Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

Abstract

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations.

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

“…Additionally, the present framework shares many features with other systems-level computational frameworks that have been previously proposed to model decisions between multiple actions [ 57 , 58 ]. However, these approaches do not incorporate the idea of dynamically integrating value information from disparate sources (with the exception of Cisek’s (2006) model [ 58 ], which demonstrated how other regions, including prefrontal cortex, influence the competition), they do not model action selection tasks with competing effectors, and they do not model the eye/hand movement trajectories generated to acquire the choices.…”

Hedging Your Bets: Intermediate Movements as Optimal Behavior in the Context of an Incomplete Decision

“…Computational models have suggested that lateral connectivity within a network representing task goals may provide a mechanism whereby intermediate movements are generated [ 27 , 43 , 44 ]. Excitatory connections between units tuned to similar movement directions can lead to two peaks of activity becoming merged and thus leading to intermediate movements.…”

Agent-causal libertarianism, statistical neural laws and wild coincidences

“…Also, it has, for instance, been observed that, as a behavior is repeated, certain motor--related neural activities become more probable under certain conditions (e.g., Costa 2007;Wickens et al 2007;Graybiel 2008;Verstynen & Sabes 2011;Hikosaka et al 2013;Kim et al 2015;Anderson 2016). together are subsequently more likely to be active together under certain conditions, and vice versa (e.g., Hebb 1949;Klaes et al 2012). These changes can happen rapidly (within milliseconds), and involve intracellular biochemical changes which influence synaptic transmission among other things (e.g., Friedman et al 2015;Cichon and Gan 2015;Svensson et al 2016).…”