Cerebellar plasticity and the automation of first-order rules

Abstract

Theories of corticocerebellar function propose roles for the cerebellum in automating motor control, a process thought to depend on plasticity in cerebellar circuits that exchange information with the motor cortex. Little is known, however, about automating behaviors beyond the motor domain. The present study tested the hypothesis that cerebellar plasticity also subserves the development of automaticity in behavior based on low-order rules. Human subjects were required to learn two sets of first-order rules in which visual stimuli of different shapes each arbitrarily instructed a particular finger movement. We used event-related functional magnetic resonance imaging to scan subjects while these response rules became increasingly automatic with practice, as assessed with a dual-task procedure. We found that the amplitude of the blood oxygenation level-dependent signal gradually decreased as a function of practice, as responses became increasingly automatic, and that this effect was greater for a set of rules that became automatic rapidly compared with a second set, which became automatic more slowly. These trial-by-trial activity changes occurred in Crus I of cerebellar cortical lobule HVIIA, in which neurons exchange information with the prefrontal cortex rather than the motor cortex. Activity in Crus I was time locked specifically to the processing of these rules, rather than to subsequent actions. The results support the hypothesis that decreases in cerebellar cortical activity underlie the automation of behavior, whether related to motor control and motor cortex or to response rules and prefrontal cortex.

Figure 1.

a, Experimental design: schematic figure of the experimental sessions. S 1, Dual-task training; S 2, prescanning behavioral training; S 3, dual task before scanning; S 4, training to automaticity during scanning; S 5, dual task after scanning and greater automaticity; orange, unrepeated training stimuli; red, low-learning condition; blue, high-learning condition (see Materials and Methods for specific details about each session). Note that the RT bar plots are schematic only; see Figure 2 for the data. b, Conditional learning trial structure. Each trial was divided into two 6 s periods (instructions occurred in the first period, and the trigger, response, and outcome (modeled as a single event) occurred in the second). Onsets of instructions and trigger-related events occurred pseudorandomly between 0.5 and 6 s within these periods, allowing us to statistically partition activity time locked to instructions from activity time locked to subsequent events (see Materials and Methods).

Figure 2.

Reaction time behavior: reaction times in each session. HL, Black; LL, gray. Subjects' reaction times were the same in HL and LL conditions in both initial training (S2) before scanning and during scanning itself (S4). However, HL trials became more automatic than LL trials (the RT differences between HL and LL were larger in S5 than in S3). It is important to note that the errors themselves did not contribute to the trial-by-condition interaction reported in the cerebellum since activity was time locked to instruction cues, not motor responses or errors, and only correctly executed trials were included in the regressors related to this contrast. The decline in error rate is therefore unlikely to explain our observation that cerebellar activity decreased.

Figure 3.

a, b, Cerebellar activations during training to automaticity: section from Schmahmann et al. (2000) cerebellum atlas (a) comparable to that in b; excitability changes in event-related BOLD activity in Crus I associated with the condition in which there are greater changes in automaticity (b); data points represent the means and SEs of estimated peak hemodynamic response amplitudes for each trial in HL_T and LL_T (c). These are derived from the subject-specific fitted responses in SPM. Lines of best fit were calculated using linear least squares. HL, Blue; LL, red.