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Flexibility of emerging face categorization at different levels of abstraction.

ABSTRACT: Categorization of visual stimuli at different levels of abstraction relies on the encoding of relevant diagnostic features present at different spatial scales. We used the Eidolon Factory, an image-manipulation algorithm that introduces random disarray fields across spatial scales, to study how such a process flexibly combines perceptual information to perform successful categorization depending on task demands. Images of animal faces, human faces, and everyday objects were disarrayed coherently (random fields correlated) or incoherently (random fields randomized) to create a family of 50 eidolons per stimulus image with increasing disarray. Participants (N = 243) viewed each family of eidolons in a smooth sequence from maximum disarray to no disarray and performed a category verification task either at the superordinate (any face type) or basic (human face only) levels at two levels of uncertainty: participants in one group used their gut feeling to respond, whereas another group had to be sure of their decision. When participants used their gut feeling to respond, we observed a superordinate-level advantage. When they were sure of their response, we observed a basic-level advantage. Coherently disarrayed sequences impaired target detection compared to incoherently disarrayed sequences for both levels of response certainty. Furthermore, participants' sensitivity in the Any Face condition increased when they observed coherently disarrayed sequences and had to be sure of their response. These results suggest that the visual system does not strictly adhere to feedforward processing but flexibly adjusts to the relevant perceptual information depending on task context.

SUBMITTER: Santo MGE

PROVIDER: S-EPMC8142708 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Visual categorization is integral for our interaction with the natural environment. In this process, similar selective responses are produced to a class of variable visual inputs. Whether categorization is supported by partial (graded) or absolute (all-or-none) neural responses in high-level human brain regions is largely unknown. We address this issue with a novel frequency-sweep paradigm probing the evolution of face categorization responses between the minimal and optimal stimulus presentation times. In a first experiment, natural images of variable non-face objects were progressively swept from 120 to 3 ?Hz (8.33-333 ?ms duration) in rapid serial visual presentation sequences. Widely variable face exemplars appeared every 1 ?s, enabling an implicit frequency-tagged face-categorization electroencephalographic (EEG) response at 1 ?Hz. Face-categorization activity emerged with stimulus durations as brief as 17 ?ms (17-83 ?ms across individual participants) but was significant with 33 ?ms durations at the group level. The face categorization response amplitude increased until 83 ?ms stimulus duration (12 ?Hz), implying graded categorization responses. In a second EEG experiment, faces appeared non-periodically throughout such sequences at fixed presentation rates, while participants explicitly categorized faces. A strong correlation between response amplitude and behavioral accuracy across frequency rates suggested that dilution from missed categorizations, rather than a decreased response to each face stimulus, accounted for the graded categorization responses as found in Experiment 1. This was supported by (1) the absence of neural responses to faces that participants failed to categorize explicitly in Experiment 2 and (2) equivalent amplitudes and spatio-temporal signatures of neural responses to behaviorally categorized faces across presentation rates. Overall, these observations provide original evidence that high-level visual categorization of faces, starting at about 100 ?ms following stimulus onset in the human brain, is variable across observers tested under tight temporal constraints, but occurs in an all-or-none fashion.

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Flexibility of emerging face categorization at different levels of abstraction.

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