Nicholas L. Port

Manual interception of moving targets I. Performance and movement initiation

Abstract We investigated the capacities of human subjects to intercept moving targets in a two-dimensional (2D) space. Subjects were instructed to intercept moving targets on a computer screen using a cursor controlled by an articulated 2D manipulandum. A target was presented in 1 of 18 combinations of three acceleration types (constant acceleration, constant deceleration, and constant velocity) and six target motion times, from 0.5 to 2.0 s. First, subjects held the cursor in a start zone located at the bottom of the screen along the vertical meridian. After a pseudorandom hold period, the target appeared in the lower left or right corner of the screen and traveled at 45º toward an interception zone located on the vertical meridian 12.5 cm above the start zone. For a trial to be considered successful, the subject’s cursor had to enter the interception zone within 100 ms of the target’s arrival at the center of the interception zone and stay inside a slightly larger hold zone. Trials in which the cursor arrived more than 100 ms before the target were classified as ”early errors,” whereas trials in which the cursor arrived more than 100 ms after the target were classified as ”late errors.” Given the criteria above, the task proved to be difficult for the subjects. Only 41.3% (1080 out of 2614) of the movements were successful, whereas the remaining 58.7% were temporal (i.e., early or late) errors. A large majority of the early errors occurred in trials with decelerating targets, and their percentage tended to increase with longer target motion times. In contrast, late errors occurred in relation to all three target acceleration types, and their percentage tended to decrease with longer target motion times. Three models of movement initiation were investigated. First, the threshold-distance model, originally proposed for optokinetic eye movements to constant-velocity visual stimuli, maintains that response time is composed of two parts, a constant processing time and the time required for the stimulus to travel a threshold distance. This model only partially fit our data. Second, the threshold-τ model, originally proposed as a strategy for movement initiation, assumes that the subject uses the first-order estimate of time-to-contact (τ) to determine when to initiate the interception movement. Similar to the threshold distance model, the threshold-τ model only partially fit the data. Finally, a dual-strategy model was developed which allowed for the adoption of either of the two strategies for movement initiation; namely, a strategy based on the threshold-distance model (”reactive” strategy) and another based on the threshold-τ model (”predictive” strategy). This model provided a good fit to the data. In fact, individual subjects preferred to use one or the other strategy. This preference was allowed to be manifested at long target motion times, whereas shorter target motion times (i.e., 0.5 s and 0.8 s) forced the subjects to use only the reactive strategy.

Manual interception of moving targets. II. On-line control of overlapping submovements

Abstract We studied the kinematic characteristics of arm movements and their relation to a stimulus moving with a wide range of velocity and acceleration. The target traveled at constant acceleration, constant deceleration, or constant velocity for 0.5–2.0 s, until it arrived at a location where it was required to be intercepted. For fast moving targets, subjects produced single movements with symmetrical, bell-shaped velocity profiles. In contrast, for slowly moving targets, hand velocity profiles displayed multiple peaks, which suggests a control mechanism that produces a series of discrete submovements according to characteristics of target motion. To analyze how temporal and spatial aspects of these submovements are influenced by target motion, we decomposed the vertical hand velocity profiles into bell-shaped velocity pulses according to the minimum-jerk model. The number of submovements was roughly proportional to the movement time, resulting in a relatively constant submovement frequency (∼2.5 Hz). On the other hand, the submovement onset asynchrony showed significantly more variability than the intersubmovement interval, indicating that the submovement onset was delayed more following a submovement with a longer duration. Examination of submovement amplitude and its relation to target motion revealed that the subjects achieved interception mainly by producing a series of submovements that would keep the displacement of the hand proportional to the first-order estimate of target position at the end of each submovement along the axis of hand movement. Finally, we did not find any evidence that information regarding target acceleration is properly utilized in the production of submovements.

Guiding contact by coupling the taus of gaps.

Abstract. Animals control contact with surfaces when locomoting, catching prey, etc. This requires sensorily guiding the rate of closure of gaps between effectors such as the hands, feet or jaws and destinations such as a ball, the ground and a prey. Control is generally rapid, reliable and robust, even with small nervous systems: the sensorimotor processes are therefore probably rather simple. We tested a hypothesis, based on general τ theory, that closing two gaps simultaneously, as required in many actions, might be achieved simply by keeping the taus of the gaps coupled in constant ratio. τ of a changing gap is defined as the time-to-closure of the gap at the current closure-rate. General τ theory shows that τ of a gap could, in principle, be directly sensed without needing to sense either the gap size or its rate of closure. In our experiment, subjects moved an effector (computer cursor) to a destination zone indicated on the computer monitor, to stop in the zone just as a moving target cursor reached it. The results indicated the subjects achieved the task by keeping τ of the gap between effector and target coupled to τ of the gap between the effector and the destination zone. Evidence of τ-coupling has also been found, for example, in bats guiding landing using echolocation. Thus, it appears that a sensorimotor process used by different species for coordinating the closure of two or more gaps between effectors and destinations entails constantly sensing the τs of the gaps and moving so as to keep the τs coupled in constant ratio.

A neural network model of cortical activity during reaching

A neural network model that produces many of the directional and spatial response properties that have been observed for cortical neurons in monkeys moving toward targets in space is described. These include motor cortex units with broad tuning in a single preferred direction, approximately linear variation in activity for different hold positions, and approximate invariance in preferred direction for different starting points in space. Association cortex units in the model are sometimes irregular and reminiscent of neurons observed in visually responsive brain areas such as the posterior parietal cortex. The model is also compatible with population analyses performed on motor cortical neurons. Across network units, the distribution of preferred directions is uniformly distributed in directional space, and the degree of tuning and response magnitude vary from unit to unit. A population code used to predict accurately the direction of arm movements from a large population of coarsely tuned individual neurons allows predictions using a simulated population of unit responses obtained from the neural network model. This code works for different starting locations in space using the same parameters.

Motor Cortical Activity during Interception of Moving Targets

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.

Control of remembered reaching sequences in monkey

Motor and premotor cortex firing patterns from 307 single neurons were recorded while monkeys made rapid sequences of three reaching movements to remembered target buttons arrayed in two-dimensional space. A primary goal was to study and compare directionally tuned responses for each of three movement periods during 12 movement sequences that uniformly sampled the directional space in front of the monkey. The majority of neurons showed maximal responses during movements in a preferred direction with smaller increases during movements close to the preferred direction. These responses showed a statistically significant regression fit to a cosine function for 72% of the neurons examined. Comparisons among tuning directions computed separately for the first, second, and third movement periods suggested the near constancy of preferred direction across a rapidly executed series of movements even though these movements began at different starting points in space. Although directionally tuned neurons were only broadly tuned for a specific direction of movement, the neuronal ensemble carried accurate directional information. A population vector computed by summing vector contributions from the entire population of tuned neurons predicted movement direction with a mean accuracy of 20°. This population code made consistent predictions for each of the 36 movements that were studied using a single set of population parameters. Most of the remaining neurons (24%) that were not tuned during movement did show significant changes in activity during other aspects of task performance. Some nontuned neurons had nondirectional increases that were sustained during movement, while others showed identical phasic bursts during the three movement periods. These nontuned neurons may control stabilizations of the shoulder, trunk, and forearm during movement, or forearm movements during button pushing.

Neuronal Clusters in the Primate Motor Cortex during Interception of Moving Targets

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.