Nathaniel Douglass Daw

Psycholinguistic tools that estimate psychological symptoms from language alone aim to provide unobtrusive assessments without needing questionnaires or clinicians. Researchers largely assume that these assessments function equivalently across demographic groups. We tested this assumption in a racially diverse sample of U.S. adults (N = 1,685; Black, Asian, Latino/Hispanic, White) who described their mental health and completed standard symptom questionnaires. Language-based markers of internalizing symptoms were often group-specific: First-person pronoun use predicted symptoms only among White participants, and sentiment varied in how strongly it tracked symptoms across groups. Machine learning models trained on White participants' language produced higher prediction error for other groups than those groups' own models did. Interpretability techniques revealed that White-trained models emphasized self-focused, agentic language more than minority groups' models. These findings indicate that linguistic tools cannot assume generalizability and must be evaluated for the populations they serve.

The neuroscience of planning has long been analogized to search algorithms in artificial intelligence (AI), which simulate future actions to guide immediate choices. We argue that advances in both neuroscience and AI suggest that planning is better understood to encompass a broader class of computations where mental simulation supports learning, often well before a decision is needed. We review three neurocomputational mechanisms that illustrate this shift. First, hippocampal replay resembles search but also often occurs prospectively or offline, likely training downstream circuits rather than directly guiding choice. Second, temporally abstract representations, such as grid cells, can enable planning without iterative search. Third, metalearning may shape how prefrontal dynamics implement task-specific planning strategies, echoing how AI systems learn to adapt across contexts. This view recasts the brain's planning machinery as a family of learning processes that leverage simulations to build representations and strategies, with forward search as one special case.

Children are adept statistical learners, capable of parsing streams of structured input into meaningful units, but the cognitive processes they engage during learning may differ from those of adults. To date, however, it is unclear how learners of different ages predict upcoming experience when navigating environments with complex structure, as well as how changes in predictive learning mechanisms influence structured knowledge acquisition. To address this question, we tested 106 children, adolescents, and adults, ages 8–22 years, on a predictive learning task, in which they experienced sequences of stimuli with a higher‐order temporal structure. After an initial learning phase, participants’ explicit knowledge of the relations between stimuli was probed via two additional task measures. We used a recently introduced computational model to characterize participants’ response times during learning, and found that all participants relied on simple, recency‐based prediction, anticipating that they would encounter stimuli they recently encountered in the past. With increasing age, however, participants demonstrated greater evidence of additionally relying on a more sophisticated learning mechanism, which captured a predictive representation of the conditional relations between stimuli. Though predictive learning changed with age, we found only weak evidence that these changes related to the acquisition of explicit knowledge of the environment. Our results suggest that the learning mechanisms through which people parse continuous streams of experience change with age, influencing their predictions about upcoming events. Summary: Children, adolescents, and adults all learn to segment continuous streams of structured perceptual input, but they may do so via different learning processes. We examined how the learning mechanisms that enable people to predict upcoming experiences change and relate to structured knowledge acquisition across development. Computational modeling revealed that in a graph‐learning task, younger participants relied on simple, recency‐based prediction, while older participants tracked temporal relations between stimuli. The extent to which participants engaged in this sophisticated form of predictive learning only weakly related to their knowledge of the task's structure.

Scientific models are widely used across the natural sciences as an interface between scientific theories and empirical data. Such models play a key role, for example, in the study of human and animal learning, where they express algorithmic hypotheses and relate them to psychology and neuroscience data. These models are traditionally handcrafted by expert researchers based on existing theory or new insights. Such handcrafted models, however, are now known to fall short of capturing the full richness of behavior, even in their narrow domains. An alternative data-driven approach has emerged, seeking to discover new insights by fitting and interpreting flexible models. However, these tools require substantial human effort to derive insight from data, and it has been unclear how to discover new ideas from data efficiently. Here, we present DataDIVER, a general approach for automatically discovering computational models from data, and demonstrate that these models surface novel mechanistic insights into human and animal learning. Our approach delivers models that take the form of short computer programs, which are optimized both to fit data well and to be simple. These programs explicitly connect with existing theoretical frameworks and are readily understandable by human scientists. They can also be used to make novel predictions, some of which we show are borne out in re-analysis of existing data. General-purpose tools for surfacing new ideas from data, especially in combination with the large datasets that are increasingly available in many fields, stand to dramatically accelerate scientific discovery.

A long-standing challenge for psychology and neuroscience is to understand the transformations by which past experiences shape future behaviour. Reward-guided learning is typically modelled using simple reinforcement learning (RL) algorithms. In RL, a handful of incrementally updated internal variables both summarize past rewards and drive future choice. Here we describe work that questions the assumptions of many RL models. We adopt a hybrid modelling approach that integrates artificial neural networks into interpretable cognitive architectures, estimating a maximally general form for each algorithmic component and systematically evaluating its necessity and sufficiency. Applying this method to a large dataset of human reward-learning behaviour, we show that successful models require independent and flexible memory variables that can track rich representations of the past. Using a modelling approach that combines predictive accuracy and interpretability, these results call into question an entire class of popular RL models based on incremental updating of scalar reward predictions.

Children are adept statistical learners, capable of parsing streams of structured input into meaningful units, but the cognitive processes they engage during learning may differ from those of adults. To date, however, it is unclear how learners of different ages predict upcoming experience when navigating environments with complex structure, as well as how changes in predictive learning mechanisms influence structured knowledge acquisition. To address this question, we tested 106 children, adolescents, and adults, ages 8 - 22 years, on a predictive learning task, in which they experienced sequences of stimuli with a higher-order temporal structure. After an initial learning phase, participants’ explicit knowledge of the relations between stimuli was probed via two additional task measures. We used a recently introduced computational model to characterize participants’ response times during learning, and found that all participants relied on simple, recency-based prediction, anticipating that they would encounter stimuli they recently encountered in the past. With increasing age, however, participants demonstrated greater evidence of additionally relying on a more sophisticated learning mechanism, which captured a predictive representation of the conditional relations between stimuli. Though predictive learning changed with age, we found only weak evidence that these changes related to the acquisition of explicit knowledge of the environment. Our results suggest that the learning mechanisms through which people parse continuous streams of experience change with age, influencing their predictions about upcoming events.

Background: The Pavlovian go/no-go task is commonly used to measure individual differences in Pavlovian biases and their interaction with instrumental learning. However, prior research has found suboptimal reliability for computational model-based performance measures for this task, limiting its usefulness in individual-differences research. These studies did not make use of several strategies previously shown to enhance task-measure reliability (e.g., task gamification, hierarchical Bayesian modeling for model estimation). Here we investigated if such approaches could improve the task’s reliability. Methods: Across two experiments, we recruited two independent samples of adult participants (N=103, N=110) to complete a novel, gamified version of the Pavlovian go/no-go task multiple times over several weeks. We used hierarchical Bayesian modeling to derive reinforcement learning model-based indices of participants' task performance, and additionally to estimate the reliability of these measures. Results: In Experiment 1, we observed considerable and unexpected practice effects, with most participants reaching near-ceiling levels of performance with repeat testing. Consequently, the test-retest reliability of some model parameters was unacceptable (range: 0.379–0.973). In Experiment 2, participants completed a modified version of the task designed to lessen these practice effects. We observed greatly reduced practice effects and improved estimates of the test-retest reliability (range: 0.696–0.989). Conclusion: The results demonstrate that model-based measures of performance on the Pavlovian go/no-go task can reach levels of reliability sufficient for use in individual- differences research. However, additional investigation is necessary to validate the modified version of the task in other populations and settings.

Learning where and when rewards like food and water are available is essential for survival ^1,2 . In the simplest cases where resource availability is stable, animals can learn reward contingencies by integrating outcomes across repeated samples of each possible action. In more natural settings, however, reward availability is governed by structured higher-order rules such as depletion and repletion over time. To adapt flexibly to such changing environments, optimal choices require meta-learning wherein animals learn how to learn from external feedback, ultimately enabling them to infer the underlying reward structure from abstract, generalizable rules rather than relying solely on recent outcomes ^3,4 . The existence of meta-learning in animal behavior is well established ^3–8 , yet the neural circuits and computations that implement it remain poorly understood ^9–11 . Here we investigated meta-learning using a spatial foraging task in which rats acquired a depletion-repletion rule that regulated reward availability, and carried out longitudinal, high-density recordings from the medial prefrontal cortex (mPFC). We show that meta-learning engages specific, systematic changes in mPFC neural dynamics that embed the learned rule and thereby alter how the network learns action values from reward outcomes. These dynamics are based on mixed coding of task structure and value in individual mPFC neurons. At the population level, this coding organizes into low-dimensional dynamical motifs that generalize across task conditions. As meta-learning progresses, these motifs are reshaped to instantiate both rule-guided inference of future states before outcome delivery and rule-based value updating during the outcome period. These results indicate that meta-learning sculpts pre-existing prefrontal dynamics to support the acquisition of new, generalizable reward-learning strategies.