| Paper: | PS-2A.24 | ||
| Session: | Poster Session 2A | ||
| Location: | H Lichthof | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Colour clustering in visual working memory | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1309-0 | ||
| Authors: | Ben Cuthbert, Martin Paré, Queen's University, Canada; Dominic Standage, University of Birmingham, United Kingdom; Gunnar Blohm, Queen's University, Canada | ||
| Abstract: | Visual working memory experiments typically involve asking a subject to memorize several visual stimuli such as coloured shapes, oriented lines, faces, or objects. Computational accounts of recall performance often assume that each stimulus presented in a trial is encoded independently, ignoring higher-level ensemble statistics that have been shown to bias recall and impact task performance. Here, we analyzed data from a delayed estimation task that required the report of all stimuli (6 coloured squares). We found evidence for serial dependencies in within-trial reports, suggesting that participants clustered similarly coloured stimuli together. These dependencies were supported by estimates of the mutual information of within-trial report distributions. We present a non-parametric clustering model to quantify the clustering properties of randomly-generated stimulus arrays. We believe this is a promising data-driven approach to characterizing the statistical properties of experimental stimuli. Together, these results provide further evidence that humans encode ensemble statistics of visual scenes in working memory. | ||