Caudate Nucleus Signals for Breaches of Expectation in a Movement Observation Paradigm

The striatum has been established as a carrier of reward-related prediction errors. This prediction error signal concerns the difference between how much reward was predicted and how much reward is gained. However, it remains to be established whether general breaches of expectation, i.e., perceptual prediction errors, are also implemented in the striatum. The current study used functional magnetic resonance imaging (fMRI) to investigate the role of caudate nucleus in breaches of expectation. Importantly, breaches were not related to the occurrence or absence of reward. Preceding the fMRI study, participants were trained to produce a sequence of whole-body movements according to auditory cues. In the fMRI session, they watched movies of a dancer producing the same sequences either according to the cue (88%) or not (12%). Caudate nucleus was activated for the prediction-violating movements. This activation was flanked by activity in posterior superior temporal sulcus, the temporo-parietal junction and adjacent angular gyrus, a network that may convey the deviating movement to caudate nucleus, while frontal areas may reflect adaptive adjustments of the current prediction. Alternative interpretations of caudate activity relating either to the saliency of breaches of expectation or to behavioral adaptation could be excluded by two control contrasts. The results foster the notion that neurons in the caudate nucleus code for a breach in expectation, and point toward a distributed network involved in detecting, signaling and adjusting behavior and expectations toward violated prediction

The role of prediction and outcomes in adaptive cognitive control

Humans adaptively perform actions to achieve their goals. This flexible behaviour requires two core abilities: the ability to anticipate the outcomes of candidate actions and the ability to select and implement actions in a goal-directed manner. The ability to predict outcomes has been extensively researched in reinforcement learning paradigms, but this work has often focused on simple actions that are not embedded in hierarchical and sequential structures that are characteristic of goal-directed human behaviour. On the other hand, the ability to select actions in accordance with high-level task goals, particularly in the presence of alternative responses and salient distractors, has been widely researched in cognitive control paradigms. Cognitive control research, however, has often paid less attention to the role of action outcomes. The present review attempts to bridge these accounts by proposing an outcome-guided mechanism for selection of extended actions. Our proposal builds on constructs from the hierarchical reinforcement learning literature, which emphasises the concept of reaching and evaluating informative states, i.e., states that constitute subgoals in complex actions. We develop an account of the neural mechanisms that allow outcome-guided action selection to be achieved in a network that relies on projections from cortical areas to the basal ganglia and back-projections from the basal ganglia to the cortex. These cortico-basal ganglia-thalamo-cortical ‘loops’ allow convergence – and thus integration – of information from non-adjacent cortical areas (for example between sensory and motor representations). This integration is essential in action sequences, for which achieving an anticipated sensory state signals the successful completion of an action. We further describe how projection pathways within the basal ganglia allow selection between representations, which may pertain to movements, actions, or extended action plans. The model lastly envisages a role for hierarchical projections from the striatum to dopaminergic midbrain areas that enable more rostral frontal areas to bias the selection of inputs from more posterior frontal areas via their respective representations in the basal ganglia.This work is supported by the Biotechnology and Biological Sciences Research Council (BBSRC) Grant BB/I019847/1, awarded to NY and FW

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts

Confidence Predictions Affect Performance Confidence and Neural Preparation in Perceptual Decision Making

International audienceDecisions are usually accompanied by a feeling of being wrong or right-a subjective confidence estimate. But what information is this confidence estimate based on, and what is confidence used for? To answer these questions, research has largely focused on confidence regarding current or past decisions, for example identifying how characteristics of the stimulus affect confidence, how confidence can be used as an internally generated feedback signal, and how communicating confidence can affect group decisions. Here, we report two studies which implemented a novel metacognitive measure: predictions of confidence for future perceptual decisions. Using computational modeling of behaviour and EEG, we established that experience-based confidence predictions are one source of information that affects how confident we are in future decision-making, and that learned confidence-expectations affect neural preparation for future decisions. Results from both studies show that participants develop precise confidence predictions informed by past confidence experience. Notably, our results also show that confidence predictions affect performance confidence rated after a decision is made; this finding supports the proposal that confidence judgments are based on multiple sources of information, including expectations. We found strong support for this link in neural correlates of stimulus preparation and processing. EEG measures of preparatory neural activity (contingent negative variation; CNV) and evidence accumulation (centro-parietal positivity; CPP) show that predicted confidence affects neural preparation for stimulus processing, supporting the proposal that one purpose of confidence judgments may be to learn about performance for future encounters and prepare accordingly

Overview of conditions and number of expositions.

<p>Overview of conditions and number of expositions.</p

Modelled BOLD for surpise over the iterations of unpredictables in the fMRI session.

<p>I3: surprise for the 3 times pre-exposed; I6: surprise for the 6 times pre-exposed; I9: surprise for the 9 times pre-exposed unpredictables.</p

Modelled BOLD for entropy over the iterations of unpredictables in the fMRI session.

<p>H3: entropy for the 3 times pre-exposed; H6: entropy for the 6 times pre-exposed; H9: entropy for the 9 times pre-exposed unpredictables.</p

Reconstructed, color-coded hippocampal ROIs in a 3-D rendered brain.

<p>Reconstructed, color-coded hippocampal ROIs in a 3-D rendered brain.</p

Reconstructed, color-coded habenular (A) and nigral (B) ROIs in a 3-D rendered brain.

<p>Reconstructed, color-coded habenular (A) and nigral (B) ROIs in a 3-D rendered brain.</p

Reconstructed, color-coded caudate ROIs in a 3-D rendered brain.

<p>Reconstructed, color-coded caudate ROIs in a 3-D rendered brain.</p