Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval.

Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action-outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this '3R' framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.

Neoadjuvant Exercise Therapy in Prostate Cancer: A Phase 1, Decentralized Nonrandomized ControlledTrial.

Importance: Observational data have shown that postdiagnosis exercise is associated with reduced risk of prostate cancer death. The feasibility and tumor biological activity of exercise therapy is not known. Objective: To identify recommended phase 2 dose of exercise therapy for patients with prostate cancer. Design, Setting, and Participants: This single-center, phase 1a dose-finding trial was conducted at a tertiary cancer center using a patientcentric, decentralized platform and included 53 inactive men with treatment-naive localized prostate cancer scheduled to undergo surgical resection between June 2019 and January 2023. Data were analyzed in June 2024. Intervention: Six escalated exercise therapy dose levels ranging from 90 to 450 minutes per week of individualized, moderate-intensity treadmill walking, allocated using adaptive continual reassessment. All exercise therapy sessions were conducted remotely with real-time monitoring. Main Outcomes and Measures: Feasibility was evaluated by relative exercise dose intensity (REDI). A dose level was considered feasible if 70% or more of patients achieved an REDI of 75% or greater. Activity end points were changes in tumor cell proliferation (Ki67) and plasma prostate-specific antigen levels between pretreatment and postintervention. Safety and changes in patient physiology were also assessed. Results: A total of 53 men were enrolled (median [IQR] age, 61 [56-66] years). All dose levels were feasible (≥75% REDI). The mean (95% CI) changes in Ki67 were 5.0% (-4.3% to 14.0%) for 90 minutes per week, 2.4% (-1.3% to 6.2%) for 150 minutes per week, -1.3% (-5.8% to 3.3%) for 225 minutes per week, -0.2% (-4.0% to 3.7%) for 300 minutes per week, -2.6% (-9.2% to 4.1%) for 375 minutes per week, and 2.2% (-0.8% to 5.1%) for 450 minutes per week. Changes in prostate-specific antigen levels were 1.0 ng/mL (-1.8 to 3.8) for 90 minutes per week, 0.2 ng/mL (-1.1 to 1.5) for 150 minutes per week, -0.5 ng/mL (-1.2 to 0.3) for 225 minutes per week, -0.2 (-1.7 to 1.3) for 300 minutes per week, -0.7 ng/mL (-1.7 to 0.4) for 375 minutes per week, and -0.9 ng/mL (-2.4 to 0.7) for 450 minutes per week. No serious adverse events were observed. Overall, 225 minutes per week (approximately 45 minutes per treatment at 5 times weekly) was selected as the recommended phase 2 dose. Conclusions and Relevance: The results of this nonrandomized clinical trial suggest that neoadjuvant exercise therapy is feasible and safe with promising activity in localized prostate cancer. Trial Registration: ClinicalTrials.gov Identifier: NCT03813615.

Artificial neural networks for model identification and parameter estimation in computational cognitive models.

Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.

Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making.

Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the ϵ-softmax policy). However, this assumption is not guaranteed to hold - for example, a subject might disengage and lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Here, we introduce a new, computationally inexpensive method to dynamically infer the levels of noise in choice behavior, under a model assumption that agents can transition between two discrete latent states (e.g., fully engaged and random). Using simulations, we show that modeling noise levels dynamically instead of statically can substantially improve model fit and parameter estimation, especially in the presence of long periods of noisy behavior, such as prolonged attentional lapses. We further demonstrate the empirical benefits of dynamic noise estimation at the individual and group levels by validating it on four published datasets featuring diverse populations, tasks, and models. Based on the theoretical and empirical evaluation of the method reported in the current work, we expect that dynamic noise estimation will improve modeling in many decision-making paradigms over the static noise estimation method currently used in the modeling literature, while keeping additional model complexity and assumptions minimal.

Artificial neural networks for model identification and parameter estimation in computational cognitive models.

Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.

Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning.

Dopamine release in the nucleus accumbens has been hypothesized to signal reward prediction error, the difference between observed and predicted reward, suggesting a biological implementation for reinforcement learning. Rigorous tests of this hypothesis require assumptions about how the brain maps sensory signals to reward predictions, yet this mapping is still poorly understood. In particular, the mapping is non-trivial when sensory signals provide ambiguous information about the hidden state of the environment. Previous work using classical conditioning tasks has suggested that reward predictions are generated conditional on probabilistic beliefs about the hidden state, such that dopamine implicitly reflects these beliefs. Here we test this hypothesis in the context of an instrumental task (a two-armed bandit), where the hidden state switches repeatedly. We measured choice behavior and recorded dLight signals reflecting dopamine release in the nucleus accumbens core. Model comparison based on the behavioral data favored models that used Bayesian updating of probabilistic beliefs. These same models also quantitatively matched the dopamine measurements better than non-Bayesian alternatives. We conclude that probabilistic belief computation plays a fundamental role in instrumental performance and associated mesolimbic dopamine signaling.

Lowered inter-stimulus discriminability hurts incremental contributions to learning.

How does the similarity between stimuli affect our ability to learn appropriate response associations for them? In typical laboratory experiments learning is investigated under somewhat ideal circumstances, where stimuli are easily discriminable. This is not representative of most real-life learning, where overlapping "stimuli" can result in different "rewards" and may be learned simultaneously (e.g., you may learn over repeated interactions that a specific dog is friendly, but that a very similar looking one isn't). With two experiments, we test how humans learn in three stimulus conditions: one "best case" condition in which stimuli have idealized and highly discriminable visual and semantic representations, and two in which stimuli have overlapping representations, making them less discriminable. We find that, unsurprisingly, decreasing stimuli discriminability decreases performance. We develop computational models to test different hypotheses about how reinforcement learning (RL) and working memory (WM) processes are affected by different stimulus conditions. Our results replicate earlier studies demonstrating the importance of both processes to capture behavior. However, our results extend previous studies by demonstrating that RL, and not WM, is affected by stimulus distinctness: people learn slower and have higher across-stimulus value confusion at decision when stimuli are more similar to each other. These results illustrate strong effects of stimulus type on learning and demonstrate the importance of considering parallel contributions of different cognitive processes when studying behavior.

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.

Intrinsic rewards explain context-sensitive valuation in reinforcement learning.

When observing the outcome of a choice, people are sensitive to the choice's context, such that the experienced value of an option depends on the alternatives: getting $1 when the possibilities were 0 or 1 feels much better than when the possibilities were 1 or 10. Context-sensitive valuation has been documented within reinforcement learning (RL) tasks, in which values are learned from experience through trial and error. Range adaptation, wherein options are rescaled according to the range of values yielded by available options, has been proposed to account for this phenomenon. However, we propose that other mechanisms-reflecting a different theoretical viewpoint-may also explain this phenomenon. Specifically, we theorize that internally defined goals play a crucial role in shaping the subjective value attributed to any given option. Motivated by this theory, we develop a new "intrinsically enhanced" RL model, which combines extrinsically provided rewards with internally generated signals of goal achievement as a teaching signal. Across 7 different studies (including previously published data sets as well as a novel, preregistered experiment with replication and control studies), we show that the intrinsically enhanced model can explain context-sensitive valuation as well as, or better than, range adaptation. Our findings indicate a more prominent role of intrinsic, goal-dependent rewards than previously recognized within formal models of human RL. By integrating internally generated signals of reward, standard RL theories should better account for human behavior, including context-sensitive valuation and beyond.

Age-related differences in prefrontal glutamate are associated with increased working memory decay that gives the appearance of learning deficits.

The ability to use past experience to effectively guide decision-making declines in older adulthood. Such declines have been theorized to emerge from either impairments of striatal reinforcement learning systems (RL) or impairments of recurrent networks in prefrontal and parietal cortex that support working memory (WM). Distinguishing between these hypotheses has been challenging because either RL or WM could be used to facilitate successful decision-making in typical laboratory tasks. Here we investigated the neurocomputational correlates of age-related decision-making deficits using an RL-WM task to disentangle these mechanisms, a computational model to quantify them, and magnetic resonance spectroscopy to link them to their molecular bases. Our results reveal that task performance is worse in older age, in a manner best explained by working memory deficits, as might be expected if cortical recurrent networks were unable to sustain persistent activity across multiple trials. Consistent with this, we show that older adults had lower levels of prefrontal glutamate, the excitatory neurotransmitter thought to support persistent activity, compared to younger adults. Individuals with the lowest prefrontal glutamate levels displayed the greatest impairments in working memory after controlling for other anatomical and metabolic factors. Together, our results suggest that lower levels of prefrontal glutamate may contribute to failures of working memory systems and impaired decision-making in older adulthood.