Temporal dynamics of decision making: A synthesis of computational and neurophysiological approaches. Issue 3 (2nd December 2021) - Harris, Alison; Hutcherson, Cendri A.
Abstract: As interest in the temporal dynamics of decision‐making has grown, researchers have increasingly turned to computational approaches such as the drift diffusion model (DDM) to identify how cognitive processes unfold during choice. At the same time, technological advances in noninvasive neurophysiological methods such as electroencephalography and magnetoencephalography now allow researchers to map the neural time course of decision making with millisecond precision. Combining these approaches can potentially yield important new insights into how choices emerge over time. Here we review recent research on the computational and neurophysiological correlates of perceptual and value‐based decision making, from DDM parameters to scalp potentials and oscillatory neural activity. Starting with motor response preparation, the most well‐understood aspect of the decision process, we discuss evidence that urgency signals and shifts in baseline activation, rather than shifts in the physiological value of the choice‐triggering response threshold, are responsible for adjusting response times under speeded choice scenarios. Research on the neural correlates of starting point bias suggests that prestimulus activity can predict biases in motor choice behavior. Finally, studies examining the time dynamics of evidence construction and evidence accumulation have identified signals at frontocentral and centroparietal electrodes associated respectively with these processes, emerging 300–500 ms after stimulus onset. These findings can inform psychological theories of decision‐making, providing empirical support for attribute weighting in value‐based choice while suggesting theoretical alternatives to dual‐process accounts. Further research combining computational and neurophysiological approaches holds promise for providing greater insight into the moment‐by‐moment evolution of the decision process. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Economics > Individual Decision‐Making Abstract : Computational model predictions (top) can be linked to distinct neurophysiological waveforms measured from EEG/MEG sensors showing peak activity in topographical scalp maps (bottom), generating novel insights into the temporal dynamics by which sensory and value‐based evidence are transformed into decisions and actions.