Encoding of Sensory Prediction Errors in the Human Cerebellum

Schlerf, John E.; Ivry, Richard B.; Diedrichsen, Jörn

doi:10.1523/jneurosci.4504-11.2012

Cited by 155 publications

(127 citation statements)

References 61 publications

(102 reference statements)

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“…S1). The visual feedback on trials in which the cursor landed outside the target constitutes a form of a sensory prediction error, a mismatch between a predicted and observed sensory outcome (7)(8)(9). We hypothesized that this could serve as a signal that the absence of an expected reward should be attributed to an error in movement execution.…”

Section: Resultsmentioning

confidence: 99%

Credit assignment in movement-dependent reinforcement learning

McDougle

Boggess

Crossley

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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When a person fails to obtain an expected reward from an object in the environment, they face a credit assignment problem: Did the absence of reward reflect an extrinsic property of the environment or an intrinsic error in motor execution? To explore this problem, we modified a popular decision-making task used in studies of reinforcement learning, the two-armed bandit task. We compared a version in which choices were indicated by key presses, the standard response in such tasks, to a version in which the choices were indicated by reaching movements, which affords execution failures. In the key press condition, participants exhibited a strong risk aversion bias; strikingly, this bias reversed in the reaching condition. This result can be explained by a reinforcement model wherein movement errors influence decision-making, either by gating reward prediction errors or by modifying an implicit representation of motor competence. Two further experiments support the gating hypothesis. First, we used a condition in which we provided visual cues indicative of movement errors but informed the participants that trial outcomes were independent of their actual movements. The main result was replicated, indicating that the gating process is independent of participants' explicit sense of control. Second, individuals with cerebellar degeneration failed to modulate their behavior between the key press and reach conditions, providing converging evidence of an implicit influence of movement error signals on reinforcement learning. These results provide a mechanistically tractable solution to the credit assignment problem.decision-making | reinforcement learning | sensory prediction error | reward prediction error | cerebellum W hen a diner reaches across the table and knocks over her coffee, the absence of anticipated reward should be attributed to a failure of coordination rather than diminish her love of coffee. Although this attribution is intuitive, current models of decision-making lack a mechanistic explanation for this seemingly simple computation. We set out to ask if, and how, selection processes in decision-making incorporate information specific to action execution and thus solve the credit assignment problem that arises when an expected reward is not obtained because of a failure in motor execution.Humans are highly capable of tracking the value of stimuli, varying their behavior on the basis of reinforcement history (1, 2), and exhibiting sensitivity to intrinsic motor noise when reward outcomes depend on movement accuracy (3-5). In real-world behavior, the underlying cause of unrewarded events is often ambiguous: A lost point in tennis could occur because the player made a poor choice about where to hit the ball or failed to properly execute the stroke. However, in laboratory studies of reinforcement learning, the underlying cause of unrewarded events is typically unambiguous, either solely dependent on properties of the stimulus or on motor noise. Thus, it remains unclear how people assign credit to either extrins...

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Section: Resultsmentioning

confidence: 99%

Credit assignment in movement-dependent reinforcement learning

McDougle

Boggess

Crossley

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…As long as the predictions stored in the loop are successfully aligned to external inputs there is no need to interrupt and update the internal model, albeit this possibility is given in the model by strong signals from V5. According to these considerations, the cerebellum would be of great importance to support the detection of prediction errors and thereby supporting predictive coding (Rao and Ballard, 1999;Langner et al, 2012;Schlerf et al, 2012). On a perceptual level, crus I has previously been associated with the cancellation of eye-movement-induced retinal image motion (Lindner et al, 2006), which might support the possible role of the cerebellum in predictive coding during visual processing.…”

Section: State Estimations Of the Cerebellum During Visual Attentionmentioning

confidence: 95%

Effective Connectivity of the Human Cerebellum during Visual Attention

et al. 2012

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Insights from both lesion and neuroimaging studies increasingly substantiate the view that the human cerebellum not only serves motor control but also supports various cognitive processes. Higher cognitive functions like working memory or executive control have been associated with the phylogenetically younger parts of the cerebellum, crus I and crus II. Functional connectivity studies corroborate this notion as activation of the cerebellum correlates with activity in numerous areas of the cerebral cortex. Moreover, these cerebrocerebellar loops were shown to be topographically organized. We used an attention-to-motion paradigm to elaborate on the effective connectivity of cerebellar crus I during visual attention. Psychophysiological interaction analyses demonstrated enhanced connectivity of the cerebellum-during attention-with dorsal visual stream regions including posterior parietal cortex (PPC) and left secondary visual cortex (V5). Dynamic causal modeling revealed a modulation of the connections from V5 to PPC and from crus I to V5 by attention. Remarkably, the influence which V5 exerted on PPC was reduced during attention, resulting in a suppression of the sensitivity of PPC to bottom-up information. Moreover, the sensitivity of V5 populations to inputs from crus I was increased under attention. This might underscore the presumed role of the cerebellum as a state estimator that provides hierarchically lower regions (V5) with top-down predictions, which in turn might be based on endogenous inputs from PPC to the cerebellum. These results are in line with formulations of attention in predictive coding, where attention increases the precision or sensitivity of hierarchically lower neuronal populations that may encode prediction error.

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“…Changes in Striatum, Basal Ganglia, and Cerebellum. In neurotypical individuals, the key brain loci that have been implicated in prediction are basal ganglia (68,69), striatum (70,71), anterior cingulate (72), and cerebellum (73,74). If predictive abilities are affected in autism, then we would expect these regions of the brain to be especially implicated.…”

Section: Predictionsmentioning

confidence: 99%

Autism as a disorder of prediction

Sinha

Kjelgaard

Gandhi

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

480

414

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A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly "magical" world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one's ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy.probabilistic processing | endophenotype | Markov models | theory | heterogeneity

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Encoding of Sensory Prediction Errors in the Human Cerebellum

Cited by 155 publications

References 61 publications

Credit assignment in movement-dependent reinforcement learning

Credit assignment in movement-dependent reinforcement learning

Effective Connectivity of the Human Cerebellum during Visual Attention

Autism as a disorder of prediction

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