The ability to pursue different goals is fundamental to adaptive decision-making, yet the mechanisms driving this capacity remain unclear. Decision-making is often thought of as having distinct valuation and choice stages. Goals could potentially act at either stage by remapping values or by remapping choices. To distinguish between these possibilities, participants completed an economic decision-making task choosing between a risky and a safe option under opposing goals to maximize or minimize points. If goals remap choices, people will simply switch to the opposite option under opposing goals. If goals remap values, people will treat losses like gains under opposing goals, leading to only sometimes switching to the opposite option. Across four studies (N=1096) and a preregistered replication (N=500), participant choices were best explained by goals remapping values. Computational modeling based on prospect theory confirmed two counterintuitive predictions of goals remapping value: first, that loss aversion acts on gains under the minimize goal, and second, that individuals systematically make identical choices despite opposing goals due to how they value options. We also found reduced overall outcome sensitivity under the novel minimize goal as measured both by choices and by happiness ratings collected periodically throughout the task. When goal changes were frequent, reduced affective outcome sensitivity predicted reduced choice outcome sensitivity, suggesting that emotional alignment across goals supports rational choice. Together, these findings reveal how goals change values, capturing the behavioral and emotional signatures of goal-directed decision-making.

Introduction: Bipolar spectrum disorders (BSDs) encompass severe and chronic mood disorders associated with social functioning difficulties. However, little work has examined more nuanced aspects of social functioning in BSDs. Methods: This investigation recruited 1,934 emerging adult college students to examine associations of self-reported bipolar spectrum risk (including both BSD risk and current mania and depressive mood symptoms) with social functioning with peers (including social network quantity and quality, social support, and social strain). Results: Self-reported BSD risk was associated with greater social strain, but also greater social network quantity (or size) and social support. Post-hoc results suggest that self-reported mood symptoms were similarly associated with increased social conflict, but also greater social network quantity (or size) and social support. Discussion: Taken together, these findings indicate a complex picture in which BSD risk and mood symptoms are associated with both social struggles as well as strengths. Implications for the involvement of social functioning in mood disturbance are discussed.

Pavlovian biases are patterns of behaviour that involve approaching stimuli associated with reward and avoiding those associated with punishment (regardless of whether this is actually optimal behaviour). Pavlovian biases are classically described as fixed and automatic, yet some studies indicate that their influence on behaviour can vary both over time and with task demands. While these results hint that people may in fact have some control over their Pavlovian biases, direct behavioural evidence is still lacking. Here, we tested a week-long cognitive bias training programme, in a preregistered, double-blind and sham-controlled design (N = 800 healthy adults). We found that the training led to significantly reduced Pavlovian biases (particularly avoidance bias) at follow-up. To our knowledge this is the first demonstration that people can learn to overcome their Pavlovian biases, and suggests greater flexibility in the way that Pavlovian biases affect cognition than had previously been appreciated.

Chatbots powered by artificial intelligence (AI) have rapidly become a significant part of everyday life, with over a quarter of American adults using them multiple times per week. While these tools offer potential benefits and risks, a fundamental question remains largely unexplored: How do conversations with AI influence subjective well-being? To investigate this, we conducted a study where participants either engaged in conversations with an AI chatbot (N = 334) or wrote journal entires (N = 193) on the same randomly assigned topics and reported their momentary happiness afterward. We found that happiness after AI chatbot conversations was higher than after journaling, particularly when discussing negative topics such as depression or guilt. Leveraging large language models for sentiment analysis, we found that the AI chatbot mirrored participants' sentiment while maintaining a consistent positivity bias. When discussing negative topics, participants gradually aligned their sentiment with the AI's positivity, leading to an overall increase in happiness. We hypothesized that the history of participants' sentiment prediction errors, the difference between expected and actual emotional tone when responding to the AI chatbot, might explain this happiness effect. Using computational modeling, we find the history of these sentiment prediction errors over the course of a conversation predicts greater post-conversation happiness, demonstrating a central role of emotional expectations during dialogue. Our findings underscore the effect that AI interactions can have on human well-being.

Whether it's choosing a tennis serve or escaping a predator, the ability to behave randomly provides a range of adaptive benefits. Decades of work explore how people both produce and detect randomness, revealing profound nonrandom biases and heuristics in our mental representations of randomness. But how is randomness realized in the mind? Do individuals have a "one-size-fits-all" conception of randomness that they employ across different tasks and time points? Or do they instead use simple context-specific strategies? Here, we develop a model that reveals individual differences in how humans attempt to generate random sequences. Then, in three experiments, we reveal that random behavior is stable across both tasks and time. In Experiment 1, participants generated sequences of random numbers and one-dimensional random locations. Behavior was remarkably consistent across the two tasks. In Experiment 2, we gave participants both a random-number-generation and a two-dimensional random-location-generation task, such that the tasks diverged in structure. We again observed stable individual differences across tasks. Finally, in Experiment 3, we collected data from the same participants as in Experiment 2, but 1 year later; we found stable individual differences across that span. Across all experiments, we find idiosyncratic behaviors that are stable across tasks and time. Thus, we suggest that a trait-like randomness generator exists in the mind. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

BackgroundHealthy social functioning relies on an ability to form accurate representations of others’ character and to utilize the representations to guide decisions. Here, we take a transdiagnostic longitudinal approach to investigate disrupted social decision-making in psychopathology. MethodsWe obtained transdiagnostic antagonism symptom scores from a battery of standardized questionnaires in an online US sample nationally representative for age, sex, and ethnicity. Participants completed a Moral Inference Task where they made a series of predictions about the moral decisions of both a selfish and a generous agent, and periodically reported their beliefs about the agents’ moral character. Participants were incentivized to form accurate representations of the agents’ tendencies as they later played an economic game where they could entrust money to the agents. We characterized the behavioral phenotypes of antagonism in forming social representations and using the representations to make adaptive social decisions.ResultsParticipants with high antagonism (1) held strong, pessimistic prior expectations, (2) were less accurate in predicting others’ moral decisions, (3) were more confident about their subjective impressions, and (4) made maladaptive trust decisions.ConclusionsWe found that disrupted social representations are specific to the latent dimension of antagonism and stable over time. The findings emphasize the role of social representations in adaptive social decisions in antagonism, providing a potential target for interventions.

The prevalence of depression is a major societal health concern, and there is an ongoing need to develop tools that predict who will become depressed. Past research suggests that depression changes the language we use, but it is unclear whether language is predictive of worsening symptoms. Here, we test whether the sentiment of brief written linguistic responses predicts changes in depression. Across two studies (N= 467), participants provided responses to neutral open-ended questions, narrating aspects of their lives relevant to depression (e.g., mood, motivation, sleep). Participants also completed the Patient Health Questionnaire (PHQ-9) to assess depressive symptoms and a risky decision-making task with periodic measurements of momentary happiness to quantify mood dynamics. The sentiment of written responses was evaluated by human raters (N= 470), Large Language Models (LLMs; ChatGPT 3.5 and 4.0), and the Linguistic Inquiry and Word Count (LIWC) tool. We found that language sentiment evaluated by human raters and LLMs, but not LIWC, predicted changes in depressive symptoms at a three-week follow-up. Using computational modeling, we found that language sentiment was associated with current mood, but language sentiment predicted symptom changes even after controlling for current mood. In summary, we demonstrate a scalable tool that combines brief written responses with sentiment analysis by AI tools that matches human performance in the prediction of future psychiatric symptoms.

Board, card, or video games have been played by virtually every individual in the world population, with both children and adults participating. Games are popular because they are intuitive and fun. These distinctive qualities of games also make them ideal as a platform for studying the mind. By being intuitive, games provide a unique vantage point for understanding the inductive biases that support behavior in more complex, ecological settings than traditional lab experiments. By being fun, games allow researchers to study new questions in cognition such as the meaning of "play'' and intrinsic motivation, while also supporting more extensive and diverse data collection by attracting many more participants. We describe both the advantages and drawbacks of using games relative to standard lab-based experiments and lay out a set of recommendations on how to gain the most from using games to study cognition. We hope this article will lead to a wider use of games as experimental paradigms, elevating the ecological validity, scale, and robustness of research on the mind.

Attributing motives to others is a crucial aspect of mentalizing, can be biased by prejudice, and is affected by common psychiatric disorders. It is therefore important to understand in depth the mechanisms underpinning it. Toward improving models of mentalizing motives, we hypothesized that people quickly infer whether other's motives are likely beneficial or detrimental, then refine their judgment (classify-refine). To test this, we used a modified Dictator game, a game theoretic task, where participants judged the likelihood of intent to harm vs. self-interest in economic decisions. Toward testing the role of serotonin in judgments of intent to harm, we delivered the task in a week-long, placebo vs. citalopram study. Computational model comparison provided clear evidence for the superiority of classify-refine models over traditional ones, strongly supporting the central hypothesis. Further, while citalopram helped refine attributions about motives through learning, it did not induce more positive initial inferences about others' motives. Finally, model comparison indicated a minimal role for racial bias within economic decisions for the large majority of our sample. Overall, these results support a proposal that classify-refine social cognition is adaptive, although relevant mechanisms of serotonergic antidepressant action will need to be studied over longer time spans.

Adaptive behavior depends on appropriate responses to environmental uncertainty. Incidental sensory events might simply be distracting and increase errors, but alternatively can lead to stereotyped responses despite their irrelevance. To evaluate these possibilities, we test whether task-irrelevant sensory prediction errors influence risky decision making in humans across seven experiments (total n = 1600). Rare auditory sequences preceding option presentation systematically increase risk taking and decrease choice perseveration (i.e., increased tendency to switch away from previously chosen options). The risk-taking and perseveration effects are dissociable by manipulating auditory statistics: when rare sequences end on standard tones, including when rare sequences consist only of standard tones, participants are less likely to perseverate after rare sequences but not more likely to take risks. Computational modeling reveals that these effects cannot be explained by increased decision noise but can be explained by value-independent risky bias and perseveration parameters, decision biases previously linked to dopamine. Control experiments demonstrate that both surprise effects can be eliminated when tone sequences are presented in a balanced or fully predictable manner, and that surprise effects cannot be explained by erroneous beliefs. These findings suggest that incidental sounds may influence many of the decisions we make in daily life. “People can quickly respond to surprising sensory events in the environment. Here, the authors show that surprising sounds, even when they are irrelevant, systematically increase risk taking, and this effect can be eliminated by changing the sensory statistics of the environment.”

Background: Intuitively, emotional states guide not only the actions we take, but also our confidence in those actions. This sets the stage for subjective confidence about the best action to take to diverge from the actual likelihood and, clinically, may give rise to over-confidence and risky behaviours during episodes of elevated mood and the reverse during depressive episodes. Whilst computational models have been proposed to explain how emotional states recursively bias perception of action outcomes, these models have not been extended to capture the impacts of mood on confidence. Here we propose a computational model that formalises confidence and its relationship with learning from outcomes and emotional states. Methods: We collected data both in a laboratory context (n=35) and in pre-registered online replication (n=106; https://osf.io/ygc4t). Participants completed a two-armed bandit task, with learning blocks before and after a mood manipulation in which participants unexpectedly received (positive mood induction) or lost (negative mood induction) a relatively large sum of money. Participants periodically reported their decision confidence throughout the task. We examined the extent to which the mood manipulation biased their confidence, predicting that positive and negative moods would lead to over- and under- confidence, respectively. We further predicted that this effect would be stronger in participants with greater propensity towards strong and changeable moods, measured by the Hypomanic Personality Scale. Moreover, we formalized a computational model in which confidence emerges as the difference between the perceived likelihood of reward for the available options. In this model, mood indirectly biases confidence through recursively biased learning of the reward likelihoods for the available options and not from simply shifting overall confidence up or down. Results: In both experiments, we confirmed that moods impacted confidence in the hypothesised direction; absent of any differences in participants' objective performance, average confidence was higher following positive mood induction, and lower following negative mood induction. This effect was larger in participants with higher levels of trait hypomania. Intriguingly, we found that the effect of mood on confidence emerged in concert with learning. Indeed, whilst the shift in mood was greatest immediately post-mood manipulation and returned to baseline by the end of the learning block, the effect of mood on confidence gradually accumulated over learning trials, peaking at the end of the block. These dynamics were captured by simulations of a 'Moody Likelihood' model. Empirically, this model simultaneously accounted for the effects of mood on choices, mood states and confidence through a mood bias parameter. Conclusion: We present a unified model in which moods recursively bias reward learning and, consequently, confidence in decision making. Moods fundamentally bias the accumulation of reward likelihood, rather than directly biasing decision confidence. Clinically, these findings have implications for understanding two core symptoms of mood disorder, suggesting that both perturbed mood and confidence about goal-directed behaviour arise from a common bias during reward learning. ### Competing Interest Statement The authors have declared no competing interest.

Emerging adulthood is characterized by marked increases in vulnerability to psychiatric illness. As such, understanding how risk and protective factors function to promote, or impede, resilience during early adulthood is critical. This pre-registered work is the first to test four extant models of resilience among emerging adults. 1,075 participants drawn from four international university sites were followed across two stressors: the transition to university and the COVID-19 pandemic. We found support for the compensatory model, which holds that risk and protective factors contribute additively to predict resilience across timepoints. Findings also support the risk-protective model, which posits that protective factors interact with risk factors in a buffering effect to reduce negative outcomes, during the university transition. Results have the potential to guide theory development by highlighting the dynamic nature of resilience and have implications for prevention and intervention efforts by underscoring the powerful influence of protective factors, regardless of risk.

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.

Background Apathy, a disabling and poorly understood neuropsychiatric symptom, is characterised by impaired self-initiated behaviour. It has been hypothesised that the opportunity cost of time (OCT) may be a key computational variable linking self-initiated behaviour with motivational status. OCT represents the amount of reward which is foregone per second if no action is taken. Using a novel behavioural task and computational modelling, we investigated the relationship between OCT, self-initiation and apathy. We predicted that higher OCT would engender shorter action latencies, and that individuals with greater sensitivity to OCT would have higher behavioural apathy. Methods We modulated the OCT in a novel task called the ‘Fisherman Game’, Participants freely chose when to self-initiate actions to either collect rewards, or on occasion, to complete non-rewarding actions. We measured the relationship between action latencies, OCT and apathy for each participant across two independent non-clinical studies, one under laboratory conditions ( n = 21) and one online ( n = 90). ‘Average-reward’ reinforcement learning was used to model our data. We replicated our findings across both studies. Results We show that the latency of self-initiation is driven by changes in the OCT. Furthermore, we demonstrate, for the first time, that participants with higher apathy showed greater sensitivity to changes in OCT in younger adults. Our model shows that apathetic individuals experienced greatest change in subjective OCT during our task as a consequence of being more sensitive to rewards. Conclusions Our results suggest that OCT is an important variable for determining free-operant action initiation and understanding apathy.

Research in computational psychiatry is dominated by models of behavior. Subjective experience during behavioral tasks is not well understood, even though it should be relevant to understanding the symptoms of psychiatric disorders. Here, we bridge this gap and review recent progress in computational models for subjective feelings. For example, happiness reflects not how well people are doing, but whether they are doing better than expected. This dependence on recent reward prediction errors is intact in major depression, although depressive symptoms lower happiness during tasks. Uncertainty predicts subjective feelings of stress in volatile environments. Social prediction errors influence feelings of self-worth more in individuals with low self-esteem despite a reduced willingness to change beliefs due to social feedback. Measuring affective state during behavioral tasks provides a tool for understanding psychiatric symptoms that can be dissociable from behavior. When smartphone tasks are collected longitudinally, subjective feelings provide a potential means to bridge the gap between lab-based behavioral tasks and real-life behavior, emotion, and psychiatric symptoms.

Humans exhibit distinct risk preferences when facing choices involving potential gains and losses. These preferences are believed to be subject to neuromodulatory influence, particularly from dopamine and serotonin. As neuromodulators manifest circadian rhythms, this suggests decision making under risk might be affected by time of day. Here, in a large subject sample collected using a smartphone application, we found that risky options with potential losses were increasingly chosen over the course of the day. We observed this result in both a within-subjects design (N = 2599) comparing risky options chosen earlier and later in the day in the same individuals, and in a between-subjects design (N = 26,720) showing our effect generalizes across ages and genders. Using computational modelling, we show this diurnal change in risk preference reflects a decrease in sensitivity to increasing losses, but no change was observed in the relative impacts of gains and losses on choice (i.e., loss aversion). Thus, our findings reveal a striking diurnal modulation in human decision making, a pattern with potential importance for real-life decisions that include voting, medical decisions, and financial investments.

Alcohol use disorder (AUD) is a major contributor to global disability and mortality. Predicting how people transition from occasional to excessive substance use remains a challenge. Previously, AUD has been linked to reduced cognitive control and increased risky decision-making in cross-sectional studies. However, these relationships can reflect changes that could either be a consequence or precedent of substance use. Thus, an untested hypothesis remains whether fluctuations in cognitive control and decision-making may temporally precede fluctuations in substance use. Here, we test this hypothesis based on a unique, preregistered, one-year longitudinal ecological momentary assessment (EMA) study. We employed EMA of real-life alcohol use in combination with a battery of smartphone-based gamified cognitive control and decision-making tests in n=288 participants with AUD. As hypothesized, we found that more risky decision-making (mixed gambles, information sampling) in a given month predicted increased alcohol consumption in the subsequent month. These results are also supported by a mechanistic computational model of information sampling in risky decision-making. Follow-up analyses further supported a specific temporal direction of the effect such that changes in decision-making preceded subsequent drinking but not vice versa. In contrast to measures of decision-making, cognitive control was not linked to subsequent alcohol consumption. However, we found, as hypothesized, that the detrimental intraindividual impact of risky decision-making on subsequent drinking was buffered in individuals with high working memory. In sum, we report first-time real-life longitudinal results from smartphone-based experiments that reveal intraindividual fluctuations in decision-making as a driving mechanism underlying subsequent fluctuations in alcohol consumption. Our smartphone-based experimental readouts can open new avenues for innovative mechanism-based and just-in-time interventions in AUD.

Adaptive behavior depends on appropriate responses to environmental uncertainty. Incidental sensory events might simply be distracting and increase errors, but alternatively could lead to stereotyped responses despite their irrelevance. To evaluate these two possibilities, we tested whether task-irrelevant sensory prediction errors influence risky decision making across multiple experiments (n=1200). Rare auditory sequences preceding option presentation systematically increased risk taking and increased switching away from previously chosen options. The two effects were dissociable by manipulating auditory statistics: when rare sequences ended on standard tones, including when rare sequences consisted only of standard tones, participants were more likely to switch options but not more likely to take risks. Computational modelling revealed that these effects could not be explained by increased decision noise but could be explained by value-independent risky bias and perseveration parameters. Our findings suggest that incidental sounds may influence many of the decisions we make in daily life.

Attributing motives to others is a crucial aspect of mentalizing, which is disturbed by prejudice and is also affected by common psychiatric disorders. Thus it is important to understand in depth the neuro-computational functions underpinning mentalizing and social reward. We hypothesized that people quickly infer whether other’s motives are likely beneficial or detrimental, then refine their judgment. Such ‘Classify-refine’, active inference models of mentalizing motives might improve on traditional models, and hence allow testing the hypothesis that serotonergic antidepressant drugs improve function partly by inducing more benign views of others. In a week-long, placebo vs. Citalopram study using an iterated dictator task, ‘Classify-refine’ models accounted for behaviour better than traditional models. Citalopram did not lead to more magnanimous attributions of motives, but we found evidence that it may help refine attributions about others’ motives through learning. With respect to social differences, model comparison clearly indicated that ethnicity-dependent, in-task biases played no role in attributing motives for the large majority of participants. This is a very encouraging result which further research should seek to replicate, and, if replicated, celebrate. Lower subjective socio-economic status was associated with lower attributions of harm intent to others. We discuss how classify-refine social cognition may be highly adaptive. Future research should examine the role of Serotonergic antidepressants in clinical studies over longer time spans. Significance Statement We developed computational models to study how, in social situations, first impressions count a lot, but people also gradually refine their views to do justice to others. In our study, such ‘classify-refine’ models clearly outperformed the ones based on simpler learning. Modeling analyses indicated that refining one’s views was facilitated by serotonergic antidepressants. We expected that those who perceived themselves as socially disadvantaged, and those interacting with people of color, would tend to attribute less benign motives to others. However in our low income, highly educated, young sample, we found evidence against both these biases. Further studies are needed to test how far these encouraging neuropharmacological and psychosocial findings apply to other populations.

Disturbances to moral inference are observed across a range of psychiatric illnesses associated with antagonism. We tested the hypothesis that strong pessimistic prior beliefs about morality characterize high antagonism and that the valence and strength of these prior beliefs can form the basis of disrupted moral inference.