Introduction
Current studies in the domain of cognitive neuroscience are devised to ensure a high signal-to-noise ratio. Measures from numerous participants are averaged to allow for commonalities to emerge, washing out possible differences. Albeit generally a good thing, a “one-size-fits-all” approach has a drawback: it fails to take inter-individual variability into account, a limitation that is no longer tenable given the increasing attention to so-called precision medicine (Schleidgen et al., 2013).
Recently, personalized approaches have become attractive to psychiatry, raising interest for which biomarkers may characterize each patient (Fernandes et al., 2017; Wium-Andersen et al., 2017; Levchenko et al., 2020). Although most research focuses on genetic and biochemical markers, attention has been paid also to the functional organization of the brain, which is deemed to be largely idiosyncratic and possibly “as unique as a fingerprint” (Finn et al., 2015). Interestingly, both functional (Mueller et al., 2013) and structural variability (Hill et al., 2010; Kanai and Rees, 2011) is larger in association compared to primary cortices, an observation that fits well with the ample differences observed in the population for higher-order cognitive functions. In cognitive terms, variability means that although all individuals eventually attain the same goal, they may do so by means of entirely different strategies (Marchette et al., 2011; Miller et al., 2012). Accordingly, information derived from cognitive styles, personality traits, and/or behavioral strategies can provide relevant clues for understanding and characterizing maladaptive behavior.
Here we discuss ritualistic behavior and body-size delusions as examples of how pathological outcomes and healthy cognitive functioning represent the extremes of a continuum. We argue that inter-individual differences in healthy cognitive style can inform on vulnerability traits or endophenotypes for disease and contribute to characterizing each patient's condition. Adapting Tolstòj's famous quote, we thus maintain that each un-healthy brain is un-healthy in its own way.
Ritualistic behavior
Ritual is a loose term used to describe series of actions that are repeated over time in rigid, stereotyped manner, and whose function and/or meaning goes beyond their immediate appearance. Repetitive behaviors mostly involve the cortical-striatal-thalamic network associated with habits formation (Graybiel, 2008), a pattern largely conserved across evolution, indicating its robust ecological function (Turbott, 1997; Tonna et al., 2019). In all species, collective rituals contribute to maintaining social norms, strengthen emotional bonding, and promote cooperation (Rossano, 2012). In humans, rituals are especially common in childhood, as part of normal development (De Caluw et al., 2020). Although most people grow out of them, forms of repetitive behaviors persist in adults, often triggered by anxiety, which they can alleviate, either by driving attention to the elementary units of the motor act and thus dispelling intrusive thoughts (Boyer and Lienard, 2008), or by restoring control over the situation and reducing uncertainty (Hirsh et al., 2012). Rituals can take the form of superstitions, which are often highly idiosyncratic, leading people to forcibly rely on personal “lucky” objects or behaviors, especially in stressful conditions (Keinan, 2002; Damisch et al., 2010). Tennis champion Rafael Nadal's courtside ritual of carefully lining up several water bottles is emblematic, but similar behaviors are described in most athletes (Dömötör et al., 2016) and a significant part of the healthy population (Muris et al., 1997). The mechanism by which personal superstitions are established is thought to arise from the (unjustified) reinforcement of purely coincidental associations (Beck and Forstmeier, 2007; Daprati et al., 2019), a process akin to the reward-based type of learning supported by basal ganglia activity (Doya, 2000). In psychiatry, “repetitive behaviors or mental acts that an individual feels driven to perform in response to an obsession or according to rules that must be applied rigidly” (DSM-5, APA, 2013) are referred to as compulsions. Although typically associated with obsessive-compulsive disorders (OCD), compulsions emerge in a variety of conditions, including autism, addiction, and anorexia. Neuroimaging studies report anomalies in the frontal-striatal-thalamic network of affected individuals, possibly in response to neuroplastic changes occurring over time, which eventually result in hyperactivation of the caudate nucleus, as would be expected by excess habit generation (Gillan et al., 2015; van den Heuvel et al., 2016; Fineberg et al., 2018; Stein et al., 2019).
Summing up, ritualistic behavior spans from ecologically relevant activities (as in collective rituals reinforcing social norms), to relatively innocuous, though possibly intrusive, routines (as in superstitions), to frankly pathological states (as in compulsions). The common thread is the reliance on the mechanisms supporting habit formation. Neurofunctional models of OCD describe an imbalance between the goal-directed and the habit system of action control, which would lead to over-reliance on the latter (Gillan and Robbins, 2014). To a lesser extent, this could be true also for superstitions, which associate with deactivation of frontal areas, possibly signaling reduced cognitive control over behavior (Rao et al., 2014).
Body-size delusions
Perceiving our body in space is instrumental to all approach/avoidance interactions with the environment (Sirigu et al., 1991; Schwoebel and Coslett, 2005; deVignemont, 2010). Anorexia Nervosa (AN), a severe eating disorder mostly reported in young women (Treasure and Frank, 2015; Dakanalis et al., 2016), seriously affects this perception. As reported in DSM-5 (APA, 2013), a major diagnostic criterion for AN is a “disturbance in the way in which one's body weight or shape is experienced,” the other criteria being significantly low weight and intense and persistent fear of becoming fat. In AN body size is generally overestimated (Schneider et al., 2009; Gardner and Brown, 2014; Mölbert et al., 2017; Brown et al., 2021), a belief that is accompanied by affective and behavioral manifestations. Emotionally, this misperception associates with negative attitudes toward the body, which is regarded as a source of distress (Vocks et al., 2007), possibly due to the tendency to make self-esteem dependent on body weight or shape (APA, 2013). Behaviorally, overestimation of bodily space emerges when anorexics are asked to judge on the possible collision with an external probe (Nico et al., 2010) or the ease with which they can pass through an aperture (Guardia et al., 2010; Keizer et al., 2013), indicating that the bias extends to the perceptual-motor level. In cognitive terms, anorexics seem to be unable to reconcile their perceived body size with the real one, failing to properly view themselves from a non-egocentric standpoint (Bora and Köse, 2016; Konstantakopoulos et al., 2020). The operations of mental rotation and visuospatial reasoning involved in these perspective-changes rely on parietal lobe activity (Nico and Daprati, 2009; Gunia et al., 2021) as do the multisensory integration processes required for a coherent and flexible body representation (Berlucchi and Aglioti, 2010; Daprati et al., 2010; Sereno and Huang, 2014). Congruently, signs of parietal dysfunction emerge in AN in both neuroimaging (Gaudio and Quattrocchi, 2012) and behavioral studies (Grunwald et al., 2002; Guardia et al., 2010, 2013; Nico et al., 2010; Keizer et al., 2013).
Overestimation of bodily space is not exclusive to AN. Young and perfectly healthy individuals misjudge their body size, particularly along the width dimension (Casper et al., 1979; Dolan et al., 1987; Urdapilleta et al., 2010; Fuentes et al., 2013; D'Amour and Harris, 2019; Longo, 2022). A primitive coding of body boundaries emerges as early as 18-h after birth (Ronga et al., 2021), testifying that delimiting bodily space clearly serves ecological purposes. In this sense, systematically perceiving oneself as wider than real size may be an asset because it increases the safety margin that protects against threats (Cooke and Graziano, 2003; deVignemont and Iannetti, 2015). If devoid of negative affective values, this mechanism is particularly advantageous considering that the body undergoes ample variations during one's lifetime, due to development (Adolph, 2008) or other physiological changes (e.g., pregnancy, Franchak and Adolph, 2012; D'Amour and Harris, 2019) and overestimation can facilitate perceptual-motor recalibration. The metric distortion is significantly larger in AN (Gardner and Brown, 2014; Mölbert et al., 2017), and deeply affective-laden (Vocks et al., 2007), but—as previously proposed for compulsions—body-size delusions can be represented as the farther end of a continuum originating in healthy behavior.
The significance of cognitive variability
So far, we provided two among many possible examples (e.g., agoraphobia, Indovina et al., 2019; fibromyalgia, Sarzi-Puttini et al., 2020) whereby a pathological symptom emerges as one extreme of a continuum stemming from basic (and strongly ecologic) cognitive mechanisms. A comprehensive description of cognitive (mal)functioning in neuropsychiatric disorders is still lacking, but systematically exploring variability in the healthy brain could inform on novel, possible susceptibility factors.
For example, detecting and learning associations, a relevant step in habit formation, varies considerably based on personality traits and cognitive style (Kaufman et al., 2010; Stillman et al., 2014; Blanco et al., 2015). Superstitious individuals are more likely to spot and exploit coincidences than non-superstitious ones (Daprati et al., 2019): this adaptive advantage could additionally represent a vulnerability trait toward developing pathological conducts. Unaffected first-degree relatives of OCD individuals can show behavioral anomalies in executive functioning (Cavedini et al., 2010) and structural changes in the fronto-striatal territory, which could similarly constitute a neurocognitive endophenotype for disease (Vaghi et al., 2017). Structure of the parietal cortex, whose relevance to body and space perception is widely known (Berlucchi and Aglioti, 2010; Sereno and Huang, 2014), shows sexual dimorphism (Levine et al., 2016) and differs widely across individuals. Gray matter density and cortical thickness can vary, and anatomical variations translate in differences in performance at tasks of attention switching (Kanai et al., 2010), mental rotation (Koscik et al., 2009) and experience of body ownership (Matuz-Budai et al., 2022), which in turn could make some individuals exceptionally vulnerable to body-size delusions.
In sum, susceptibility factors for pathology may be nested within cognitive variability and—though still poorly explored—should be considered alongside psychosocial and biological markers (Jacobi et al., 2004; Levchenko et al., 2020).
Conclusion
Recent neuropsychological approaches to psychiatry have underlined the multifactorial origin of mental illnesses, drawing attention to cognitive variables (Wood et al., 2009). Besides permitting a more comprehensive view of disease, exploring cognition provides objective, quantitative measures that can by-pass top-down influences produced by psycho-affective attitudes, an obvious advantage when self-report is affected for example, by denial of illness. The next step forward is feeding information on cognitive variability into the newly developing models of disease.
The study of cognitive variability is notoriously laden by methodological and reliability issues (Hedge et al., 2018). Nevertheless, guidelines are rapidly emerging regarding experimental paradigms, populations, and statistical analyses (Mollon et al., 2017; Hedge et al., 2018; Goodhew, 2020), which warrant strong internal reliability, minimize confounds, and allow discriminating between state and trait variables (Goodhew, 2020). Though still scant, information obtained from cognitive variability could thus shed light on vulnerability traits or endophenotypes for disease, contributing to personalizing diagnostic and remediation pathways.
Consider the example of body-size delusions. Describing one's body implies reporting beliefs and/or attitudes related to it and applying the visuospatial skills required to view oneself “from the outside” (deVignemont, 2010; Mölbert et al., 2017). While the first aspect is routinely assessed via clinical interviews and scales, the latter is rarely approached, though there are advantages in collecting measures that are quantifiable and less permeable to emotions. Poor perspective-taking abilities can reduce the capacity to take a non-egocentric view about the self, impairing illness awareness and reducing insight on the real state of one's body (Bora and Köse, 2016; Konstantakopoulos et al., 2020). As such, they may both support the symptom and constitute a vulnerability trait for disease. Detecting this trait within the healthy population (whereby variability exists; Samuel et al., 2022) can improve screening and diagnostic protocols. Likewise, many remediation programs now employ virtual reality: suitability for these approaches is affected by structural variability in parietal areas (Hosoda et al., 2021). Assessing skills relying on parietal functioning may thus help singling out which individuals will benefit from these therapies. Similar reasoning applies to associative learning: sensitivity to detecting coincidences can belong in an endophenotype for developing ritualistic behavior, as hinted by associations between implicit learning, reward processing and polymorphism for BDNF genes (see Daprati et al., 2019 for a discussion).
Thus, while not implying a causal link between one cognitive style and development of a psychiatric condition, investigating cognitive variability could prove fruitful in characterizing disease and informing on the most probable direction malfunctioning could take, should other factors co-occur.
Statements
Author contributions
ED drafted the paper. All authors discussed the ideas presented in the paper. All authors reviewed and approved the submitted version.
Conflict of interest
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.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
Adolph K. E. (2008). The growing body in action, in Embodiment, Ego-Space, and Action, eds KlatzyR. et al. (Mahwah, NJ: Erlbaum), 275–321.
2
APA . (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th Edn.Washinton, DC: American Psychiatric Association
3
Beck J. Forstmeier W. (2007). Superstition and belief as inevitable by-products of an adaptive learning strategy. Hum. Nat. 18, 35e46. 10.1007/BF02820845
4
Berlucchi G. Aglioti S. M. (2010). The body in the brain revisited. Exp. Brain Res. 200, 25–35. 10.1007/s00221-009-1970-7
5
Blanco F. Barberia I. Matute H. (2015). Individuals who believe in the paranormal expose themselves to biased information and develop more causal illusions than non-believers in the laboratory. PLoS ONE10, e0131378. 10.1371/journal.pone.0131378
6
Bora E. Köse S. (2016). Meta-analysis of theory of mind in anorexia nervosa and bulimia nervosa: A specific impairment of cognitive perspective taking in anorexia nervosa?Int. J. Eat. Disord. 49, 739–74010.1002/eat.22572
7
Boyer P. Lienard P. (2008). Ritual behavior in obsessive and normal individuals. Curr. Dir. Psychol. Sci. 17, 291–294. 10.1111/j.1467-8721.2008.00592.x
8
Brown T. A. Shott M. E. Frank G. K. (2021). Body size overestimation in anorexia nervosa. Psychiatry Res. 297, 11370510.1016/j.psychres.2021.113705
9
Casper R. C. Halmi K. A. Goldberg S. C. Eckert E. D. Davis J. M. (1979). Disturbances in body image estimation as related to other characteristics and outcome in anorexia nervosa. Br. J. Psychiatry134, 60–66.
10
Cavedini P. Zorzi C. Piccinni M. Cavallini M. C. Bellodi L. (2010). Executive dysfunctions in obsessive-compulsive patients and unaffected relatives. Biol. Psychiatry67, 1178–1184. 10.1016/j.biopsych.2010.02.012
11
Cooke D. F. Graziano M. S. (2003). Defensive movements evoked by air puff in monkeys. J. Neurophysiol. 90, 3317–332910.1152/jn.00513.2003
12
Dakanalis A. Gaudio S. Serino S. Clerici M. Carr,à G. Riva G. (2016). Body-image distortion in anorexia nervosa. Nat. Rev. Dis. Prim.2, 16026. 10.1038/nrdp.2016.26
13
Damisch L. Stoberock B. Mussweiler T. (2010). Keep your fingers crossed: how superstition improves performance. Psychol. Sci. 21, 1014–1020. 10.1177/0956797610372631
14
D'Amour S. Harris L. R. (2019). The representation of body size. Front. Psychol. 10, 2805. 10.3389/fpsyg.2019.02805
15
Daprati E. Sirigu A. Desmurget M. Nico D. (2019). Superstitious beliefs and the associative mind. Consc. Cogn. 75, 10282210.1016/j.concog.2019.102822
16
Daprati E. Sirigu A. Nico D. (2010). Body and movement: consciousness in the parietal lobes. Neuropsychologia48, 756–762. 10.1016/j.neuropsychologia.2009.10.008
17
De Caluw,é E. Vergauwe J. Decuyper M. Bogaerts S. Rettew D. C. De Clercq B. (2020). The relation between normative rituals/routines and obsessive-compulsive symptoms at a young age. Dev. Rev. 56, 100913. 10.1016/j.dr.2020.100913
18
deVignemont F. (2010). Body schema and body image: pros and cons. Neuropsychologia48, 669–680. 10.1016/j.neuropsychologia.2009.09.022
19
deVignemont F. Iannetti G. D. (2015). How many peripersonal spaces?Neuropsychologia70, 327–334. 10.1016/j.neuropsychologia.2014.11.018
20
Dolan B. M. Birtchnell S. A. Lacey J. H. (1987). Body image distortion in non-eating disordered women and men. J. Psychosom. Res. 31, 513–520.
21
Dömötör Z. Ruíz-Barquín R. Szabo A. (2016). Superstitious behavior in sport. Scand. J. Psychol. 57, 368–382. 10.1111/sjop.12301
22
Doya K. (2000). Complementary roles of basal ganglia and cerebellum in learning and motor control. Curr. Opin. Neurobiol. 10, 732–739. 10.1016/S0959-4388(00)00153-7
23
Fernandes B. S. Williams L. M. Steiner J. Leboyer M. Carvalho A. F. Berk M. (2017). The new field of 'precision psychiatry'. BMC Med. 15, 80. 10.1186/s12916-017-0849-x
24
Fineberg N. A. Apergis-Schoute A. M. Vaghi M. M. Banca P. Gillan C. M. Voon V. et al . (2018). Mapping compulsivity in the DSM-5 obsessive compulsive and related disorders. Int. J. Neuropsychopharmacol. 21, 42–58. 10.1093/ijnp/pyx088
25
Finn E. S. Shen X. Scheinost D. Rosenberg M. D. Huang J. Chun M. M. et al . (2015). Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671. 10.1038/nn.4135
26
Franchak J. M. Adolph K. E. (2012). What infants know and what they do: Perceiving possibilities for walking through openings. Dev. Psychol. 48, 1254–1261. 10.1037/a0027530
27
Fuentes C. T. Longo M. R. Haggard P. (2013). Body image distortions in healthy adults. Acta Psychol. 144, 344–351. 10.1016/j.actpsy.2013.06.012
28
Gardner R. M. Brown D. L. (2014). Body size estimation in anorexia nervosa. Psychiatry Res. 219, 407–410. 10.1016/j.psychres.2014.06.029
29
Gaudio S. Quattrocchi C. C. (2012). Neural basis of a multidimensional model of body image distortion in anorexia nervosa. Neurosci. Biobehav. Rev. 36, 1839–1847. 10.1016/j.neubiorev.2012.05.003
30
Gillan C. M. Apergis-Schoute A. M. Morein-Zamir S. Urcelay G. P. Sule A. Fineberg N. A. et al . (2015). Functional neuroimaging of avoidance habits in obsessive-compulsive disorder. Am. J. Psychiatry172, 284–293. 10.1176/appi.ajp.2014.14040525
31
Gillan C. M. Robbins T. W. (2014). Goal-directed learning and obsessive-compulsive disorder. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130475. 10.1098/rstb.2013.0475
32
Goodhew S. C. (2020). Applying an individual-differences lens to understanding human cognition. Consc. Cogn. 79, 102883. 10.1016/j.concog.2020.102883
33
Graybiel A. M. (2008). Habits, rituals, and the evaluative brain. Ann. Rev. Neurosci. 31, 359–387. 10.1146/annurev.neuro.29.051605.112851
34
Grunwald M. Ettrich C. Busse F. Assmann B. Dähne A. Gertz H. J. (2002). Angle paradigm: a new method to measure right parietal dysfunctions in anorexia nervosa. Arch. Clin. Neuropsychol. 17, 485–496. 10.1093/arclin/17.5.485
35
Guardia D. Carey A. Cottencin O. Thomas P. Luyat M. (2013). Disruption of spatial task performance in anorexia nervosa. PLoS ONE. 8, e54928. 10.1371/journal.pone.0054928
36
Guardia D. Lafargue G. Thomas P. Dodin V. Cottencin O. Luyat M. (2010). Anticipation of body-scaled action is modified in anorexia nervosa. Neuropsychologia48, 3961–3966. 10.1016/j.neuropsychologia.2010.09.004
37
Gunia A. Moraresku S. Vlček K. (2021). Brain mechanisms of visuospatial perspective-taking in relation to object mental rotation and the theory of mind. Behav. Brain Res. 407, 113247. 10.1016/j.bbr.2021.113247
38
Hedge C. Powell G. Sumner P. (2018). The reliability paradox. Behav. Res. Methods50, 1166–118610.3758/s13428-017-0935-1
39
Hill J. Dierker D. Neil J. Inder T. Knutsen A. Harwell J. et al . (2010). A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants. J. Neurosci. 30, 2268–2276. 10.1523/JNEUROSCI.4682-09.2010
40
Hirsh J. B. Mar R. A. Peterson J. B. (2012). Psychological entropy: a framework for understanding uncertainty-related anxiety. Psychol. Rev. 119, 304320. 10.1037/a0026767
41
Hosoda C. Futami K. Hosokawa K. Isogaya Y. Terada T. Maruya K. et al . (2021). The structure of the superior and inferior parietal lobes predicts inter-individual suitability for virtual reality. Sci. Rep. 11, 2368810.1038/s41598-021-02957-x
42
Indovina I. Conti A. Lacquaniti F. Staab J. P. Passamonti L. Toschi N. (2019). Lower functional connectivity in vestibular-limbic networks in individuals with subclinical agoraphobia. Front. Neurol. 10, 874. 10.3389/fneur.2019.00874
43
Jacobi C. Hayward C. de Zwaan M. Kraemer H. C. Agras W. S. (2004). Coming to terms with risk factors for eating disorders: application of risk terminology and suggestions for a general taxonomy. Psychol. Bull. 130, 19–65. 10.1037/0033-2909.130.1.19
44
Kanai R. Bahrami B. Rees G. (2010). Human parietal cortex structure predicts individual differences in perceptual rivalry. Curr. Biol.20, 1626–1630. 10.1016/j.cub.2010.07.027
45
Kanai R. Rees G. (2011). The structural basis of inter-individual differences in human behavior and cognition. Nat. Rev. Neurosci. 12, 231–242. 10.1038/nrn3000
46
Kaufman S. B. DeYoung C. G. Gray J. R. Jiménez L. Brown J. Mackintosh N. (2010). Implicit learning as an ability. Cognition116, 321–340. 10.1016/j.cognition.2010.05.011
47
Keinan G. (2002). The effects of stress and desire for control on superstitious behavior. Pers. Soc. Psychol. Bull. 28, 102–108. 10.1177/0146167202281009
48
Keizer A. Smeets M. A. Dijkerman H. C. Uzunbajakau S. A. van Elburg A. Postma A. (2013). Too fat to fit through the door: first evidence for disturbed body-scaled action in anorexia nervosa during locomotion. PLoS ONE8, e64602. 10.1371/journal.pone.0064602
49
Konstantakopoulos G. Ioannidi N. Patrikelis P. Gonidakis F. (2020). The impact of theory of mind and neurocognition on delusionality in anorexia nervosa. J. Clin. Exp. Neuropsychol. 42, 611–621. 10.1080/13803395.2020.1786504
50
Koscik T. O'Leary D. Moser D. J. Andreasen N. C. Nopoulos P. (2009). Sex differences in parietal lobe morphology. Brain Cogn. 69, 451–459. 10.1016/j.bandc.2008.09.004
51
Levchenko A. Nurgaliev T. Kanapin A. Samsonova A. Gainetdinov R. R. (2020). Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders. Heliyon6, e03990. 10.1016/j.heliyon.2020.e03990
52
Levine S. C. Foley A. Lourenco S. Ehrlich S. Ratliff K. (2016). Sex differences in spatial cognition: advancing the conversation. Wiley Interdiscip. Rev. Cogn. Sci. 7, 127–155. 10.1002/wcs.1380
53
Longo M. R. (2022). Distortion of mental body representations. Trends Cogn. Sci. 26, 241–254. 10.1016/j.tics.2021.11.005
54
Marchette S. A. Bakker A. Shelton A. L. (2011). Cognitive mappers to creatures of habit. J. Neurosci. 31, 15264–15268. 10.1523/JNEUROSCI.3634-11.2011
55
Matuz-Budai T. Lábadi B. Kohn E. Matuz A. Zsid,ó A. N. Inhóf O. et al . (2022). Individual differences in the experience of body ownership are related to cortical thickness. Sci. Rep. 12, 808. 10.1038/s41598-021-04720-8
56
Miller M. B. Donovan C. L. Bennett C. M. Aminoff E. M. Mayer R. E. (2012). Individual differences in cognitive style and strategy predict similarities in the patterns of brain activity between individuals. Neuroimage59, 83–93. 10.1016/j.neuroimage.2011.05.060
57
Mölbert S. C. Klein L. Thaler A. Mohler B. J. Brozzo C. Martus P. et al . (2017). Depictive and metric body size estimation in anorexia nervosa and bulimia nervosa: a systematic review and meta-analysis. Clin. Psychol. Rev. 57, 21–31. 10.1016/j.cpr.2017.08.005
58
Mollon J. D. Bosten J. M. Peterzell D. H. Webster M. A. (2017). Individual differences in visual science. Vis. Res. 141, 4–15. 10.1016/j.visres.2017.11.001
59
Mueller S. Wang D. Fox M. D. Yeo B. T. Sepulcre J. Sabuncu M. R. et al . (2013). Individual variability in functional connectivity architecture of the human brain. Neuron77, 586–595. 10.1016/j.neuron.2012.12.028
60
Muris P. Merckelbach H. Clavan M. (1997). Abnormal and normal compulsions. Behav. Res. Ther. 35, 249–252.
61
Nico D. Daprati E. (2009). The egocentric reference for visual exploration and orientation. Brain Cogn. 69, 227–235. 10.1016/j.bandc.2008.07.011
62
Nico D. Daprati E. Nighoghossian N. Carrier E. Duhamel J. R. Sirigu A. (2010). The role of the right parietal lobe in anorexia nervosa. Psychol. Med. 40, 1531–1539. 10.1017/S0033291709991851
63
Rao L. L. Zheng Y. Zhou Y. Li S. (2014). Probing the neural basis of superstition. Brain Topogr. 27, 766–770. 10.1007/s10548-013-0332-8
64
Ronga I. Galigani M. Bruno V. Noel J. P. Gazzin A. Perathoner C. et al . (2021). Spatial tuning of electrophysiological responses to multisensory stimuli reveals a primitive coding of the body boundaries in newborns. Proc. Natl. Acad. Sci. U. S. A.118, e2024548118. 10.1073/pnas.2024548118
65
Rossano M. J. (2012). The essential role of ritual in the transmission and reinforcement of social norms. Psychol. Bull. 138, 529–549. 10.1037/a0027038
66
Samuel S. Cole G. Eacott M. (2022). It's not you, it's me: a review of individual differences in visuospatial perspective taking. Perspect. Psychol. Sci.2022, 17456916221094545. 10.1177/17456916221094545
67
Sarzi-Puttini P. Giorgi V. Atzeni F. Gorla R. Kosek E. Choy E. H. et al . (2020). Fibromyalgia. Nat. Rev. Rheum. 16, 645–660. 10.1038/s41584-020-00506-w
68
Schleidgen S. Klingler C. Bertram T. Rogowski W. H. Marckmann G. (2013). What is personalized medicine. BMC Med. Ethics. 14, 55. 10.1186/1472-6939-14-55
69
Schneider N. Frieler K. Pfeiffer E. Lehmkuhl U. Salbach-Andrae H. (2009). Comparison of body size estimation in adolescents with different types of eating disorders. Eur. Eat. Disord. Rev. 17, 468–475. 10.1002/erv.956
70
Schwoebel J. Coslett H. B. (2005). Evidence for multiple, distinct representations of the human body. J. Cogn. Neurosci. 17, 543–553. 10.1162/0898929053467587
71
Sereno M. I. Huang R. S. (2014). Multisensory maps in parietal cortex. Curr. Opin. Neurobiol. 24, 39–46. 10.1016/j.conb.2013.08.014
72
Sirigu A. Grafman J. Bressler K. Sunderland T. (1991). Multiple representations contribute to body knowledge processing. Evidence from a case of autotopagnosia. Brain114, 629–642.
73
Stein D. J. Costa D. L. Lochner C. Miguel E. C. Reddy Y. C. Shavitt R. G. et al . (2019). Obsessive-compulsive disorder. Nat. Rev. Dis. Prim.5, 52. 10.1038/s41572-019-0102-3
74
Stillman C. M. Feldman H. Wambach C. G. Howard J. H. Howard D. V. (2014). Dispositional mindfulness is associated with reduced implicit learning. Consc. Cogn.28, 141–150. 10.1016/j.concog.2014.07.002
75
Tonna M. Marchesi C. Parmigiani S. (2019). The biological origins of rituals: An interdisciplinary perspective. Neurosci. Biobehav. Rev. 98, 95–106. 10.1016/j.neubiorev.2018.12.031
76
Treasure J. Frank G. K. (2015). Anorexia nervosa. Nat. Rev. Dis. Prim.1, 15074. 10.1038/nrdp.2015.74
77
Turbott J. (1997). The meaning and function of ritual in psychiatric disorder, religion and everyday behavior. Aust. NZJ Psychiatry31, 835–843.
78
Urdapilleta I. Aspavlo D. Masse L. Docteur A. (2010). Use of a picture distortion technique to examine perceptive and ideal body image in male and female competitive swimmers. Psychol. Sport Exerc. 11, 568–573. 10.1016/j.psychsport.2010.06.006
79
Vaghi M. M. Hampshire A. Fineberg N. A. Kaser M. Brühl A. B. Sahakian B. J. et al . (2017). Hypoactivation and dysconnectivity of a frontostriatal circuit during goal-directed planning as an endophenotype for obsessive-compulsive disorder. Biol. Psychiatry Cogn. Neurosci. Neuroimag.2, 655–663. 10.1016/j.bpsc.2017.05.005
80
van den Heuvel O. A. van Wingen G. Soriano-Mas C. Alonso P. Chamberlain S. R. Nakamae T. et al . (2016). Brain circuitry of compulsivity. Eur. Neuropsychopharmacol. 26, 810–827. 10.1016/j.euroneuro.2015.12.005
81
Vocks S. Legenbauer T. Wächter A. Wucherer M. Kosfelder J. (2007). What happens in the course of body exposure? Emotional, cognitive, and physiological reactions to mirror confrontation in eating disorders. J. Psychosom. Res. 62, 231–239. 10.1016/j.jpsychores.2006.08.007
82
Wium-Andersen I. K. Vinberg M. Kessing L. V. McIntyre R. S. (2017). Personalized medicine in psychiatry. Nord. J. Psychiatry71, 12–19. 10.1080/08039488.2016.1216163
83
Wood S. J. Allen N. B. Pantelis C. (2009). The Neuropsychology of Mental Illness. Cambridge: Cambridge University Press10.1017/CBO9780511642197
Summary
Keywords
individual differences, Anorexia Nervosa, ritualistic behavior, associative learning, body representation, precision psychiatry, neurocognitive markers
Citation
Daprati E and Nico D (2022) Vulnerability factors and neuropsychiatric disorders: What could be learned from individual variability in cognitive functions. Front. Psychol. 13:1019030. doi: 10.3389/fpsyg.2022.1019030
Received
14 August 2022
Accepted
07 December 2022
Published
22 December 2022
Volume
13 - 2022
Edited by
Bernhard Hommel, University Hospital Carl Gustav Carus, Germany
Reviewed by
Robert Blumenfeld, California State Polytechnic University, United States; Fengyu Zhang, Global Clinical and Translational Research Institute, United States
Updates
Copyright
© 2022 Daprati and Nico.
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) and the copyright owner(s) 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: Elena Daprati ✉elena.daprati@uniroma2.it
This article was submitted to Cognition, a section of the journal Frontiers in Psychology
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.