Recognizing facial slivers

Sharon Gilad-Gutnick, Elia Samuel Harmatz, Kleovoulos Tsourides, Galit Yovel, Pawan Sinha

Research output: Contribution to journalArticlepeer-review

Abstract

We report here an unexpectedly robust ability of healthy human individuals (n = 40) to recognize extremely distorted needle-like facial images, challenging the well-entrenched notion that veridical spatial configuration is necessary for extracting facial identity. In face identification tasks of parametrically compressed internal and external features, we found that the sum of performances on each cue falls significantly short of performance on full faces, despite the equal visual information available from both measures (with full faces essentially being a superposition of internal and external features). We hypothesize that this large deficit stems from the use of positional information about how the internal features are positioned relative to the external features. To test this, we systematically changed the relations between internal and external features and found preferential encoding of vertical but not horizontal spatial relationships in facial representations (n = 20). Finally, we employ magnetoencephalography imaging (n = 20) to demonstrate a close mapping between the behavioral psychometric curve and the amplitude of the M250 face familiarity, but not M170 face-sensitive evoked response field component, providing evidence that the M250 can be modulated by faces that are perceptually identifiable, irrespective of extreme distortions to the face’s veridical configuration. We theorize that the tolerance to compressive distortions has evolved from the need to recognize faces across varying viewpoints. Our findings help clarify the important, but poorly defined, concept of facial configuration and also enable an association between behavioral performance and previously reported neural correlates of face perception.

Original languageEnglish
Pages (from-to)951-962
Number of pages12
JournalJournal of Cognitive Neuroscience
Volume30
Issue number7
DOIs
StatePublished - 1 Jul 2018

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience

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