Vignash Tharmaratnam¹, Dirk B. Walther², Jonathan S. Cant¹; ¹University of Toronto Scarborough, ²University of Toronto

We have previously demonstrated that participants can rapidly extract average scene temperature (i.e., how hot or cold scenes would feel on average; VSS, 2024), without reliance on color, contrast, or low spatial frequencies. Furthermore, this occurred without utilizing visual working memory (VWM) resources, a hallmark of ensemble processing. In the present study, we furthered this investigation by examining whether average scene temperature could be extracted without reliance on high spatial frequency content. Given the established importance of low spatial frequencies in the rapid formation of scene gist representations (Oliva, 2005), we predicted that average scene temperature could be extracted when high spatial-frequency information was filtered out of scene images, and, similar to our previous results, this would occur without reliance on VWM. Participants rated the average temperature of scene ensembles that were gray-scaled and had a low spatial frequency filter applied (< 1 cycle/degree). 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, with all 6 scenes being integrated into their summary statistics. This occurred without relying on VWM, as fewer than 0.9 scenes were remembered on average. These results reveal that computing cross-modal summary statistics (i.e., average temperature) does not rely on high spatial frequency information or VWM resources, and that abstract multisensory information can be rapidly retrieved from complex visual stimuli.