Generating Options and Choosing Between Them Depend on Distinct Forms of Value Representation

Humans have a remarkable capacity for flexible decision-making, deliberating among actions by modeling their likely outcomes. This capacity allows us to adapt to the specific features of diverse circumstances. In real-world decision-making, however, people face an important challenge: There are often an enormous number of possibilities to choose among, far too many for exhaustive consideration. There is a crucial, understudied prechoice step in which, among myriad possibilities, a few good candidates come quickly to mind. How do people accomplish this? We show across nine experiments ( N = 3,972 U.S. residents) that people use computationally frugal cached value estimates to propose a few candidate actions on the basis of their success in past contexts (even when irrelevant for the current context). Deliberative planning is then deployed just within this set, allowing people to compute more accurate values on the basis of context-specific criteria. This hybrid architecture illuminates how typically valuable thoughts come quickly to mind during decision-making.

Brain Reward Systems and Apathy

Reward is essential for motivating goal-directed behaviour. Impairment in the processing of reward is therefore a promising candidate for understanding apathy which has been defined as a loss of motivation and a quantitative reduction of goal-directed behaviour. This chapter employs the recently updated Research Domain Criteria framework for positive valence systems to provide an overview of reward system functions that have been associated with apathy, including reward anticipation, reward consumption, learning and prediction error, value representation, and integration of effort. For each construct, the concept and the measures on the behavioural and neural level are discussed. The chapter then provides examples from the schizophrenia literature on the association of apathy with these functions and gives a transdiagnostic perspective on the role of reward system dysfunction.

Human value learning and representation reflects rational adaptation to task demands

When making decisions about goods and actions, humans and animals often rely on internally represented values. However, to be useful across a wide range of contexts, these values need to be represented on an absolute scale – a coding scheme that is computationally costly. By contrast, representing values in a way that depends entirely on context is highly computationally efficient, but can lead to irrational behaviour when values need to be compared across contexts. Thus, an efficient learner would allocate limited computational resources only when needed according to their expectations about the future. Here, we test the hypothesis that value representation is not fixed, but rationally adapted to expectations in two human learning experiments. Unlike most lab-based tasks, participants could use their initial experience (Phase 1) to optimise behaviour (Phase 2). Phase 1 was designed to cause one group to expect only decisions within local contexts (relative code sufficient), and another group to expect choices across local contexts (relative code insufficient). Despite identical learning experiences, the group whose expectations included choices across local contexts, went on to learn absolute value representations, and learned more absolute-like representations than the other group. Human value representation is neither relative nor absolute, but efficiently and rationally tuned to task demands.

Transforming absolute value to categorical choice in primate superior colliculus during value-based decision making

Value-based decision making involves choosing from multiple options with different values. Despite extensive studies on value representation in various brain regions, the neural mechanism for how multiple value options are converted to motor actions remains unclear. To study this, we developed a multi-value foraging task with varying menu of items in non-human primates using eye movements that dissociates value and choice, and conducted electrophysiological recording in the midbrain superior colliculus (SC). SC neurons encoded “absolute” value, independent of available options, during late fixation. In addition, SC neurons also represent value threshold, modulated by available options, different from conventional motor threshold. Electrical stimulation of SC neurons biased choices in a manner predicted by the difference between the value representation and the value threshold. These results reveal a neural mechanism directly transforming absolute values to categorical choices within SC, supporting highly efficient value-based decision making critical for real-world economic behaviors.