Correspondence between population coding andpsychophysical scaling models of working memory
Bays, P. M.
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A mathematical idealization of the way neural populations encode sensory information has been found to provide a parsimonious account of errors made by human observers on perceptual and short-term memory tasks. This includes the effects of set size and flexible prioritization of items within a set (Bays, 2014), the frequency and identity of \"swap\" or misbinding errors (Schneegans & Bays, 2017), subjective judgments of confidence (Bays, 2016; van den Berg et al., 2017), and biases and variation in precision due to serial dependent and stimulus-specific effects (Bliss et al., 2017; Taylor & Bays, 2018). A superficially quite different account of short-term recall has recently been proposed in work by Schurgin et al. (2018), who argue that taking into account the differences between physical and perceptual distance in a feature space reduces recall to a classical signal detection problem. Here I document a remarkable similarity between the two models, demonstrating a favourable convergence of neural- and cognitive-level accounts of working memory.
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