A Probabilistic Palimpsest Model of Visual Short-term Memory

Abstract

Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ.

Parallel encoding of information into visual short-term memory

“…This speaks against computational models that assume serial complete encoding, and illustrates the biological plausibility of models that allow for simultaneous encoding of several items (e.g. (Matthey et al, 2015)…”

“…There exist two main theories: The serial hypothesis is central to feature integration theory, and postulates that items are attended to and consequently encoded one-by-one (Treisman & Gelade, 1980;Wheeler & Treisman, 2002), whereas the parallel hypothesis states that all items simultaneously compete for attention and access to short-term memory (Bundesen, 1990). These theories have informed contemporary computational models of short-term memory that either implicitly assume serial encoding (Manohar, Zokaei, Fallon, Vogels, & Husain, 2017), or allow parallel encoding (Matthey, Bays, & Dayan, 2015).…”

In search of the focus of attention in working memory: 13 years of the retro-cue effect

“…Several computational models already exist for visual WM (Bays, 2014;Matthey, Bays, & Dayan, 2015;Swan & Wyble, 2014;van den Berg, Shin, Chou, George, & Ma, 2012). We recently proposed a computational model of attention to items in verbal WM (Oberauer, 2013;Oberauer, Souza, Druey, & Gade, 2013) that explains the effects of switching attention between items in WM and of swapping entire memory sets into and out of WM.…”

A Unifying Model of Orientation Crowding in Peripheral Vision

“…In Figure 2, we summarize a processing stage that differentially weights orientation signals according to their distance from the stimulus center. The population response thus reflects probability functions based on biologically-motivated filters (see also [28] and [29]). With this single quantification, we compute the probability of any given perceptual report under various viewing conditions.…”