Data from: Decisions reduce sensitivity to subsequent information

Behavioural studies over half a century indicate that making categorical
choices alters beliefs about the state of the world. People seem biased to
confirm previous choices, and to suppress contradicting information. These
choice-dependent biases imply a fundamental bound of human rationality.
However, it remains unclear whether these effects extend to lower level
decisions, and only little is known about the computational mechanisms
underlying them. Building on the framework of sequential-sampling models
of decision-making, we developed novel psychophysical protocols that
enable us to dissect quantitatively how choices affect the way
decision-makers accumulate additional noisy evidence. We find robust
choice-induced biases in the accumulation of abstract numerical
(experiment 1) and low-level perceptual (experiment 2) evidence. These
biases deteriorate estimations of the mean value of the numerical sequence
(experiment 1) and reduce the likelihood to revise decisions (experiment
2). Computational modelling reveals that choices trigger a reduction of
sensitivity to subsequent evidence via multiplicative gain modulation,
rather than shifting the decision variable towards the chosen alternative
in an additive fashion. Our results thus show that categorical choices
alter the evidence accumulation mechanism itself, rather than just its
outcome, rendering the decision-maker less sensitive to new information.