The motivational cost of inequality: Opportunity gaps reduce the willingness to work.

Factors beyond a person's control, such as demographic characteristics at birth, often influence the availability of rewards an individual can expect for their efforts. We know surprisingly little how such differences in opportunities impact human motivation. To test this, we designed a study in which we arbitrarily varied the reward offered to each participant in a group for performing the same task. Participants then had to decide whether or not they were willing to exert effort to receive their reward. Across three experiments, we found that the unequal distribution of offers reduced participants' motivation to pursue rewards even when their relative position in the distribution was high, and despite the decision being of no benefit to others and reducing the reward for oneself. Participants' feelings partially mediated this relationship. In particular, a large disparity in rewards was associated with greater unhappiness, which was associated with lower willingness to work-even when controlling for absolute reward and its relative value, both of which also affected decisions to work. A model that incorporated a person's relative position and unfairness of rewards in the group fit better to the data than other popular models describing the effects of inequality. Our findings suggest opportunity-gaps can trigger psychological dynamics that hurt productivity and well-being of all involved.

Evidence accumulation is biased by motivation: A computational account.

To make good judgments people gather information. An important problem an agent needs to solve is when to continue sampling data and when to stop gathering evidence. We examine whether and how the desire to hold a certain belief influences the amount of information participants require to form that belief. Participants completed a sequential sampling task in which they were incentivized to accurately judge whether they were in a desirable state, which was associated with greater rewards than losses, or an undesirable state, which was associated with greater losses than rewards. While one state was better than the other, participants had no control over which they were in, and to maximize rewards they had to maximize accuracy. Results show that participants' judgments were biased towards believing they were in the desirable state. They required a smaller proportion of supporting evidence to reach that conclusion and ceased gathering samples earlier when reaching the desirable conclusion. The findings were replicated in an additional sample of participants. To examine how this behavior was generated we modeled the data using a drift-diffusion model. This enabled us to assess two potential mechanisms which could be underlying the behavior: (i) a valence-dependent response bias and/or (ii) a valence-dependent process bias. We found that a valence-dependent model, with both a response bias and a process bias, fit the data better than a range of other alternatives, including valence-independent models and models with only a response or process bias. Moreover, the valence-dependent model provided better out-of-sample prediction accuracy than the valence-independent model. Our results provide an account for how the motivation to hold a certain belief decreases the need for supporting evidence. The findings also highlight the advantage of incorporating valence into evidence accumulation models to better explain and predict behavior.

Goal-directed, habitual and Pavlovian prosocial behavior.

Although prosocial behaviors have been widely studied across disciplines, the mechanisms underlying them are not fully understood. Evidence from psychology, biology and economics suggests that prosocial behaviors can be driven by a variety of seemingly opposing factors: altruism or egoism, intuition or deliberation, inborn instincts or learned dispositions, and utility derived from actions or their outcomes. Here we propose a framework inspired by research on reinforcement learning and decision making that links these processes and explains characteristics of prosocial behaviors in different contexts. More specifically, we suggest that prosocial behaviors inherit features of up to three decision-making systems employed to choose between self- and other- regarding acts: a goal-directed system that selects actions based on their predicted consequences, a habitual system that selects actions based on their reinforcement history, and a Pavlovian system that emits reflexive responses based on evolutionarily prescribed priors. This framework, initially described in the field of cognitive neuroscience and machine learning, provides insight into the potential neural circuits and computations shaping prosocial behaviors. Furthermore, it identifies specific conditions in which each of these three systems should dominate and promote other- or self- regarding behavior.