Neural Underpinnings of Decision Strategy Selection: A Review and a Theoretical Model

Abstract

In multi-attribute choice, decision makers use decision strategies to arrive at the final choice. What are the neural mechanisms underlying decision strategy selection? The first goal of this paper is to provide a literature review on the neural underpinnings and cognitive models of decision strategy selection and thus set the stage for a neurocognitive model of this process. The second goal is to outline such a unifying, mechanistic model that can explain the impact of noncognitive factors (e.g., affect, stress) on strategy selection. To this end, we review the evidence for the factors influencing strategy selection, the neural basis of strategy use and the cognitive models of this process. We also present the Bottom-Up Model of Strategy Selection (BUMSS). The model assumes that the use of the rational Weighted Additive strategy and the boundedly rational heuristic Take The Best can be explained by one unifying, neurophysiologically plausible mechanism, based on the interaction of the frontoparietal network, orbitofrontal cortex, anterior cingulate cortex and the brainstem nucleus locus coeruleus. According to BUMSS, there are three processes that form the bottom-up mechanism of decision strategy selection and lead to the final choice: (1) cue weight computation, (2) gain modulation, and (3) weighted additive evaluation of alternatives. We discuss how these processes might be implemented in the brain, and how this knowledge allows us to formulate novel predictions linking strategy use and neural signals.

Neural Signatures of Rational and Heuristic Choice Strategies: A Single Trial ERP Analysis

“…Adopting this perspective, we suggest that the preference for a particular choice strategy is at least partly determined by the LC-NE gain control mechanism. Our theoretical model of decision strategy selection ( Wichary and Smolen, 2016 ) assumes that phasic gain changes in response to decision cues constitute the mechanism of differential attentional weighting of decision cues during option evaluation, and thus shape the strategy for pre-decisional information processing. Norepinephrine might be one neuromodulator responsible for the observed effects, however, P3 amplitude was also linked with activity in ventral striatum ( Pogarell et al, 2011 ; Pfabigan et al, 2014 ), suggesting that also dopamine is involved in the modulation of this component.…”

“…We extended this line of theorizing and proposed that the LC-NE activity is linked to strategy selection in multi-attribute choice. Our theoretical model (Bottom Up Model of Strategy Selection, BUMSS; Wichary and Smolen, 2016 ) proposes that the process of strategy selection is shaped in a bottom up manner by brain-wide gain modulation mediated by LC-NE. According to the model gain modulation coupled with lateral inhibition in the cortex, is responsible for attentional selection of some decision cues over others ( Eldar et al, 2013 ; Warren et al, 2015 ; Mather et al, 2016 ).…”

“…We analyzed the relationships between spontaneous decision strategy use and the EEG signal amplitude to consecutive decision cues. Based on the literature linking P3 with attention allocation and based on our theoretical model of strategy selection ( Wichary and Smolen, 2016 ), we expected that the use of the simple heuristic TTB would be associated with high P3 amplitudes to the most important decision cues and low P3 amplitudes to less important cues. In contrast, the use of WADD strategy should be associated with similar P3 amplitudes to all decision cues.…”

Individual differences in decisiveness: ERP correlates of response inhibition and error monitoring

“…To our knowledge, no study has examined these two crucial cognitive control mechanisms in relation to individual differences in decisiveness. Moreover, despite the growing interest in the cerebral basis of decision making (for reviews, see Krawczyk, ; Venkatraman & Huettel, ; Wichary & Smoleń, ), no study has looked at decisiveness‐related neural correlates of response inhibition and error monitoring. The aim of this study is to fill this gap by investigating the links between decisiveness and selected behavioral measures, as well as ERP components, which represent well‐validated indexes of cognitive control functions.…”

The Psychophysiology of Action: A Multidisciplinary Endeavor for Integrating Action and Cognition

“…To reach this goal, psychophysiological models of motor heuristics and embodied choices have to be formulated. Different models of the psychophysiology of movement have been studied in animals ( Cisek and Kalaska, 2010 ) and humans ( Wichary and Smolen, 2016 ) and can serve as a starting point for theory testing, integration, and development. Now, neuroscientific studies integrating different methodological challenges ( Kyathanahally et al, 2017 ) target decision making ( Chand and Dhamala, 2017 ; Muraskin et al, 2017 ), and find correlations of decision making and somatosensory networks ( Harris and Lim, 2016 ), however, the aspect of movement or “real” (i.e., dynamic) action is still rarely taken into account.…”