Adolescent and adult mice use both incremental reinforcement learning and short term memory when learning concurrent stimulus-action associations.

Computational modeling has revealed that human research participants use both rapid working memory (WM) and incremental reinforcement learning (RL) (RL+WM) to solve a simple instrumental learning task, relying on WM when the number of stimuli is small and supplementing with RL when the number of stimuli exceeds WM capacity. Inspired by this work, we examined which learning systems and strategies are used by adolescent and adult mice when they first acquire a conditional associative learning task. In a version of the human RL+WM task translated for rodents, mice were required to associate odor stimuli (from a set of 2 or 4 odors) with a left or right port to receive reward.

An algorithmic account for how humans efficiently learn, transfer, and compose hierarchically structured decision policies.

Learning structures that effectively abstract decision policies is key to the flexibility of human intelligence. Previous work has shown that humans use hierarchically structured policies to efficiently navigate complex and dynamic environments. However, the computational processes that support the learning and construction of such policies remain insufficiently understood.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Troubleshooting Bayesian cognitive models.

Using Bayesian methods to apply computational models of cognitive processes, or , is an important new trend in psychological research. The rise of Bayesian cognitive modeling has been accelerated by the introduction of software that efficiently automates the Markov chain Monte Carlo sampling used for Bayesian model fitting-including the popular Stan and PyMC packages, which automate the dynamic Hamiltonian Monte Carlo and No-U-Turn Sampler (HMC/NUTS) algorithms that we spotlight here. Unfortunately, Bayesian cognitive models can struggle to pass the growing number of diagnostic checks required of Bayesian models.

Credit assignment in hierarchical option transfer.

Humans have the exceptional ability to efficiently structure past knowledge during learning to enable fast generalization. Xia and Collins (2021) evaluated this ability in a hierarchically structured, sequential decision-making task, where participants could build "options" (strategy "chunks") at multiple levels of temporal and state abstraction. A quantitative model, the Option Model, captured the transfer effects observed in human participants, suggesting that humans create and compose hierarchical options and use them to explore novel contexts.

Choice Type Impacts Human Reinforcement Learning.

In reinforcement learning (RL) experiments, participants learn to make rewarding choices in response to different stimuli; RL models use outcomes to estimate stimulus-response values that change incrementally. RL models consider any response type indiscriminately, ranging from more concretely defined motor choices (pressing a key with the index finger), to more general choices that can be executed in a number of ways (selecting dinner at the restaurant). However, does the learning process vary as a function of the choice type? In Experiment 1, we show that it does: Participants were slower and less accurate in learning correct choices of a general format compared with learning more concrete motor actions.

The interpretation of computational model parameters depends on the context.

Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences, promising to explain behavior from simple conditioning to complex problem solving, to shed light on developmental and individual differences, and to anchor cognitive processes in specific brain mechanisms. However, the RL literature increasingly reveals contradictory results, which might cast doubt on these claims. We hypothesized that many contradictions arise from two commonly-held assumptions about computational model parameters that are actually often invalid: That parameters between contexts (e.

Activation, but not inhibition, of the indirect pathway disrupts choice rejection in a freely moving, multiple-choice foraging task.

The dorsomedial striatum (DMS) plays a key role in action selection, but less is known about how direct and indirect pathway spiny projection neurons (dSPNs and iSPNs, respectively) contribute to choice rejection in freely moving animals. Here, we use pathway-specific chemogenetic manipulation during a serial choice foraging task to test the role of dSPNs and iSPNs in learned choice rejection. We find that chemogenetic activation, but not inhibition, of iSPNs disrupts rejection of nonrewarded choices, contrary to predictions of a simple "select/suppress" heuristic.

Reinforcement learning and Bayesian inference provide complementary models for the unique advantage of adolescents in stochastic reversal.

During adolescence, youth venture out, explore the wider world, and are challenged to learn how to navigate novel and uncertain environments. We investigated how performance changes across adolescent development in a stochastic, volatile reversal-learning task that uniquely taxes the balance of persistence and flexibility. In a sample of 291 participants aged 8-30, we found that in the mid-teen years, adolescents outperformed both younger and older participants.

Event segmentation reveals working memory forgetting rate.

We encounter the world as a continuous flow and effortlessly segment sequences of events into episodes. This process of event segmentation engages working memory (WM) for tracking the flow of events and impacts subsequent memory accuracy. WM is limited in how much information (i.

The Role of Executive Function in Shaping Reinforcement Learning.

Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports some aspects of learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human decision making, including the generalization of learned information, one-shot learning, and the synthesis of task information in complex environments..

What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience.

Reinforcement learning (RL) is a concept that has been invaluable to fields including machine learning, neuroscience, and cognitive science. However, what RL entails differs between fields, leading to difficulties when interpreting and translating findings. After laying out these differences, this paper focuses on cognitive (neuro)science to discuss how we as a field might over-interpret RL modeling results.

How the Mind Creates Structure: Hierarchical Learning of Action Sequences.

Humans have the astonishing capacity to quickly adapt to varying environmental demands and reach complex goals in the absence of extrinsic rewards. Part of what underlies this capacity is the ability to flexibly reuse and recombine previous experiences, and to plan future courses of action in a psychological space that is shaped by these experiences. Decades of research have suggested that humans use hierarchical representations for efficient planning and flexibility, but the origin of these representations has remained elusive.