  Perceptual decision making is the subject of many experimental and
theoretical studies. Whereas most modeling analysis are based on statistical
processes of accumulation of evidence, less attention is being devoted to the
modeling with attractor network dynamics, even though they describe well
psychophysical and neurophysiological data. In particular, very few works
confront attractor models predictions with data from continuous sequences of
trials. Recently however, a biophysical competitive attractor network model has
been used to describe such sequences of decision trials, and has been shown to
reproduce repetition biases observed in perceptual decision experiments. Here
we propose an extension of the reduced attractor network model of Wong and Wang
(2006) to get more insights into such effects. We make explicit the conditions
under which such network can perform a succession of decisions, and show that
the model provides a mathematical framework for studying the effects of a trial
on the decision made on the next one. We study in details the reaction times
properties during a sequence of decision trials, and show that the model
reproduces behavioral data, both qualitatively and quantitatively. In
particular, we find that the decision made on the current trial is biased
toward the one made on the previous trial. More remarkably, we show that, in
the absence of any feedback about the correctness of the decision, the network
exhibits post-error slowing, a subtle effect in agreement with empirical data.
