Humans can infer social preferences from decision speed alone.

Humans are known to be capable of inferring hidden preferences and beliefs of their conspecifics when observing their decisions. While observational learning based on choices has been explored extensively, the question of how response times (RT) impact our learning of others' social preferences has received little attention. Yet, while observing choices alone can inform us about the direction of preference, they reveal little about the strength of this preference.

Comparing experience- and description-based economic preferences across 11 countries.

Recent evidence indicates that reward value encoding in humans is highly context dependent, leading to suboptimal decisions in some cases, but whether this computational constraint on valuation is a shared feature of human cognition remains unknown. Here we studied the behaviour of n = 561 individuals from 11 countries of markedly different socioeconomic and cultural makeup. Our findings show that context sensitivity was present in all 11 countries.

The functional form of value normalization in human reinforcement learning.

Reinforcement learning research in humans and other species indicates that rewards are represented in a context-dependent manner. More specifically, reward representations seem to be normalized as a function of the value of the alternative options. The dominant view postulates that value context-dependence is achieved via a divisive normalization rule, inspired by perceptual decision-making research.

Outcome context-dependence is not WEIRD: Comparing reinforcement- and description-based economic preferences worldwide.

Recent evidence indicates that reward value encoding in humans is highly context-dependent, leading to suboptimal decisions in some cases. But whether this computational constraint on valuation is a shared feature of human cognition remains unknown. To address this question, we studied the behavior of individuals from across 11 countries of markedly different socioeconomic and cultural makeup using an experimental approach that reliably captures context effects in reinforcement learning.

Spontaneous instrumental avoidance learning in social contexts.

Adaptation to our social environment requires learning how to avoid potentially harmful situations, such as encounters with aggressive individuals. Threatening facial expressions can evoke automatic stimulus-driven reactions, but whether their aversive motivational value suffices to drive instrumental active avoidance remains unclear. When asked to freely choose between different action alternatives, participants spontaneously-without instruction or monetary reward-developed a preference for choices that maximized the probability of avoiding angry individuals (sitting away from them in a waiting room).

Contextual influence of reinforcement learning performance of depression: evidence for a negativity bias?

Backgrounds: Value-based decision-making impairment in depression is a complex phenomenon: while some studies did find evidence of blunted reward learning and reward-related signals in the brain, others indicate no effect. Here we test whether such reward sensitivity deficits are dependent on the overall value of the decision problem.

Methods: We used a two-armed bandit task with two different contexts: one 'rich', one 'poor' where both options were associated with an overall positive, negative expected value, respectively.

Why unchosen options linger in our minds.

In the 1930s, philosopher John Dewey stated: “We do not learn from experience… we learn from reflecting on experience.” The question of how we learn from the consequences of our actions has been investigated for decades. When deliberating between options, it is assumed that the outcome of our choice is used as a feedback signal to learn the value of the chosen option.

Two sides of the same coin: Beneficial and detrimental consequences of range adaptation in human reinforcement learning.

Evidence suggests that economic values are rescaled as a function of the range of the available options. Although locally adaptive, range adaptation has been shown to lead to suboptimal choices, particularly notable in reinforcement learning (RL) situations when options are extrapolated from their original context to a new one. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio.

Assessing inter-individual differences with task-related functional neuroimaging.

Explaining and predicting individual behavioural differences induced by clinical and social factors constitutes one of the most promising applications of neuroimaging. In this Perspective, we discuss the theoretical and statistical foundations of the analyses of inter-individual differences in task-related functional neuroimaging. Leveraging a five-year literature review (July 2013-2018), we show that researchers often assess how activations elicited by a variable of interest differ between individuals.

Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences.

In economics and perceptual decision-making contextual effects are well documented, where decision weights are adjusted as a function of the distribution of stimuli. Yet, in reinforcement learning literature whether and how contextual information pertaining to decision states is integrated in learning algorithms has received comparably little attention. Here, we investigate reinforcement learning behavior and its computational substrates in a task where we orthogonally manipulate outcome valence and magnitude, resulting in systematic variations in state-values.