Getting stuck in a rut as an emergent feature of a dynamic decision-making system

Materials

https://osf.io/jcgqk/

Preprint

https://doi.org/10.1101/2020.06.02.127860

Other details

Presented at Progress in Motor Control 2019.

APA7 Citation

Warburton, M., Brookes, J., Hasan, M., Leonetti, M., Dogar, M., Wang, H., Cohn, A. G., Mushtaq, F., & Mon-Williams, M. (2024). Getting stuck in a rut as an emergent feature of a dynamic decision-making system. Royal Society Open Science, 11(4), 231550.

Abstract

Human sensorimotor decision making has a tendency to get ‘stuck in a rut’, being biased towards selecting a previously implemented action structure (hysteresis). Existing explanations propose this is the consequence of an agent efficiently modifying an existing plan, rather than creating a new plan from scratch. Instead, we propose that hysteresis is an emergent property of a system learning from the consequences of its actions. To examine this, 152 participants moved a cursor to a target on a tablet device while avoiding an obstacle. Hysteresis was observed when the obstacle moved sequentially across the screen between trials, whereby the participant continued moving around the same side of the obstacle despite it now requiring a larger movement than the alternative. Two further experiments (n = 20) showed an attenuation when time and resource constraints were eased. We created a simple computational model capturing probabilistic estimate updating that showed the same patterns of results. This provides, to our knowledge, the first computational demonstration of how sensorimotor decision making can get ‘stuck in a rut’ through the updating of the probability estimates associated with actions.