Cerebral changes during performance of overlearned arbitrary visuomotor associations

Abstract

The posterior parietal cortex (PPC) is known to be involved in the control of automatic movements that are spatially guided, such as grasping an apple. We considered whether the PPC might also contribute to the performance of visuomotor associations in which stimuli and responses are linked arbitrarily, such as producing a certain sound for a typographical character when reading aloud or pressing pedals according to the color of a traffic light when driving a motor vehicle. The PPC does not appear to be necessary for learning new arbitrary visuomotor associations, but with extensive training, the PPC can encode nonspatial sensory features of task-relevant cues. Accordingly, we have tested whether the contributions of the PPC might become apparent once arbitrary sensorimotor mappings are overlearned. We have used functional magnetic resonance imaging to measure cerebral activity while subjects were learning novel arbitrary visuomotor associations, overlearning known mappings, or attempting to learn frequently changing novel mappings. To capture the dynamic features of cerebral activity related to the learning process, we have compared time-varying modulations of activity between conditions rather than average (steady-state) responses. Frontal, striatal, and intraparietal regions showed decreasing or stable activity when subjects learned or attempted to learn novel associations, respectively. Importantly, the same frontal, striatal, and intraparietal regions showed time-dependent increases in activity over time as the mappings become overlearned, i.e., despite time-invariant behavioral responses. The automaticity of these mappings predicted the degree of intraparietal changes, indicating that the contribution of the PPC might be related to a particular stage of the overlearning process. We suggest that, as the visuomotor mappings become robust to interference, the PPC may convey relevant sensory information toward the motor cortex. More generally, our findings illustrate how rich cerebral dynamics can underlie stable behavior.

Figure 1.

Experimental setup. A, Task setup. Subjects were asked to associate visual stimuli (white line patterns on black background) with motor responses (flexion of one of four fingers of the right hand to press a button on a four-button keypad). After presentation of a visual stimulus, the subjects had to flex one of four fingers of the right hand. After the motor response, visual feedback stimuli indicated whether the movement was incorrect (red square; example 1), correct (green square; example 2), or too late (blue square; example 3). B, Experimental setup. During fMRI scanning, trials from three different conditions were intermixed pseudorandomly. In the visuomotor overlearned condition (overlearning), subjects retrieved a set of visuomotor associations learned before scanning (set 1; 2630 trials over 3 d). In the visuomotor learning condition (learning; set 4), subjects learned novel visuomotor associations between four new visual patterns and the four finger movements. In the continuous learning task (continuous), subjects attempted to learn novel visuomotor associations. In this latter condition, novel visual patterns (unseen during the training) were introduced constantly and removed from the stimulus set. To assess the degree of automaticity achieved during overlearning, we compared performance during a dual-task procedure involving overlearning trials (set 1) and a set of learned trials (set 3) (Fig. 3). ISI, Interstimulus interval.

Figure 2.

Behavioral performance. Average ERs and RTs over scanning time (binned in blocks of 20 trials; intersubject mean ± SEM) for overlearning (•), learning (□), and continuous (○). During overlearning trials, performance was stable and virtually error free. During learning, ERs dropped from chance level to 10%. During continuous, subjects' learning rate was reduced significantly as compared with learning.

Figure 3.

Dual-task performance. Before starting the fMRI measurements, we used a dual-task procedure to assess the degree of automaticity of overlearning performance. This test required concurrent performance of the visuomotor associative task and a verbal fluency task (see Materials and Methods). This figure shows the average ERs (intersubject mean ± SEM) (lightgray, incorrect responses; black, missed responses) and RTs (intersubject mean±SEM) of the visuomotor associative task for both overlearned associations (left) and newly learned associations (right) during concurrent performance of a nounrepetition task (repeat) and a verb generation task (generate). During overlearning trials, subjects were faster and made fewer errors compared with the learned condition. Note that on each trial of the dual-task procedure, there were two concurrent sensory inputs (auditory nouns and visual patterns) and two concurrent motor responses (vocal utterances and finger presses). Furthermore, the subjects were given explicit instructions to give priority to the verbal fluency task. Accordingly, our goal here was to show that performance of the overlearned associations suffered less interference from a concurrent task as compared with performance of newly learned associations. This can be contrasted with other uses of dual-task techniques, as when one wants to show that performance of a given primary task is not affected by a secondary task (Poldrack et al., 2005).

Figure 4.

Imaging results. Differential time-related changes of cerebral activity across conditions, relative to the contrast between time-dependent increases and time-dependent decreases in activity during correct performance of overlearning and learning trials. Right column, Peak BOLD signal change over scanning time (binned in blocks of 20 trials; intersubject mean ± SEM) for overlearning (red), learning (green), and continuous (blue). Left column, SPM{t} of the relevant contrast superimposed on anatomical sections of a representative subject. A, Left intraparietal sulcus (–36, –48, 46); B, left superior precentral sulcus (–20, 2, 62); C, left inferior frontal sulcus (–40, 28, 28); D, left caudate nucleus (–10, 12, –2). a.u., Arbitrary units.

Figure 5.

Relation between behavioral and cerebral effects. Relation between the time-related change in cerebral activity observed during overlearning trials and the degree of automaticity of the visuomotor transformation evoked in that condition. The cerebral effect (y-axis) denotes the variation in signal over time for each subject, as indexed by the standardized (SE units) parameter estimate of the linear change over time in BOLD signal. The behavioral effect (x-axis) denotes the amount of interference generated by the dual-task procedure for each subject, as indexed by the difference in error rates evoked during over learning and learned trials when a word is generated as compared with being simply repeated. This figure illustrates a significant nonlinear relationship between dual-task performance and parietal increase in BOLD signal (•). Parietal activity decreased over time (negative cerebral effect) for those subjects with a poor degree of automaticity during overlearning (negative behavioral effect; this indicates that the verb generation task hampered performance of the learned trials less than performance of the overlearning trials). Conversely, parietal activity increased over time (positive cerebral effect) for those subjects with a good degree of automaticity during overlearning (moderately positive behavioral effects). Importantly, parietal activity remained constant over time (zero cerebral effect) for those subjects with an excellent degree of automaticity during overlearning (extremely positive behavioral effects). The dashed line indicates the least square fit of a fourth-order polynomial (R² = 0.41).