The face-in-the-crowd effect: Threat detection versus iso-feature suppression and collinear facilitation.

Are people biologically prepared for the rapid detection of threat posed by an angry facial expression, even when it is conveyed in the form of a schematic line drawing? Based on visual search times, the current literature would suggest that the answer is yes. But are there low-level explanations for this effect? Here, we present visual search results for schematic faces using current best practice, based on a concentric search array and set size manipulation. Using this approach, we replicate the classic search advantage for angry over happy faces. However, we also report a comparable effect when abstract plus- and square-shaped stimuli-derived from the angry and happy schematic faces respectively-are used within the same paradigm. We then go on to demonstrate that, while reduced, the effect remains after removal of the circular surround, bringing us closer to the source of the effect. We explore the possibility that the source of this search asymmetry could be the iso-feature suppression and collinear facilitation model proposed in Li's (1999a, 1999b, and 2002) bottom-up model of saliency. Simulations with this model using the abstract stimuli align with the corresponding behavioral results (i.e., the plus shape was found to be more salient than the square). Given the deliberate similarities between these abstract shapes and the respective face stimuli, we propose that the underlying cause for the asymmetries typically found using schematic faces, may be more related to early visual processing of line orientation than threat detection.

[1]  J. Wolfe Asymmetries in visual search: An introduction , 2001, Perception & psychophysics.

[2]  Daniel Smilek,et al.  Visual search for faces with emotional expressions. , 2008, Psychological bulletin.

[3]  A. Ohman,et al.  The face in the crowd revisited: a threat advantage with schematic stimuli. , 2001, Journal of personality and social psychology.

[4]  Joseph R. Rausch,et al.  Sample size planning for statistical power and accuracy in parameter estimation. , 2008, Annual review of psychology.

[5]  O. Lipp,et al.  Visual search for schematic emotional faces: Angry faces are more than crosses , 2014, Cognition & emotion.

[6]  Stefanie I. Becker,et al.  Of toothy grins and angry snarls--open mouth displays contribute to efficiency gains in search for emotional faces. , 2012, Journal of vision.

[7]  Zhaoping Li,et al.  A Neural Model of Contour Integration in the Primary Visual Cortex , 1998, Neural Computation.

[8]  C. H. Hansen,et al.  Finding the face in the crowd: an anger superiority effect. , 1988, Journal of personality and social psychology.

[9]  J. Wolfe,et al.  What attributes guide the deployment of visual attention and how do they do it? , 2004, Nature Reviews Neuroscience.

[10]  Stefanie I. Becker,et al.  Different faces in the crowd: a happiness superiority effect for schematic faces in heterogeneous backgrounds. , 2014, Emotion.

[11]  D. Lundqvist,et al.  Finding the face in a crowd: Relationships between distractor redundancy, target emotion, and target gender , 2010 .

[12]  D. Levi,et al.  Visual crowding: a fundamental limit on conscious perception and object recognition , 2011, Trends in Cognitive Sciences.

[13]  Preeti Verghese,et al.  The psychophysics of visual search , 2000, Vision Research.

[14]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[15]  V. Lakshminarayanan,et al.  Basic and Clinical Applications of Vision Science , 1997, Documenta Ophthalmologica Proceedings Series.

[16]  P. Dayan,et al.  Multi-level visual adaptation: Dissociating curvature and facial-expression aftereffects produced by the same adapting stimuli , 2012, Vision Research.

[17]  D. Purcell,et al.  It Takes a Confounded Face to Pop Out of a Crowd , 1996, Perception.

[18]  B. Julesz Textons, the elements of texture perception, and their interactions , 1981, Nature.

[19]  Stefanie I. Becker,et al.  Perceptual grouping, not emotion, accounts for search asymmetries with schematic faces. , 2011, Journal of experimental psychology. Human perception and performance.

[20]  Susan L. Franzel,et al.  Guided search: an alternative to the feature integration model for visual search. , 1989, Journal of experimental psychology. Human perception and performance.

[21]  Li Zhaoping,et al.  A clash of bottom-up and top-down processes in visual search: the reversed letter effect revisited. , 2011, Journal of experimental psychology. Human perception and performance.

[22]  Gernot Horstmann,et al.  Preattentive face processing: What do visual search experiments with schematic faces tell us? , 2007 .

[23]  Z Li,et al.  Contextual influences in V1 as a basis for pop out and asymmetry in visual search. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[24]  J. P. Thomas,et al.  A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays , 2000, Perception & psychophysics.

[25]  Andrew W Young,et al.  The eyebrow frown: a salient social signal. , 2002, Emotion.

[26]  U. Polat,et al.  Collinear stimuli regulate visual responses depending on cell's contrast threshold , 1998, Nature.

[27]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[28]  David J. Field,et al.  Contour integration by the human visual system: Evidence for a local “association field” , 1993, Vision Research.

[29]  D. V. van Essen,et al.  Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. , 1992, Journal of neurophysiology.

[30]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[31]  J. Eastwood,et al.  Differential attentional guidance by unattended faces expressing positive and negative emotion , 2001, Perception & psychophysics.

[32]  Li Zhaoping,et al.  Understanding Vision: Theory, Models, and Data , 2014 .

[33]  Jeremy M. Wolfe,et al.  Guided Search 4.0: Current Progress With a Model of Visual Search , 2007, Integrated Models of Cognitive Systems.

[34]  M. Carrasco,et al.  The eccentricity effect: Target eccentricity affects performance on conjunction searches , 1995, Perception & psychophysics.

[35]  Zhaoping Li A saliency map in primary visual cortex , 2002, Trends in Cognitive Sciences.

[36]  J. Wolfe,et al.  Why are there eccentricity effects in visual search? Visual and attentional hypotheses , 1998, Perception & psychophysics.

[37]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.

[38]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[39]  P Cavanagh,et al.  Familiarity and pop-out in visual search , 1994, Perception & psychophysics.

[40]  A. Ohman,et al.  Face the beast and fear the face: animal and social fears as prototypes for evolutionary analyses of emotion. , 1986, Psychophysiology.

[41]  L. Isbell,et al.  Snakes as agents of evolutionary change in primate brains. , 2006, Journal of human evolution.

[42]  D. Purcell,et al.  Probing "pop-out": Another look at the face-in-the-crowd effect. , 1989 .

[43]  P. Ekman Pictures of Facial Affect , 1976 .

[44]  H. Nothdurft Faces and Facial Expressions do not Pop Out , 1993, Perception.

[45]  C. Gilbert,et al.  Contour Saliency in Primary Visual Cortex , 2006, Neuron.

[46]  Guy Wallis,et al.  The face-in-the-crowd effect: when angry faces are just cross(es). , 2011, Journal of vision.

[47]  Gernot Horstmann,et al.  Visual search for schematic affective faces: Stability and variability of search slopes with different instances , 2009 .

[48]  G. Gendolla,et al.  Detecting emotional faces and features in a visual search paradigm: are faces special? , 2006, Emotion.

[49]  C. Gilbert,et al.  Improvement in visual sensitivity by changes in local context: Parallel studies in human observers and in V1 of alert monkeys , 1995, Neuron.

[50]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[51]  Z Li,et al.  Visual segmentation by contextual influences via intra-cortical interactions in the primary visual cortex. , 1999, Network.

[52]  Murray White,et al.  Preattentive analysis of facial expressions of emotion , 1995 .

[53]  E. Fox,et al.  Facial Expressions of Emotion: Are Angry Faces Detected More Efficiently? , 2000, Cognition & emotion.

[54]  J. Palmer Attention in Visual Search: Distinguishing Four Causes of a Set-Size Effect , 1995 .

[55]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[56]  Stefanie I. Becker,et al.  A reversal of the search asymmetry favouring negative schematic faces , 2010 .

[57]  D. Levi Crowding—An essential bottleneck for object recognition: A mini-review , 2008, Vision Research.