Consequence assessment and behavioral patterns of inhibition in decision-making: modelling its underlying mechanisms

Abstract Learning to make decisions depends on exploring options, experiencing their consequence, and reassessing the strategy. Several studies have analyzed various aspects of value-based decision-making, focusing on cued and immediate gratification. By contrast, how the brain gauges delayed consequence for decision-making remains poorly understood. We designed a decision-making task in which decisions altered future options. In the absence of any explicit performance feedback, participants had to test and internally assess specific criteria to make optimal decisions. This task was designed to specifically study how the assessment of consequence forms and influences decisions as learning progresses. We analyzed behavior results to characterize individual differences in reaction times, decision strategies, and learning rates. We formalized this operation mathematically by means of a multi-layered decision-making model. The first layer described the dynamics of two populations of neurons characterizing the decision-making process. The other two layers modulated the decision-making policy by dynamically adapting an oversight learning mechanism. The model was validated by fitting individual participants’ behavior and it faithfully predicted non-trivial patterns of decision-making. These findings provided an explanation to how delayed consequence may be computed and incorporated into the neural dynamics of decision-making, and to how learning occurs in the absence of explicit feedback.