Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.02.127860v1?rss=1 Authors: Warburton, M., Brookes, J., Hasan, M., Leonetti, M., Dogar, M., Wang, H., Cohn, A., Mushtaq, F., Mon-Williams, M. 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 cannot provide a principled account of when hysteresis will occur. We propose that hysteresis is an emergent property of a dynamical system learning from the consequences of its actions. To examine this, 152 participants moved a cursor to a target on a tablet device whilst avoiding an obstacle. Hysteresis was observed when the obstacle moved sequentially across the screen between trials, but not with random obstacle placement. Two further experiments (n = 20) showed an attenuation when time and resource constraints were eased. We created a simple computational model capturing dynamic probabilistic estimate updating that showed the same patterns of results. This provides the first computational demonstration of how sensorimotor decision-making can get 'stuck in a rut' through the dynamic updating of its probability estimates. Copy rights belong to original authors. Visit the link for more info