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A Probabilistic Clustering Theory of the Organization of Visual Short-Term Memory
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Abstract
Some models of visual short-term memory (VSTM) assume that memories for individual items are independent. Recent experimental evidence indicates that this assumption is false. People's memories for individual items are influenced by the other items in a scene. We develop a Probabilistic Clustering Theory (PCT) for modeling the organization of VSTM. PCT states that VSTM represents a set of items in terms of a probability distribution over all possible clusterings or partitions of those items. Because PCT considers multiple possible partitions, it can represent an item at multiple granularities or scales simultaneously. Moreover, using standard probabilistic inference, it automatically determines the appropriate partitions for the particular set of items at hand, and the probabilities or weights that should be allocated to each partition. A consequence of these properties is that PCT accounts for experimental data that have previously motivated hierarchical models of VSTM, thereby providing an appealing alternative to hierarchical models with pre-speci?ed, ?xed structures. Meeting abstract presented at VSS 2013
@article{jacobs2013probabilistic,
title = {A Probabilistic Clustering Theory of the Organization of Visual Short-Term Memory},
author = {Robert A. Jacobs and A. Emin Orhan},
year = {2013},
journal = {Journal of Vision},
publisher = {Association for Research in Vision and Ophthalmology},
volume = {13},
number = {9},
pages = {1368},
doi = {10.1167/13.9.1368},
}