Motivation dynamically increases noise resistance by internal feedback during movement. (4th February 2019) - Manohar, Sanjay G.; Muhammed, Kinan; Fallon, Sean J.; Husain, Masud
Abstract: Motivation improves performance, pushing us beyond our normal limits. One general explanation for this is that the effects of neural noise can be reduced, at a cost. If this were possible, reward would promote investment in resisting noise. But how could the effects of noise be attenuated, and why should this be costly? Negative feedback may be employed to compensate for disturbances in a neural representation. Such feedback would increase the robustness of neural representations to internal signal fluctuations, producing a stable attractor. We propose that encoding this negative feedback in neural signals would incur additional costs proportional to the strength of the feedback signal. We use eye movements to test the hypothesis that motivation by reward improves precision by increasing the strength of internal negative feedback. We find that reward simultaneously increases the amplitude, velocity and endpoint precision of saccades, indicating true improvement in oculomotor performance. Analysis of trajectories demonstrates that variation in the eye position during the course of saccades is predictive of the variation of endpoints, but this relation is reduced by reward. This indicates that motivation permits more aggressive correction of errors during the saccade, so that they no longer affect the endpoint. We suggest that such increases in internal negative feedback allow attractor stability, albeit at a cost, and therefore may explain how motivation improves cognitive as well as motor precision. Highlights: Motivation can increase speed and reduce behavioural variability. This requires stabilising neural representations so they are robust to noise. Stable representations or attractors in neural systems may come at the cost of stronger negative feedback. Examination of trajectory correlations demonstrates that reward increases negative feedback. We propose that the cost of stabilising signals explain why effort is expensive.