Noise in Neural Populations Accounts for Errors in Working Memory

Abstract

Errors in short-term memory increase with the quantity of information stored, limiting the complexity of cognition and behavior. In visual memory, attempts to account for errors in terms of allocation of a limited pool of working memory resources have met with some success, but the biological basis for this cognitive architecture is unclear. An alternative perspective attributes recall errors to noise in tuned populations of neurons that encode stimulus features in spiking activity. I show that errors associated with decreasing signal strength in probabilistically spiking neurons reproduce the pattern of failures in human recall under increasing memory load. In particular, deviations from the normal distribution that are characteristic of working memory errors and have been attributed previously to guesses or variability in precision are shown to arise as a natural consequence of decoding populations of tuned neurons. Observers possess fine control over memory representations and prioritize accurate storage of behaviorally relevant information, at a cost to lower priority stimuli. I show that changing the input drive to neurons encoding a prioritized stimulus biases population activity in a manner that reproduces this empirical tradeoff in memory precision. In a task in which predictive cues indicate stimuli most probable for test, human observers use the cues in an optimal manner to maximize performance, within the constraints imposed by neural noise.

The Poisson shot noise model of visual short-term memory and choice response time: Normalized coding by neural population size

“…The dilemma this creates is that a trace-strength power model of VSTM growth can realize the sample-size model predictions, but possibly at the expense of poorer RT predictions. One way to resolve this dilemma is to assume, as proposed by Bays (2014), that VSTM normalization acts on the Poisson rates representing stimuli rather than on the VSTM traces themselves. This would simultaneously yield the predictions of the samplesize model, via the signal-to-noise properties of Poisson processes, and an exponential VSTM growth function, via the properties of linear shunting equations.…”

Changing concepts of working memory

“…Many sources could potentially contribute to variability in precision, including stimulus differences 71 , waxing and waning of alertness, covert attention shifts 25 , grouping and other configural effects 72 , and variability arising during maintenance 16,61 . One proposal is that deviations from normality in the distribution of working memory errors may arise from the same source of stochasticity as the errors themselves, namely Poisson variability in neural spiking 73 .…”

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.…”