Literature DB >> 31622473

The influence of sequential predictions on scene-gist recognition.

Maverick E Smith1, Lester C Loschky1.   

Abstract

Past research suggests that recognizing scene gist, a viewer's holistic semantic representation of a scene acquired within a single eye fixation, involves purely feed-forward mechanisms. We investigated whether expectations can influence scene categorization. To do this, we embedded target scenes in more ecologically valid, first-person-viewpoint image sequences, along spatiotemporally connected routes (e.g., an office to a parking lot). We manipulated the sequences' spatiotemporal coherence by presenting them either coherently or in random order. Participants identified the category of one target scene in a 10-scene-image rapid serial visual presentation. Categorization accuracy was greater for targets in coherent sequences. Accuracy was also greater for targets with more visually similar primes. In Experiment 2, we investigated whether targets in coherent sequences were more predictable and whether predictable images were identified more accurately in Experiment 1 after accounting for the effect of prime-to-target visual similarity. To do this, we removed targets and had participants predict the category of the missing scene. Images were more accurately predicted in coherent sequences, and both image predictability and prime-to-target visual similarity independently contributed to performance in Experiment 1. To test whether prediction-based facilitation effects were solely due to response bias, participants performed a two-alternative forced-choice task in which they indicated whether the target was an intact or a phase-randomized scene. Critically, predictability of the target category was irrelevant to this task. Nevertheless, results showed that sensitivity, but not response bias, was greater for targets in coherent sequences. Predictions made prior to viewing a scene facilitate scene-gist recognition.

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Year:  2019        PMID: 31622473     DOI: 10.1167/19.12.14

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  1 in total

1.  The N300: An Index for Predictive Coding of Complex Visual Objects and Scenes.

Authors:  Manoj Kumar; Kara D Federmeier; Diane M Beck
Journal:  Cereb Cortex Commun       Date:  2021-04-21
  1 in total

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