From recency to the central tendency bias in working memory: a unifying attractor network model

The central tendency bias, or contraction bias is a phenomenon where the judgment of the magnitude of items held in working memory is biased towards the average of past observations. This phenomenon has been first described more than a century ago [1] and since then, has been replicated in various decision making tasks in humans [2–10], and rodents [11, 12]. Contraction bias is assumed to be an optimal strategy by the brain, given the noisy nature of working memory. From a Bayesian perspective [7], the progressive shift of the noisy memory towards the mean of a prior distribution built from past sensory experience helps with more accurate estimates of the memory. In this work, we propose an alternative account, via short-term history biases (serial dependence) [12–15]. Our model is motivated and inspired by recent results from an auditory delayed-discrimination task in rats, where the posterior parietal cortex (PPC) has been shown to be critical to these memory effects [12]. The dynamics of our model suggests that contraction bias can emerge as a result of a volatile working memory content which makes it susceptible to shifting to the past sensory experience. The errors, at the level of individual trials, are sampled from the full distribution of the stimuli, and are not due to a gradual shift of the memory towards the distribution’s mean. Our model explains both short-term history biases, as well as contraction bias towards the sensory mean for the averaged performance. The results are consistent with the role of the PPC in encoding such sensory history biases, and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas.