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.},
  author = {Szymon Wichary and Tomasz Smolen},
  doi = {10.3389/fnins.2016.00500},
  issn = {1662-453X},
  journal = {Frontiers in Neuroscience},
  keywords = {arousal,decision-making,gain,gain modulation,multi-attribute choice,neurocognitive model,strategy selection},
  number = {November},
  pages = {1--13},
  title = {{Neural Underpinnings of Decision Strategy Selection: A Review and a Theoretical Model}},
  url = {http://journal.frontiersin.org/article/10.3389/fnins.2016.00500/full},
  volume = {10},
  year = {2016}