Learning the value of environmental stimuli from reward experience allows animals to make advantageous choices. Existing biological accounts of reinforcement learning (RL) often assume that this value is represented as a single, motor-system-invariant neural signal. Here we recorded neuronal activity across eight nodes of the macaque cortico-basal ganglia system — from limbic to neocortical regions — while monkeys learned stimulus-reward associations using either saccades or reaches as the required motor response. The geometry of population activity revealed largely distinct value-coding dimensions for saccades and reaches, including in limbic regions typically associated with motor-system-invariant value coding. Consequently, value information was substantially reduced when read out across motor systems. These results provide circuit-wide evidence that challenges existing neural implementations of RL. They link biological accounts of value learning to embodied frameworks in cognitive science and artificial intelligence, in which behavior is grounded in an agent’s physical structure rather than abstracted from it. ### Competing Interest Statement The authors have declared no competing interest. Custom code used to generate the results reported in this study is publicly available at <https://github.com/GiarroccoFranco/Cortico-BasalGanglia>. The data supporting the findings of this study will be made publicly available in a suitable repository upon publication. Prior to publication, the data are available from the corresponding authors upon request. NIH, ZIAMH002928

Sensory feedback is essential for the fine-tuning of motor actions, and speech production is no exception. It depends on continuous self-monitoring...

The problem wasn’t just the perfectly polished, yet mediocre prose. It’s what’s lost when we surrender the struggle to translate thought into words

Sweeping new investigations probe the replication, robustness and reproducibility of results across the behavioural and social sciences.

SCORE shows that there is no shortcut to producing credible research findings, and there is no single indicator of trustworthiness. Research progress depends on transparency, rigor, and establishing r...

Alt: a little girl wearing sunglasses and a blue shirt is excited

Recognizing that certain acts are communicative is a key requirement of the development of communicative skills. Newborn infants have been shown to selectively respond to certain ostensive cues, incl...

The Reward Positivity (RewP) is an electroencephalogram (EEG) feature that emerges following performance feedback and is commonly understood to index both positive and negative reward-prediction error (RPE+ and RPE−, respectively) signals. In contrast to this dominant perspective, we argue that the RewP is an independent EEG feature that selectively responds to positive RPE and is superimposed on a common background signal. We further propose that the RewP signals a goal prediction error: it is elicited by abstract signals instead of by hedonic 'rewards'. This goal prediction error appears to be produced by a critic-like architecture that is associated with the actor–critic framework in reinforcement learning. This perspective emphasizes the role of the RewP in goal attainment and cognitive control as opposed to being a simple indicator of reward receipt.

One of the greatest (and, in my opinion, most overlooked) aspects of science is that it is a process inherently founded on and driven by community. Almost all ongoing research questions are derived…

UC San Diego alumnus Robert J. Glushko, Ph.D. and Pamela Samuelson have created four endowed faculty chairs in honor of transformational scholars whose work shaped both modern cognitive science and Gl...

The new Leverhulme Centre for Algorithmic Life, led by Louise Amoore at Durham Univeristy, launched in October. My role is to lead the data cluster from here at the University of York. The centre h…

The Reform UK leader doubts people can recall their abuse from years ago. I’ve never forgotten his – it felt malicious

a gureckislab joint.

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ALIGN 2.0: Modern Python package for multi-level linguistic alignment analysis. Faster, streamlined, and feature-rich while maintaining full compatibility with the original ALIGN methodology (Duran...

The University of Nottingham has suspended applications to all languages degree programmes, with a view to permanent closure of all of its undergraduate languages degrees. Under the proposals, it…

Perceptual experience depends on recurrent interactions between lower and higher cortices. One theory, predictive coding, posits that feedback from higher to lower brain regions decreases neuronal activity predicted by higher-level representations. Despite the widespread adoption of predictive coding in neuroscience, the correspondence to neurophysiological findings in sensory cortices remains elusive. Here, we review how the canonical patterns of intra- and inter-cortical interactions that occur during perception and shifts of attention deviate from those predicted by predictive coding. We argue that these circuit interactions are better captured by alternative theories, which we summarize under the umbrella term BELIEF. We review how BELIEF theories account for the inter-areal interactions during attentive perception.

The brain constantly generates predictions based on one’s knowledge of the world, as captured in memory. When these predictions are in error, our know…

Prediction in language is often about objects in the language users’ visual surroundings. Previous research suggests that linguistic working memory limitations in such task environments constrain l...

Xiaochen Wang, Heming Xia, Jialin Song, Longyu Guan, Qingxiu Dong, Rui Li, Yixin Yang, Yifan Pu, Weiyao Luo, Yiru Wang, Xiangdi Meng, Wenjie Li, Zhifang Sui. Findings of the Association for Computatio...

Abstract. In this functional magnetic resonance imaging study, we investigated whether language production and understanding recruit similar phoneme-specif