Event Abstract

Back to Event

Does repetition suppression index face recognition?

Mirta Stantic1*, Eri Ichijo1, Caroline Catmur2 and Geoffrey Bird3, 4*
  • 1 Department of Experimental Psychology, Medical Sciences Division, University of Oxford, United Kingdom
  • 2 Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
  • 3 University of Oxford, United Kingdom
  • 4 Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom

Social ability has long been argued to be a key determinant of success within the education system and in later life (Shakeshaft, 2016). Perhaps the most fundamental social skill is the ability to individuate our conspecifics; to recognize who somebody is such that we may better understand and predict how they are likely to think, feel and behave. One of the key mechanisms by which we individuate others is face recognition; it is therefore surprising that, as yet, we do not have good models to explain individual differences in this area - why some people are so good at recognizing faces and others so bad. Identifying a neural marker of face recognition would provide a useful tool to investigate the psychological and neural systems responsible for individual differences in face recognition and may explain why face recognition fails so spectacularly in individuals with developmental prosopagnosia (DP). Problematically, however, neural markers of individual differences in face recognition ability have been hard to identify (Avidan, Tanzer, Hadj-Bouziane, Liu, Ungerleider, & Behrmann, 2014). Repetition effects observed in neuroimaging data appear promising in providing such a marker. They include Repetition Suppression (RS), in which repetition of a stimulus causes a reduced neural response to the second stimulus, and Repetition Enhancement (RE), where repetition of a stimulus causes an increased response to the second stimulus (Fox, Hanif, Moon, Iaria, & Barton, 2010; Henson, Shallice, Gorno-Tempini, & Dolan, 2002). Whether or not repetition effects are observed to pairs of stimuli can therefore be a useful indicator as to whether the neural signal of interest is sensitive to variance on a particular dimension. As an illustration, consider the comparison of neural activity evoked by a photograph of Jane’s face when preceded either by the same photo of Jane’s face (repeat condition), or by a photo of Barbara’s face (non-repeat condition). If the presentation of Jane’s face evokes less neural activity in the repeat condition than the non-repeat condition (i.e., if RS occurs), then it is usually assumed that the neural signal being measured is sensitive to variance in facial identity. Such a signal would, therefore, appear to be a good neural marker of face recognition. Comparisons between repeated and non-repeated pairs of stimuli are not the only design used to investigate face recognition through repetition effects. For example, Baseler et al. (2016) compared blocks in which several face stimuli were presented. They were either all faces of the same identity (repeat blocks), or different identities (non-repeat blocks). If neural activity evoked by repeat and non-repeat blocks differ (i.e., either RS or RE effects occur), then again repetition effects would appear to be a potential neural marker of individual differences in face recognition. There is, however, a logical problem with the assumption that repetition effects are a marker of face recognition: although the difference in signal between repeat and non-repeat conditions could be a product of face recognition, it could also be a product of attentional modulation or differences in the extent of face processing caused by increased processing time. These possibilities are outlined below (Figure 1). Classic models of RS effects suggest that the reduced signal seen in fMRI data for repeated stimuli (Grill-Spector, Henson, & Martin, 2006) results from either neural fatigue for those neurons tuned to the repeated stimulus, or, more likely, sharpening of neural tuning curves or increased network efficiency for neural units involved in processing of that stimulus. Any of these models could be characterised as reflecting face recognition – Jane’s face is recognised as Jane’s face the second time it is presented and causes activity solely of those neurons that are preferentially tuned for Jane’s face (Figure 1, left panel). Alternatively, decreased activity on repeat vs. non-repeat trials (or blocks of trials) could be caused by attentional modulation of identity-general face responses caused by a change detection signal arising on non-repeat trials because two faces are discriminated, although neither is recognised (Figure 1, right panel). More concretely, increased signal on a non-repeat trial (B→A) compared to a repeat trial (A→A) be due to a judgement that the “the second face is different than the first” rather than recognition of either Face A, Face B, or both faces. The recognition that the face has changed might prompt increased attention to the second face, boosting the neural signal on non-repeat trials (Wojciulik, Kanwisher, & Driver, 1998). Of course, the attentional modulation account outlined above cannot explain RE effects, where increased activity is observed on repeat trials compared to non-repeat trials. In our opinion, such effects are more likely to be a product of face recognition than RS effects, however, it is possible to explain even RE effects without recourse to processes indexing face recognition. For example, presentation of a face may prompt any of a number of theorised processes which contribute to face recognition but could not be described as a marker of it. Such processes might include the construction of a view-independent model of the face (Bruce & Young, 1986) or determining the location of a face within a multi-dimensional space representing one’s experience with faces (Valentine, 1991). If any of these processes (or their neural representations) are modulated by structural properties of the particular face, then RE effects may reflect extended activation of those neural systems responsible for these processes rather than those reflecting recognition of the face. This explanation is made more likely by the fact, as far as we are aware, RE effects for facial identity have only been observed for unfamiliar faces and not for familiar faces, for which these processes have presumably already taken place (Henson et al., 2000). In addition to the conceptual point, several practical aspects of repetition effects make them problematic as potential neural markers of individual differences in face recognition ability. Most obvious is the fact that repetition effects are typically incredibly small, allowing little scope for variance across individuals to be observed. Additionally, the magnitude, location, and direction (RS vs. RE) of repetition effects have been shown to be influenced by a number of factors including familiarity, viewpoint and emotion (Henson et al., 2002; Ewbank, Smith, Hancock, & Andrews, 2008; Winston et al., 2009; Andrews & Ewbank, 2004). While this is not a problem per se, it does suggest that any repetition effect neural marker of face recognition may be incredibly variable across studies depending on the specific stimuli used. More problematic is the fact that individuals with DP, a group who are defined by their face recognition impairments, have been shown to exhibit typical repetition effects when compared to their neurotypical counterparts (Minnebusch et al., 2007; Avidan et al., 2005). If repetition effects are problematic as neural markers of individual differences in face recognition are there more promising approaches? In considering this question it is first worth mentioning the existence of a third type of paradigm designed to investigate the existence of repetition effects. This paradigm looks for evidence of repetition effects over longer durations with intervening stimuli (i.e. repetition of a face occurs after several intervening face stimuli have been presented; Henson, Rylands, Ross, Vuilleumeir, & Rugg, 2004). This design does not suffer from the logical problem associated with the possible confounding effect of attentional modulation described above (as all stimuli, whether classified as repeat or non-repeat stimuli, are immediately preceded by presentation of a different face), and so RS effects observed using this paradigm (Henson et al., 2004) are likely to provide a true index of face recognition. However, repetition effects observed using this paradigm are even smaller and less reliable than those observed with paired or blocked designs, making them of limited use. In contrast, Fast Periodic Visual Stimulation (FPVS), an EEG paradigm in which stimuli are presented at a periodic frequency (Rossion, 2014), shares those features of lagged designs that allow it to be concluded with confidence that neural signals truly reflect face recognition, but provides a large-enough signal such that individual differences in face recognition can be observed (Coll, Murphy, Catmur, Bird, Brewer, in press). In FPVS paradigms, stimuli are presented at different frequencies such that one type of stimulus (‘base’ stimulus) is presented at one frequency, while another type (‘oddball’ stimulus) is presented at a lower frequency. If base stimuli consist of a number of different facial identities, whereas oddball stimuli are exemplars of a specific facial identity, then any signal observed at the oddball frequency must index the repeated recognition of that identity when it appears. Importantly, previous research has shown that individual differences in ‘gold standard’ tests of face recognition correlate with variance in the size of neural markers of face recognition observed using FPVS (Xu, Liu-Shuang, Rossion, & Tanaka, 2017), and the only published study of a DP individual using the FPVS technique reported weaker neural signals of face recognition in this individual (Liu-Shuang, Torfs, & Rossion, 2016). For these reasons, FPVS techniques may be better able to provide a neural marker of individual differences in face recognition than RE/RS methods.

Figure 1
Image

Acknowledgements

MS is funded by ESRC DTP studentship and a Wilfrid Knapp Science Scholarship. GB is supported by Baily Thomas Charitable Trust.

References

Andrews, T. J., & Ewbank, M. P. (2004). Distinct representations for facial identity and changeable aspects of faces in the human temporal lobe. Neuroimage, 23(3), 905-913. Avidan, G., Tanzer, M., Hadj-Bouziane, F., Liu, N., Ungerleider, L. G., & Behrmann, M. (2013). Selective dissociation between core and extended regions of the face processing network in congenital prosopagnosia. Cerebral Cortex, 24(6), 1565-1578. Baseler, H. A., Young, A. W., Jenkins, R., Burton, A. M., & Andrews, T. J. (2016). Face-selective regions show invariance to linear, but not to non-linear, changes in facial images. Neuropsychologia, 93, 76-84. Bruce, V., & Young, A. (1986). Understanding face recognition. British journal of psychology, 77(3), 305-327. Coll, M. P., Murphy, J., Catmur, C., Bird, G., Brewer, R., & Oxford, O. (2018). The importance of stimulus variability when studying face processing using Fast Periodic Visual Stimulation: A novel ‘Mixed-Emotions’ paradigm. PsyArXiv. November, 9. Ewbank, M. P., Smith, W. A., Hancock, E. R., & Andrews, T. J. (2007). The M170 reflects a viewpoint-dependent representation for both familiar and unfamiliar faces. Cerebral Cortex, 18(2), 364-370. Fox, C. J., Hanif, H. M., Iaria, G., Duchaine, B. C., & Barton, J. J. (2011). Perceptual and anatomic patterns of selective deficits in facial identity and expression processing. Neuropsychologia, 49(12), 3188-3200. Grill-Spector, K., Henson, R., & Martin, A. (2006). Repetition and the brain: neural models of stimulus-specific effects. Trends in cognitive sciences, 10(1), 14-23. Henson, R. N. A., Shallice, T., Gorno-Tempini, M. L., & Dolan, R. J. (2002). Face repetition effects in implicit and explicit memory tests as measured by fMRI. Cerebral Cortex, 12(2), 178-186. Henson, R. N., Rylands, A., Ross, E., Vuilleumeir, P., & Rugg, M. D. (2004). The effect of repetition lag on electrophysiological and haemodynamic correlates of visual object priming. Neuroimage, 21(4), 1674-1689. Liu-Shuang, J., Torfs, K., & Rossion, B. (2016). An objective electrophysiological marker of face individualisation impairment in acquired prosopagnosia with fast periodic visual stimulation. Neuropsychologia, 83, 100–113. Minnebusch, D. A., Suchan, B., Ramon, M., & Daum, I. (2007). Event‐related potentials reflect heterogeneity of developmental prosopagnosia. European Journal of Neuroscience, 25(7), 2234-2247. Rossion, B. (2014). Understanding individual face discrimination by means of fast periodic visual stimulation. Experimental brain research, 232(6), 1599-1621. Valentine, T. (1991). A unified account of the effects of distinctiveness, inversion, and race in face recognition. The Quarterly Journal of Experimental Psychology Section A, 43(2), 161-204. Wojciulik, E., Kanwisher, N., & Driver, J. (1998). Covert visual attention modulates face-specific activity in the human fusiform gyrus: fMRI study. Journal of Neurophysiology, 79(3), 1574-1578. Xu, B., Liu-Shuang, J., Rossion, B., & Tanaka, J. (2017). Individual Differences in Face Identity Processing with Fast Periodic Visual Stimulation. Journal of Cognitive Neuroscience, 29(8), 1368–1377. Figure 1. Alternative explanations for repetition suppression. Traditional explanation of repetition suppression (in green) poses that reduction in neural signal on repeat trials is caused by increased network efficiency for neural units involved in processing of the repeated stimulus. An alternative explanation (in blue) suggests that change detection on non-repeat trials causes a modulation of attention to the novel stimulus, thereby increasing neural signal. As can be seen from the top panel, the modulation of neural signal (the only parameter typically measured) is indistinguishable between these explanations.

Keywords: face processing, repetition suppression effect, Repetition enhancement and suppression, Face recognition (FR), prosopagnosia (PA)

Conference: 4th International Conference on Educational Neuroscience, Abu Dhabi, United Arab Emirates, 10 Mar - 11 Mar, 2019.

Presentation Type: Oral Presentation (invited speakers only)

Topic: Educational Neuroscience

Citation: Stantic M, Ichijo E, Catmur C and Bird G (2019). Does repetition suppression index face recognition?. Conference Abstract: 4th International Conference on Educational Neuroscience. doi: 10.3389/conf.fnhum.2019.229.00015

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 10 Feb 2019; Published Online: 27 Sep 2019.

* Correspondence:
Miss. Mirta Stantic, Department of Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, England, OX1 3PH, United Kingdom, mirta.stantic@psy.ox.ac.uk
Prof. Geoffrey Bird, University of Oxford, Oxford, United Kingdom, geoff.bird@psy.ox.ac.uk