Our choice is influenced by choices we made in the past, but the mechanism responsible for the choice bias remains elusive. Here we show that the history-dependent choice bias can be explained by an autonomous learning rule whereby an estimate of the likelihood of a choice to be made is updated in each trial by comparing between the actual and expected choices. We found that in perceptual decision making without performance feedback, a decision on an ambiguous stimulus is repeated on the subsequent trial more often than a decision on a salient stimulus. This inertia of decision was not accounted for by biases in motor response, sensory processing, or attention. The posterior cingulate cortex and frontal eye field represent choice prediction error and choice estimate in the learning algorithm, respectively. Interactions between the two regions during the intertrial interval are associated with decision inertia on a subsequent trial.

Our voluntary behaviors are thought to be controlled by top-down signals from the prefrontal cortex that modulate neural processing in the posterior cortices according to the behavioral goal. However, we have insufficient evidence for the causal effect of the top-down signals. We applied a single-pulse transcranial magnetic stimulation over the human prefrontal cortex and measured the strength of the top-down signals as an increase in the efficiency of neural impulse transmission. The impulse induced by the stimulation transmitted to different posterior visual areas depending on the domain of visual features to which subjects attended. We also found that the amount of impulse transmission was associated with the level of attentional preparation and the performance of visual selective-attention tasks, consistent with the causal role of prefrontal top-down signals.

In many natural environments the value of a choice gradually gets better or worse as circumstances change. Discerning such trends makes predicting future choice values possible. We show that humans track such trends by comparing estimates of recent and past reward rates, which they are able to hold simultaneously in the dorsal anterior cingulate cortex (dACC). Comparison of recent and past reward rates with positive and negative decision weights is reflected by opposing dACC signals indexing these quantities. The relative strengths of time-linked reward representations in dACC predict whether subjects persist in their current behaviour or switch to an alternative. Computationally, trend-guided choice can be modelled by using a reinforcement-learning mechanism that computes a longer-term estimate (or expectation) of prediction errors. Using such a model, we find a relative predominance of expected prediction errors in dACC, instantaneous prediction errors in the ventral striatum and choice signals in the ventromedial prefrontal cortex.