Impact Factor 2.089

The world's most-cited Multidisciplinary Psychology journal

Review ARTICLE

Front. Psychol., 27 February 2017 | https://doi.org/10.3389/fpsyg.2017.00267

Are Older Adults Less Embodied? A Review of Age Effects through the Lens of Embodied Cognition

  • 1Department of Psychology, University of Hartford, West Hartford, CT, USA
  • 2Department of Psychology, Central Michigan University, Mount Pleasant, MI, USA

Embodied cognition is a theoretical framework which posits that cognitive function is intimately intertwined with the body and physical actions. Although the field of psychology is increasingly accepting embodied cognition as a viable theory, it has rarely been employed in the gerontological literature. However, embodied cognition would appear to have explanatory power for aging research given that older adults typically manifest concurrent physical and mental changes, and that research has indicated a correlative relationship between such changes. The current paper reviews age-related changes in sensory processing, mental representation, and the action-perception relationship, exploring how each can be understood through the lens of embodied cognition. Compared to younger adults, older adults exhibit across all three domains an increased tendency to favor visual processing over bodily factors, leading to the conclusion that older adults are less embodied than young adults. We explore the significance of this finding in light of existing theoretical models of aging and argue that embodied cognition can benefit gerontological research by identifying further factors that can explain the cause of age-related declines.

Introduction

Embodied cognition theory argues that cognitive and perceptual processes are grounded in the organism’s sensorimotor capacities. It rests upon a rejection of the classic cognitivist division between mind and body, and holds that the two are intertwined in often unexpected ways (Foglia and Wilson, 2013). Traditional (non-embodied) models of cognition operate through manipulation of symbolic mental representations governed by logical and computational rules. Under this traditional model, the body is not given a privileged position; it is merely the means by which sensory-level inputs are stored and later processed as mental representations. These representations are understood conceptually as independent of the physicality of the body. Embodied cognition rejects this traditional account, and argues that the physicality of the body in action is not merely a vehicle for computational processes, but rather is a co-producer of cognitive processes.

Embodied cognition is generally viewed as a broad theoretical framework for understanding mind-body relations, rather than as a singular theory serving to generate explicit hypotheses. It does not employ any singular experimental methodology, but instead serves as a backdrop to help interpret existing research such as in cognitive, perceptual, and bodily action studies. In this regard, embodied cognition theorists generally draw upon traditional ‘non-embodied’ research, and therefore may be understood as largely consonant with traditional cognitive methodologies (Goldman, 2012)1. However, insofar as it rejects some of the central tenets of classical cognitivist theory, embodied cognition retains something of an outsider status in cognitive science. Indeed, for some the ambiguous nature of embodied cognition makes it too difficult to effectively employ (Aizawa, 2015; Mahon, 2015). However, embodied cognition has become increasingly accepted as a viable theoretical option for researchers investigating sensorimotor function and situated cognition (Creem-Regehr and Kunz, 2010; Glenberg et al., 2013; Barsalou, 2016).

Embodied cognition would seem to be a natural fit for gerontology, as aging typically manifests as concurrent physical and cognitive changes (Roberts and Allen, 2016). Physically, older adults suffer from a range of changes to the body and action systems. Older adults experience height decreases, postural changes, decreased bone density, and increased bone brittleness (DiPietro, 2001). The quantity and quality of the muscular system show marked declines with advanced age (Metter et al., 1999), with reductions in muscle mass, strength, and mobility (Visser et al., 2002). These bodily changes are matched by decreases in motor function (cf., Seidler et al., 2010), with older adults exhibiting slower motor responsiveness (Falkenstein et al., 2006), more variable and slower physical movements (Mau-Moeller et al., 2013), and reduced gait speed (Studenski et al., 2011). Mobility is decreased in older adults (Tinetti, 1986), and physical actions are complicated by declines in balance control (Laughton et al., 2003). Accordingly, older adults exhibit decreased walking stability (Lord and Menz, 2000; Lord and Sturnieks, 2005) and increased falls (Kannus et al., 2005).

Cognitively, even healthy older adults exhibit a number of changes, including declines in working memory (Zacks, 1989), selective attention (Verhaeghen and Cerella, 2002), and response inhibition (Kramer et al., 1994). Perhaps the most expected age-related deficit in cognitive processing is in processing speed, with widespread slowdowns in basic cognitive and motor performance (Salthouse, 1996). Cognitive slowdowns for seniors are especially evident in visuospatial processing (Jenkins et al., 2000); relatedly, older adults exhibit declines in visuospatial reasoning and spatial navigation (Moffat, 2009; Klencklen et al., 2012). Interestingly, cognitive slowdowns have been linked to the type of physical activity levels described above (Kraft, 2012), and both cardiovascular health (Hillman et al., 2008) and walking speed (Atkinson et al., 2007) are predictive of cognition in older adults.

In short, there is ample evidence that for older adults, the mind and body are interconnected in a manner that resonates with embodied cognition theory. However, gerontological research has largely avoided the direct application of this framework. The goal of this review is to introduce the embodied cognition framework to the field of gerontological research and to argue that it can be used to explain a wide variety of cognitive aging effects. Given that embodied cognition is a broad field of study, the scope of this review article will be limited to addressing how aging affects three subdomains of specific relevance to embodied cognition: sensory processing, mental imagery, and the perception-action link. By way of preview, a central conclusion from this review is a finding common across all three of these subdomains: compared to young adults, older adults exhibit (1) an increased reliance on visual processing (what we will call a visual dominance effect), and (2) a decreased reliance on bodily (kinesthetic, tactile, proprioceptive) factors. At the end of this review, we will discuss the significance of this finding and how it may affect existing models of sensorimotor processing in older adults.

Foundations of Embodiment

Embodied cognition theorists have argued that sensorimotor factors serve as the groundwork for cognition. For instance, concepts have been characterized as grounded in situated, bodily action and are instantiated by neural reactivations of sensorimotor cortices used in active sensorimotor function (Barsalou, 2008). This embodied view of cognition is generally viewed in contradistinction to traditional cognitivist models in which cognitive processing operates through amodal and abstract mental representations that are conceptually separated from the sensory modalities (Fodor et al., 1975). Because embodied cognition theory argues that higher-level cognition is grounded in sensorimotor processing, it would predict that any changes at this earliest stage will result in corollary changes in more complex cognition. Accordingly, we start our review with age-related sensory changes.

Sensory Perception and Integration

While the sensory system is subject to age effects across all five senses (Fozard and Gordon-Salant, 2001), our focus will be on declines in visual and tactile ability. Vision and tactition are particularly important senses for embodied cognition, as they provide information about the surrounding environment in which the body is located: vision provides accurate spatial feedback about both near- and far-body space, and tactition provides real-time feedback about the progress and success of physical interactions. Both sensory modalities exhibit decreased sensitivity with advanced age, with documented age-related declines in vision (Owsley, 2011) and tactile processing (Wickremaratchi and Llewelyn, 2006).

Although age-related unimodal sensory declines are of obvious importance, our perception of the world typically involves more than one sensory modality. Multisensory integration (MSI) is the combination of two or more sensory modalities into a unified percept, a process driven by a neuronal system designed to process multiple sensory inputs simultaneously (Calvert et al., 2004). MSI is particularly important for embodied cognition: in order to have knowledge of the body’s motor capabilities and the origin of sensory stimuli, there must be seamless integration of both external stimuli (visual and tactile information) and internal representations (motor commands, proprioceptive processing, and vestibular control). Thus, any change in the ability to integrate multiple sensory inputs carries with it changes in the accuracy of embodied representations.

Research indicates that younger and older adults both benefit from multisensory inputs (compared to unisensory inputs) when those inputs provide redundant information, yet this benefit is even greater in the older adults (cf., Freiherr et al., 2013). For instance, Laurienti et al. (2006) had participants discriminate between red and blue stimuli that were presented under both unimodal (either visually as colored circles or auditorily as the verbalized color words) and bimodal (both auditory and visual presentations) conditions. Although all participants evidenced reaction time facilitation in the bimodal compared to unimodal conditions, the gain was significantly greater for older adults. Several subsequent studies have also replicated the finding that older adults gain more than younger adults from bimodal conditions compared to unimodal conditions (Peiffer et al., 2007; Mahoney et al., 2011, 2012). However, when the inputs are unrelated or conflicting, older adults are more negatively affected by the distracting information than young adults (Setti et al., 2011). For example, Poliakoff et al. (2006) tested a crossmodal cuing task that required young and older participants to report the spatial location of vibrotactile stimuli while visual stimuli were presented in either congruent or incongruent locations. Older adults were significantly more distracted by spatially incongruent visual stimuli compared to young participants.

Thus, while older adults can gain from MSI-based facilitation effects, they also exhibit stronger debilitation under MSI-based distraction conditions. What explains these divergent effects? There are likely several contributing factors. First, with deficits in unimodal perception, older adults are likely compensating by relying on the additional information provided by MSI. Consider Mahoney et al. (2014), who found that around 75% of older adults tested evidenced MSI enhancement (faster performance in visual-somatosensory testing conditions than in either visual or somatosensory alone conditions). However, the remaining 25% did not show MSI enhancement; rather, their MSI responses were as fast as their unimodal somatosensory responses, which were in turn similar to speeds reported for younger adults in prior literature (e.g., Laurienti et al., 2006; Mahoney et al., 2011). This faster group of elders did not need to draw on multisensory benefit given their command of unimodal processing, whereas the slower older adults drew on the second sensory modality to reinforce the original weaker one.

Second, when vision is one of the modalities being integrated, older adults often appear to prioritize it over audition and touch. Throughout this paper, we will be using the phrase visual dominance to describe this over-reliance on visual processing over other sensory modalities. It is important to point out that visual processing factors dominate over the other sensory modalities for all normally sighted humans, not just older adults (Posner et al., 1976). The findings of this literature review is that visual dominance holds greater sway for older adults when compared to younger adults. Although this is not readily apparent when information from the two modalities provide synchronous information, this bias emerges more clearly during times of conflict (i.e., increased distraction for older adults when visual information conflicts with tactile). This age-related increase in visual capture has been well-explored in crossmodal attention research, indicating that older adult processing is dominated by the visual components under MSI testing conditions (Thompson and Malloy, 2004; Mahoney et al., 2012). A key study in this regard is Diaconescu et al. (2013), who assessed age group differences in brain activity during crossmodal processing using magnetoencephalography (MEG). In the task, younger and older adults made categorization judgments for stimuli presented either unimodally (auditory or visual) or bimodally (auditory + visual). Although both age groups benefited from the addition of visual inputs compared to auditory alone, the benefit was greater for older adults. MEG recordings indicated a probable explanation: multisensory gains in the older adults were associated with increased medial prefrontal and posterior parietal activity, with the latter region implicated in visuo-motor performance. The authors characterized these results as indicative of visual dominance, a compensatory mechanism in which older adults draw more heavily on visual processing brain regions to offset age-related reductions in cortical and subcortical gray matter volume.

Prioritizing visual over somatosensory information may help explain why older adults exhibit balance control issues and increased falls (Kannus et al., 2005). Standing balance is compromised in older adults (Laughton et al., 2003), a finding which has been attributed to their overemphasis of visual processing factors (Horak et al., 1989). When attempting to maintain an upright posture, older adults exhibit increased susceptibility to sway based on visual manipulations (Wade et al., 1995; Prioli et al., 2005; Toledo and Barela, 2014). It is not surprising, then, that walking is also compromised in aging. Efficient walking requires a complex integration of visual, tactile, and vestibular inputs with motor sequencing commands. Yet when walking, older adults tend to focus more on the ground and thus draw more heavily on optic flow information (Anderson et al., 1998), with greater attention to visual factors (Sparrow et al., 2002) which may reflect the decreased processing power of proprioceptive systems (Huitema et al., 2005). Fall-prone older adults appear to rely heavily on visual processing due to interoceptive processing failures (Barrett et al., 2013), and MSI control failures in older adults have been linked to both poor balance (Stapleton et al., 2014) and increased falls (Mahoney et al., 2014).

Yet this visual dominance affecting older adults is neither inevitable nor fixed. Recent research has shown that MSI in older adults is highly plastic, and MSI-based training can result in improved walking and balance performance (Setti et al., 2014). For instance, Merriman et al. (2015) explored whether a balance intervention program could help MSI problems in older adults. They found that older adults benefited from the intervention, with improved balance control when compared to similarly matched peers. Importantly, fall-prone older adults showed significant improvement in MSI performance based upon the balance control intervention. Thus, behavioral intervention holds the promise of offsetting the visual dominance exhibited by older adults and reducing the potential for falls.

The Body Schema and Peripersonal Space

The body schema and peripersonal space are concepts used to explain how humans understand the physical body and the external space immediately surrounding it. While these terms are treated as functionally distinct, they share many similarities and in fact are often used somewhat interchangeably. The body schema is the internalized ‘map’ of spatial relations for one’s own body based on kinesthetic, proprioceptive, and tactile information, which is ultimately used to facilitate successful action; peripersonal space, on the other hand, is the space immediately surrounding the body through which we interact with the surrounding environment. Ambiguity in the exact boundaries of these constructs is understandable: when examining the understanding of physical actions (a body schema question), that movement necessarily takes place in peripersonal space, and the influence of that space is difficult to disentangle from the body schema itself (c.f., Cardinali et al., 2009 for a full review).

Both the body schema and peripersonal space are essential to embodiment theory, for they form the foundation for the intersection of sensory experience and bodily action (Borghi and Cimatti, 2010). Both the body schema and the formation of peripersonal space are mediated through MSI, the result of combining weighted signals from visual, tactile, and proprioceptive domains (Blanke, 2012). Additionally, both allow for multiple action-perception relationships, for the capacity to perform bodily actions requires an awareness of one’s own body in space (Reed and Farah, 1995). Accordingly, the body schema and peripersonal space are plastic in nature, and can flexibly adapt to differing physical environments and bodily demands.

It is reasonable to expect that aging will affect the body schema and peripersonal space given that they are based on underlying factors that are vulnerable to aging. First, they represent the holistic integration of multisensory inputs within the body’s spatiality (Maravita et al., 2003), a process known to change during aging. Second, body schema mapping and the localization of objects in peripersonal space necessitate accurate proprioception (Walsh et al., 2011; Proske and Gandevia, 2012), which is known to decline with advanced age (Goble et al., 2009). Finally, the body schema is continuously updated by vestibular signals (Lopez et al., 2012), and vestibular control declines with advanced age (Alexander, 1994). Taken together, one would expect profound changes to the body schema and peripersonal space with advanced aging.

Studies examining peripersonal space in young adults have sought to identify perceptual differences between near-body and far-body (extrapersonal) space. One common difference is that in peripersonal space, young adults deploy attention asymmetrically, over-emphasizing the left visual field over the right. This is thought to occur because spatial attention in peripersonal (but not extrapersonal) space is primarily controlled by the right-hemisphere (Fink et al., 2001; Bjoertomt et al., 2002). On tasks such as line bisection, in which participants indicate the center point on a straight horizontal line, it manifests as a slight leftward bias when the line is presented in peripersonal space (Jewell and McCourt, 2000). In extrapersonal space, where the asymmetry does not occur, young adults bisect lines at veridical center or with a slight rightward bias (Varnava et al., 2002; Garza et al., 2008). Older adults, on the other hand, exhibit a rightward bias in peripersonal space (Fujii et al., 1995; Jewell and McCourt, 2000). This occurs even when manual abilities are eliminated and when implicit tasks are used to account for the possibility of strategy differences (Barrett and Craver-Lemley, 2008). This may be occurring because older adults do not have right-hemisphere dominance for coding peripersonal space, requiring activation of the left hemisphere to compensate (Cherry et al., 2005; Chen et al., 2011).

Ghafouri and Lestienne (2000) used a sensorimotor experimental design to evaluate peripersonal space representations in older adults, having participants draw imaginary ellipses in the air in different planes in the space in front of their bodies. The authors argued that representations of 3D space can be revealed by examining goal-directed movements within that space: if space is not represented veridically, the movements should reflect those inaccuracies. The authors compared the abilities of young and older adults to orient ellipses in three different planes by first showing participants a template and asking them to reproduce it. In comparing the plane of motion of each participant’s finger, they found that older adults had smaller plane volumes than young adults, indicating a compression in the representation of peripersonal space. Furthermore, by comparing performance across the different planes, they were able to show that the motor component was not responsible for the errors; rather, older adults were representing the spatial coordinates incorrectly.

Although these behavioral tests are in line with peripersonal representations changing with age, not all studies find age differences. Older adults have been found to exhibit leftward biases when drawing or arranging items in peripersonal space, similar to young adults (Barrett and Craver-Lemley, 2008). While these measures were implicit tasks (unlike line bisection), other studies using seemingly implicit dependent measures such as ellipse drawing have shown these differences, suggesting that changes in performance with age are not just due to changing strategies. It is unclear what is causing these divergent results, and further research is needed to clarify why peripersonal space representations sometimes, but not always, show age-related changes.

With regard to the body schema proper, little research has directly explored how it may be affected by aging. Gilpin et al. (2015) recently tested how osteoarthritis affects the body schema of older adults. In their study, arthritis sufferers and non-arthritic controls viewed live video feed of their own hands while their hands were manipulated to be ‘stretched’ (the imaged hand was digitally elongated while physically pulled), ‘shrunk’ (digitally contracted while being pushed), or presented without manipulation. Participants were then asked to resize digital photographs of their hands to veridical sizes. Analyses indicated two key findings. First, the non-arthritic controls exhibited the expected shrinking and stretching illusion, whereas this manipulation was muted in the arthritis sufferers. Second, arthritic seniors reported smaller hand sizes than non-arthritic controls, suggesting that pain contracted the spatial representation of the hand. Although this study indicates the alteration of the body schema under a common older adult disability, the results point to body schema plasticity based upon age-related pathology (i.e., arthritis) rather than aging itself. However, it indicates that changes in the body and physical ability have the capacity to alter the body schema. Further research is needed to determine how the body and physical changes that accompany healthy aging also alter the body schema.

Embodiment in Mental Representations

Having detailed the age-related changes to sensory-level processing, we turn now to how aging alters mental representations based on that sensory processing. The embodied cognition framework regards mental concepts as either heavily interactive with sensorimotor systems or as directly related to sensorimotor function (Glenberg, 2015), whereby concepts are instantiated by reactivations of sensory- and/or action-based neural pathways (Barsalou, 1999, 2008). As such, mental representations are emblematic of sensorimotor function. This is in contrast with the traditional cognitive science model, wherein perceptual processing of sensory inputs occurs through mental representations that are understood as abstract and amodal symbols for the represented percepts (Fodor et al., 1975; Pylyshyn, 1984). To explore how aging affects the influence of embodiment on mental representations, we will examine age-related differences in mental imagery, motor imagery, and action observation.

Mental Imagery

Mental imagery is the creation, maintenance, and activation of an internal representation. Although seemingly simple in definition, mental imagery depends upon multiple cognitive subcomponents including sensory processing (the image is typically either visual or auditory in nature), cognitive processing (the image is understood in terms of its physical construction and/or semantic content), and working memory (mental images can be reproduced, maintained, and elaborated upon; Kosslyn, 1995). Mental imagery shares a similar underlying neuroarchitecture with perceptual processing, such that imagining an object elicits similar brain activations as directly perceiving the object (Kosslyn et al., 2001; Wilson-Mendenhall et al., 2013). This close relationship between the perceptual processing and mental imagery suggests that older adult deficits in unisensory and multisensory processing may yield similar deficits in mental representations.

Perhaps the canonical experimental paradigm for mental representation study has been the mental rotation task, first developed by Shepard and Metzler (1971). In such tasks, participants are shown an object on a screen and are required to mentally rotate it, typically responding by selecting the target angle from an array of variously rotated items. Early research on age-related differences in mental rotation found that older adults committed greater errors and yielded slower performance in mental rotation compared to young adults (Cerella et al., 1981; Hertzog and Rypma, 1991), indicative of a generalized decline in mental imagery (Craik and Dirkx, 1992). However, later work found that age effects on mental rotation are not a global failure, but rather are evident primarily under the cognitive strain of difficult rotation angles and increased complexity (McDowd and Craik, 1988; Inui, 1997; Sit and Fisk, 1999). Indeed, recent work from an embodied cognition perspective has found that both older and younger adults have mental representations that are similarly grounded in sensory processing (Vallet et al., 2011, 2013). Age-related deficits in mental rotation, when present, may therefore reflect the increased cognitive demands of spatial processing.

The mental rotation of the body is of particular importance to embodied cognition theory given that it represents the mental manipulation of the internalized body schema (Kaltner et al., 2014). Research with young adults has indicated that such imagery is constrained by body spatiality and bodily actions (Amorim et al., 2006; Steggemann et al., 2011). Importantly, older adult performance in mental rotation worsens when the target shape is a body. Devlin and Wilson (2010) assessed the effect of aging on mental rotations with three different imagery types: an alphanumeric stimuli set (5 or F), a 2D image of a single hand, and 3D whole-body image. Although age effects were evident across all three image types, older adults were significantly worse performing the whole-body rotations, with increasing reaction times and errors when rotating at larger angles. In this case, the whole-body stimuli represented not only a more complex stimuli set, but one intimately linked with the body schema.

While it may not always be clear whether older adult difficulties in whole-body mental rotation are due to task complexity or poor body representations, there is evidence that egocentric perspectives are specifically problematic. First, when the mental rotation requires an egocentric perspective (such as rotating a body image by means of one’s own body), older adults show deficits compared to young adults (Jansen and Kaltner, 2014; Kaltner and Jansen, 2016). Second, studies in spatial learning have found that older adults show marked deficits in learning spatial environments when operating through an embodied and first-person perspective (Inagaki et al., 2002; Borella et al., 2015). For instance, Yamamoto and DeGirolamo (2012) had young and older participants encode landmarks in a simulated environment either through a first-person or third-person perspective. When participants were later asked to reconstruct the spatial layouts, older adults were less accurate than young adults in the first-person, but not the third-person, encoding conditions. Finally, when older adults are required to make mental rotations of hand stimuli, their performance is significantly worse compared to younger adults when the rotations required an egocentric (as opposed to allocentric) rotation strategy (De Simone et al., 2013). Taken together, we can interpret these multiple instances of age-related decreases in first-person perspective to indicate that older adults may be downgrading their bodily inputs, resulting in decreases in egocentric (first-person) processing and consequent increases in allocentric (third-person) processing.

Motor Imagery

A subtype of mental imagery is motor imagery, the mental simulation of an action when there are no overt physical actions being made. Motor imagery is arguably a more complex variant of mental imagery for it includes an implicit action plan (Kosslyn, 1987). Motor imagery is integral to embodied cognition because it combines external representations of the environment and peripersonal space with representations of the body schema to construct simulations. Motor imagery is critical to efficient intentional actions, because it serves as an internal simulation of actions that can moderate overt motoric action (Wolpert, 1997; Wolpert and Kawato, 1998) by working as forward-models in the planning of physical actions (Beauchet et al., 2010).

Our understanding of motor imagery has been greatly influenced by Jeannerod, who theorized that covert (mentally simulated) actions are neurologically similar to overt motor actions, such that both elicit comparable cortical activation patterns (Jeannerod, 1994, 2001). Indeed, there has been strong empirical support for this neurological overlap (Decety, 1996; Szameitat et al., 2007; Kraeutner et al., 2014). Motor regions activate when merely viewing action-based objects (Chao and Martin, 2000), and motor imagery elicits sensorimotor brain responses (Hauk et al., 2004). Jeannerod’s motor simulation theory is broadly compatible with the embodied cognition framework, and is conceptually akin to action-perception theories that similarly emphasize the close connection between sensation, action, and cognition (Hommel et al., 2001; Proffitt and Linkenauger, 2013).

Research indicates that motor imagery declines are evident in healthy aging (Personnier et al., 2008; Skoura et al., 2008; Saimpont et al., 2009). For instance, Gabbard et al. (2011) asked young and older adults to declare whether a target was within their arm’s reach after mentally simulating the action. Older adults overestimated their reach compared to young adults, which may reflect age-related deficits in brain regions critical to motor imagery (Munzert et al., 2009). Declines in motor imagery appear linked to handedness, with older adults exhibiting greater errors when operating with the non-dominant compared to the dominant arm (Skoura et al., 2008; Saimpont et al., 2009).

Perhaps not surprisingly, there are many similarities between the age-related declines in both mental imagery and motor imagery. Like mental imagery, the aging effect on motor imagery is also affected by both the complexity and perspective of the imagery. Under simple motor imagery conditions, older adults perform equivalent to young adults (cf., Saimpont et al., 2013). However, as the motor sequences to be imagined become more challenging, older adult imagery becomes less accurate compared to young adults (Skoura et al., 2005).

One specific form of additional complexity for older adults are first-person motor imagery visualizations. Mulder et al. (2007) had young and older participants report visualization strengths of their own or other’s actions. When older adults visualized their own first-person actions (as opposed to a third-person perspective visualization), the vividness in the images decreased. Importantly, this decline in the first-person perspective reflected the declining physical powers of the older adults: imagery vividness scores were correlated with measures of motor capacity and mental and physical speed measures. The effect of aging on motor imagery, therefore, is subtle: relatively preserved in cases of simple motor images without dynamic physical constraints, but degraded under more complex imagined action conditions (Personnier et al., 2008; Saimpont et al., 2013), particularly those involving egocentric perspectives. This result is consistent with theories of aging in which older adults suffer from a diminution of mental resources, resulting in increased difficulties from distraction under complex task requirements (Lustig et al., 2007).

Visual dominance of motor imagery in older adults has also been identified in neuroimaging studies. When using motor imagery, both young and older adults engage a network of neural pathways that include visual imagery and motor pathways as well as frontoparietal, subcortical, and cerebellar areas. However, activation intensity and specificity does differ by age group, suggesting that age effects in motor imagery reflect the changes to these brain networks (Saimpont et al., 2013). During motor imagery, older adult brains respond with larger and more diffuse activation patterns (Léonard and Tremblay, 2007; Nedelko et al., 2010; Sharma and Baron, 2014; Reuter et al., 2015), suggesting degraded or less-specific motor representations leading to compensatory brain recruitment (Zapparoli et al., 2016). When performing overt motor actions, older adults exhibit increased frontal and prefrontal (pre-SMA) activations during more cognitively complex actions, whereas when mentally simulating actions older adults exhibited increased activations in occipito-temporo-parietal areas (Zapparoli et al., 2013). This latter finding suggests that older adults rely more heavily on visual processing while performing motor imagery, consistent with our earlier finding of older adult over-reliance on visual processing in multisensory perception. Thus, behavioral evidence of visual dominance in older adults is matched by neuroimaging results suggesting increased activity in visual processing regions.

Action Observation

Jeannerod’s (2001) motor simulation theory stipulates not only that motor imagery and physical actions share a common neural substrate – it also predicts that there is a similarly shared activation network when observing such actions in others. Indeed, there is mounting evidence indicating that merely observing someone performing an action results in the activation of the sensorimotor pathways responsible for producing that action (Buccino et al., 2004; Lui et al., 2008). This shared cortical activation pattern may offer a twofold advantage: allowing superior anticipation of the actions of others and providing information on one’s own actions (Jeannerod, 2001; Jeannerod and Anquetil, 2008).

Given the age-related decline in mental and motor imagery, it is not surprising that older adults do not benefit from action observation to the same extent as young adults. Aging decreases the efficiency in learning and then executing novel motor sequences (Coats et al., 2013) with specific losses for action observation (Maryott and Sekuler, 2009). For instance, Maguinness et al. (2013) explored age-related differences in a task in which participants viewed videos of either a hand or a full body lifting weighted boxes and had to indicate the amount of weight being lifted. Older adults were overall less sensitive for weight prediction than young adults, but particularly so when there were fewer visual details on the biomechanics of the lift. Accordingly, the authors argued that older adult performance declines indicate that seniors relied on a more visual-based (rather than felt-based) strategy.

Older adults may rely on visual processing components because their internal action models are degraded (Boisgontier and Nougier, 2013). Diersch et al. (2012) explored this possibility with a task assessing differences between young and older adults in the temporal parameters of action prediction. In the task, participants were shown video clips of everyday action sequences with the scenes interrupted by a black-screen occluder. After the occluder was removed, participants rejoined the original scene, although the timing parameters were manipulated such that the pre- and post-occluder scenes were either temporally continuous or discontinuous with the preceding scene. Participants were tasked with identifying the amount of time that had been altered based on the occluder. Older adults were less accurate than young adults in predicting the time course of another’s action, a result that the authors argued indicated a specific age-related decline in action representations. More recently, the same research group found that older adults exhibited increased activity in visual regions when performing action prediction (Diersch et al., 2013), with more diffuse brain activity indicative of less-specific internal models of motor actions (Diersch et al., 2016).

Embodiment in Perception and Action

Our review thus far has focused largely on the bottom-up factors (i.e., physiological systems and sensory processing) and the mental representations that underlie embodied cognition. We have found that aging affects these systems profoundly, with a common finding of visual prioritization over somatosensory processing in older adults. In cases of multisensory perception, older adult deficits in unisensory processing typically result in gains in multisensory inputs, although visual processing is dominant. A similar tendency toward visual dominance occurs in mental imagery (most especially in motor imagery and action-perception), for visual processing factors dominate the motoric representations in older adults. Thus, basic sensorimotor deficits in older adults result in increased reliance on visual processing over other sensory modalities, and this visual dominance alters the nature of action-based mental representations. We now turn to how bodily action alters perceptual experience, a topic that is central to the embodied cognition framework (Glenberg et al., 2013).

Action-Specific Theory of Perception

A central tenet of embodied cognition is that the mind and the body have reciprocal influence: not only can the mind control and direct the body as traditionally thought, but the body can also direct and influence the mind through its movement, posture, and sensory input. Embodied cognition theorists have long argued that action alters perceptual judgment, and research has confirmed that perceptual judgments of object size and distance correlate with our capacity to act upon the object (Proffitt et al., 2003; Kirsch and Kunde, 2013; Linkenauger et al., 2013). This close link between perception and action is central to both embodied cognition theory (Proffitt, 2006; Glenberg et al., 2013) and ecological theories of perception (Richardson et al., 2008).

Witt (2011) has formalized this general dictum into a specific theory on how action affordances can alter spatial cognition. Her action-specific account of visual perception argues that visual information of an object (in terms of object size or distance) is scaled relative to an observer’s effort or ability to effectively interact with that object (Witt et al., 2004). Thus, perceptual appraisals are always scaled as a phenotypic expression of bodily capacity (Proffitt and Linkenauger, 2013) made in relation to action affordances (Kirsch and Kunde, 2013). In line with the action-specific perception theory, physical capacity of the perceiver has been found to be related to perceptual scaling, presumably to maximize efficiency in motor response. Several studies have found that athletes exhibit perceptual recalibrations that correlate with their performance when playing sports such as tennis (Witt and Sugovic, 2010), golf (Witt et al., 2008), softball (Witt and Proffitt, 2005), and football (Witt and Dorsch, 2009).

Given that the older adult body is limited in its physical capacity, it is reasonable to expect age-related changes in perceptual scaling. In an early study by Bhalla and Proffitt (1999)2 on the relationship between aging and perceptual capacity, younger and older participants provided slope estimates while looking up from the bottom of variously angled hills. Young adult slope estimates were steeper when placed in physically demanding conditions, a result consistent with the action-specific perception hypothesis. However, older adults provided steeper hill slope estimates than young adults even under typical physical conditions, suggesting that their reduced physiological potential led to perceptual bias. Perceptual scaling tasks have also found that older adults are sensitive to environmental impediments that would complicate walking (Sugovic and Witt, 2013), essentially indicative of increased cautiousness. Similar findings of visuo-motor cautiousness have been documented in gait analyses of older adults (Grabiner et al., 2001; Owings and Grabiner, 2004) and shoulder rotation movements when walking through door apertures (Hackney and Cinelli, 2011).

What explains this increasing cautiousness in the action-specific perception responses of older adults? While the answer is not yet clear, the action-specific account of visual perception would point to the physical limitations of older adults as determinative to perceptual scaling. If true, the number of potential physiological factors affecting the older adult perception-action relationship may be surprisingly broad. Action-specific perception research has generated a dizzying array of physical factors vulnerable to aging that can influence task performance, including strength (Linkenauger et al., 2009), pain levels (Witt et al., 2009), and even respiration capacity (Daviaux et al., 2015). All these factors tie directly to the changing bodies of older adults. Consider the more speculative possibility that the metabolism of older adults might affect their action-perception relationship. It is known that giving participants sugary drinks has been found to decrease judgments of hill steepness (Schnall et al., 2010), presumably due to altered energy levels (although cf., Durgin et al., 2012). Older adults have marked declines in energy intake levels (Vaughan et al., 1991) and metabolism rate decreases (Wilson and Morley, 2003). The cause for this age effect on the regulation of metabolism is diverse, ranging from the delayed absorption of macronutrients and to reduced sensitivity of taste and smell (Roberts and Rosenberg, 2006). This raises the intriguing possibility that such seemingly tangential physiological and/or sensory factors could alter the perceptual judgments of older adults.

Tool Use

Embodied cognition argues not merely that the body and bodily action is formative toward cognition and perception, but that the mind itself is unconstrained by the physical boundary of the brain and is extended out into the body in action (Noë,, 2009; Clark, 2010). Accordingly, it is logical to assume that the body may not be the only tool for mind-extension. Indeed, embodied cognition theorists have argued that the mind extends itself through a wide array of tools; this goes beyond mere physical objects and includes even metaphorical tools such as cultural artifacts (Malafouris, 2004; Wilson, 2010) and language (Mirolli and Parisi, 2009; Borghi et al., 2013).

The study of how tool use is represented in the brain began largely with primate research using single-cell recordings of neuronal sensitivity. Such work has found that when monkeys use tools, the receptive fields of neurons normally coding the spatial extent of the hand became extended into the more distant space of the tool’s reach (cf., Baccarini and Maravita, 2013). An early report indicated that when monkeys were trained to retrieve distant food using a rake, the receptive fields for bimodal (visual and somatosensory) neurons within the postcentral gyrus elongated to include the functional reach of the rake (Iriki et al., 1996). Consequent work has indicated that such tool-based changes to neuronal function are evidence of the plasticity of body representations and the coding of spatial relations (Obayashi et al., 2001; Hihara et al., 2006). Collectively this research indicates that the body schema is instantiated in the brain through multimodal (typically visual and tactile) coding that is subject to alteration based upon tool use (Maravita and Iriki, 2004).

Studies with human participants using tools have found similar results of body schema adaption under both behavioral (Maravita et al., 2002) and neuroimaging (Järveläinen et al., 2004; Peeters et al., 2009) testing conditions. When using tools, peripersonal space becomes extended into extrapersonal space (Berti and Frassinetti, 2000; Farnè and Làdavas, 2000; Maravita et al., 2001; Farnè et al., 2007). Cognitive processes alter in accordance with tool use, as both visual attention (Farnè et al., 2005; Witt and Brockmole, 2012) and memory (Davoli et al., 2012a) become refocused to the space at the functional end of the tool. For instance, participants report the distance of targets to be closer to themselves when wielding a tool directed to the target (Reed et al., 2010). This spatial-contraction effect is evident even when participants merely intend to act upon the target with a tool (Witt et al., 2005) and when the tool offers only remote access (Davoli et al., 2012b).

How might aging affect the ability to incorporate a tool into the body schema? One source of evidence comes from pantomime studies, in which participants are asked to identify or act out a physical action (e.g., “show me how to cut with a knife”). In a manner consistent with our earlier review of action observation research, pantomiming research has found that older adults exhibit general declines in pantomiming human actions (Cavalcante and Caramelli, 2009), especially when the pantomiming involves use of a tool (Mozaz et al., 2002, 2009). When pantomiming tool use, older adults make a characteristic body-part-as-object (BPO) error, wherein body parts rather than simulated actions are used to represent tool use (Ska and Nespoulous, 1987; Peigneux and van der Linden, 1999). For example, when asked to pantomime scissors, older adults are more likely to represent the index and middle fingers as scissor blades than to grip imaginary scissors handles. These age-related increases in BPO errors may simply reflect the cognitive demands of mimicking a tool-wielded action (Cavalcante and Caramelli, 2009; Maryott and Sekuler, 2009; Mozaz et al., 2009), a pantomime complicated by required mimicry of both the motor intention and detailed hand kinematics. Conversely, older adult deficits in pantomiming may represent a specific deficit, akin to the ideomotor apraxia common to stroke patients (Mizelle and Wheaton, 2010). Yet direct empirical evidence has been minimal.

Costello et al. (2015) recently examined whether aging affects the spatial-contraction effect that occurs during tool use. In their first task, young and older adults were shown target circles at near-body distances (ranging from 44 to 89 cm away) and asked to estimate their distance after pointing to the target either with their hand or with a reach-extending tool. Only the young adults displayed the expected tool-based spatial-contraction effect, in which the targets appeared closer when the participants were using the tool. In a second task, distance estimates were derived for far distances (ranging from 3.4 to 25.3 m away) after either pointing with the hand or with a laser pointer, which has been found to produce spatial contraction (Davoli et al., 2012b). Similar to the first experiment, only the young adults displayed the expected spatial-contraction effect when using the laser pointer on the targets. Taken together, the two tasks indicate a generalized phenomenon in which older adults fail to exhibit tool-based plasticity of body representations.

What might explain older adults’ inability to incorporate tools into the body schema? If we assume that tool use is a complex function with multiple cognitive, sensory, and motor components, age effects at lower sensorimotor levels may disrupt the overt performance of using a tool. After all, tool use requires the smooth integration of visual and tactile inputs, and as already discussed, older adults struggle in MSI-based tasks. There is evidence to this effect. Heuer and colleagues have investigated age-related differences in multisensory processing during tool use with a visuo-motor rotation paradigm. In such tasks, participants look at a display screen while moving a mouse cursor to a target, but vision of the hand movements themselves are hidden under an occluder. On some trials, the cursor pathway is diverted from the actual pathway of the hand movement. After the participants complete their reach and return to the home position, the pathway directions of either the cursor or the hand are displayed. Participants are then asked to modify the displayed pathways to the correct location for the remembered target location. Perhaps not surprisingly, under such conditions older adults commit greater errors than young adults (Bock, 2005; Heuer and Hegele, 2008; Heuer et al., 2013). But more importantly, older adults were more heavily influenced by the visual information of the monitor cursor than the tactile information of their own hand movements (Rand and Heuer, 2013; Heuer and Hegele, 2014). Thus, the lack of spatial contraction based upon tool use (Costello et al., 2015) may be due to the aforementioned visual dominance in older adults: because their sensorimotor calibration is more heavily influenced by visual processing, the proprioceptive feedback from the hand using the tool is accordingly diminished.

Secondly, tool use is typically performed with the hand, and there is evidence that the cognitive system of older adults is desensitized to their hands. Young adults appear to automatically orient attention toward their hands both immediately prior to and during a reach (Tipper et al., 1992; Meegan and Tipper, 1998). Older adults, however, do not orient toward the reaching hand; instead, attention remains centered on the space around the trunk (Bloesch et al., 2013). Older adults, in other words, have an attentional reference frame that is less sensitive to their hands, a finding that is consonate with a similar result discussed earlier: when older adults perform mental rotations of hand-based stimuli their errors were greatest for egocentric (as opposed to allocentric) rotations, suggesting that the older adults struggle to extract sensorimotor information of their own hands to assist their mental rotation of the target hand (De Simone et al., 2013). If sensorimotor information about the hands is less available during aging, it would be less likely that representations are flexible enough to easily incorporate tools into the body schema.

One final factor that may also contribute to older adults’ ability to understand and use tools is the type of knowledge necessary for this complicated task. Effective tool use relies on both semantic knowledge, information connecting the purpose of a tool to a to-be-acted-upon object, and mechanical knowledge, information about the functional allowances of a tool-based on the properties of both the tool itself and the to-be-acted-upon object (Lesourd et al., 2016). Additionally, tool use depends on reasoning skills that allow for mechanical knowledge to be drawn upon given the tools available and the situational demands (Reynaud et al., 2016). While chronological age is predictive of declines in both types of tool-related knowledge, the relationship is considerably stronger for semantic than mechanical. Further, cognitive functioning mediates age-related declines in semantic, but not mechanical, tasks (Lesourd et al., 2016). Thus, tool use failures in older adults may be partially explained by an inability to access knowledge about a tool’s purpose or the type of task a specific tool is used to complete, rather than an inability to access the action affordances of the tool.

Are Older Adults Less Embodied?

This review has explored how the framework of embodied cognition can be applied to the study of aging by detailing research falling under three subdomains pertinent to embodied cognition: sensory perception (with a focus on MSI), mental representation (with a focus on motor imagery), and the effect of action on perceptual judgments. Embodied cognition theory predicts that as the body and sensory systems decline, there will be corollary changes in perception and cognition. Older adulthood is a critical period of physical decline, and therefore embodied cognition is a valuable theoretical prism by which to view aging (Vallet, 2015). As we’ve described in the prior sections, the aging mind-body relationship is indeed seen consistently across a variety of paradigms measuring different aspects of cognition. The results indicate that aging has profound effect on embodiment, as summarized below.

Embodiment Changes in Older Adults

While aging is associated with a host of physical changes that produce a variety of effects on embodiment, a consistent finding in the literature is that older adults are influenced largely by visual processing over tactile and kinesthetic processing. This visual dominance effect was evident across all three subdomains we covered. Older adults are more heavily influenced by visual processing components during MSI tasks (Diaconescu et al., 2013), which may explain the increase in falls for older adults (Mahoney et al., 2014; Stapleton et al., 2014). Age-related failures in postural control have been attributed to older adults prioritizing visual inputs over other sensory modalities (Wade et al., 1995; Prioli et al., 2005) due to decreased efficiency in control over body position (Toledo AND Barela, 2014).

Similar instances of visual dominance in older adults have been found when performing tasks involving action observation (Maguinness et al., 2013; Boisgontier et al., 2014), again across a number of domains. Older adults have increased activity in visual processing regions during action prediction (Diersch et al., 2016) and greater reliance on visual over somatosensory signals on visuo-motor tasks (Rand and Heuer, 2013; Rand et al., 2013; Heuer and Hegele, 2014). Similarly, sensorimotor learning in older adults is more strongly directed by visual inputs compared to young adults (Teixeira and Lima, 2009). This over-reliance on visual processing may explain why older adults exhibit declines in spatial cognition: MSI is down-weighting tactile/proprioceptive inputs relative to visual inputs. In essence, due to age-related declines in physical capacity, a compensatory mechanism arises that increases the influence of visual processing, resulting in sensorimotor calibrations that are scaled with either distorted tactile/kinematic inputs, or the tactile/kinematic inputs are reduced in their weighted contribution. Older adults, in relying more heavily on a visual signal and less on an internal, body-based signal, are effectively less embodied than young adults.

An important corollary phenomenon is the age-related change in perspective-taking: older adults struggle when visualizing from their own first-person compared to a third-person perspective, displaying an insensitivity for egocentric awareness. This was primarily evident in motor imagery studies which have found decreases in multiple areas: in first as opposed to third person perspectives (Mulder et al., 2007), in whole-body mental rotation with egocentric requirements (Devlin and Wilson, 2010), and when mentally rotating hand-based stimuli (De Simone et al., 2013). Thus, the older adults are less ‘inhabited’ within their body and less comfortable when forced to imagine their body in alternative viewpoints. How does this relate to the aforementioned visual dominance effect in older adults? One, first-person perspectives would encompass the body state of the older adult. If older adults have deficiencies in access to their body states, then the visualizations that depend on that perspective would consequently falter. Two, if older adults are less embodied and more objectivist in their perspective, their increased visual processing would force them ‘outside’ rather than staying in the body. In that sense, mentally seeing the world in the third-person would likely be clearer to older adults compare to an egocentric perspective.

In short, the body’s influence on cognition alters in older adults based on their reliance on visual signals over bodily control. Embodied representations rely on externally originating visual and tactile sensory signals as well as internally originating proprioceptive and vestibular sensory signals. These signals will be weighted differently depending on the task and the quality of the signal. For older adults the internal signals are often degraded – proprioception declines with advanced age (Goble et al., 2009), as does vestibular control (Alexander, 1994). Although vision declines with age as well, it can be corrected to normal more easily than these other senses. While it is the case that normally sighted humans, not just older adults, prioritize vision (Posner et al., 1976), when creating embodied representations the visual signal can be balanced with internal signals. If internal signals are less reliable or less available, as they are for older adults, it may be the case that the visual signal becomes most strongly weighted regardless of the task.

This scenario would predict that older adults would be most likely to prioritize visual information because it is the most readily available and often the most spatially and temporally precise. However, in cases where vision is not the most reliable modality, vision should follow perception, not lead it. While that is quite difficult to accomplish in healthy older adults, it is found in some clinical populations. For example, bilateral parietal lobe damage (Balint’s Syndrome) can impair the ability to visually localize and attend to stimuli while leaving these abilities relatively intact in the auditory modality. Phan et al. (2000) had both healthy participants and a patient with Balint’s Syndrome localize visual and auditory stimuli that were presented simultaneously. Healthy participants were captured by the visual stimuli, which is expected given the spatial superiority of vision over audition. However, the patient’s performance was captured by the auditory stimuli, which is consistent with the prediction that the modality that is most reliable and accurate will be the one whose signal is prioritized. Given the myriad unisensory and physical declines that accompany aging, it is reasonable for vision to be the most reliable; thus, it would make sense for vision to be the default modality.

Note that a similar reliance on visual processing components over somatosensory processing has been identified in older adults suffering from Parkinson’s disease (PD) in order to compensate for their physical declines. Imaging studies have found that older adults with PD not only perform worse on motor imagery tasks, but also show increased brain activity in extrastriate visual areas (Helmich et al., 2007). The physical limitations of the patients, in other words, is compensated with additional neural resources drawn from visual processing. At the behavioral level, this manifests in increasing dependence on a visual processing strategy (Poliakoff et al., 2010). Accordingly, motor imagery declines in PD are typically localized to the physical actions most debilitated by PD (Dominey et al., 1995; Helmich et al., 2007). Finally, Conson et al. (2014) found that PD patients had increased difficulty in whole-body mental rotations when the rotation type required a somatosensory ‘feel’ for the rotated body stimuli in contrast to a more visual grasp. This finding is similar in kind to healthy older adult deficits in first-person perspectives in body schema.

Interestingly, while older adults display visual dominance, infants, and children may display the exact opposite effect – a tendency to favor physical factors above visual factors. For instance, Frick et al. (2009) had 5-year-olds, 8-year-olds, 11-year-olds, and young adults (M age = 37 years) perform a jigsaw puzzle while working a hand crank. In some trials, the crank rotation matched the expected rotation of the puzzle piece, whereas in other trials the crank rotation was in the opposite direction of the puzzle rotation. When the crank and puzzle rotations matched, performance was improved in the 5- and 8-year-old children, but not the older children or young adults, indicating that motor factors were weighted more heavily early in development. This is consistent with other studies showing that the mental imagery of young children is more heavily influenced by motor processes over visual components (Frick et al., 2005; Funk et al., 2005). Infant research using looking-measure paradigms have similarly found that physical exploration is critical for infants to perceptually discriminate visual scenes (Frick and Möhring, 2013; Möhring and Frick, 2013), suggesting that mental imagery is linked with motor expression from early on. Although there is debate on the age of onset for the visuo-motor matching (cf., Krüger and Krist, 2009; Frick et al., 2013), the evidence points to an increased influence of motor factors on perceptual judgments for infants and children. Thus, from a lifespan perspective, the integration of visual perception and action systems is complex, favoring motor influences early in life, a more leveled integration in young adult period, and diminished motor and increased visual influence in older age.

The claim of visual dominance in older adults is based on the preponderance of evidence that we have reported in this review, although not surprisingly there are empirical results that run counter to this theme. Recall that we earlier reported an age-related increase in MSI-based errors during the Sound-Induced Flash Illusion. Recently, McGovern et al. (2014) found that the greatest age effects were found in the fusion illusion (in which two flashes paired with one beep is experienced as one flash) compared to the fission illusion (in which one flash paired with two beeps is reported as two flashes). If the action-perception system of older adults is more heavily driven by visual processing factors, one would have expected performance to be worse in the latter condition. Additionally, results from studies testing the action-specific theory of perception, in which older adults judge hills to be steeper than young adults (Bhalla and Proffitt, 1999) and distances to be greater when walking is difficult (Sugovic and Witt, 2013), appear to run counter to visually dominated perception. If older adults were up-weighting the visual signal, environmental factors that impede physical movements should have less of an impact on perception, not more.

It is difficult to know how to interpret these contrary findings. One possibility could be that differing results are due to the relative demands of different tasks. When judging hill steepness or target distance, explicit judgments are required that may lead to age-related strategy differences, a problem which has been documented in other domains (Geary et al., 1993). These types of perceptual judgments also occur on a much larger environmental scale than tasks assessing peripersonal space, motor imagery, or action execution. Perhaps in these circumstances older adults are able to up-weight internal sensory signals to inform perception. It is important to note here that we are not arguing that older adults do not have access to internal sensory signals. Rather, we are suggesting that the internal signal is discounted relative to vision due to vision’s reliability.

An Embodied Cognition Model for Aging

Our review has indicated that older adults operate with a model of embodied cognition that is differently weighted than younger adults: emphasizing visual processing components and deemphasizing bodily (tactile, kinematic, proprioceptive) factors. Although it is beyond the purview of this review article, a logical continuation would be to formally develop (perhaps via computational modeling) an explicit model of how aging alters the manifold factors operative in sensorimotor-cognitive processing. Developing such a model will be a difficult task given the range of potential age-related causal factors. First, there are a host of ‘non-embodied’ (as traditionally viewed) cognitive and brain factors that must play a role in how embodiment is altered in aging. As briefly stated in the beginning of this review, older adults exhibit a range of cognitive and brain-based changes. This review has focused almost exclusively on three subdomains pertinent to embodied cognition, but these subdomains are not independent of such factors. Second, social factors are likely operative in the mind-body relationship in aging, although almost no research has been conducted to determine this possibility. This is particularly important considering how negative societal views on aging (i.e., ageism) might alter performance of the older adults. If an older adult believes that he or she is out of shape or cognitively slow, we can expect that participant performance recorded through most behavioral measures will be affected (e.g., Levy, 2003). Third, older adult differences in embodied cognition may reflect cohort differences in strategy. Older adults perform tasks more conservatively, requiring increased evidence prior to making decisions (Ratcliff et al., 2004). It should be noted, however, that these age-related differences in response criterion may themselves simply reflect the age effect in embodied cognition.

We need to be cautious, then, with the concept of an age-related visual dominance. We do not want to say that in all tasks older adults will favor visual processing. Compensation can take multiple forms depending on the context, and in cases in which the visual inputs are minimized, we would expect that older adults will take advantage of motor factors. Consider Roski et al. (2014), who examined brain activation responses in young and older adults with two task conditions: a motor-based and a visual attention task. In the motor-based task, older adults had decreased activation for motor regions but greater in visual processing regions – an expected result given the visual dominance effect in older adults. However, in the visual attention task there was the opposite effect – activation increased for motor and decreased for visual processing brain regions. In short, older adults were displaying a dedifferentiated brain response in which the distinct functionality of these regions was less defined for older adults, perhaps due to compensatory recruitment. An age-related visual dominance effect, in other words, may simply be a compensatory draw on any and all available sensory inputs, with visual inputs being the quickest and most salient in most task conditions.

We can best understand this age-related change in sensorimotor control as a change to the internal model of older adults. The internal model is the mental calibration of many inputs, resulting in a controlled and purposeful response (Wolpert et al., 1995). Because older adults are vulnerable to cognitive, perceptual, physiological, and action-based deficits, the calibration of the internal action model is altered, with weaker physiological inputs and consequent over-reliance on visual processing. Several research groups have argued that aging results in degraded internal action models (cf., Cressman et al., 2010; Boisgontier and Nougier, 2013; Lafargue et al., 2013). Degradation in the internal action model may also explain the characteristic dedifferentiated brain response in older adults. If embodied cognitive effects in young adults reflect the efficient integration of sensory, cognitive, and motor systems, the older adult brain is characterized by the failure to recruit specialized neuronal modules and the adoption of a more diffuse cortical response (Mott et al., 2014; Roski et al., 2014). Research indicates that the older adult motor system is affected by dedifferentiation (Bernard and Seidler, 2012; Koppelmans et al., 2015; Reuter et al., 2015). Further, motor performance in older adults is affected by cerebellar changes, leading to ‘degraded internal models’ (Bernard and Seidler, 2014). Dedifferentiation, which is associated with a lack of specialized neuronal modules, may compromise the ability to calibrate internal and external sensory signals, resulting in inaccurate weighting. Inaccurate or altered weighting may explain the age-related behavioral changes in embodiment we’ve described in the review above.

Future Directions

As we have highlighted throughout the review, virtually every area of aging would benefit from further research using an embodied cognition perspective. Given that, here we will highlight just a few areas of practical importance. One question that has not yet been explored is the age at which changes in embodiment begin to take place. If age-related changes are based on neural dedifferentiation or compensation, then it would be expected that the changes are gradual and positively correlated with age. Relatedly, given that there are individual differences in the onset and rate of neural change with age, there should be corollary individual differences on tasks that tap into embodied effects. Indeed, there is preliminary support for this, with older adults exhibiting wide variability in the relative impact of the body during visuo-motor performance (Bloesch et al., 2013). Not surprisingly, young adults also show individual differences, which are often overlooked by only examining group differences. By expanding age groups to include middle-aged individuals and testing for individual differences, we can begin to achieve a more nuanced understanding of how and when changes in embodiment occur across the lifespan.

It is clear that older adults have altered MSI, and that this impacts not just sensory processing but also higher-level cognition and physical ability. For example, there is a link between MSI and balance, with training on one factor improving performance on the other (Setti et al., 2014; Merriman et al., 2015). Given that falls are a concern for older adults, anything that can reliably improve balance and decrease fall risk is potentially meaningful. Unfortunately, there is still a paucity of research on this topic. The MSI conditions that have been trained are visual-auditory, but given the link between visual-tactile integration and action it may be the case that other types of MSI training would improve balance, and potentially other types of action as well. Visual-tactile integration is also important for successful tool use, which older adults struggle with compared to young adults. While many older adults experience benefits from the use of canes and walkers to assist in mobility, many others report that these tools hinder them and, in fact, increase their risk of falls (Bateni and Maki, 2005). If this struggle is caused in part by a decreased ability to integrate visual-tactile information about the mobility device and bring it into the body schema, training this type of MSI in older adults may be beneficial.

Embodied cognition claims that there is a causal link between physical ability and cognition, so it should not be surprising that studies have found correlations between older adults’ physical capacities and their perceptions (e.g., Sugovic and Witt, 2013). However, many of the studies explicitly examining the relationship between physical declines and the specific cognitive factors assumed to be impacted based on the predictions of EC (including the majority of the studies included in this review) are correlational, leaving unanswered the direction of the relationship or the potential for a third variable. An avenue in which this relationship has been explored experimentally is in fitness training with older adults, which has found that physical interventions such as aerobic exercise have the potential to improve cognitive performance across a variety of tasks, notably processing speed, and visuospatial ability (for recent reviews, see Bherer et al., 2013; Bamidis et al., 2014). Because these are variables that have been implicated in older adult declines on embodied tasks, improving cardiovascular health would perhaps improve embodiment in older adults as well. If this is true, older adults in better physical health should also show greater embodiment than older adults in poor health, a question which can begin to be answered by examining individual differences.

There are many avenues still available for exploration in applying the embodied cognition theoretical framework to understand aging. Indeed, it would appear that we are just scratching the surface of applications to aging, and already we are gaining a richer understanding of age-related declines and the causes of them. The history of gerontological research has focused on isolating and then assessing sensory, cognitive, and physical abilities; it is now more clearly understood that these three systems interact, and that assessing changes means assessing those interactions as well.

Author Contributions

MC compiled original literature review and wrote primary manuscript. EB contributed to both literature review and manuscript preparation.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

  1. ^At its extreme theoretical variants, embodied cognition theory is both non-computational and non-representational (cf., Chemero, 2009), although this is not a universal position within the embodied cognition field (Clark, 1997). Given that we will be drawing upon a broad array of research in this review article, we employ a more inclusive variant of embodied cognition in which traditional cognitivist terms such as ‘mental representations’ are utilized.
  2. ^Note that this work has been criticized as reflecting participant effects in how participants respond rather than actual measures of perceptual alteration (Firestone, 2013; see Witt et al. (2016) for a reply). Recently, Philbeck and Witt (2015) have offered a more measured action-perception model.

References

Aizawa, K. (2015). What is this cognition that is supposed to be embodied? Philos. Psychol. 28, 1–21. doi: 10.1080/09515089.2013.875280

CrossRef Full Text | Google Scholar

Alexander, N. B. (1994). Postural control in older adults. J. Am. Geriatr. Soc. 42, 93–108. doi: 10.1111/j.1532-5415.1994.tb06081.x

CrossRef Full Text | Google Scholar

Amorim, M. A., Isableu, B., and Jarraya, M. (2006). Embodied spatial transformations: “body analogy” for the mental rotation of objects. J. Exp. Psychol. 135, 327–347. doi: 10.1037/0096-3445.135.3.327

PubMed Abstract | CrossRef Full Text | Google Scholar

Anderson, P. G., Nienhuis, B., Mulder, T., and Hulstijn, W. (1998). Are older adults more dependent on visual information in regulating self-motion than younger adults? J. Mot. Behav. 30, 104–113. doi: 10.1080/00222899809601328

PubMed Abstract | CrossRef Full Text | Google Scholar

Atkinson, H. H., Rosano, C., Simonsick, E. M., Williamson, J. D., Davis, C., Ambrosius, W. T., et al. (2007). Cognitive function, gait speed decline, and comorbidities: the health, aging and body composition study. J. Gerontol. 62, 844–850. doi: 10.1093/gerona/62.8.844

CrossRef Full Text | Google Scholar

Baccarini, M., and Maravita, A. (2013). “Beyond the boundaries of the body: plasticity of the body representation following tool use,” in The Hand, an Organ of the Mind: What the Manual Tells the Mental, ed. Z. Radman (Cambridge, MA: MIT Press), 77–99.

Google Scholar

Bamidis, P. D., Vivas, A. B., Styliadis, C., Frantzidis, C., Klados, M., Schlee, W., et al. (2014). A review of physical and cognitive interventions in aging. Neurosci. Biobehav. Rev. 44, 206–220. doi: 10.1016/j.neubiorev.2014.03.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Barrett, A. M., and Craver-Lemley, C. E. (2008). Is it what you see, or how you say it? Spatial bias in young and aged subjects. J. Int. Neuropsychol. Soc. 14, 562–570. doi: 10.1017/S1355617708080764

PubMed Abstract | CrossRef Full Text | Google Scholar

Barrett, M. M., Doheny, E. P., Setti, A., Maguinness, C., Foran, T. G., Kenny, R. A., et al. (2013). Reduced vision selectively impairs spatial updating in fall-prone older adults. Multisens. Res. 26, 69–94. doi: 10.1163/22134808-00002412

PubMed Abstract | CrossRef Full Text | Google Scholar

Barsalou, L. W. (1999). Perceptual symbol systems. Behav. Brain Sci. 22, 577–660. doi: 10.1017/s0140525x99002149

CrossRef Full Text | Google Scholar

Barsalou, L. W. (2008). Grounded cognition. Annu. Rev. Psychol. 59, 617–645. doi: 10.1146/annurev.psych.59.103006.093639

CrossRef Full Text | Google Scholar

Barsalou, L. W. (2016). “Situated conceptualization: theory and application,” in Foundations of Embodied Cognition, eds Y. Coello and M. H. Fisher (East Sussex: Psychology Press), 11–37.

Google Scholar

Bateni, H., and Maki, B. E. (2005). Assistive devices for balance and mobility: benefits, demands, and adverse consequences. Arch. Phys. Med. Rehabil. 86, 134–145. doi: 10.1016/j.apmr.2004.04.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Beauchet, O., Annweiler, C., Assal, F., Bridenbaugh, S., Herrmann, F. R., Kressig, R. W., et al. (2010). Imagined timed up & go test: a new tool to assess higher-level gait and balance disorders in older adults? J. Neurol. Sci. 294, 102–106. doi: 10.1016/j.jns.2010.03.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Bernard, J. A., and Seidler, R. D. (2012). Evidence for motor cortex dedifferentiation in older adults. Neurobiol. Aging 33, 1890–1899. doi: 10.1016/j.neurobiolaging.2011.06.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Bernard, J. A., and Seidler, R. D. (2014). Moving forward: age effects on the cerebellum underlie cognitive and motor declines. Neurosci. Biobehav. Rev. 42, 193–207. doi: 10.1016/j.neubiorev.2014.02.011

PubMed Abstract | CrossRef Full Text | Google Scholar

Berti, A., and Frassinetti, F. (2000). When far becomes near: remapping of space by tool use. J. Cogn. Neurosci. 12, 415–420. doi: 10.1162/089892900562237

CrossRef Full Text | Google Scholar

Bhalla, M., and Proffitt, D. R. (1999). Visual–motor recalibration in geographical slant perception. J. Exp. Psychol. Hum. Percept. Perform. 25, 1076–1096. doi: 10.1037/0096-1523.25.4.1076

CrossRef Full Text | Google Scholar

Bherer, L., Erickson, K. I., and Liu-Ambrose, T. (2013). A review of the effects of physical activity and exercise on cognitive and brain functions in older adults. J. Aging Res. 2013:657508. doi: 10.1155/2013/657508

PubMed Abstract | CrossRef Full Text | Google Scholar

Bjoertomt, O., Cowey, A., and Walsh, V. (2002). Spatial neglect in near and far space investigated by repetitive transcranial magnetic stimulation. Brain 125, 2012–2022. doi: 10.1093/brain/awf211

CrossRef Full Text | Google Scholar

Blanke, O. (2012). Multisensory brain mechanisms of bodily self-consciousness. Nat. Rev. Neurosci. 13, 556–572. doi: 10.1038/nrn3292

PubMed Abstract | CrossRef Full Text | Google Scholar

Bloesch, E. K., Davoli, C. C., and Abrams, R. A. (2013). Age-related changes in attentional reference frames for peripersonal space. Psychol. Sci. 24, 557–561. doi: 10.1177/0956797612457385

PubMed Abstract | CrossRef Full Text | Google Scholar

Bock, O. (2005). Components of sensorimotor adaptation in young and elderly subjects. Exp. Brain Res. 160, 259–263. doi: 10.1007/s00221-004-2133-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Boisgontier, M. P., and Nougier, V. (2013). Ageing of internal models: from a continuous to an intermittent proprioceptive control of movement. Age 35, 1339–1355. doi: 10.1007/s11357-012-9436-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Boisgontier, M. P., Van Halewyck, F., Corporaal, S. H., Willacker, L., Van Den Bergh, V., Beets, I. A., et al. (2014). Vision of the active limb impairs bimanual motor tracking in young and older adults. Front. Aging Neurosci. 6:320. doi: 10.3389/fnagi.2014.00320

PubMed Abstract | CrossRef Full Text | Google Scholar

Borella, E., Meneghetti, C., Muffato, V., and De Beni, R. (2015). Map learning and the alignment effect in young and older adults: how do they gain from having a map available while performing pointing tasks? Psychol. Res. 79, 104–119. doi: 10.1007/s00426-014-0543-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Borghi, A. M., and Cimatti, F. (2010). Embodied cognition and beyond: acting and sensing the body. Neuropsychologia 48, 763–773. doi: 10.1016/j.neuropsychologia.2009.10.029

PubMed Abstract | CrossRef Full Text | Google Scholar

Borghi, A. M., Scorolli, C., Caligiore, D., Baldassarre, G., and Tummolini, L. (2013). The embodied mind extended: using words as social tools. Front. Psychol. 4:214. doi: 10.3389/fpsyg.2013.00214

PubMed Abstract | CrossRef Full Text | Google Scholar

Buccino, G., Binkofski, F., and Riggio, L. (2004). The mirror neuron system and action recognition. Brain Lang. 89, 370–376. doi: 10.1016/S0093-934X(03)00356-0

CrossRef Full Text | Google Scholar

Calvert, G., Spence, C., and Stein, B. E. (2004). The Handbook of Multisensory Processes. Cambridge, MA: MIT Press.

Google Scholar

Cardinali, L., Brozzoli, C., and Farne, A. (2009). Peripersonal space and body schema: two labels for the same concept? Brain Topogr. 21, 252–260. doi: 10.1007/s10548-009-0092-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Cavalcante, K. R., and Caramelli, P. (2009). Evaluation of the performance of normal elderly in a limb praxis protocol: influence of age, gender, and education. J. Int. Neuropsychol. Soc. 15, 618–622. doi: 10.1017/S1355617709090663

PubMed Abstract | CrossRef Full Text | Google Scholar

Cerella, J., Poon, L. W., and Fozard, J. L. (1981). Mental rotation and age reconsidered. J. Gerontol. 36, 620–624. doi: 10.1093/geronj/36.5.620

PubMed Abstract | CrossRef Full Text | Google Scholar

Chao, L. L., and Martin, A. (2000). Rapid communication representation of manipulable man-made objects in the dorsal stream. Neuroimage 12, 478–484. doi: 10.1006/nimg.2000.0635

PubMed Abstract | CrossRef Full Text | Google Scholar

Chemero, A. (2009). Radical Embodied Cognitive Science. Cambridge, MA: MIT Press.

Google Scholar

Chen, P., Goedert, K. M., Murray, E., Kelly, K., Ahmeti, S., and Barrett, A. M. (2011). Spatial bias and right hemisphere function: sex-specific changes with aging. J. Int. Neuropsychol. Soc. 17, 455–462. doi: 10.1017/S135561771100004X

PubMed Abstract | CrossRef Full Text | Google Scholar

Cherry, B. J., Adamson, M., Duclos, A., and Hellige, J. B. (2005). Aging and individual variation in interhemispheric collaboration and hemispheric asymmetry. Aging Neuropsychol. Cogn. 12, 316–339.

Google Scholar

Clark, A. (2010). “Memento’s revenge: the extended mind, extended,” in The Extended Mind, ed. R. Menary (Cambridge, MA: MIT Press), 43–66.

Google Scholar

Clark, A. (1997). Being There: Putting Brain, Body and World Together Again. Cambridge, MA: MIT Press.

Google Scholar

Coats, R. O., Snapp-Childs, W., Wilson, A. D., and Bingham, G. P. (2013). Perceptuo-motor learning rate declines by half from 20s to 70/80s. Exp. Brain Res. 225, 75–84. doi: 10.1007/s00221-012-3349-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Conson, M., Trojano, L., Vitale, C., Mazzarella, E., Allocca, R., Barone, P., et al. (2014). The role of embodied simulation in mental transformation of whole-body images: evidence from Parkinson’s disease. Hum. Mov. Sci. 33, 343–353. doi: 10.1016/j.humov.2013.10.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Costello, M. C., Bloesch, E. K., Davoli, C. C., Panting, N. D., Abrams, R. A., and Brockmole, J. R. (2015). Spatial representations in older adults are not modified by action: evidence from tool use. Psychol. Aging 30, 656–668. doi: 10.1037/pag0000029

PubMed Abstract | CrossRef Full Text | Google Scholar

Craik, F. I. M., and Dirkx, E. (1992). Age-related differences in three tests of visual imagery. Psychol. Aging 7, 661–665. doi: 10.1037/0882-7974.7.4.661

CrossRef Full Text | Google Scholar

Creem-Regehr, S. H., and Kunz, B. R. (2010). Perception and action. Wiley Interdiscip. Rev. Cogn. Sci. 1, 800–810. doi: 10.1002/wcs.82

PubMed Abstract | CrossRef Full Text | Google Scholar

Cressman, E. K., Salomonczyk, D., and Henriques, D. Y. (2010). Visuomotor adaptation and proprioceptive recalibration in older adults. Exp. Brain Res. 205, 533–544. doi: 10.1007/s00221-010-2392-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Daviaux, Y., Cremoux, S., Tallet, J., Amarantini, D., Cornu, C., and Deschamps, T. (2015). An enhanced experimental procedure to rationalize on the impairment of perception of action capabilities. Psychol. Res. 80, 1–11. doi: 10.1007/s00426-015-0653-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Davoli, C. C., Brockmole, J. R., and Goujon, A. (2012a). A bias to detail: how hand position modulates visual learning and visual memory. Mem. Cogn. 40, 352–359. doi: 10.3758/s13421-011-0147-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Davoli, C. C., Brockmole, J. R., and Witt, J. K. (2012b). Compressing perceived distance with remote tool-use: real, imagined, and remembered. J. Exp. Psychol. Hum. Percept. Perform. 38, 80–89. doi: 10.1037/a0024981

PubMed Abstract | CrossRef Full Text | Google Scholar

De Simone, L., Tomasino, B., Marusic, N., Eleopra, R., and Rumiati, R. I. (2013). The effects of healthy aging on mental imagery as revealed by egocentric and allocentric mental spatial transformations. Acta Psychol. 143, 146–156. doi: 10.1016/j.actpsy.2013.02.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Decety, J. (1996). Do imagined and executed actions share the same neural substrate? Cogn. Brain Res. 3, 87–93.

PubMed Abstract | Google Scholar

Devlin, A. L., and Wilson, P. H. (2010). Adult age differences in the ability to mentally transform object and body stimuli. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 17, 709–729. doi: 10.1080/13825585.2010.510554

PubMed Abstract | CrossRef Full Text | Google Scholar

Diaconescu, A. O., Hasher, L., and McIntosh, A. R. (2013). Visual dominance and multisensory integration changes with age. Neuroimage 65, 152–166. doi: 10.1016/j.neuroimage.2012.09.057

PubMed Abstract | CrossRef Full Text | Google Scholar

Diersch, N., Cross, E. S., Stadler, W., Schütz-Bosbach, S., and Rieger, M. (2012). Representing others’ actions: the role of expertise in the aging mind. Psychol. Res. 76, 525–541. doi: 10.1007/s00426-011-0404-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Diersch, N., Jones, A. L., and Cross, E. S. (2016). The timing and precision of action prediction in the aging brain. Hum. Brain Mapp. 37, 54–66. doi: 10.1002/hbm.23012

PubMed Abstract | CrossRef Full Text | Google Scholar

Diersch, N., Mueller, K., Cross, E. S., Stadler, W., Rieger, M., and Schütz-Bosbach, S. (2013). Action prediction in younger versus older adults: neural correlates of motor familiarity. PLoS ONE 8:e64195. doi: 10.1371/journal.pone.0064195

PubMed Abstract | CrossRef Full Text | Google Scholar

DiPietro, L. (2001). Physical activity in aging: changes in patterns and their relationship to health and function. J. Gerontol. 56, 13–22. doi: 10.1093/gerona/56.suppl_2.13

CrossRef Full Text | Google Scholar

Dominey, P., Decety, J., Broussolle, E., Chazot, G., and Jeannerod, M. (1995). Motor imagery of a lateralized sequential task is asymmetrically slowed in hemi-Parkinson’s patients. Neuropsychologia 33, 727–741. doi: 10.1016/0028-3932(95)00008-Q

CrossRef Full Text | Google Scholar

Durgin, F. H., Klein, B., Spiegel, A., Strawser, C. J., and Williams, M. (2012). The social psychology of perception experiments: hills, backpacks, glucose, and the problem of generalizability. J. Exp. Psychol. Hum. Percept. Perform. 38, 1582–1595. doi: 10.1037/a0027805

PubMed Abstract | CrossRef Full Text | Google Scholar

Falkenstein, M., Yordanova, J., and Kolev, V. (2006). Effects of aging on slowing of motor-response generation. Int. J. Psychophysiol. 59, 22–29. doi: 10.1016/j.ijpsycho.2005.08.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Farnè, A., Iriki, A., and Làdavas, E. (2005). Shaping multisensory action–space with tools: evidence from patients with cross-modal extinction. Neuropsychologia 43, 238–248. doi: 10.1016/j.neuropsychologia.2004.11.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Farnè, A., and Làdavas, E. (2000). Dynamic size-change of hand peripersonal space following tool use. Neuroreport 11, 1645–1649. doi: 10.1097/00001756-200006050-00010

CrossRef Full Text | Google Scholar

Farnè, A., Serino, A., and Làdavas, E. (2007). Dynamic size-change of peri-hand space following tool-use: determinants and spatial characteristics revealed through cross-modal extinction. Cortex 43, 436–443. doi: 10.1016/S0010-9452(08)70468-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Fink, G. R., Marshall, J. C., Weiss, P. H., and Zilles, K. (2001). The neural basis of vertical and horizontal line bisection judgments: an fMRI study of normal volunteers. Neuroimage 14, S59–S67. doi: 10.1006/nimg.2001.0819

PubMed Abstract | CrossRef Full Text | Google Scholar

Firestone, C. (2013). How “Paternalistic” is spatial perception? Why wearing a heavy backpack doesn’t–and couldn’t–make hills look steeper. Perspect. Psychol. Sci. 8, 455–473. doi: 10.1177/1745691613489835

PubMed Abstract | CrossRef Full Text | Google Scholar

Fodor, J. D., Fodor, J. A., and Garrett, M. F. (1975). The psychological unreality of semantic representations. Linguist. Inq. 6, 515–531.

Google Scholar

Foglia, L., and Wilson, R. A. (2013). Embodied cognition. Wiley Interdiscip. Rev. Cogn. Sci. 4, 319–325. doi: 10.1002/wcs.1226

PubMed Abstract | CrossRef Full Text | Google Scholar

Fozard, J. L., and Gordon-Salant, S. (2001). “Changes in vision and hearing with aging,” in Handbook of the Psychology of Aging, 5th Edn, eds J. E. Birren and K. W. Schaie (San Diego, CA: Academic Press), 241–266.

Google Scholar

Freiherr, J., Lundstrom, J. N., Habel, U., and Reetz, K. (2013). Multisensory integration mechanisms during aging. Front. Hum. Neurosci. 7:863. doi: 10.3389/fnhum.2013.00863

PubMed Abstract | CrossRef Full Text | Google Scholar

Frick, A., Daum, M. M., Walser, S., and Mast, F. W. (2009). Motor processes in children’s mental rotation. J. Cogn. Dev. 10, 18–40. doi: 10.1080/15248370902966719

CrossRef Full Text | Google Scholar

Frick, A., Ferrara, K., and Newcombe, N. S. (2013). Using a touch screen paradigm to assess the development of mental rotation between 3½ and 5½ years of age. Cogn. Process. 14, 117–127. doi: 10.1007/s10339-012-0534-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Frick, A., Huber, S., Reips, U.-D., and Krist, H. (2005). Task-specific knowledge of the law of pendulum motion in children and adults. Swiss J. Psychol. 64, 103–114. doi: 10.1024/1421-0185.64.2.103

CrossRef Full Text | Google Scholar

Frick, A., and Möhring, W. (2013). Mental object rotation and motor development in 8-and 10-month-old infants. J. Exp. Child Psychol. 115, 708–720. doi: 10.1016/j.jecp.2013.04.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Fujii, T., Fukatsu, R., Yamadori, A., and Kimura, I. (1995). Effect of age on the line bisection test. J. Clin. Exp. Neuropsychol. 17, 941–944. doi: 10.1080/01688639508402443

PubMed Abstract | CrossRef Full Text | Google Scholar

Funk, M., Brugger, P., and Wilkening, F. (2005). Motor processes in children’s imagery: the case of mental rotation of hands. Dev. Sci. 8, 402–408. doi: 10.1111/j.1467-7687.2005.00428.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Gabbard, C., Cacola, P., and Cordova, A. (2011). Is there an advanced aging effect on the ability to mentally represent action? Arch. Gerontol. Geriatr. 53, 206–209. doi: 10.1016/j.archger.2010.10.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Garza, J. P., Eslinger, P. J., and Barratt, A. M. (2008). Perceptual-attentional and motor-intentional bias in near and far space. Brain Cogn. 68, 9–14. doi: 10.1016/j.bandc.2008.02.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Geary, D. C., Frensch, P. A., and Wiley, J. G. (1993). Simple and complex mental subtraction: strategy choice and speed-of-processing differences in younger and older adults. Psychol. Aging 8, 242–256. doi: 10.1037/0882-7974.8.2.242

PubMed Abstract | CrossRef Full Text | Google Scholar

Ghafouri, M., and Lestienne, F. G. (2000). Altered representation of peripersonal space in the elderly human subject: a sensorimotor approach. Neurosci. Lett. 289, 193–196. doi: 10.1016/S0304-3940(00)01280-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Gilpin, H. R., Moseley, G. L., Stanton, T. R., and Newport, R. (2015). Evidence for distorted mental representation of the hand in osteoarthritis. Rheumatology 54, 678–682. doi: 10.1093/rheumatology/keu367

PubMed Abstract | CrossRef Full Text | Google Scholar

Glenberg, A. M. (2015). Few believe the world is flat: how embodiment is changing the scientific understanding of cognition. Can. J. Exp. Psychol. 69, 165–171. doi: 10.1037/cep0000056

PubMed Abstract | CrossRef Full Text | Google Scholar

Glenberg, A. M., Witt, J. K., and Metcalfe, J. (2013). From the revolution to embodiment: 25 years of cognitive psychology. Perspect. Psychol. Sci. 8, 573–585. doi: 10.1177/1745691613498098

PubMed Abstract | CrossRef Full Text | Google Scholar

Goble, D. J., Coxon, J. P., Wenderoth, N., Impe, A. V., and Swinnen, S. P. (2009). Proprioceptive sensibility in the elderly: degeneration, functional consequences and plastic-adaptive processes. Neurosci. Behav. Rev. 33, 271–278. doi: 10.1016/j.neubiorev.2008.08.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Goldman, A. I. (2012). A moderate approach to embodied cognitive science. Rev. Philos. Psychol. 3, 71–88. doi: 10.1007/s13164-012-0089-0

CrossRef Full Text | Google Scholar

Grabiner, P. C., Biswas, S. T., and Grabiner, M. D. (2001). Age-related changes in spatial and temporal gait variables. Arch. Phys. Med. Rehabil. 82, 31–35. doi: 10.1053/apmr.2001.18219

PubMed Abstract | CrossRef Full Text | Google Scholar

Hackney, A. L., and Cinelli, M. E. (2011). Action strategies of older adults walking through apertures. Gait Posture 33, 733–736. doi: 10.1016/j.gaitpost.2011.02.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Hauk, O., Johnsrude, I., and Pulvermüller, F. (2004). Somatotopic representation of action words in human motor and premotor cortex. Neuron 41, 301–317. doi: 10.1016/S0896-6273(03)00838-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Helmich, R. C., de Lange, F. P., Bloem, B. R., and Toni, I. (2007). Cerebral compensation during motor imagery in Parkinson’s disease. Neuropsychologia 45, 2201–2215. doi: 10.1016/j.neuropsychologia.2007.02.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Hertzog, C., and Rypma, B. (1991). Age differences in components of mental-rotation task performance. Bull. Psychon. Soc. 29, 209–212. doi: 10.3758/BF03342680

CrossRef Full Text | Google Scholar

Heuer, H., and Hegele, M. (2008). Adaptation to visuomotor rotations in younger and older adults. Psychol. Aging 23, 190–202. doi: 10.1037/0882-7974.23.1.190

PubMed Abstract | CrossRef Full Text | Google Scholar

Heuer, H., and Hegele, M. (2014). Age-related variations of visuo-motor adaptation beyond explicit knowledge. Front. Aging Neurosci. 6:152. doi: 10.3389/fnagi.2014.00152

PubMed Abstract | CrossRef Full Text | Google Scholar

Heuer, H., Hegele, M., and Rand, M. K. (2013). “Age-related variations in the control of electronic tools,” in Age-Differentiated Work Systems, eds C. M. Schlick, E. Frieling, and J. Wegge (Berlin: Springer), 369–390. doi: 10.1007/978-3-642-35057-3_16

CrossRef Full Text | Google Scholar

Hihara, S., Notoya, T., Tanaka, M., Ichinose, S., Ojima, H., Obayashi, S., et al. (2006). Extension of corticocortical afferents into the anterior bank of the intraparietal sulcus by tool-use training in adult monkeys. Neuropsychologia 44, 2636–2646. doi: 10.1016/j.neuropsychologia.2005.11.020

PubMed Abstract | CrossRef Full Text | Google Scholar

Hillman, C. H., Erickson, K. I., and Kramer, A. F. (2008). Be smart, exercise your heart: exercise effects on brain and cognition. Neuroscience 9, 59–65. doi: 10.1038/nrn2298

PubMed Abstract | CrossRef Full Text | Google Scholar

Hommel, B., Musseler, J., Aschersleben, G., and Prinz, W. (2001). The theory of event coding (TEC): a framework for perception and action planning. Behav. Brain Sci. 24, 849–937. doi: 10.1017/S0140525X01000103

CrossRef Full Text

Horak, F. B., Shupert, C. L., and Mirka, A. (1989). Components of postural dyscontrol in the elderly: a review. Neurobiol. Aging 10, 727–738. doi: 10.1016/0197-4580(89)90010-9

CrossRef Full Text | Google Scholar

Huitema, R. B., Brouwer, W. H., Mulder, T., Dekker, R., Hof, A. L., and Postema, K. (2005). Effect of ageing on the ability to adapt to a visual distortion during walking. Gait Posture 21, 440–446. doi: 10.1016/j.gaitpost.2004.05.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Inagaki, H., Meguro, K., Shimada, M., Ishizaki, J., Okuzumi, H., and Yamadori, A. (2002). Discrepancy between mental rotation and perspective-taking abilities in normal aging assessed by Piaget’s three-mountain task. J. Clin. Exp. Neuropsychol. 24, 18–25. doi: 10.1076/jcen.24.1.18.969

PubMed Abstract | CrossRef Full Text | Google Scholar

Inui, N. (1997). Simple reaction times and timing of serial reactions of middle-aged and old men. Percept. Mot. Skills 84, 219–225. doi: 10.2466/pms.1997.84.1.219

PubMed Abstract | CrossRef Full Text | Google Scholar

Iriki, A., Tanaka, M., and Iwamura, Y. (1996). Coding of modified body schema during tool use by macaque postcentral neurones. Neuroreport 7, 2325–2330. doi: 10.1097/00001756-199610020-00010

PubMed Abstract | CrossRef Full Text | Google Scholar

Jansen, P., and Kaltner, S. (2014). Object-based and egocentric mental rotation performance in older adults: the importance of gender differences and motor ability. Aging Neuropsychol. Cogn. 21, 296–316. doi: 10.1080/13825585.2013.805725

PubMed Abstract | CrossRef Full Text | Google Scholar

Järveläinen, J., Schuermann, M., and Hari, R. (2004). Activation of the human primary motor cortex during observation of tool use. Neuroimage 23, 187–192. doi: 10.1016/j.neuroimage.2004.06.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Jeannerod, M. (1994). The representing brain: neural correlates of motor intention and imagery. Behav. Brain Sci. 17, 187–202. doi: 10.1017/S0140525X00034026

CrossRef Full Text | Google Scholar

Jeannerod, M. (2001). Neural simulation of action: a unifying mechanism for motor cognition. Neuroimage 14, 103–109. doi: 10.1006/nimg.2001.0832

PubMed Abstract | CrossRef Full Text | Google Scholar

Jeannerod, M., and Anquetil, T. (2008). Putting oneself in the perspective of the other: a framework for self–other differentiation. Soc. Neurosci. 3, 356–367. doi: 10.1080/17470910701563715

PubMed Abstract | CrossRef Full Text | Google Scholar

Jenkins, L., Myerson, J., Joerding, J. A., and Hale, S. (2000). Converging evidence that visuospatial cognition is more age-sensitive than verbal cognition. Psychol. Aging 15, 157–175. doi: 10.1037/0882-7974.15.1.157

PubMed Abstract | CrossRef Full Text | Google Scholar

Jewell, G., and McCourt, M. E. (2000). Pseudoneglect: a review and meta-analysis of performance factors in line bisection tasks. Neuropsychologia 38, 93–110. doi: 10.1016/S0028-3932(99)00045-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaltner, S., and Jansen, P. (2016). Developmental changes in mental rotation: a dissociation between object-based and egocentric transformations. Adv. Cogn. Psychol. 12, 67–78. doi: 10.5709/acp-0187-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaltner, S., Riecke, B. E., and Jansen, P. (2014). Embodied mental rotation: a special link between egocentric transformation and the bodily self. Front. Psychol 5:505. doi: 10.3389/fpsyg.2014.00505

PubMed Abstract | CrossRef Full Text | Google Scholar

Kannus, P., Sievänen, H., Palvanen, M., Järvinen, T., and Parkkari, J. (2005). Prevention of falls and consequent injuries in elderly people. Lancet 366, 1885–1893. doi: 10.1016/S0140-6736(05)67604-0

CrossRef Full Text | Google Scholar

Kirsch, W., and Kunde, W. (2013). Moving further moves things further away in visual perception: position-based movement planning affects distance judgments. Exp. Brain Res. 226, 431–440. doi: 10.1007/s00221-013-3455-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Klencklen, G., Després, O., and Dufour, A. (2012). What do we know about aging and spatial cognition? Reviews and Perspectives Ageing Res. Rev. 11, 123–135. doi: 10.1016/j.arr.2011.10.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Koppelmans, V., Hirsiger, S., Mérillat, S., Jäncke, L., and Seidler, R. D. (2015). Cerebellar gray and white matter volume and their relation with age and manual motor performance in healthy older adults. Hum. Brain Mapp. 36, 2352–2363. doi: 10.1002/hbm.22775

PubMed Abstract | CrossRef Full Text | Google Scholar

Kosslyn, S. M. (1987). Seeing and imagining in the cerebral hemispheres: a computational approach. Psychol. Rev. 94, 148–175. doi: 10.1037/0033-295X.94.2.148

CrossRef Full Text | Google Scholar

Kosslyn, S. M. (1995). “Mental imagery,” in An Invitation to Cognitive Science: Visual Cognition, 2nd edn, eds S. M. Kosslyn and D. N. Osherson (Cambridge, MA: MIT Press).

Google Scholar

Kosslyn, S. M., Ganis, G., and Thompson, W. L. (2001). Neural foundations of imagery. Nat. Rev. Neurosci. 2, 635–642. doi: 10.1038/35090055

PubMed Abstract | CrossRef Full Text | Google Scholar

Kraeutner, S., Gionfriddo, A., Bardouille, T., and Boe, S. (2014). Motor imagery-based brain activity parallels that of motor execution: evidence from magnetic source imaging of cortical oscillations. Brain Res. 1588, 81–91. doi: 10.1016/j.brainres.2014.09.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Kraft, E. (2012). Cognitive function, physical activity, and aging: possible biological links and implications for multimodal interventions. Aging Neuropsychol. Cogn. 19, 248–263. doi: 10.1080/13825585.2011.645010

PubMed Abstract | CrossRef Full Text | Google Scholar

Kramer, A. F., Humphrey, D. G., Larish, J. F., Logan, G. D., and Strayer, D. L. (1994). Aging and inhibition: beyond a unitary view of inhibitory processing in attention. Psychol. Aging 9, 491–512. doi: 10.1037/0882-7974.9.4.491

CrossRef Full Text | Google Scholar

Krüger, M., and Krist, H. (2009). Imagery and motor processes–When are they connected? The mental rotation of body parts in development. J. Cogn. Dev. 10, 239–261. doi: 10.1080/15248370903389341

CrossRef Full Text | Google Scholar

Lafargue, G., Noël, M., and Luyat, M. (2013). In the elderly, failure to update internal models leads to over-optimistic predictions about upcoming actions. PLoS ONE 8:e51218. doi: 10.1371/journal.pone.0051218

PubMed Abstract | CrossRef Full Text | Google Scholar

Laughton, C. A., Slavin, M., Katdare, K., Nolan, L., Bean, J. F., Kerrigan, D. C., et al. (2003). Aging, muscle activity, and balance control: physiologic changes associated with balance impairment. Gait Posture 18, 101–108. doi: 10.1016/S0966-6362(02)00200-X

PubMed Abstract | CrossRef Full Text | Google Scholar

Laurienti, P. J., Burdette, J. H., Maldjian, J. A., and Wallace, M. T. (2006). Enhanced multisensory integration in older adults. Neurobiol. Aging 27, 1155–1163. doi: 10.1016/j.neurobiolaging.2005.05.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Léonard, G., and Tremblay, F. (2007). Corticomotor facilitation associated with observation, imagery and imitation of hand actions: a comparative study in young and old adults. Exp. Brain Res. 177, 167–175. doi: 10.1007/s00221-006-0657-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Lesourd, M., Baumard, J., Jarry, C., Le Gall, D., and Osiurak, F. (2016). A cognitive-based model of tool use in normal aging. Aging Neuropsychol. Cogn. doi: 10.1080/13825585.2016.1218822 [Epub ahead of print].

PubMed Abstract | CrossRef Full Text | Google Scholar

Levy, B. R. (2003). Mind matters: cognitive and physical effects of aging self-stereotypes. J. Gerontol. B Psychol. Sci. Soc. Sci. 58, 203–211. doi: 10.1093/geronb/58.4.P203

PubMed Abstract | CrossRef Full Text | Google Scholar

Linkenauger, S. A., Leyrer, M., Bülthoff, H. H., and Mohler, B. J. (2013). Welcome to wonderland: the influence of the size and shape of a virtual hand on the perceived size and shape of virtual objects. PLoS ONE 8:e68594. doi: 10.1371/journal.pone.0068594

PubMed Abstract | CrossRef Full Text | Google Scholar

Linkenauger, S. A., Witt, J. K., Stefanucci, J. K., Bakdash, J. Z., and Proffitt, D. R. (2009). The effects of handedness and reachability on perceived distance. J. Exp. Psychol. 35, 1649–1660. doi: 10.1037/a0016875

PubMed Abstract | CrossRef Full Text | Google Scholar

Lopez, C., Schreyer, H. M., Preuss, N., and Mast, F. W. (2012). Vestibular stimulation modifies the body schema. Neuropsychologia 50, 1830–1837. doi: 10.1016/j.neuropsychologia.2012.04.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Lord, S. R., and Menz, H. B. (2000). Visual contributions to postural stability in older adults. Gerontology 46, 306–310. doi: 10.1159/000022182

CrossRef Full Text | Google Scholar

Lord, S. R., and Sturnieks, D. L. (2005). The physiology of falling: assessment and prevention strategies for older people. J. Sci. Med. Sport 8, 35–42. doi: 10.1016/S1440-2440(05)80022-2

CrossRef Full Text | Google Scholar

Lui, F., Buccino, G., Duzzi, D., Benuzzi, F., Crisi, G., Baraldi, P., et al. (2008). Neural substrates for observing and imagining non-object-directed actions. Soc. Neurosci. 3, 261–275. doi: 10.1080/17470910701458551

PubMed Abstract | CrossRef Full Text | Google Scholar

Lustig, C., Hasher, L., and Zacks, R. (2007). “Inhibitory deficit theory: recent developments in a “New View,” in Inhibition and Cognition, eds D. Gorfein and C. MacLeod (Washington, DC: American Psychological Association), 145–162. doi: 10.1037/11587-008

CrossRef Full Text | Google Scholar

Maguinness, C., Setti, A., Roudaia, E., and Kenny, R. A. (2013). Does that look heavy to you? Perceived weight judgment in lifting actions in younger and older adults. Front. Hum. Neurosci. 7:795. doi: 10.3389/fnhum.2013.00795

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahon, B. Z. (2015). The burden of embodied cognition. Can. J. Exp. Psychol. 69, 172–178. doi: 10.1037/cep0000060

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahoney, J. R., Holtzer, R., and Verghese, J. (2014). Visual-somatosensory integration and balance: evidence for psychophysical integrative differences in aging. Multisens. Res. 27, 17–42. doi: 10.1163/22134808-00002444

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahoney, J. R., Li, P. C. C., Oh-Park, M., Verghese, J., and Holtzer, R. (2011). Multisensory integration across the senses in young and old adults. Brain Res. 1426, 43–53. doi: 10.1016/j.brainres.2011.09.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahoney, J. R., Verghese, J., Dumas, K., Wang, C., and Holtzer, R. (2012). The effect of multisensory cues on attention in aging. Brain Res. 1472, 63–73. doi: 10.1016/j.brainres.2012.07.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Malafouris, L. (2004). “The cognitive basis of material engagement: where brain, body and culture conflate,” in Rethinking Materiality: The Engagement of Mind with the Material World, eds E. DeMarrais, C. Gosden, and C. Renfrew (Cambridge: McDonald Institute for Archaeological Research), 53–61.

Google Scholar

Maravita, A., Husain, M., Clarke, K., and Driver, J. (2001). Reaching with a tool extends visual–tactile interactions into far space: evidence from cross-modal extinction. Neuropsychologia 39, 580–585. doi: 10.1016/S0028-3932(00)00150-0

CrossRef Full Text | Google Scholar

Maravita, A., and Iriki, A. (2004). Tools for the body (schema). Trends Cogn. Sci. 8, 79–86. doi: 10.1016/j.tics.2003.12.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Maravita, A., Spence, C., and Driver, J. (2003). Multisensory integration and the body schema: close to hand and within reach. Curr. Biol. 13, R531–R539. doi: 10.1016/S0960-9822(03)00449-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Maravita, A., Spence, C., Kennett, S., and Driver, J. (2002). Tool-use changes multimodal spatial interactions between vision and touch in normal humans. Cognition 83, B25–B34. doi: 10.1016/S0010-0277(02)00003-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Maryott, J., and Sekuler, R. (2009). Age-related changes in imitating sequences of observed movements. Psychol. Aging 24, 476–486. doi: 10.1037/a0015266

PubMed Abstract | CrossRef Full Text | Google Scholar

Mau-Moeller, A., Behrens, M., Linder, T., Bader, R., and Bruhn, S. (2013). Age-related changes in neuromuscular function of the quadriceps muscle in physically active adults. J. Electromyogr. Kinesiol. 23, 640–648. doi: 10.1016/j.jelekin.2013.01.009

PubMed Abstract | CrossRef Full Text | Google Scholar

McDowd, J. M., and Craik, F. I. (1988). Effects of aging and task difficulty on divided attention performance. J. Exp. Psychol. 14, 267–280. doi: 10.1037/0096-1523.14.2.267

CrossRef Full Text | Google Scholar

McGovern, D. P., Roudaia, E., Stapleton, J., McGinnity, T. M., and Newell, F. N. (2014). The sound-induced flash illusion reveals dissociable age-related effects in multisensory integration. Front. Aging Neurosci. 6:250. doi: 10.3389/fnagi.2014.00250

PubMed Abstract | CrossRef Full Text | Google Scholar

Meegan, D. V., and Tipper, S. P. (1998). Reaching into cluttered visual environments: spatial and temporal influences of distracting objects. Q. J. Exp. Psychol. 51, 225–249. doi: 10.1080/02724989843000004

PubMed Abstract | CrossRef Full Text | Google Scholar

Merriman, N. A., Whyatt, C., Setti, A., Craig, C., and Newell, F. N. (2015). Successful balance training is associated with improved multisensory function in fall-prone older adults. Comput. Human Behav. 45, 192–203. doi: 10.1016/j.chb.2014.12.017

CrossRef Full Text | Google Scholar

Metter, E. J., Lynch, N., Conwit, R., Lindle, R., Tobin, J., and Hurley, B. (1999). Muscle quality and age: cross-sectional and longitudinal comparisons. J. Gerontol. A Biol. Sci. Med. Sci. 54, B207–B218. doi: 10.1093/gerona/54.5.b207

PubMed Abstract | CrossRef Full Text | Google Scholar

Mirolli, M., and Parisi, D. (2009). Language as a cognitive tool. Minds Mach. 19, 517–528. doi: 10.1007/s11023-009-9174-2

CrossRef Full Text | Google Scholar

Mizelle, J. C., and Wheaton, L. A. (2010). The neuroscience of storing and molding tool action concepts: how “plastic” is grounded cognition. Front. Psychol. 1:195. doi: 10.3389/fpsyg.2010.00195

PubMed Abstract | CrossRef Full Text | Google Scholar

Moffat, S. D. (2009). Aging and spatial navigation: what do we know and where do we go? Neuropsychol. Rev. 19, 478–489. doi: 10.1007/s11065-009-9120-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Möhring, W., and Frick, A. (2013). Touching up mental rotation: effects of manual experience on 6-month-old infants’ mental object rotation. Child Dev. 84, 1554–1565. doi: 10.1111/cdev.12065

PubMed Abstract | CrossRef Full Text | Google Scholar

Mott, K. K., Alperin, B. R., Holcomb, P. J., and Daffner, K. R. (2014). Age-related decline in differentiated neural responses to rare target versus frequent standard stimuli. Brain Res. 1587, 97–111. doi: 10.1016/j.brainres.2014.08.057

PubMed Abstract | CrossRef Full Text | Google Scholar

Mozaz, M., Rothi, L. J. G., Anderson, J. M., Crucian, G. P., and Heilman, K. M. (2002). Postural knowledge of transitive pantomimes and intransitive gestures. J. Int. Neuropsychol. Soc. 8, 958–962. doi: 10.1017/S1355617702870114

PubMed Abstract | CrossRef Full Text | Google Scholar

Mozaz, M. J., Crucian, G. P., and Heilman, K. M. (2009). Age-related changes in arm–hand postural knowledge. Cogn. Neuropsychol. 26, 675–684. doi: 10.1080/02643291003651571

PubMed Abstract | CrossRef Full Text | Google Scholar

Mulder, T., Hochstenbach, J. B. H., Van Heuvelen, M. J. G., and den Otter, A. R. (2007). Motor imagery: the relation between age and imagery capacity. Hum. Mov. Sci. 26, 203–211. doi: 10.1016/j.humov.2007.01.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Munzert, J., Lorey, B., and Zentgraf, K. (2009). Cognitive motor processes: the role of motor imagery in the study of motor representations. Brain Res. Rev. 60, 306–326. doi: 10.1016/j.brainresrev.2008.12.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Nedelko, V., Hassa, T., Hamzei, F., Weiller, C., Binkofski, F., Schoenfeld, M. A., et al. (2010). Age-independent activation in areas of the mirror neuron system during action observation and action imagery: a fMRI study. Restor. Neurol. Neurosci. 28, 737–747. doi: 10.3233/RNN-2010-0542

PubMed Abstract | CrossRef Full Text | Google Scholar

Noë, A. (2009). Out of Our Heads: Why you are not your Brain, and other Lessons From the Biology of Consciousness. New York, NY: Hill and Wang.

Google Scholar

Obayashi, S., Suhara, T., Kawabe, K., Okauchi, T., Maeda, J., Akine, Y., et al. (2001). Functional brain mapping of monkey tool use. Neuroimage 14, 853–861. doi: 10.1006/nimg.2001.0878

PubMed Abstract | CrossRef Full Text | Google Scholar

Owings, T. M., and Grabiner, M. D. (2004). Variability of step kinematics in young and older adults. Gait Posture 20, 26–29. doi: 10.1016/S0966-6362(03)00088-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Owsley, C. (2011). Aging and vision. Vis. Res. 51, 1610–1622. doi: 10.1016/j.visres.2010.10.020

PubMed Abstract | CrossRef Full Text | Google Scholar

Peeters, R., Simone, L., Nelissen, K., Fabbri-Destro, M., Vanduffel, W., Rizzolatti, G., et al. (2009). The representation of tool use in humans and monkeys: common and uniquely human features. J. Neurosci. 29, 11523–11539. doi: 10.1523/JNEUROSCI.2040-09.2009

PubMed Abstract | CrossRef Full Text | Google Scholar

Peiffer, A. M., Mozolic, J. L., Hugenschmidt, C. E., and Laurienti, P. J. (2007). Age-related multisensory enhancement in a simple audiovisual detection task. Neuroreport 18, 1077–1081. doi: 10.1097/WNR.0b013e3281e72ae7

PubMed Abstract | CrossRef Full Text | Google Scholar

Peigneux, P., and van der Linden, M. (1999). Influence of ageing and educational level on the prevalence of body-part-as-objects in normal subjects. J. Clin. Exp. Neuropsychol. 21, 547–552. doi: 10.1076/jcen.21.4.547.881

PubMed Abstract | CrossRef Full Text | Google Scholar

Personnier, P., Paizis, C., Ballay, Y., and Papaxanthis, C. (2008). Mentally represented motor actions in normal aging II. The influence of the gravity-inertial context on the duration of overt and covert arm movements. Behav. Brain Res. 186, 273–283. doi: 10.1016/j.bbr.2007.08.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Phan, M. L., Schendel, K. L., Recanzone, G. H., and Robertson, L. C. (2000). Auditory and visual spatial localization deficits following bilateral parietal lobe lesions in a patient with Balint’s syndrome. J. Cogn. Neurosci. 12, 583–600. doi: 10.1162/089892900562354

CrossRef Full Text | Google Scholar

Philbeck, J. W., and Witt, J. K. (2015). Action-specific influences on perception and postperceptual processes: present controversies and future directions. Psychol. Bull. 141, 1120–1144. doi: 10.1037/a0039738

PubMed Abstract | CrossRef Full Text | Google Scholar

Poliakoff, E., Ashworth, S., Lowe, C., and Spence, C. (2006). Vision and touch in ageing: crossmodal selective attention and visuotactile spatial interactions. Neuropsychologia 44, 507–517. doi: 10.1016/j.neuropsychologia.2005.07.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Poliakoff, E., Galpin, A. J., Dick, J. P. R., and Tipper, S. P. (2010). Does Parkinson’s disease affect judgement about another person’s action? Exp. Brain Res. 204, 327–331. doi: 10.1007/s00221-009-1976-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Posner, M. I., Nissen, M. J., and Klein, R. M. (1976). Visual dominance: an information-processing account of its origins and significance. Psychol. Rev. 83, 157–171. doi: 10.1037/0033-295X.83.2.157

CrossRef Full Text | Google Scholar

Prioli, A. C., Freitas Júnior, P. B., and Barela, J. E. A. (2005). Physical activity and postural control in the elderly: coupling between visual information and body sway. Gerontology 51, 145–148. doi: 10.1159/000083984

PubMed Abstract | CrossRef Full Text | Google Scholar

Proffitt, D. R. (2006). Embodied perception and the economy of action. Perspect. Psychol. Sci. 1, 110–122. doi: 10.1111/j.1745-6916.2006.00008.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Proffitt, D. R., and Linkenauger, S. A. (2013). “Perception viewed as a phenotypic expression,” in Action Science: Foundations of an Emerging Discipline, eds W. Prinz, M. Beisert, and A. Herwig (Cambridge, MA: MIT Press), 171–227. doi: 10.7551/mitpress/9780262018555.003.0007

CrossRef Full Text | Google Scholar

Proffitt, D. R., Stefanucci, J., Banton, T., and Epstein, W. (2003). The role of effort in perceiving distance. Psychol. Sci. 14, 106–112. doi: 10.1111/1467-9280.t01-1-01427

PubMed Abstract | CrossRef Full Text | Google Scholar

Proske, U., and Gandevia, S. C. (2012). The proprioceptive senses: their roles in signaling body shape, body position and movement, and muscle force. Physiol. Rev. 92, 1651–1697. doi: 10.1152/physrev.00048.2011

PubMed Abstract | CrossRef Full Text | Google Scholar

Pylyshyn, Z. W. (1984). Computation and Cognition. Cambridge, MA: MIT Press.

Google Scholar

Rand, M. K., and Heuer, H. (2013). Implicit and explicit representations of hand position in tool use. PLoS ONE 8:e68471. doi: 10.1371/journal.pone.0068471

PubMed Abstract | CrossRef Full Text | Google Scholar

Rand, M. K., Wang, L., Müsseler, J., and Heuer, H. (2013). Vision and proprioception in action monitoring by young and older adults. Neurobiol. Aging 34, 1864–1872. doi: 10.1016/j.neurobiolaging.2013.01.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Ratcliff, R., Thapar, A., Gomez, P., and McKoon, G. (2004). A diffusion model analysis of the effects of aging in the lexical-decision task. Psychol. Aging 19, 278–289. doi: 10.1037/0882-7974.19.2.278

PubMed Abstract | CrossRef Full Text | Google Scholar

Reed, C. L., Betz, R., Garza, J. P., and Roberts, R. J. (2010). Grab it! Biased attention in functional hand and tool space. Atten. Percept. Psychophys. 72, 236–245. doi: 10.3758/APP.72.1.236

PubMed Abstract | CrossRef Full Text | Google Scholar

Reed, C. L., and Farah, M. J. (1995). The psychological reality of the body schema: a test with normal participants. J. Exp. Psychol. 21, 334–343. doi: 10.1037/0096-1523.21.2.334

PubMed Abstract | CrossRef Full Text | Google Scholar

Reuter, E. M., Behrens, M., and Zschorlich, V. R. (2015). Age-related differences in corticomotor facilitation indicate dedifferentiation in motor planning. Exp. Gerontol. 65, 79–84. doi: 10.1016/j.exger.2015.03.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Reynaud, E., Lesourd, M., Navarro, J., and Osiurak, F. (2016). On the neurocognitive origins of human tool use: a critical review of neuroimaging data. Neurosci. Biobehav. Rev. 64, 421–437. doi: 10.1016/j.neubiorev.2016.03.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Richardson, M. J., Shockley, K., Fajen, B. R., Riley, M. A., and Turvey, M. T. (2008). “Ecological psychology: six principles for an embodied-embedded approach to behavior,” in Handbook of Cognitive Science: An Embodied Approach, eds P. Calvo and T. Gomila (Oxford: Elsevier), 161–187. doi: 10.1016/b978-0-08-046616-3.00009-8

CrossRef Full Text | Google Scholar

Roberts, K. L., and Allen, H. A. (2016). Perception and cognition in the ageing brain: a brief review of the short- and long-term links between perceptual and cognitive decline. Front. Aging Neurosci. 8:39. doi: 10.3389/fnagi.2016.00039

PubMed Abstract | CrossRef Full Text | Google Scholar

Roberts, S. B., and Rosenberg, I. (2006). Nutrition and aging: changes in the regulation of energy metabolism with aging. Physiol. Rev. 86, 651–667. doi: 10.1152/physrev.00019.2005

PubMed Abstract | CrossRef Full Text | Google Scholar

Roski, C., Caspers, S., Lux, S., Hoffstaedter, F., Bergs, R., Amunts, K., et al. (2014). Activation shift in elderly subjects across functional systems: an fMRI study. Brain Struct. Funct. 219, 707–718. doi: 10.1007/s00429-013-0530-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Saimpont, A., Malouin, F., Tousignant, B., and Jackson, P. L. (2013). Motor imagery and aging. J. Mot. Behav. 45, 21–29. doi: 10.1080/00222895.2012.740098

PubMed Abstract | CrossRef Full Text | Google Scholar

Saimpont, A., Pozzo, T., and Papaxanthis, C. (2009). Aging affects the mental rotation of left and right hands. PLoS ONE 4:e6714. doi: 10.1371/journal.pone.0006714

PubMed Abstract | CrossRef Full Text | Google Scholar

Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychol. Rev. 103, 403–428. doi: 10.1037/0033-295X.103.3.403

CrossRef Full Text | Google Scholar

Schnall, S., Zadra, J. R., and Proffitt, D. R. (2010). Direct evidence for the economy of action: glucose and the perception of geographical slant. Perception 39, 464–482. doi: 10.1068/p6445

PubMed Abstract | CrossRef Full Text | Google Scholar

Seidler, R. D., Bernard, J. A., Burutolu, T. B., Fling, B. W., Gordon, M. T., Gwin, J. T., et al. (2010). Motor control and aging: links to age-related brain structural, functional, and biochemical effects. Neurosci. Biobehav. Rev. 34, 721–733. doi: 10.1016/j.neubiorev.2009.10.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Setti, A., Burke, K. E., Kenny, R. A., and Newell, F. N. (2011). Is inefficient multisensory processing associated with falls in older people? Exp. Brain Res. 209, 375–384. doi: 10.1007/s00221-011-2560-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Setti, A., Stapleton, J., Leahy, D., Walsh, C., Kenny, R. A., and Newell, F. N. (2014). Improving the efficiency of multisensory integration in older adults: audio-visual temporal discrimination training reduces susceptibility to the sound-induced flash illusion. Neuropsychologia 61, 259–268. doi: 10.1016/j.neuropsychologia.2014.06.027

PubMed Abstract | CrossRef Full Text | Google Scholar

Sharma, N., and Baron, J. C. (2014). Effects of healthy ageing on activation pattern within the primary motor cortex during movement and motor imagery: an fMRI study. PLoS ONE 9:e88443. doi: 10.1371/journal.pone.0088443

PubMed Abstract | CrossRef Full Text | Google Scholar

Shepard, R. N., and Metzler, J. (1971). Mental rotation of three-dimensional objects. Science 171, 701–703. doi: 10.1126/science.171.3972.701

CrossRef Full Text | Google Scholar

Sit, R. A., and Fisk, A. D. (1999). Age-related performance in a multiple-task environment. Hum. Factors 41, 26–34. doi: 10.1518/001872099779577345

PubMed Abstract | CrossRef Full Text | Google Scholar

Ska, B., and Nespoulous, J. L. (1987). Pantomimes and aging. J. Clin. Exp. Neuropsychol. 9, 754–766. doi: 10.1080/01688638708405214

PubMed Abstract | CrossRef Full Text | Google Scholar

Skoura, X., Papaxanthis, C., Vinter, A., and Pozzo, T. (2005). Mentally represented motor actions in normal aging I. Age effects on the temporal features of overt and covert execution of actions. Behav. Brain Res. 165, 229–239. doi: 10.1016/j.bbr.2005.07.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Skoura, X., Personnier, P., Vinter, A., Pozzo, T., and Papaxanthis, C. (2008). Decline in motor prediction in elderly subjects: right versus left arm differences in mentally simulated motor actions. Cortex 44, 1271–1278. doi: 10.1016/j.cortex.2007.07.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Sparrow, W. A., Bradshaw, E. J., Lamoureux, E., and Tirosh, O. (2002). Ageing effects on the attention demands of walking. Hum. Mov. Sci. 21, 961–972. doi: 10.1016/S0167-9457(02)00154-9

CrossRef Full Text | Google Scholar

Stapleton, J., Setti, A., Doheny, E. P., Kenny, R. A., and Newell, F. N. (2014). A standing posture is associated with increased susceptibility to the sound-induced flash illusion in fall-prone older adults. Exp. Brain Res. 232, 423–434. doi: 10.1007/s00221-013-3750-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Steggemann, Y., Engbert, K., and Weigelt, M. (2011). Selective effects of motor expertise in mental body rotation tasks: comparing object-based and perspective transformations. Brain Cogn. 76, 97–105. doi: 10.1016/j.bandc.2011.02.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Studenski, S., Perera, S., Patel, K., Rosano, C., Faulkner, K., Inzitari, M., et al. (2011). Gait speed and survival in older adults. JAMA 305, 50–58. doi: 10.1001/jama.2010.1923

PubMed Abstract | CrossRef Full Text | Google Scholar

Sugovic, M., and Witt, J. K. (2013). An older view on distance perception: older adults perceive walkable extents as farther. Exp. Brain Res. 226, 383–391. doi: 10.1007/s00221-013-3447-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Szameitat, A. J., Shen, S., and Sterr, A. (2007). Motor imagery of complex everyday movements. An fMRI study. Neuroimage 34, 702–713. doi: 10.1016/j.neuroimage.2006.09.033

PubMed Abstract | CrossRef Full Text | Google Scholar

Teixeira, L. A., and Lima, S. (2009). Greater specificity of sensorimotor learning in the elderly when acquiring an interceptive task. Curr. Aging Sci. 2, 60–66. doi: 10.2174/1874609810902010060

PubMed Abstract | CrossRef Full Text | Google Scholar

Thompson, L. A., and Malloy, D. (2004). Attention resources and visible speech encoding in older and younger adults. Exp. Aging Res. 30, 241–252. doi: 10.1080/03610730490447877

PubMed Abstract | CrossRef Full Text | Google Scholar

Tinetti, M. E. (1986). Performance-oriented assessment of mobility problems in elderly patients. J. Am. Geriatr. Soc. 34, 119–126. doi: 10.1111/j.1532-5415.1986.tb05480.x

CrossRef Full Text | Google Scholar

Tipper, S. P., Lortie, C., and Baylis, G. C. (1992). Selective reaching: evidence for action-centered attention. J. Exp. Psychol. 18, 891–905. doi: 10.1037/0096-1523.18.4.891

CrossRef Full Text | Google Scholar

Toledo, D. R., and Barela, J. A. (2014). Age-related differences in postural control: effects of the complexity of visual manipulation and sensorimotor contribution to postural performance. Exp. Brain Res. 232, 493–502. doi: 10.1007/s00221-013-3756-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Vallet, G. T. (2015). Embodied cognition of aging. Front. Psychol. 6:6. doi: 10.3389/fpsyg.2015.00463

PubMed Abstract | CrossRef Full Text | Google Scholar

Vallet, G. T., Simard, M., and Versace, R. (2011). Sensory-dependent knowledge in young and elderly adults: arguments from the cross-modal priming effect. Curr. Aging Sci. 4, 137–149. doi: 10.2174/1874609811104020137

PubMed Abstract | CrossRef Full Text | Google Scholar

Vallet, G. T., Simard, M., Versace, R., and Mazza, S. (2013). The perceptual nature of audiovisual interactions for semantic knowledge in young and elderly adults. Acta Psychol. 143, 253–260. doi: 10.1016/j.actpsy.2013.04.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Varnava, A., McCarthy, M., and Beaumont, J. G. (2002). Line bisection in normal adults: direction of attentional bias for near and far space. Neuropsychologia 40, 1372–1378. doi: 10.1016/S0028-3932(01)00204-4

CrossRef Full Text | Google Scholar

Vaughan, L., Zurlo, F., and Ravussin, E. (1991). Aging and energy expenditure. Am. J. Clin. Nutr. 53, 821–825.

Google Scholar

Verhaeghen, P., and Cerella, J. (2002). Aging, executive control, and attention: a review of meta-analyses. Neurosci. Biobehav. Rev. 26, 849–857. doi: 10.1016/S0149-7634(02)00071-4

CrossRef Full Text | Google Scholar

Visser, M., Pluijm, S. M. F., Stel, V. S., Bosscher, R. J., and Deeg, D. J. H. (2002). Physical activity as a determinant of change in mobility performance: the longitudinal aging study Amsterdam. J. Am. Geriatr. Soc. 50, 1774–1781. doi: 10.1046/j.1532-5415.2002.50504.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Wade, M. G., Lindquist, R., Taylor, J. R., and Treat-Jacobson, D. (1995). Optical flow, spatial orientation, and the control of posture in the elderly. J. Gerontol. B Psychol. Sci. Soc. Sci. 50, 51–54. doi: 10.1093/geronb/50B.1.P51

PubMed Abstract | CrossRef Full Text | Google Scholar

Walsh, L. D., Moseley, G. L., Taylor, J. L., and Gandevia, S. C. (2011). Proprioceptive signals contribute to the sense of body ownership. J. Physiol. 12, 3009–3021. doi: 10.1113/jphysiol.2011.204941

CrossRef Full Text | Google Scholar

Wickremaratchi, M. M., and Llewelyn, J. G. (2006). Effects of ageing on touch. Postgrad. Med. J. 82, 301–304. doi: 10.1136/pgmj.2005.039651

PubMed Abstract | CrossRef Full Text | Google Scholar

Wilson, M. (2010). The re-tooled mind: how culture re-engineers cognition. Soc. Cogn. Affect. Neurosci. 5, 180–187. doi: 10.1093/scan/nsp054

PubMed Abstract | CrossRef Full Text | Google Scholar

Wilson, M. M. G., and Morley, J. E. (2003). Invited review: aging and energy balance. J. Appl. Physiol. 95, 1728–1736. doi: 10.1152/japplphysiol.00313.2003

PubMed Abstract | CrossRef Full Text | Google Scholar

Wilson-Mendenhall, C. D., Simmons, W. K., Martin, A., and Barsalou, L. W. (2013). Contextual processing of abstract concepts reveals neural representations of nonlinguistic semantic content. J. Cogn. Neurosci. 25, 920–935. doi: 10.1162/jocn_a_00361

PubMed Abstract | CrossRef Full Text | Google Scholar

Witt, J. K. (2011). Action’s effect on perception. Curr. Dir. Psychol. Sci. 20, 201–206. doi: 10.1177/0963721411408770

CrossRef Full Text | Google Scholar

Witt, J. K., and Brockmole, J. R. (2012). Action alters object identification: wielding a gun increases the bias to see guns. J. Exp. Psychol. 38, 1159–1167. doi: 10.1037/a0027881

PubMed Abstract | CrossRef Full Text | Google Scholar

Witt, J. K., and Dorsch, T. E. (2009). Kicking to bigger uprights: field goal kicking performance influences perceived size. Perception 38, 1328–1340. doi: 10.1068/p6325

PubMed Abstract | CrossRef Full Text | Google Scholar

Witt, J. K., Linkenauger, S. A., Bakdash, J. Z., Augustyn, J. S., Cook, A., and Proffitt, D. R. (2009). The long road of pain: chronic pain increases perceived distance. Exp. Brain Res. 192, 145–148. doi: 10.1007/s00221-008-1594-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Witt, J. K., Linkenauger, S. A., and Wickens, C. D. (2016). Action-specific effects in perception and their potential applications: a reply to commentaries. J. Appl. Res. Mem. Cogn. 1, 88–93. doi: 10.1016/j.jarmac.2016.01.002

CrossRef Full Text | Google Scholar

Witt, J. K., Linkenauger, S. A., Bakdash, J. Z., and Proffitt, D. R. (2008). Putting to a bigger hole: golf performance relates to perceived size. Psychon. Bull. Rev. 15, 581–585. doi: 10.3758/PBR.15.3.581

PubMed Abstract | CrossRef Full Text | Google Scholar

Witt, J. K., Proffitt, D. R., and Epstein, W. (2004). Perceiving distance: a role of effort and intent. Perception 33, 577–590. doi: 10.1068/p5090

PubMed Abstract | CrossRef Full Text | Google Scholar

Witt, J. K., Proffitt, D. R., and Epstein, W. (2005). Tool use affects perceived distance, but only when you intend to use it. J. Exp. Psychol. 31, 880–888. doi: 10.1037/0096-1523.31.5.880

PubMed Abstract | CrossRef Full Text | Google Scholar

Witt, J. K., and Proffitt, D. R. (2005). See the ball, hit the ball: apparent ball size is correlated with batting average. Psychol. Sci. 16, 937–938. doi: 10.1111/j.1467-9280.2005.01640.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Witt, J. K., and Sugovic, M. (2010). Performance and ease influence perceived speed. Perception 39, 1341–1353. doi: 10.1068/p6699

PubMed Abstract | CrossRef Full Text | Google Scholar

Wolpert, D. M. (1997). Computational approaches to motor control. Trends Cogn. Sci. 1, 209–216. doi: 10.1016/S1364-6613(97)01070-X

CrossRef Full Text | Google Scholar

Wolpert, D. M., Ghahramani, Z., and Jordan, M. I. (1995). An internal model for sensorimotor integration. Science 269, 1880–1882. doi: 10.1126/science.7569931

CrossRef Full Text | Google Scholar

Wolpert, D. M., and Kawato, M. (1998). Multiple paired forward and inverse models for motor control. Neural Netw. 11, 1317–1329. doi: 10.1016/S0893-6080(98)00066-5

CrossRef Full Text | Google Scholar

Yamamoto, N., and DeGirolamo, G. J. (2012). Differential effects of aging on spatial learning through exploratory navigation and map reading. Front. Aging Neurosci. 4:14. doi: 10.3389/fnagi.2012.00014

PubMed Abstract | CrossRef Full Text | Google Scholar

Zacks, R. T. (1989). Working memory, comprehension, and aging: a review and a new view. Psychol. Learn. Motiv. 22, 193–225.

Google Scholar

Zapparoli, L., Invernizzi, P., Gandola, M., Verardi, M., Berlingeri, M., Sberna, M., et al. (2013). Mental images across the adult lifespan: a behavioural and fMRI investigation of motor execution and motor imagery. Exp. Brain Res. 224, 519–540. doi: 10.1007/s00221-012-3331-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Zapparoli, L., Saetta, G., De Santis, C., Gandola, M., Zerbi, A., Banfi, G., et al. (2016). When I am (almost) 64: the effect of normal ageing on implicit motor imagery in young elderlies. Behav. Brain Res. 303, 137–151. doi: 10.1016/j.bbr.2016.01.058

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: embodiment, aging, cognition, perception, embodied cognition, older adults, gerontology

Citation: Costello MC and Bloesch EK (2017) Are Older Adults Less Embodied? A Review of Age Effects through the Lens of Embodied Cognition. Front. Psychol. 8:267. doi: 10.3389/fpsyg.2017.00267

Received: 30 October 2016; Accepted: 10 February 2017;
Published: 27 February 2017.

Edited by:

Annalisa Setti, University College Cork, Ireland

Reviewed by:

Martin H. Fischer, University of Potsdam, Germany
Guillaume T. Vallet, Laboratoire de Psychologie Sociale et Cognitive (CNRS), France

Copyright © 2017 Costello and Bloesch. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Matthew C. Costello, mcostello@hartford.edu