The Cerebellum: Adaptive Prediction for Movement and Cognition

Abstract

Over the past 30 years, cumulative evidence has indicated that cerebellar function extends beyond sensorimotor control. This view has emerged from studies of neuroanatomy, neuroimaging, neuropsychology, and brain stimulation, with the results implicating the cerebellum in domains as diverse as attention, language, executive function, and social cognition. Although the literature provides sophisticated models of how the cerebellum helps refine movements, it remains unclear how the core mechanisms of these models can be applied when considering a broader conceptualization of cerebellar function. In light of recent multidisciplinary findings, we examine how two key concepts that have been suggested as general computational principles of cerebellar function- prediction and error-based learning- might be relevant in the operation of cognitive cerebro-cerebellar loops.

Keywords: cerebellum; cognition; language; learning; prediction; social cognition.

Figure 1. Functional Topography of the Cerebellar Cortex

The cerebellar cortical sheet (E), when graphically flattened into a 2-D map has a huge surface area. Resting state functional connectivity (rsFC) studies show that each cerebellar area can be linked to a region in the cerebral cortex (A–D). There appears to be a repeated mapping with, for example, prefrontal cortical areas (label 16 in C, D) represented in anterior and posterior lobes of the cerebellum (E). Adapted from [12], [178] and [19], with permissions.

Figure 2. Forward Models and Prediction

(A) The motor cerebellum has been hypothesized to operate as a forward model or state estimator, using efferent copies of motor commands to predict the sensory consequences of actions. Sensory prediction errors, the difference between the predicted and actual outcome, are conveyed to the cerebellum through the climbing fibres of the inferior olive (IO). Whether cerebellar output is the state estimate or a signal updating a state estimate represented elsewhere, for example in motor or parietal cortex, is unclear. (B) By analogy, the cognitive cerebellum might predict changes in perceptual or mental states, and feed these updates to associative areas. It is uncertain if the calculation of errors (comparator) is within the IO or elsewhere, and fed to the cerebellum via the IO. For example, there is a loop between mesodiencephalic junction, inferior olive, cerebellar cortex, DCN and back to the mesodiencephalic junction that may contribute to this computation. Although these diagrams only depict cortical inputs to the pons, there are many other sources of pontine input. DCN: deep cerebellar nuclei; Th: thalamus.

Figure 3. The Cerebellum and Linguistic Prediction

(A) The predictability of visually presented sentences can be high. (B) A small region in right posterior lateral cerebellum was more active in the high predictable condition compared to when predictability was reduced by scrambling the order of the presented words [82]. If the expected ending of the sentence was violated (“Two plus two is apple”), a broad, bilateral region of the cerebellar cortex was activated. (C, contrast between incongruent and scrambled trials). (D,E) Evidence that right posterior lateral cerebellum is causally involved in linguistic prediction [83]. The latency advantage in fixating the object of the predictable spoken sentences compared to unpredictable control sentences was significantly reduced after rTMS to the right lateral cerebellum. Illustrations adapted and reprinted with permissions.

Figure 4. Social Cognition and the Cerebellum

(A) Summary of cerebellar findings from meta-analysis of 350 whole-brain neuroimaging studies of social cognition. Most consistent cerebellar activation is elicited by abstraction in trait inferences, in bilateral Crus I, with a right hemispheric predominance. Bilateral activation is also found during mirroring (observation of human body motion). In contrast, event (e.g., observation of social interaction depicted by moving geometric shapes) and person trait mentalizing appear left- and right-lateralized, respectively. Adapted from [108], with permission. (B–F) Series of studies specifically focused on cerebellar engagement in social cognition. (B) Observation of human body motion represented by point lights on the head and the main joints (reprinted from [179] with automatic permission from SAGE Publications) elicits activation in the left cerebellar lobules (C) Crus I and (D) VIIB. Reprinted from [30] with permission by Oxford University Press. (E+F) Patients with tumors topographically overlapping regions left lateral cerebellum exhibit deficits in perception of body motion. Reprinted from [116] with permission by Oxford University Press. (G) Illustration of geometric shapes that move as if they would socially interact (upper panel) or in a random fashion (control; lower panel). Adapted from [180]. (H) As with visual perception of body motion, observation of the interacting geometric shapes results in activation of left cerebellar Crus I/II, and effective connectivity with the right superior temporal sulcus (STS). Adapted from [117], with permission by Oxford University Press. (I) Diffusion tensor imaging revealing a structural loop between the left lateral cerebellar lobule Crus I and right STS. Reprinted from [31] with permission by Oxford University Press.

Figure I. Interactions between Cerebellum and Cognitive Cortical Networks

The cerebellum receives input from cortical areas via the pons and projects back to similar areas via the thalamus, forming a closed-loop architecture (black arrows). Complex cognitive processes such as language or social cognition require interactions between distributed regions in the cerebral cortex (colored, dashed arrows). Thus, the closed-loop architecture would suggest that the cerebellar outputs modulate singular network components, instead of affecting a complex cognitive process as a whole. However, cortico-cortical connections provide a means to expand the influence of the cerebellum.