Goals, usefulness and abstraction in value-based choice

Electrophysiological correlates of (mis)judging social information

Social information can be used to optimize decision-making. However, the simultaneous presentation of multiple sources of advice can lead to a distinction bias in judging the validity of the information. While the involvement of event-related potential (ERP) components in social information processing has been studied, how they are modulated by (mis)judging an advisor's information validity remains unknown. In two experiments participants performed a decision-making task with highly accurate or inaccurate cues. Each experiment consisted of an initial, learning, and test phase. During the learning phase, three advice cues were simultaneously presented and the validity of them had to be assessed. The effect of different cue constellations on ERPs was investigated. In the subsequent test phase, the willingness to follow or oppose an advice cue was tested. Results demonstrated the distinction bias with participants over or underestimating the accuracy of the most uncertain cues. The P2 amplitude was significantly increased during cue presentation when advisors were in disagreement as compared to when all were in agreement, regardless of cue validity. Further, a larger P3 amplitude during outcome presentation was found when advisors were in disagreement and increased with more informative cues. As such, the most uncertain cues were related to the smallest P3 amplitude. The findings hint at the possible role of P3 in judging and learning the predictability of social cues. This study provides novel insights into the role of P2 and P3 components during the judgment of social information validity.

The computational structure of consummatory anhedonia

Anhedonia is a reduction in enjoyment, motivation, or interest. It is common across mental health disorders and a harbinger of poor treatment outcomes. The enjoyment aspect, termed 'consummatory anhedonia', in particular poses fundamental questions about how the brain constructs rewards: what processes determine how intensely a reward is experienced? Here, we outline limitations of existing computational conceptualisations of consummatory anhedonia. We then suggest a richer reinforcement learning (RL) account of consummatory anhedonia with a reconceptualisation of subjective hedonic experience in terms of goal progress. This accounts qualitatively for the impact of stress, dysfunctional cognitions, and maladaptive beliefs on hedonic experience. The model also offers new views on the treatments for anhedonia.

The neural representation of metacognition in preferential decision-making

Humans regularly assess the quality of their judgements, which helps them adjust their behaviours. Metacognition is the ability to accurately evaluate one's own judgements, and it is assessed by comparing objective task performance with subjective confidence report in perceptual decisions. However, for preferential decisions, assessing metacognition in preference-based decisions is difficult because it depends on subjective goals rather than the objective criterion. Here, we develop a new index that integrates choice, reaction time, and confidence report to quantify trial-by-trial metacognitive sensitivity in preference judgements. We found that the dorsomedial prefrontal cortex (dmPFC) and the right anterior insular were more activated when participants made bad metacognitive evaluations. Our study suggests a crucial role of the dmPFC-insula network in representing online metacognitive sensitivity in preferential decisions.

Rationality, preferences, and emotions with biological constraints: it all starts from our senses

Is the role of our sensory systems to represent the physical world as accurately as possible? If so, are our preferences and emotions, often deemed irrational, decoupled from these 'ground-truth' sensory experiences? We show why the answer to both questions is 'no'. Brain function is metabolically costly, and the brain loses some fraction of the information that it encodes and transmits. Therefore, if brains maximize objective functions that increase the fitness of their species, they should adapt to the objective-maximizing rules of the environment at the earliest stages of sensory processing. Consequently, observed 'irrationalities', preferences, and emotions stem from the necessity for our early sensory systems to adapt and process information while considering the metabolic costs and internal states of the organism.

Multiple timescales of temporal context in risky choice: Behavioral identification and relationships to physiological arousal

Context-dependence is fundamental to risky monetary decision-making. A growing body of evidence suggests that temporal context, or recent events, alters risk-taking at a minimum of three timescales: immediate (e.g. trial-by-trial), neighborhood (e.g. a group of consecutive trials), and global (e.g. task-level). To examine context effects, we created a novel monetary choice set with intentional temporal structure in which option values shifted between multiple levels of value magnitude ("contexts") several times over the course of the task. This structure allowed us to examine whether effects of each timescale were simultaneously present in risky choice behavior and the potential mechanistic role of arousal, an established correlate of risk-taking, in context-dependency. We found that risk-taking was sensitive to immediate, neighborhood, and global timescales: risk-taking decreased following large (vs. small) outcome amounts, increased following large positive (but not negative) shifts in context, and increased when cumulative earnings exceeded expectations. We quantified arousal with skin conductance responses, which were related to the global timescale, increasing with cumulative earnings, suggesting that physiological arousal captures a task-level assessment of performance. Our results both replicate and extend prior research by demonstrating that risky decision-making is consistently dynamic at multiple timescales and that the role of arousal in risk-taking extends to some, but not all timescales of context-dependence.

Motivational context determines the impact of aversive outcomes on mental effort allocation

It is well known that people will exert effort on a task if sufficiently motivated, but how they distribute these efforts across different strategies (e.g., efficiency vs. caution) remains uncertain. Past work has shown that people invest effort differently for potential positive outcomes (rewards) versus potential negative outcomes (penalties). However, this research failed to account for differences in the context in which negative outcomes motivate someone - either as punishment or reinforcement. It is therefore unclear whether effort profiles differ as a function of outcome valence, motivational context, or both. Using computational modeling and our novel Multi-Incentive Control Task, we show that the influence of aversive outcomes on one's effort profile is entirely determined by their motivational context. Participants (N:91) favored increased caution in response to larger penalties for incorrect responses, and favored increased efficiency in response to larger reinforcement for correct responses, whether positively or negatively incentivized.

A goal-centric outlook on learning

Goals play a central role in human cognition. However, computational theories of learning and decision-making often take goals as given. Here, we review key empirical findings showing that goals shape the representations of inputs, responses, and outcomes, such that setting a goal crucially influences the central aspects of any learning process: states, actions, and rewards. We thus argue that studying goal selection is essential to advance our understanding of learning. By following existing literature in framing goal selection within a hierarchy of decision-making problems, we synthesize important findings on the principles underlying goal value attribution and exploration strategies. Ultimately, we propose that a goal-centric perspective will help develop more complete accounts of learning in both biological and artificial agents.

Accounting for multiscale processing in adaptive real-world decision-making via the hippocampus

For adaptive real-time behavior in real-world contexts, the brain needs to allow past information over multiple timescales to influence current processing for making choices that create the best outcome as a person goes about making choices in their everyday life. The neuroeconomics literature on value-based decision-making has formalized such choice through reinforcement learning models for two extreme strategies. These strategies are model-free (MF), which is an automatic, stimulus-response type of action, and model-based (MB), which bases choice on cognitive representations of the world and causal inference on environment-behavior structure. The emphasis of examining the neural substrates of value-based decision making has been on the striatum and prefrontal regions, especially with regards to the "here and now" decision-making. Yet, such a dichotomy does not embrace all the dynamic complexity involved. In addition, despite robust research on the role of the hippocampus in memory and spatial learning, its contribution to value-based decision making is just starting to be explored. This paper aims to better appreciate the role of the hippocampus in decision-making and advance the successor representation (SR) as a candidate mechanism for encoding state representations in the hippocampus, separate from reward representations. To this end, we review research that relates hippocampal sequences to SR models showing that the implementation of such sequences in reinforcement learning agents improves their performance. This also enables the agents to perform multiscale temporal processing in a biologically plausible manner. Altogether, we articulate a framework to advance current striatal and prefrontal-focused decision making to better account for multiscale mechanisms underlying various real-world time-related concepts such as the self that cumulates over a person's life course.

Having multiple selves helps learning agents explore and adapt in complex changing worlds

Satisfying a variety of conflicting needs in a changing environment is a fundamental challenge for any adaptive agent. Here, we show that designing an agent in a modular fashion as a collection of subagents, each dedicated to a separate need, powerfully enhanced the agent's capacity to satisfy its overall needs. We used the formalism of deep reinforcement learning to investigate a biologically relevant multiobjective task: continually maintaining homeostasis of a set of physiologic variables. We then conducted simulations in a variety of environments and compared how modular agents performed relative to standard monolithic agents (i.e., agents that aimed to satisfy all needs in an integrated manner using a single aggregate measure of success). Simulations revealed that modular agents a) exhibited a form of exploration that was intrinsic and emergent rather than extrinsically imposed; b) were robust to changes in nonstationary environments, and c) scaled gracefully in their ability to maintain homeostasis as the number of conflicting objectives increased. Supporting analysis suggested that the robustness to changing environments and increasing numbers of needs were due to intrinsic exploration and efficiency of representation afforded by the modular architecture. These results suggest that the normative principles by which agents have adapted to complex changing environments may also explain why humans have long been described as consisting of "multiple selves."

The effect of memory load on object reconstruction: Insights from an online mouse-tracking task

Why can't we remember everything that we experience? Previous work in the domain of object memory has suggested that our ability to resolve interference between relevant and irrelevant object features may limit how much we can remember at any given moment. Here, we developed an online mouse-tracking task to study how memory load influences object reconstruction, testing participants synchronously over virtual conference calls. We first tested up to 18 participants concurrently, replicating memory findings from a condition where participants were tested individually. Next, we examined how memory load influenced mouse trajectories as participants reconstructed target objects. We found interference between the contents of working memory and what was perceived during object reconstruction, an effect that interacted with visual similarity and memory load. Furthermore, we found interference from previously studied but currently irrelevant objects, providing evidence of object-to-location binding errors. At the greatest memory load, participants were nearly three times more likely to move their mouse cursor over previously studied nontarget objects, an effect observed primarily during object reconstruction rather than in the period before the final response. As evidence of the dynamic interplay between working memory and perception, these results show that object reconstruction behavior may be altered by (i) interference between what is represented in mind and what is currently being viewed, and (ii) interference from previously studied but currently irrelevant information. Finally, we discuss how mouse tracking can provide a rich characterization of participant behavior at millisecond temporal resolution, enormously increasing power in cognitive psychology experiments.