Distinct motivations to seek out information in healthy individuals and problem gamblers

Information seeking and the expected utility of information about COVID-19 can be associated with uncertainty and related attitudes

Understanding how people decide when to seek out information can offer important insights into best practices for scientific communication, which may be critical in the face of global challenges like the COVID-19 pandemic. We examined how expected information utility, affective characteristics, and attitudes predicted COVID-19 information seeking behavior in a sample of 191 midwestern undergraduate students in late 2020. Participants completed five rounds of an information seeking task in which they read about a potential gap in their knowledge about COVID-19 and chose whether to read an excerpt that could fill that information gap. We found that information seeking in a given round (i.e. "round-wise information seeking") was best predicted by expected cognitive utility (i.e., expected reduced uncertainty). When collapsed across rounds, information seeking was positively correlated with COVID-19 preventive behaviors and trust in science, which also correlated with each other. Additionally, exploratory analyses regressing round-wise utility ratings on personality variables revealed that intolerance of uncertainty was associated with higher ratings of all three information utilities. Together, these results suggest that pandemic-related information seeking may have been especially driven by how individuals relate to and manage uncertainty. We discuss how these findings relate to extant literature on information utility and seeking behaviors and highlight the potential for work in this area to improve scientific communication.

Multiple and subject-specific roles of uncertainty in reward-guided decision-making

Decision-making in noisy, changing, and partially observable environments entails a basic tradeoff between immediate reward and longer-term information gain, known as the exploration-exploitation dilemma. Computationally, an effective way to balance this tradeoff is by leveraging uncertainty to guide exploration. Yet, in humans, empirical findings are mixed, from suggesting uncertainty-seeking to indifference and avoidance. In a novel bandit task that better captures uncertainty-driven behavior, we find multiple roles for uncertainty in human choices. First, stable and psychologically meaningful individual differences in uncertainty preferences actually range from seeking to avoidance, which can manifest as null group-level effects. Second, uncertainty modulates the use of basic decision heuristics that imperfectly exploit immediate rewards: a repetition bias and win-stay-lose-shift heuristic. These heuristics interact with uncertainty, favoring heuristic choices under higher uncertainty. These results, highlighting the rich and varied structure of reward-based choice, are a step to understanding its functional basis and dysfunction in psychopathology.

Surprise and novelty in the brain

Notions of surprise and novelty have been used in various experimental and theoretical studies across multiple brain areas and species. However, 'surprise' and 'novelty' refer to different quantities in different studies, which raises concerns about whether these studies indeed relate to the same functionalities and mechanisms in the brain. Here, we address these concerns through a systematic investigation of how different aspects of surprise and novelty relate to different brain functions and physiological signals. We review recent classifications of definitions proposed for surprise and novelty along with links to experimental observations. We show that computational modeling and quantifiable definitions enable novel interpretations of previous findings and form a foundation for future theoretical and experimental studies.

Computational approaches to modeling gambling behaviour: Opportunities for understanding disordered gambling

Computational modeling has become an important tool in neuroscience and psychiatry research to provide insight into the cognitive processes underlying normal and pathological behavior. There are two modeling frameworks, reinforcement learning (RL) and drift diffusion modeling (DDM), that are well-developed in cognitive science, and have begun to be applied to Gambling Disorder. RL models focus on explaining how an agent uses reward to learn about the environment and make decisions based on outcomes. The DDM is a binary choice framework that breaks down decision making into psychologically meaningful components based on choice reaction time analyses. Both approaches have begun to yield insight into aspects of cognition that are important for, but not unique to, gambling, and thus relevant to the development of Gambling Disorder. However, these approaches also oversimplify or neglect various aspects of decision making seen in real-world gambling behavior. Gambling Disorder presents an opportunity for 'bespoke' modeling approaches to consider these neglected components. In this review, we discuss studies that have used RL and DDM frameworks to investigate some of the key cognitive components in gambling and Gambling Disorder. We also include an overview of Bayesian models, a methodology that could be useful for more tailored modeling approaches. We highlight areas in which computational modeling could enable progression in the investigation of the cognitive mechanisms relevant to gambling.

People adaptively use information to improve their internal states and external outcomes

Information can strongly impact people's affect, their level of uncertainty and their decisions. It is assumed that people seek information with the goal of improving all three. But are they successful at achieving this goal? Answering this question is important for assessing the impact of self-driven information consumption on people's well-being. Here, over five experiments (total N = 727) we show that participants accurately predict the impact of information on their internal states (e.g., affect and cognition) and external outcomes (e.g., material rewards), and use these predictions to guide information-seeking choices. A model incorporating participants' subjective expectations regarding the impact of information on their affective, cognitive, and material outcomes accounted for information-seeking choices better than a model that included only objective proxies of those measures. This model also accounted for individual differences in information-seeking choices. By balancing considerations of the impact of information on affective, cognitive and material outcomes when seeking knowledge, participants became happier, more certain and made better decisions when they sought information relative to when they did not, suggesting that the actual consequences of receiving information aligned with their subjective expectations.

Value-free random exploration is linked to impulsivity

Deciding whether to forgo a good choice in favour of exploring a potentially more rewarding alternative is one of the most challenging arbitrations both in human reasoning and in artificial intelligence. Humans show substantial variability in their exploration, and theoretical (but only limited empirical) work has suggested that excessive exploration is a critical mechanism underlying the psychiatric dimension of impulsivity. In this registered report, we put these theories to test using large online samples, dimensional analyses, and computational modelling. Capitalising on recent advances in disentangling distinct human exploration strategies, we not only demonstrate that impulsivity is associated with a specific form of exploration—value-free random exploration—but also explore links between exploration and other psychiatric dimensions.

The Stage 1 protocol for this Registered Report was accepted in principle on 19/03/2021. The protocol, as accepted by the journal, can be found at 10.6084/m9.figshare.14346506.v1.

Deciding between known rewarding options and exploring novel avenues is central to decision making. Humans show variability in their exploration. Here, the authors show that impulsivity is associated to an increased usage of a cognitively cheap (and sometimes sub-optimal) exploration strategy.

Independent and interacting value systems for reward and information in the human brain

Theories of prefrontal cortex (PFC) as optimizing reward value have been widely deployed to explain its activity in a diverse range of contexts, with substantial empirical support in neuroeconomics and decision neuroscience. Similar neural circuits, however, have also been associated with information processing. By using computational modeling, model-based functional magnetic resonance imaging analysis, and a novel experimental paradigm, we aim at establishing whether a dedicated and independent value system for information exists in the human PFC. We identify two regions in the human PFC that independently encode reward and information. Our results provide empirical evidence for PFC as an optimizer of independent information and reward signals during decision-making under realistic scenarios, with potential implications for the interpretation of PFC activity in both healthy and clinical populations.