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Context-based modulations of 3D vision are expertise dependent.

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An object's identity can influence depth-position judgments. The mechanistic underpinnings underlying this phenomenon are largely unknown. Here, we asked whether context-dependent modulations of stereoscopic depth perception are expertise dependent. In… Click to show full abstract

An object's identity can influence depth-position judgments. The mechanistic underpinnings underlying this phenomenon are largely unknown. Here, we asked whether context-dependent modulations of stereoscopic depth perception are expertise dependent. In 2 experiments, we tested whether training that attaches meaning (i.e. classification labels) to otherwise novel, stereoscopically presented objects changes observers' sensitivity for judging their depth position. In Experiment 1, observers were randomly assigned to 3 groups: a Greeble-classification training group, an orientation-discrimination training group, or a no-training group, and were tested on their stereoscopic depth sensitivity before and after training. In Experiment 2, participants were tested before and after training while fMRI responses were concurrently imaged. Behaviorally, stereoscopic performance was significantly better following Greeble-classification (but not orientation-discrimination, or no-) training. Using the fMRI data, we trained support vector machines to predict whether the data were from the pre- or post-training sessions. Results indicated that classification accuracies in V4 were higher for the Greeble-classification group as compared with the orientation-discrimination group for which accuracies were at chance level. Furthermore, classification accuracies in V4 were negatively correlated with response times for Greeble identification. We speculate that V4 is implicated in an expertise-dependent, object-tuning manner that allows it to better guide stereoscopic depth retrieval.

Keywords: classification; expertise dependent; group; stereoscopic depth

Journal Title: Cerebral cortex
Year Published: 2023

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