A Biologically Plausible Computational Theory for Value Integration and Action Selection in Decisions with Competing Alternatives

Abstract

Decision making is a vital component of human and animal behavior that involves selecting between alternative options and generating actions to implement the choices. Although decisions can be as simple as choosing a goal and then pursuing it, humans and animals usually have to make decisions in dynamic environments where the value and the availability of an option change unpredictably with time and previous actions. A predator chasing multiple prey exemplifies how goals can dynamically change and compete during ongoing actions. Classical psychological theories posit that decision making takes place within frontal areas and is a separate process from perception and action. However, recent findings argue for additional mechanisms and suggest the decisions between actions often emerge through a continuous competition within the same brain regions that plan and guide action execution. According to these findings, the sensorimotor system generates concurrent action-plans for competing goals and uses online information to bias the competition until a single goal is pursued. This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time. We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions. We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation.

Motor planning flexibly optimizes performance under uncertainty about task goals

“…The same neural data that gave rise to computational theories of movement averaging have also been interpreted more broadly as evidence of parallel motor planning142930. That is, parallel activity associated with multiple targets is thought to represent parallel motor plans prepared for each goal29.…”

Understanding the nonlinear physiological and behavioral effects of tDCS through computational neurostimulation

“…Second, the model provides a mesoscopic level of description regarding the neural causes that generate behavior, which reduces the tractability of the problem but not at the expense of being biophysical plausible. Finally, we note that there is now a rich panoply of models for many behaviors at these levels of description (Arbib, 1995;Christopoulos et al, 2015;Trappenberg, 2009), thus allowing for the interrogation of the impact of NIBS in such systems, without the requirement of a strong intellectual investment in any of these models.…”

“…Computational models with sufficient biological plausibility can thus bridge the gap between stimulation and behavior by simulating the changes in neural activity that mediate between the two. The use of such models is indeed now common in other fields of neuroscience (Bonaiuto and Arbib, 2010;Christopoulos et al, 2015;Itti and Koch, 2001;Riesenhuber and Poggio, 2000;Wolpert and Ghahramani, 2000), yet strikingly few such approaches have been adopted for the field of NIBS.…”

The sequential encoding of competing action goals involves dynamic restructuring of motor plans in working memory

“…Such motor encoding of competing action goals could facilitate the incorporation of movement-related costs and constraints into decisions related to action selection and may enable more rapid responding once the target is selected (Christopoulos et al 2015;Cisek 2006;Cisek and Pastor-Bernier 2014;Cos et al 2011Cos et al , 2012Cos et al , 2014. In contrast to experiments described above, in the real world, due to continuous shifts of overt or covert attention, potential action goals often arise sequentially over time with uncertainty about the number of options that will become available before we need to act.…”