Value learning modulates goal-directed actions

Abstract

With experience, particular objects can predict good or bad outcomes. This alters our perceptual response to them: Reliable predictors of salient outcomes are recognized faster and better than unreliable predictors, regardless of the value (gain, loss) of the outcome they predict. When attentional resources are constrained, learned value associations matter, causing recognition of gain-associated objects to be spared. Here, we ask how learned predictiveness and value change the way we interact with potentially rewarding objects. After associating virtual objects (drinking flutes) with monetary gains or losses, reaching for and grasping corresponding real objects depended on the object's learned value. Action was faster when directed at objects that previously predicted outcomes more rather than less consistently, regardless of value. Conversely, reaches were more direct for gain- than for loss-associated objects, regardless of their predictiveness. Action monitoring thus reveals how value learning components become accessible during action.

Keywords:

• Value learning
• Goal-directed actions

Notes

¹ Peak grasp value main effect, F(1, 13) = 0.287, p = .601; predictiveness main effect F(1, 13)  = 1.252, p = .283; Value × Predictiveness interaction, F(1, 13)  = 0.242, p = .631. Grasp closing velocity value main effect, F(1, 13)  = 0.745, p = .6404; predictiveness main effect, F(1, 13)  = 1.329, p = .576; Value × Predictiveness interaction, F(1, 13)  = 2.476, p = .14. Movement duration value main effect, F(1, 13)  = 0.188, p = .672; predictiveness main effect, F(1, 13)  = 0.578, p = .461; Value × Predictiveness interaction, F(1, 13)  = 0.160, p = .696. Time to peak velocity value main effect, F(1, 13) = 0.994, p = .333; predictiveness main effect, F(1, 13)  = 0.142, p = .711; Value × Predictiveness interaction, F(1, 13)  = 0.961, p =.341. Time to peak deceleration value main effect, F(1, 13) = 0.828, p =.376; predictiveness main effect, F(1, 13)  = 0.668, p = .425; Value × Predictiveness interaction, F(1, 13)  = 2.012, p = .174.

² We thank Reviewer 2 for this suggestion.