The Influence of Semantics on Figure Assignment: Unmasked Primes, Masked Primes, and Context

Skocypec, Rachel; Peterson, Mary A.

doi:10.1167/19.10.35b

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“…For instance, the border could be perceived as a joint between two slanted surfaces, the boundary between two colored areas on a two-dimensional surface, or a shadow border). Figure assignment is archetypal object detection because before borders are assigned, patterns are present, but objects are not [ 12 , 31 , 32 , 33 ].…”

Section: The Present Experimentsmentioning

confidence: 99%

Semantic Expectation Effects on Object Detection: Using Figure Assignment to Elucidate Mechanisms

Skocypec

Peterson

2022

Vision

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Recent evidence suggesting that object detection is improved following valid rather than invalid labels implies that semantics influence object detection. It is not clear, however, whether the results index object detection or feature detection. Further, because control conditions were absent and labels and objects were repeated multiple times, the mechanisms are unknown. We assessed object detection via figure assignment, whereby objects are segmented from backgrounds. Masked bipartite displays depicting a portion of a mono-oriented object (a familiar configuration) on one side of a central border were shown once only for 90 or 100 ms. Familiar configuration is a figural prior. Accurate detection was indexed by reports of an object on the familiar configuration side of the border. Compared to control experiments without labels, valid labels improved accuracy and reduced response times (RTs) more for upright than inverted objects (Studies 1 and 2). Invalid labels denoting different superordinate-level objects (DSC; Study 1) or same superordinate-level objects (SSC; Study 2) reduced accuracy for upright displays only. Orientation dependency indicates that effects are mediated by activated object representations rather than features which are invariant over orientation. Following invalid SSC labels (Study 2), accurate detection RTs were longer than control for both orientations, implicating conflict between semantic representations that had to be resolved before object detection. These results demonstrate that object detection is not just affected by semantics, it entails semantics.

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Section: The Present Experimentsmentioning

confidence: 99%