Faster, stronger, lateralized: Low spatial frequency information supports face processing

Awasthi, Bhuvanesh; Friedman, Jason; Williams, Mark A.

doi:10.1016/j.neuropsychologia.2011.08.027

Cited by 30 publications

(18 citation statements)

References 60 publications

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“…Preattentive mechanisms that scan the environment to detect faces automatically (Elder, Prince, Hou, Sizintsev, & Olevskiy, 2007;Lewis & Edmonds, 2003;'t Hart, Abresch, & Einhäuser, 2011) may exist to compensate for the difficulty in distinguishing subtle facial characteristics at periphery or in unattended central locations (Devue, Laloyaux, et al, 2009). This could be achieved through magnocellular channels that extract low spatial frequencies that are used for holistic processing (Awasthi, Friedman, & Williams, 2011a, 2011bCalvo, Beltrán, & Fernández-Martín, 2014;Girard & Koenig-Robert, 2011;Goffaux, Hault, Michel, Vuong, & Rossion, 2005;Johnson, 2005;Taubert, Apthorp, AagtenMurphy, & Alais, 2011). Holistic processing can be demonstrated as fast as 50 ms after exposure to a face (Richler, Mack, Gauthier, & Palmeri, 2009;Taubert et al, 2011) and is indexed by an early face-specific P100 ERP component (Nakashima et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

“…By bringing the face into foveal vision, this orienting reflex could facilitate the extraction of further information conveyed by medium and high spatial frequency information (Awasthi et al, 2011a(Awasthi et al, , 2011bDeruelle & Fagot, 2005;Gao & Maurer, 2011; see also Underwood, Templeman, Lamming, & Foulsham, 2008, for a similar argument with objects within complex scenes) to complement the partial information gathered at periphery via low spatial frequencies, for example, about familiarity (Smith et al, 2016) or facial expression (Vuilleumier et al, 2003). Fixating a face enables finer facial discrimination (e.g., wrinkles associated with facial expressions; see Johnson, 2005) and may help, nay be necessary, to reach a definite decision about the meaning of the face in terms of facial expression, identity, gender, race, or intentions.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Faces are special, but facial expressions aren’t: Insights from an oculomotor capture paradigm

Devue

Grimshaw

2017

Atten Percept Psychophys

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We compared the ability of angry and neutral faces to drive oculomotor behaviour as a test of the widespread claim that emotional information is automatically prioritized when competing for attention. Participants were required to make a saccade to a colour singleton; photos of angry or neutral faces appeared amongst other objects within the array, and were completely irrelevant for the task. Eye-tracking measures indicate that faces drive oculomotor behaviour in a bottom-up fashion; however, angry faces are no more likely to capture the eyes than neutral faces are. Saccade latencies suggest that capture occurrs via reflexive saccades and that the outcome of competition between salient items (colour singletons and faces) may be subject to fluctuations in attentional control. Indeed, although angry and neutral faces captured the eyes reflexively on a portion of trials, participants successfully maintained goal-relevant oculomotor behaviour on a majority of trials. We outline potential cognitive and brain mechanisms underlying oculomotor capture by faces.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Faces are special, but facial expressions aren’t: Insights from an oculomotor capture paradigm

Devue

Grimshaw

2017

Atten Percept Psychophys

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“…Since these differences are intrinsic to faces, a self-organizing system would naturally tune face selective cells to lower frequency bandpass filters (Keil et al, 2008). This low frequency bias is regarded by some as the driving force behind the holistic processing of faces, a proposal which they have backed up through behavioral experimentation (Goffaux and Rossion, 2006; Awasthi et al, 2011). I would nonetheless argue that although the bias may be a contributing factor, and one which a self-organizing system could replicate, it does not offer a simple explanation for why other-race faces are processed non-holistically (Michel et al, 2006), since they should possess a comparable spectral bias to own-race faces.…”

Section: A Model Of Face Recognitionmentioning

confidence: 99%

Toward a unified model of face and object recognition in the human visual system

Wallis¹

2013

Front. Psychol.

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Our understanding of the mechanisms and neural substrates underlying visual recognition has made considerable progress over the past 30 years. During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. In the psychological literature, in particular, this dissociation has led to a palpable disconnect between theories of how we process and represent the two classes of object. This paper follows a trend in part of the recognition literature to try to reconcile what we know about these two forms of recognition by considering the effects of learning. Taking a widely accepted, self-organizing model of object recognition, this paper explains how such a system is affected by repeated exposure to specific stimulus classes. In so doing, it explains how many aspects of recognition generally regarded as unusual to faces (holistic processing, configural processing, sensitivity to inversion, the other-race effect, the prototype effect, etc.) are emergent properties of category-specific learning within such a system. Overall, the paper describes how a single model of recognition learning can and does produce the seemingly very different types of representation associated with faces and objects.

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“…Pre-attentive mechanisms that scan the environment to detect faces automatically ('t Hart, Abresch, & Einhäuser, 2011;Elder, Prince, Hou, Sizintsev, & Olevskiy, 2007;Lewis & Edmonds, 2003) may exist to compensate for the difficulty in distinguishing subtle facial characteristics at periphery or in unattended central locations (Devue, Laloyaux, et al, 2009). This could be achieved through magnocellular channels that extract low spatial frequencies that are used for holistic processing (Awasthi, Friedman, & Williams, 2011a, 2011bCalvo, Beltrán, & Fernández-Martín, 2014;Girard & Koenig-Robert, 2011;Goffaux, Hault, Michel, Vuong, & Rossion, 2005;Johnson, 2005;Taubert, Apthorp, Aagten-Murphy, & Alais, 2011). Holistic processing can be demonstrated as fast as 50 ms after exposure to a face (Richler, Mack, Gauthier, & Palmeri, 2009;Taubert et al, 2011) and is indexed by an early face-specific P100 ERP component (Nakashima et al, 2008).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Faces are special, but facial expressions aren’t: Insights from an oculomotor capture paradigm

Devue¹,

Grimshaw²

2018

Preprint

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We compared the ability of angry and neutral faces to drive oculomotor behaviour, as a test of the widespread claim that emotional information is automatically prioritized when competing for attention. Participants were required to make a saccade to a colour singleton; photos of angry or neutral faces appeared amongst other objects within the array, and were completely irrelevant for the task. Eye-tracking measures indicate that faces drive oculomotor behaviour in a bottom-up fashion; however angry faces are no more likely to capture the eyes than neutral faces. Saccade latencies suggest that capture occurred via reflexive saccades and that the outcome of competition between salient items (colour singletons and faces) may be subject to fluctuations in attentional control. Indeed, although angry and neutral faces captured the eyes reflexively on a portion of trials, participants successfully maintained goal-relevant oculomotor behaviour on a majority of trials. We outline potential cognitive and brain mechanisms underlying oculomotor capture by faces.

show abstract

Faster, stronger, lateralized: Low spatial frequency information supports face processing

Cited by 30 publications

References 60 publications

Faces are special, but facial expressions aren’t: Insights from an oculomotor capture paradigm

Faces are special, but facial expressions aren’t: Insights from an oculomotor capture paradigm

Toward a unified model of face and object recognition in the human visual system

Faces are special, but facial expressions aren’t: Insights from an oculomotor capture paradigm

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