This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Not great, not terrible: A reward “landscape” analysis of time-varying decision thresholds

Mr. Erik Stuchlý
University of Hamburg ~ Faculty of Psychology and Movement Sciences
Casimir Ludwig
University of Bristol ~ School of Psychological Science
Gaurav Malhotra
University of Bristol, United Kingdom

Normative models of perceptual decision-making predict that time-varying decision policies, such as collapsing decision thresholds, represent the optimal strategy in certain contexts. Nevertheless, experimental studies often reveal systematic differences between the model-inferred optimal threshold and the thresholds adopted by participants. Malhotra et al. (2018, J. Exp. Psychol. Gen.) computed the reward rate of decision thresholds with different intercepts and gradients – the ‘reward landscape’ - and found that the optimal policy in their task was adjacent to policies with extremely low reward rate. They proposed that the observed choice of sub-optimal thresholds is a result of satisficing, whereby participants explore this landscape and settle for policies distant enough from those which yield low reward rate, while still being near-optimal. If this hypothesis holds, then lowering the reward rate of all non-optimal policies, while keeping the optimal policy unchanged, should motivate participants to adopt thresholds closer to the optimal policy. We report findings from Monte Carlo simulations used to generate the reward landscape, which identified two task parameters that change the reward rate of thresholds around the optimal policy, while keeping the optimal policy unchanged: monetary penalty and inter-trial interval for incorrect decisions. We manipulated these parameters in an experimental task to identify participants’ position on the reward landscape and to examine how sensitive they are to changes in this landscape. By considering a broad range of decision policies in this fashion, we can reach a better understanding of why and how time-varying decision strategies are used.



decision thresholds
optimal policy
reward rate
time-varying policy
decision strategy

There is nothing here yet. Be the first to create a thread.

Cite this as:

Stuchlý, E., Ludwig, C., & Malhotra, G. (2021, July). Not great, not terrible: A reward “landscape” analysis of time-varying decision thresholds. Paper presented at Virtual MathPsych/ICCM 2021. Via