Striatal Prediction Error Modulates Cortical Coupling
Open accessAbstract
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.
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The bilinear terms in DCM for fMRI allow for representing trial-wise modulation of connection strengths, making it well-suited for studying how connection strengths vary as a function of trial-by-trial prediction errors.
“The bilinear terms in DCM for fMRI allow for representing trial-wise modulation of connection strengths; this makes it well-suited for studying how connection strengths vary as a function of trial-by-trial prediction errors, where the latter are typically derived from a separate model applied to behavior or stimuli (e.g., den Ouden et al., 2010).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as den Ouden et al., 2010
The Data Analysis section (page not specified) explains that DCM’s B matrices represent input-induced changes in connection strengths. The paper also uses trial-by-trial estimates from a separate Bayesian learning model, but its prediction-error-dependent coupling result comes from nonlinear DCM, in which putamen activity gates connections through D matrices. It therefore does not establish the claimed application of bilinear terms to trial-by-trial prediction errors. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA number of human studies have demonstrated that magnitude of stimulus-evoked responses is inversely proportional to degree to which stimulus was expected.
“and a number of human studies have demonstrated that the magnitude of stimulus‐evoked responses is inversely proportional to the degree to which the stimulus was expected 75 , 76 , 83 , 84”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [75]
Many studies have reported that stimulus-evoked responses are inversely proportional to degree to which stimulus was expected, although this effect is not universally observed.
“Indeed, as mentioned above, many studies have reported that stimulus‐evoked responses are inversely proportional to the degree to which the stimulus was expected, 47 , 75 , 76 , 83 , 84 , 85 , 98 although this effect is not universally observed. 79 , 82”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [75]
There is evidence that predictability alters the functional connectivity between neural regions associated with its processing.
“Although there is evidence that the predictability of a stimulus modulates activity in low‐level sensory areas 103 , 128 and alters the functional connectivity between neural regions associated with its processing, 75 , 83 , 155 , 195 efforts to trace the diffusion of these effects across the processing hierarchy have produced mixed results.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [75]
Neural responses to uncertainty or surprise have been reported in multiple structures and linked with variables such as arousal, anxiety, risk preference, or global learning rates.
“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; Preuschoff et al., 2008; Schultz et al., 2008; So and Stuphorn, 2012; Tobler et al., 2009) and have been linked with variables such as arousal, anxiety, risk preference,”
From Attention, learning, and the value of information (2012) · cites this paper as den Ouden et al., 2010
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References 80 · Cited by 304
Research works printed but not linked: 2
- Hutton C Deichmann R Turner R Anderson JM Combined correction for geometric distortion and its interaction with head motion in fMRI 2004 Proceedings of ISMRM 12 Kyoto, Japan
- McLaren IP Kaye H Mackintosh NJ Morris RGM An associative theory of the representation of stimuli: Applications to perceptual learning and latent inhibition Parallel distributed processing: Implications for psychology and neurobiology 1989 102 120 Clarendon Press Oxford
- Authors
- den Ouden, H. E. M., Daunizeau, J., Roiser, J., Friston, K. J., & Stephan, K. E.
- Affiliations
- University College London (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2010-03-03
- Volume
- 30
- Issue
- 9
- Pages
- 3210-3219
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3044875
- OpenAlex
- W2079366972
- Cited by
- 304
- References
- 80
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 13 · 2023: 12 · 2022: 12 · 2021: 7 · 2020: 40 · 2019: 14 · 2018: 15 · 2017: 14 · 2016: 28 · 2015: 23 · 2014: 28 · 2013: 26 · 2012: 21
- FWCI
- 15.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Wellcome Trust (075047, 088130)
- Universität Zürich
- SystemsX.ch
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- predictive coding, visuomotor learning, premotor cortex, surprise, dynamic causal modeling
- MeSH terms
- Acoustic Stimulation, Adaptation, Psychological, Adult, Bayes Theorem, Brain Mapping, Cerebral Cortex, Computer Simulation, Corpus Striatum, Cues, Female, Humans, Judgment, Learning, Magnetic Resonance Imaging, Male, Movement, Nerve Net, Neural Pathways, Neuronal Plasticity, Neuropsychological Tests, Photic Stimulation, Psychomotor Performance, Nonlinear Dynamics, Young Adult
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