Perceptual Decision-Making: Biases in Post-Error Reaction Times Explained by Attractor Network Dynamics

Abstract

Perceptual decision-making is the subject of many experimental and theoretical studies. Most modeling analyses are based on statistical processes of accumulation of evidence. In contrast, very few works confront attractor network models' predictions with empirical data from continuous sequences of trials. Recently however, numerical simulations of a biophysical competitive attractor network model have shown that such network can describe sequences of decision trials and reproduce repetition biases observed in perceptual decision experiments. Here we get more insights into such effects by considering an extension of the reduced attractor network model of Wong and Wang (2006), taking into account an inhibitory current delivered to the network once a decision has been made. We make explicit the conditions on this inhibitory input for which the network can perform a succession of trials, without being either trapped in the first reached attractor, or losing all memory of the past dynamics. We study in details how, during a sequence of decision trials, reaction times and performance depend on the nonlinear dynamics of the network, and we confront the model behavior with empirical findings on sequential effects. Here we show that, quite remarkably, the network exhibits, qualitatively and with the correct orders of magnitude, post-error slowing and post-error improvement in accuracy, two subtle effects reported in behavioral experiments in the absence of any feedback about the correctness of the decision. Our work thus provides evidence that such effects result from intrinsic properties of the nonlinear neural dynamics. Significance statementMuch experimental and theoretical work is being devoted to the understanding of the neural correlates of perceptual decision making. In a typical behavioral experiment, animals or humans perform a continuous series of binary discrimination tasks. To model such experiments, we consider a biophysical decision-making attractor neural network, taking into account an inhibitory current delivered to the network once a decision is made. Here we provide evidence that the same intrinsic properties of the nonlinear network dynamics underpins various sequential effects reported in experiments. Quite remarkably, in the absence of feedback on the correctness of the decisions, the network exhibits post-error slowing (longer reaction times after error trials) and post-error improvement in accuracy (smaller error rates after error trials).

Nonlinear Neural Network Dynamics Accounts for Human Confidence in a Sequence of Perceptual Decisions

“…First we note that the attractor neural network captures the variation of response times with respect to angle orientation, as expected from Wong and Wang [2006]. Second, the dependency in the choice history (through the repetition of responses) is correctly reproduced, in agreement with a previous study of these effects Berlemont and Nadal [2019]. Finally, the effect of confidence on response times is significant, with negative slopes as for the experiment and with the same order of magnitude.…”

“…Perceptual decisions made by humans in behavioral experiments have been shown to depend not only on the current sensory input, but also on the choices made at previous trials. Various sequential effects have been reported Fernberger [1920], Laming [1979], Gold et al [2008], Leopold et al [2002], and different models have been proposed to account for them Cho et al [2002], Angela and Cohen [2009], Glaze et al [2015], Bonaiuto et al [2016], Berlemont and Nadal [2019]. When the decision-maker does not receive feedback, confidence in one's decision might be important for controlling future behaviors Yeung and Summerfield [2012], Meyniel et al [2015b].…”

“…7 we illustrate how the nonlinear neural dynamics lead to confidence-specific sequential effects. The analysis is here similar to the one done in Berlemont and Nadal [2019] for the analysis of post-error effects in the same neural model. On each panel we compare the (mean) neural dynamics for post-low and post-high confidence trials (respectively red and blue lines).…”

“…1 F. For a biologically relevant range of parameters, relaxation is not complete at the beginning of a new trial, hence the decision made at this trial will depend on the one at the previous trial. Previously, we showed Berlemont and Nadal [2019] that he model accounts for the main sequential and post-error effects observed in perceptual decision making experiments in human and monkeys.…”

“…We show that a biophysically inspired attractor neural network is able to accurately reproduce response times and accuracy across participants. We fit an attractor neural network Wong and Wang [2006], Berlemont and Nadal [2019] on behavioral data (response times and accuracy). We show that behavioral effects of confidence can be accurately estimated for each participant by estimating confidence as a function of the differential activity between the two neural populations Wei and Wang [2015] at the time of the decision.…”