Behavioral / Systems / Cognitive Striatal Prediction Error Modulates Cortical Coupling

By H. D. den Ouden, Jean Daunizeau, Jonathan P. Roiser, Karl J. Friston, Klaas E. Stephan. 2010

319 citations recorded by the source corpus.

Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulus-induced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trial-by-trial prediction error activity controls the efficacy of visuomotor connections and thus the influence of surprising stimuli on premotor activity. This finding substantially advances our understanding of striatal function and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity.

Subfields: Cognitive Neuroscience

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Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, positive): “Other experiments show that a tone can be used as a cue to predict the orientation of an upcoming grating (Kok et al., 2012a), and either a colored frame or an auditory tone can predict whether an upcoming image will be a face or a house (den Ouden et al., 2010; Egner et al., 2010).”
Attention, learning and the value of information (2012, background): “Neural responses to uncertainty or surprise have been reported in multiple structures (den Ouden et al., 2010; Fiorillo et al., 2003; Kepecs et al., 2008; McCoy and Platt, 2005; O’Neill and Schultz, 2010; Preuschoff et al., 2006, 2008; Schultz et al., 2008; So and Stuphorn, 2012; Tobler et al.,…”
Predictive Coding and the Neural Response to Predictable Stimuli (2010, background): “Viewed in parallel, Alink et al. (2010) and den Ouden et al. (2010) provide evidence that across different time scales, experimental contexts, and levels of the visual hierarchy, the brain is not only highly sensitive to predictability, but that this predictability can lead to a reduction of…”
Repetition Suppression and Expectation Suppression Are Dissociable in Time in Early Auditory Evoked Fields (2012, background): “…activity in area V1, in connection with expected, simple visual stimuli (den Ouden et al., 2008; Alink et al., 2010; Kok et al., 2011); in the fusiform face area, in connection with faces; and in the parahippocampal place area, in connection with houses (Den Ouden et al., 2010; Egner et al., 2010).”
Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream (2010, background): “…that some portion of population activity in visual cortices can be attributed to prediction error signals, in general support of predictive coding models (Murray et al., 2002, 2003; Summerfield et al., 2006, 2008; Summerfield and Koechlin, 2008; den Ouden et al., 2009, 2010; Alink et al., 2010).”
How Prediction Errors Shape Perception, Attention, and Motivation (2012, background): “In the first of these studies we showed that PE signals in response to stimuli that required an update were present in the putamen, which is known to connect to cortical motor planning areas (den Ouden et al., 2010).”
Bayesian model selection for group studies - Revisited (2014, unknown): “These two summary statistics typically constitute the results of random effects BMS (see, for example, den Ouden et al., 2010). While the random effects BMS procedure suggested in Stephan et al. (2009) and Penny et al.”
Observing the Observer (II): Deciding When to Decide (2010, methodology): “The experimental data and procedures have been reported previously as part of a functional magnetic resonance imaging study of audio-visual associative learning [12].”