People often learn about risks from other people. In the current study, we investigated the impact of social learning on risky decisions from experience by incorporating direct observational learning. Participants were placed in pairs – one participant observed the other participant sampling from different options, and then both made decisions based on this personal/observed experience. Participants tended to underweight rare outcomes less when learning from observed experience, particularly with high-value rare outcomes. This difference was not reliably significant, however, suggesting a subtle effect. The study discusses potential contributing factors such as active hypothesis testing, psychological distance, social environment, competitiveness, and goal alignment to explain the results. Overall, the findings contribute to understanding the dynamics of social learning in risky decision-making.

Habits form a crucial component of behavior. In recent years, key computational models have conceptualized habits as arising from model-free reinforcement learning mechanisms, which typically select between available actions based on the future value expected to result from each. Traditionally, however, habits have been understood as behaviors that can be triggered directly by a stimulus, without requiring the animal to evaluate expected outcomes. Here, we develop a computational model instantiating this traditional view, in which habits develop through the direct strengthening of recently taken actions rather than through the encoding of outcomes. We demonstrate that this model accounts for key behavioral manifestations of habits, including insensitivity to outcome devaluation and contingency degradation, as well as the effects of reinforcement schedule on the rate of habit formation. The model also explains the prevalent observation of perseveration in repeated-choice tasks as an additional behavioral manifestation of the habit system. We suggest that mapping habitual behaviors onto value-free mechanisms provides a parsimonious account of existing behavioral and neural data. This mapping may provide a new foundation for building robust and comprehensive models of the interaction of habits with other, more goal-directed types of behaviors and help to better guide research into the neural mechanisms underlying control of instrumental behavior more generally. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

When faced with delayed, uncertain rewards, humans andother animals usually prefer to know the eventual outcomesin advance. This preference for cues providing advance infor-mation can lead to seemingly suboptimal choices, where lessreward is preferred over more reward. Here, we introduce areinforcement-learning model of this behavior, the anticipatedprediction error (APE) model, based on the idea that predic-tion errors themselves can be rewarding. As a result, animalswill sometimes pick options that yield large prediction errors,even when the expected rewards are smaller. We compare theAPE model against an alternative information-bonus model,where information itself is viewed as rewarding. These mod-els are evaluated against a newly collected dataset with humanparticipants. The APE model fits the data as well or betterthan the other models, with fewer free parameters, thus provid-ing a more robust and parsimonious account of the suboptimalchoices. These results suggest that anticipated prediction er-rors can be an important signal underpinning decision making.

Good explanations can be distinguished from bad ones in different ways, for instance by how much of the available information they can explain (i.e., maximise the likelihood of) the available data. Here, we consider two different components of likelihood: descriptiveness (the likelihood of the individual data points) and co-explanation (the likelihood of the specific subset of data under consideration). We consider whether people prefer explanations that are high in descriptiveness vs. coexplanation. Moreover, we consider whether people who endorse conspiracy theories prefer explanations for either quality. In a medical diagnosis task, participants make binary choices between two fictional disease variants: one higher in descriptiveness versus another higher in co-explanation. Overall, participants displayed a weak preference for descriptiveness. This preference, however, did not vary across increasing levels of descriptiveness. Moreover, such preferences were unrelated to conspiracy mentality. Thus, both explanatory virtues may play a role in the appeal of likely explanations.

Adaptive decisions require that decision makers factor in the subjective values of different possible outcomes, and the probability of these outcomes occurring. Subjective values depend, among other things, on how far an outcome is away in time. This can be captured by assessing an individual’s delay discounting of different options. An individual’s risk preference also affects how attractive particular choice options appear to them. In humans, probability discounting and delay discounting are often related. People who show more risky behaviors also tend to be more impulsive and less patient. Based on such findings, single-process models of delay discounting and probability discounting have been suggested. In the current study, we tested if this relationship is equally present in chimpanzees, one of human’s closest extant evolutionary relatives. We presented 23 chimpanzees with a patience task and a risky-choice task. The patience task was designed to explicitly distinguish between delay preference and self-control (i.e., the ability to wait a given delay). Still, we found no strong correlations between risk and delay preferences. As this task has not been used with humans before, we implemented a computerized version and tested it in a sample of twenty adult participants. Initial results indicate that the task is well suited to capture patience, and it makes a promising candidate to be used in behavioral delay discounting experiments in humans.