The three biggest obstacles to solving motivated behavior, and how to overcome them
Year: 2026
Type: preprint
Abstract: Humans and other animals have a wide repertoire of actions they can engage in at any given moment, ranging from moving their limbs to generating utterances to controlling the flow of information from perception to action. What motivates them to engage some of these actions and not others at any given moment? To answer this question, motivation scientists have historically leveraged three kinds of explanatory variables: goals (what directs action), value (what determines a good or bad action), and affect (what it feels like if an action is or isn’t taken). I will argue that each of these variables faces significant challenges in explaining motivated behavior: Goals can explain how actions are directed, but not what directs goals; value can be assigned to an action, but it is a mystery how these values are assigned and how they are shaped by both “cold” and “hot” systems; and affect can explain what actions will make you feel best, but under most definitions affect is neither specific nor reliable enough to guide action selection. My Affective Gradient Hypothesis (AGH) proposes a way out of this entanglement, by re-centering motivation on two sets of processes: prediction and optimization. AGH suggests that motivated behavior arises from the continual representation of potential future states and their affective value, and the dynamic adjustment of available actions and control states to achieve states that are anticipated to be most positive and/or least negative. This account revises traditional definitions of goals, value, and affect to overcome existing obstacles and to provide a complete, coherent, empirically grounded, and generalizable account of what drives motivated behavior.
Author: Amitai Shenhav
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7214174192