Modelling the magnitude sensitivity effect in confidence and response time
Mon-B22-Talk II-04
Presented by: Sebastian Hellmann
Magnitude sensitivity refers to the effect that decisions between two alternatives/stimuli tend to be faster when the intensities of both alternatives (e.g., luminance, size, or preference) are increased even if their difference is kept constant. Previous studies proposed several computational models to describe decision and response time distributions in experimental paradigms with changes of stimulus magnitude. However, with only two dependent variables, i.e., responses and response times, there is a high degree of model mimicry. We suggest to include confidence judgments as an additional dependent variable in experiments and models. We generalized several previously proposed dynamical models of confidence and response time to account for magnitude sensitivity by adding intensity-dependent noise parameters. We present three experiments, two brightness discrimination tasks and a motion discrimination task, in which the intensities of both alternatives were varied and confidence judgments were recorded. The data show that confidence increases with stimulus magnitude, even if accuracy remains constant. Previous studies explained increasing confidence but constant accuracy with stimulus magnitude by a positive evidence bias, i.e. for the computation of confidence, people allegedly rely only on the evidence supporting their decision and ignore evidence for the alternative. However, according to the present study, the apparent positive evidence bias can be alternatively explained as a result of the dynamics of the decision process. We suggest that identification of computational models of decision making can be improved by considering decisions, reaction times, and confidence at the same time.
Keywords: sequential sampling models, confidence, magnitude sensitivity, computational modeling, response times, decision
making, drift diffusion model