The single-unit activity of 831 cells was recorded in the arm area of the motor cortex of tow monkeys while the monkeys intercepted a moving visual stimulus (interception task) or remained immobile during presentation of the same moving stimulus (no-go task). The moving target traveled on an oblique path from either lower corner of a screen toward the vertical meridian, and its movement time (0.5,1.0, or 1.5 sec) and velocity profile (accelerating, decelerating, or constant velocity) were pseudorandomly varied. The moving target had to be intercepted within 130 msec of target arrival at an interception point. By comparing motor cortical activity at the single-neuron tasks, we tested whether information about parameters of moving target is represented in the primary motor cortex to generate appropriate motor responses. A substantial number of neurons displayed modulation of their activity during the no-go task, and this activity was often affected by the stimulus parameters. These results suggest a role of motor cortex in specifying the timing of movement initiation based on information about target motion. In addition, there was a lack of systematic relation between the onset times of neural activity in the interception and no-go task, suggesting that processing of information concerning target motion and generation of hand movement occurs in parallel. Finally, the activity in the most motor cortical neurons was modulated according to an estimate of the time-to-target interception, raising the possibility that time-to-interception may be coded in the motor cortical activity.

Two rhesus monkeys were trained to intercept a moving target at a fixed location with a feedback cursor controlled bya 2-D manipulandum. The direction from which the target appeared, the time from the target onset to its arrival at the interception point, and the target acceleration were randomized for each trial, thus requiring the animal to adjust its movement according to the visual input on a trail-by-trail basis. The two animals adopted different strategies, similar to those identified previously in human subjects. Single-cell activity was recorded from the arm area of the primary motor cortex in these two animals, and the neurons were classified based on the temporal patterns in their activity, using a nonhierarchical cluster analysis. Results of this analysis revealed differences in the complexity and diversity of motor cortical activity between the two animals that paralleled those of behavioral strategies. Most clusters displayed activity closedly related to the kinematics of hand movements. In addition, some clusters displayed patterns of activation that conveyed additional information necessary for successful performance of the task, such as the initial target velocity and the interval between successive submovements, suggesting that such information is represented in selective subpopulations of neurons in the primary motor cortex. These results also suggest that conversion of information about target motion into movement-related signals takes place in a broad network of cortical areas including the primary motor cortex.

We studied the brain activation patterns in two visual image processing tasks requiring judgements on object construction (FIT task) or object sameness (SAME task). Eight right-handed healthy human subjects (four women and four men) performed the two tasks in a randomized block design while 5-mm, multislice functional images of the whole brain were acquired using a 4-tesla system using blood oxygenation dependent (BOLD) activation. Pairs of objects were picked randomly from a set of 25 oriented fragments of a square and presented to the subjects approximately every 5 sec. In the FIT task, subjects had to indicate, by pushing one of two buttons, whether the two fragments could match to form a perfect square, whereas in the SAME task they had to decide whether they were the same or not. In a control task, preceding and following each of the two tasks above, a single square was presented at the same rate and subjects pushed any of the two keys at random. Functional activation maps were constructed based on a combination of conservative criteria. The areas with activated pixels were identified using Talairach coordinates and anatomical landmarks, and the number of activated pixels was determined for each area. Altogether, 379 pixels were activated. The counts of activated pixels did not differ significantly between the two tasks or between the two genders. However, there were significantly more activated pixels in the left (n = 218) than the right side of the brain (n = 161). Of the 379 activated pixels, 371 were located in the cerebral cortex. The Talairach coordinates of these pixels were analyzed with respect to their overall distribution in the two tasks. These distributions differed significantly between the two tasks. With respect to individual dimensions, the two tasks differed significantly in the anterior-posterior and superior-inferior distributions but not in the left-right (including mediolateral, within the left or right side) distribution. Specifically, the FIT distribution was, overall, more anterior and inferior than that of the SAME task. A detailed analysis of the counts and spatial distributions of activated pixels was carried out for 15 brain areas (all in the cerebral cortex) in which a consistent activation (in ≥ 3 subjects) was observed (n = 323 activated pixels). We found the following. Except for the inferior temporal gyrus, which was activated exclusively in the FIT task, all other areas showed activation in both tasks but to different extents. Based on the extent of activation, areas fell within two distinct groups (FIT or SAME) depending on which pixel count (i.e., FIT or SAME) was greater. The FIT group consisted of the following areas, in decreasing FIT/SAME order (brackets indicate ties): GTi, GTs, GC, GFi, GFd, [GTm, GF], GO. The SAME group consisted of the following areas, in decreasing SAME/FIT order: GOi, LPs, Sca, GPrC, GPoC, [GFs, GFm]. These results indicate that there are distributed, graded, and partially overlapping patterns of activation during performance of the two tasks. We attribute these overlapping patterns of activation to the engagement of partially shared processes. Activated pixels clustered to three types of clusters: FIT-only (111 pixels), SAME-only (97 pixels), and FIT + SAME (115 pixels). Pixels contained in FIT-only and SAME-only clusters were distributed approximately equally between the left and right hemispheres, whereas pixels in the SAME + FIT clusters were located mostly in the left hemisphere. With respect to gender, the left-right distribution of activated pixels was very similar in women and men for the SAME-only and FIT + SAME clusters but differed for the FIT-only case in which there was a prominent left side preponderance for women, in contrast to a right side preponderance for men. We conclude that (a) cortical mechanisms common for processing visual object construction and discrimination involve mostly the left hemisphere, (b) cortical mechanisms specific for these tasks engage both hemispheres, and (c) in object construction only, men engage predominantly the right hemisphere whereas women show a left-hemisphere preponderance.

We sought to determine how a visual maze is mentally solved. Human subjects ( N = 13) viewed mazes with orthogonal, unbranched paths; each subject solved 200-600 mazes in any specific experiment below. There were four to six openings at the perimeter of the maze, of which four were labeled: one was the entry point and the remainder were potential exits marked by Arabic numerals. Starting at the entry point, in some mazes the path exited, whereas in others it terminated within the maze. Subjects were required to type the number corresponding to the true exit (if the path exited) or type zero (if the path did not exit). In all cases, the only required hand movement was a key press, and thus the hand never physically traveled through the maze. Response times (RT) were recorded and analyzed using a multiple linear regression model. RT increased as a function of key parameters of the maze, namely the length of the main path, the number of turns in the path, the direct distance from entry to termination, and the presence of an exit. The dependence of RT on the number of turns was present even when the path length was fixed in a separate experiment ( N = 10 subjects). In a different experiment, subjects solved large and small mazes ( N = 3 subjects). The former was the same as the latter but was scaled up by 1.77 times. Thus both kinds of mazes contained the same number of squares but each square subtended 1.77° of visual angle (DVA) in the large maze, as compared to 1 DVA in the small one. We found that the average RT was practically the same in both cases. A multiple regression analysis revealed that the processing coefficients related to maze distance (i.e., path length and direct distance) were reduced by approximately one-half when solving large mazes, as compared to solving small mazes. This means that the efficiency in processing distance-related information almost doubled for scaled-up mazes. In contrast, the processing coefficients for number of turns and exit status were practically the same in the two cases. Finally, the eye movements of three subjects were recorded during maze solution. They consisted of sequences of saccades and fixations. The number of fixations in a trial increased as a linear function of the path length and number of turns. With respect to the fixations themselves, eyes tended to fixate on the main path and to follow it along its course, such that fixations occurring later in time were positioned at progressively longer distances from the entry point. Furthermore, the time the eyes spent at each fixation point increased as a linear function of the length and number of turns in the path segment between the current and the upcoming fixation points. These findings suggest that the maze segment from the current fixation spot to the next is being processed during the fixation time (FT), and that a significant aspect of this processing relates to the length and turns in that segment. We interpreted these relations to mean that the maze was mentally traversed. We then estimated the distance and endpoint of the path mentally traversed within a specific FT; we also hypothesized that the next portion of the main path would be traversed during the ensuing FT, and so on for the whole path. A prediction of this hypothesis is that the upcoming saccade would land the eyes at or near the locus on the path where the mental traversing ended, so that “the eyes would pick up where the mental traversal left off.” In this way, a portion of the path would be traversed during a fixation and successive such portions would be strung together closely along the main path to complete the processing of the whole path. We tested this prediction by analyzing the relations between the path distance of mental traverse and the distance along the path between the current and the next fixation spot. Indeed, we found that these distances were practically the same and that the endpoint of the hypothesized mental path traversing was very close to the point where the eye landed by the saccade to initiate a new mental traversing. This forward progression of fixation points along the maze path, coupled with the ongoing analysis of the path between successive fixation points, would constitute an algorithm for the routine solution of a maze.

The functional equivalence of overt movements and dynamic imagery is of fundamental importance in neuroscience. Here, we investigated the participation of the neocortical motor areas in a classic task of dynamic imagery, Shepard and Metzler's mental rotation task, by time-resolved single-trial functional Magnetic Resonance Imaging (fMRI). The subjects performed the mental-rotation task 16 times, each time with different object pairs. Functional images were acquired for each pair separately, and the onset times and widths of the activation peaks in each area of interest were compared to the response times. We found a bilateral involvement of the superior parietal lobule, lateral premotor area, and supplementary motor area in all subjects; we found, furthermore, that those areas likely participate in the very act of mental rotation. We also found an activation in the left primary motor cortex, which seemed to be associated with the right-hand button press at the end of the task period.

We studied the performance and cortical activation patterns during a mental rotation task (Shepard & Metzler, 1971) using functional magnetic resonance imaging (fMlU) at high field (4 Tesla). Twenty-four human subjects were imaged (fMRI group), whereas six additional subjects performed the task without being imaged (control group). All subjects were shown pairs of perspective drawings of 31, objects and asked to judge whether they were the same or mirror images. The measures of performance examined included (1) the percentage of errors, (2) the speed of performance, calculated as the inverse of the average response time, and (3) the rate of rotation for those object pairs correctly identified as “same.” We found the following: (1) Subjects in the fMRI group performed well outside and inside the magnet, and, in the latter case, before and during data acquisition. Moreover, performance over time improved in the same manner as in the control group. These findings indicate that exposure to high magnetic fields does not impair performance in mental rotation. (2) Functional activation data were analyzed from 16 subjects of the fMRI goup. Several cortical areas were activated during task performance. The relations between the measures of performance above and the magnitude of activation of specific cortical areas were investigated by anatomically demarcating these areas of interest and calculating a normalized activation for each one of them. (3) We used the multivariate technique of hierarchical tree modeling to determine functional clustering among areas of interest and performance measures. Two main branches were distinguished: One comprised areas in the right hemisphere and the extrastriate and superior parietal lobules bilaterally, whereas the other comprised areas of the left hemisphere and the frontal pole bilaterally; all three performance measures above clustered with the former branch. Specifically, performance outcome (“percentage of errors”) clustered with the parieto-occipital subcluster, whereas both the speed of performance and the rate of mental rotation clustered with the right precentral gyms. We conclude that the mental rotation paradigm used involves the cooperative interaction of functional groups of cortical areas of which some are probably more specifically associated with performance, whereas others may serve a more general function within the task constraints.