Jacqueline Gottlieb

Curiosity, a key driver of exploration and learning, is reinforced by reward-related neurochemical systems, yet the role of the opioidergic system in modulating this behavior remains unclear. Music, as a highly rewarding stimulus, offers a unique context to investigate the neurochemical basis of curiosity, particularly the unexplored role of opioids in music-driven exploration. To fill this gap, we performed a double-blind within-subject pharmacological design, in which 26 participants received, in two different sessions, either a placebo or the opioid antagonist naltrexone. During each session, participants engaged in a music exploration/exploitation trade-off paradigm designed to assess their willingness to pay for exploring unfamiliar electronic music. Using logistic regression mixed-effects models, we found that while naltrexone did not affect overall curiosity ratings, it significantly reduced exploratory behavior in states of heightened curiosity. These findings suggest that the opioidergic system plays a critical role in regulating the relationship between curiosity and exploration, particularly in the context of novel and rewarding stimuli like music. Overall, the present research provides new and compelling evidence on the important relationship between curiosity and exploration and its regulation with the opioidergic neurotransmitter subsystem. Significance Statement: The present research aimed to advance our understanding of the neurochemical mechanisms underlying curiosity and information seeking. In our study, we employed a pharmacological design to examine the role of the opioidergic system in music-related exploration. Using a novel music exploration/exploitation paradigm, we found that while naltrexone, an opioid antagonist, did not affect baseline curiosity ratings, it markedly reduced exploratory behavior during high-curiosity states in the presence of potential monetary losses. These results provide new evidence that opioidergic modulation plays a critical role in regulating curiosity-driven exploration. This new evidence might be relevant in the future for better understanding how neurochemical systems shape learning, motivation, and affective responses in complex cognitive domains such as music.

Curiosity drives information-seeking behavior and shapes memory, yet its role in real-world contexts remains underexplored. This study addresses this question by investigating music-driven curiosity and its influence on exploration and long-term memory across laboratory and real-world (concert) settings. In Experiment 1, 63 participants engaged in a new exploration/exploitation paradigm assessing willingness to pay (WTP) to explore unfamiliar music. In Experiment 2, 150 participants attended a live DJ set, rating their curiosity and WTP for real-world musical experiences. Music-driven curiosity increased WTP and enhanced long-term memory, with stronger effects when exploration incurred potential costs, suggesting that curiosity biases decision-making by selectively promoting information seeking when potential costs are at stake. Furthermore, a machine learning classifier trained on laboratory data effectively predicted exploration behavior in the natural setting, highlighting the robustness of the results across contexts. By integrating controlled and real-world environments through an innovative paradigm, this study sheds light on the cognitive and motivational dynamics of curiosity in the aesthetic domain, providing novel insights into its potential to enhance learning and memory in applied settings.

Functional accounts of mind, brain, and behavior have profoundly shaped thinking across many disciplines. Despite this common lens, the processes driving adaptive outcomes are often studied in isolation, leading to narrow and localized explanations. This paper brings together perspectives across fields by identifying six dynamics that enable agents to behave adaptively. These dynamics span multiple timescales and systems, including individual-level processes—thinking, learning, and development—and population-level processes—market selection, cultural evolution, and genetic evolution. We examine the reasons behind functional explanations of cognition and behavior, the conditions that foster rationality, and the interactions between adaptive processes. Our goal is to bring the cognitive, social, biological, and computer sciences into closer conversation by exposing researchers to a wide range of perspectives on adaptive decision-making.

Effective decisions require not only choosing between alternative options but selecting evidence that is informative for those options. Humans often deviate from strategies that maximize information gain, but the mechanisms of these deviations are not understood. We tested adult participants in a task in which they chose which question to inspect to prepare for a test. Participants received two alternative questions and could identify the more informative question based on its uncertainty (Unc) and probability of appearing at test (PTest). Despite the simplicity and transparency of the task, participants systematically deviated from the normative strategy. Computational modeling showed that most participants underweighted Unc and PTest and relied more strongly on PTest vs Unc. Strikingly, a substantial minority systematically preferred theleast-informative question, suggesting that they incorrectly reasoned about how uncertainty impacted informativeness. Moreover, in the absence of external feedback, sub-optimal strategies were amplified over time. The findings highlight the importance of abstract reasoning, task understanding, and self-reinforcing learning in constraining human information demand.

Deciding whether, when, and which information to sample is critical for making effective decisions, yet the cognitive mechanisms of this process are not well understood. Here, we propose that key aspects of human information demand are explained by non-linear subjective perceptions of probabilistic losses or gains. Using behavioral testing and quantitative model comparisons across three independent participant samples (N = 50, 50, and 150), we show that a model that incorporates non-linear probability and value perception outperforms a model based on a linear mixture of motives in explaining instrumental and non-instrumental information demand. Moreover, individual non-linearities that best explained information demand were correlated with personality traits and with non-linearities explaining risk seeking/aversion in standard choice tasks. The results suggest that a computational framework rooted in the subjective perception of probability furthers our understanding of information demand and its relationship with decision making under risk and uncertainty.

In natural environments, associations that indicate true learnable regularities are intermixed with those that arise from random and ultimately unlearnable relationships between events. To efficiently allocate cognitive resources and avoid inferring spurious patterns, organisms must distinguish learnable from unlearnable associations, but the mechanisms underlying this ability are not understood. We recently showed that monkeys performing a transitive inference task, while discovering the true hidden order in learnable image sets, also behaved to varying degrees as if they inferred subjective order in objectively random (unlearnable) image sets. Here, we show that the ability to detect learnability is encoded by neurons in the dorsal anterior cingulate cortex (dACC, area 24c). dACC neurons responded strongly after a decision outcome as reported in previous studies and, additionally, signaled whether a trial was from a learnable vs unlearnable set before outcome delivery, and showed interactions whereby their selectivity for the outcome (reward vs lack of reward) was stronger for learnable versus unlearnable sets. Learnability and interaction responses were independent of sensory or reward cues (which were equated for learnable and unlearnable sets) but their strength correlated with the monkeys’ ability to avoid inferring false order in unlearnable sets. The findings suggest that the dACC is part of a network that monitors learnability and enables animals to appropriately focus learning on true patterns while avoiding false inferences about spurious and random associations.

Deciding whether, when, and which information to sample is critical for making effective decisions, yet the cognitive mechanisms of this process are not well understood. Here, we propose that key aspects of human information demand are explained by non-linear subjective perceptions of probabilistic losses or gains. Using behavioral testing and quantitative model comparisons, we show that a new model that incorporates non-linearities in perceived probability and value outperforms a mixed-motive approach in explaining deviations from normative information demand. Across participants, individual subjective non-linearities that best accounted for information demand were correlated with personality traits and with non-linearities explaining risk seeking/aversion in standard choice tasks. The results support a novel computational framework that is rooted in the subjective probabilistic perception and furthers our understanding of information demand and its relationship with decision making under risk and uncertainty.

Understanding the psychological computations underlying motivation can shed light onto motivational constructs as emergent phenomena. According to Murayama and Jach, reward-learning is a key candidate mechanism. However, there's no such thing as a free lunch: Not only benefits (like reward), but also costs inherent to motivated behaviors (like effort, or uncertainty) are an essential part of the picture.

When making adaptive decisions, we actively demand information, but relatively little is known about the mechanisms of active information gathering. An open question is how the brain prospectively estimates the information gains that are expected to accrue from various sources by integrating simpler quantities of prior certainty and the reliability (diagnosticity) of a source. We examine this question using fMRI in a task in which people placed bids to obtain information in conditions that varied independently in the rewards, decision uncertainty, and information diagnosticity. We show that, consistent with value of information theory, the participants’ bids are sensitive to prior certainty (the certainty about the correct choice before gathering information) and expected posterior certainty (the certainty expected after gathering information). Expected posterior certainty is decoded from multivoxel activation patterns in the posterior parietal and extrastriate cortices. This representation is independent of instrumental rewards and spatially overlaps with distinct representations of prior certainty and expected information gains. The findings suggest that the posterior parietal and extrastriate cortices are candidates for mediating the prospection of posterior probabilities as a key step to anticipating information gains during active gathering of instrumental information.