Sasmita, K. | bioRxiv Science

bioRxiv2019

Neurobehavioral Correlates of How Time-on-task and Sleep Deprivation Modulate Deployment of Cognitive Effort

Sustaining attention is highly demanding and can falter if there is a shift in willingness to exert effort. Motivated attentional performance and effort preference were tracked in relation to increasing time-on-task (Experiment 1) and sleep deprivation (Experiment 2). Performance decrement with time-on-task was attenuated with reward, while preference to deploy effort decreased with longer task duration. Sleep deprivation, accentuated performance decline with time-on-task, and was accompanied by greater effort-discounting. Motivated attention performance was associated with higher fronto-parietal activation, in both normal and sleep deprived conditions. However, after sleep deprivation modulation of activation by reward was reduced in the anterior cingulate cortex (ACC) and left anterior insula (aIns). Together, these results depict how motivational decline affects performance when one gets tired after sustained task performance and/or sleep deprivation.

neuroscience↗

bioRxiv2017

Electrophysiological indices reflect switches between Bayesian and heuristic strategies in perceptual learning

Belief updating entails the incorporation of new information about the environment into internal models of the world. Bayesian inference is the statistically optimal strategy for performing belief updating in the presence of uncertainty. An important open question is whether the use of cognitive strategies that implement Bayesian inference is dependent upon motivational state and, if so, how this is reflected in electrophysiological signatures of belief updating in the brain. Here we recorded the electroencephalogram of participants performing a simple reward learning task with both monetary and non-monetary instructive feedback conditions. Our aim was to distinguish the influence of the rewarding properties of feedback on belief updating from the information content of the feedback itself. A Bayesian updating model allowed us to quantify different aspects of belief updating across trials, including the size of belief updates and the uncertainty of beliefs. Faster learning rates were observed in the monetary feedback condition compared to the instructive feedback condition, while belief updates were generally larger, and belief uncertainty smaller, with monetary compared to instructive feedback. Larger amplitudes in the monetary feedback condition were found for three event-related potential components: the P3a, the feedback-related negativity (FRN) and the late positive potential (LPP). These findings suggest that motivational state influences inference strategies in reward learning, and this is reflected in the electrophysiological correlates of belief updating.

neuroscience↗