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Poster F133

Average Temperature can be Extracted from Visual Scene Ensembles without Reliance on Contrast

Poster Session F - Tuesday, April 16, 2024, 8:00 – 10:00 am EDT, Sheraton Hall ABC

yuanze huang1 (, Vignash Tharmaratnam1, Dirk walther2, Jonathan Cant1; 1Univeristy of Toronto Scarborough, 2University of Toronto

Summary statistics for groups (i.e., ensembles) of faces or objects can be rapidly extracted to optimize visual processing, without reliance of visual working memory (VWM). Recently, Tharmaratnam and colleagues (VSS 2019) demonstrated that average scene content and spatial boundary of scene ensembles can also be extracted. Furthermore, Jung and Walther (2021) have shown that non-visual attributes (i.e., apparent temperature: how hot or cold a scene would feel) of single scenes are represented in the prefrontal cortex and are accurately rated by observers. Given the flexibility of ensemble encoding, we examined if temperature summary statistics could be extracted by human participants, and whether lower-level features like visual contrast mediated this process. Participants rated the average temperature of scene ensembles, with either gray-scaled stimuli (Exp. 1) or gray-scaled stimuli with a 75% contrast reduction (Exp. 2). In both experiments, we varied set size by randomly presenting 1, 2, 4, or 6 scenes to participants on each trial, and measured VWM capacity using a 2-AFC task. Participants were able to accurately extract average temperature in both experiments, with all 6 scenes being integrated into their summary statistics. Consistent with previous results, this occurred without relying on VWM, as less than 1 scene was remembered on average. These results reveal that computing cross-modal summary statistics (i.e., average temperature) does not rely on lower-level visual features (i.e., contrast). Overall, these results suggest that rapid multisensory ensemble processing occurs through higher cognitive processes, while being independent from one’s VWM.

Topic Area: PERCEPTION & ACTION: Vision


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April 13–16  |  2024