Recognizing Facial Slivers
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 par...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MIT Press - Journals
2020
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| Online Access: | https://hdl.handle.net/1721.1/124989 |
| _version_ | 1811096871996751872 |
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| author | Gilad-Gutnick, Sharon Harmatz, Elia Samuel Tsourides, Kleovoulos Yovel, Galit Sinha, Pawan |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Gilad-Gutnick, Sharon Harmatz, Elia Samuel Tsourides, Kleovoulos Yovel, Galit Sinha, Pawan |
| author_sort | Gilad-Gutnick, Sharon |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:50:21Z |
| format | Article |
| id | mit-1721.1/124989 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:50:21Z |
| publishDate | 2020 |
| publisher | MIT Press - Journals |
| record_format | dspace |
| spelling | mit-1721.1/1249892022-10-03T08:40:52Z Recognizing Facial Slivers Gilad-Gutnick, Sharon Harmatz, Elia Samuel Tsourides, Kleovoulos Yovel, Galit Sinha, Pawan Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Cognitive Neuroscience 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. National Institutes of Health (U.S.) (Grant R01EY02051) 2020-05-04T15:07:35Z 2020-05-04T15:07:35Z 2018-05 2019-10-08T16:31:05Z Article http://purl.org/eprint/type/JournalArticle 0898-929X 1530-8898 https://hdl.handle.net/1721.1/124989 Gilad-Gutnick, Sharon et al. "Recognizing Facial Slivers." Journal of Cognitive Neuroscience 30, 7 (July 2018): 951-962 © 2018 Massachusetts Institute of Technology. en http://dx.doi.org/10.1162/jocn_a_01265 Journal of Cognitive Neuroscience Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf MIT Press - Journals MIT Press |
| spellingShingle | Cognitive Neuroscience Gilad-Gutnick, Sharon Harmatz, Elia Samuel Tsourides, Kleovoulos Yovel, Galit Sinha, Pawan Recognizing Facial Slivers |
| title | Recognizing Facial Slivers |
| title_full | Recognizing Facial Slivers |
| title_fullStr | Recognizing Facial Slivers |
| title_full_unstemmed | Recognizing Facial Slivers |
| title_short | Recognizing Facial Slivers |
| title_sort | recognizing facial slivers |
| topic | Cognitive Neuroscience |
| url | https://hdl.handle.net/1721.1/124989 |
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