The cone method: Inferring decision times from single-trial 3D movement trajectories in choice behavior

Superior performance by two new methods in identifying the online reaction time of reaching movements

Reaching movements can be redirected during their progress to handle unexpected visual changes, such as a change in target location. It is important to know when these redirections start, i.e., the online reaction time (oRT), but this information is not readily evident since redirections are embedded within a time-varying baseline movement that differs from trial to trial. The one previous study that evaluated the performance of different oRT identification methods utilized simulated redirections with the exact same onset, rather than a range of onsets as would be typically encountered. We addressed this gap by utilizing batches of "hybrid" trials with temporal spread in their oRTs. Each hybrid trial combined a sampled baseline movement with an idealized corrective response. Two new methods had the most accurate identification of online reaction times: 1) a threshold-aligned grand mean regression, and 2) a template-based approach we term the canonical correction search. The threshold-aligned grand mean regression is simple to implement and effective. The canonical correction search is a more complex procedure but arguably better linked to the underlying response. Applying the two methods to a published dataset revealed more delayed oRTs than was previously reported along with new information such as the width of oRT distributions. Taken together, our results demonstrate the utility of two new methods for dissecting corrective action from ongoing movement.NEW & NOTEWORTHY Advancing our understanding of visual feedback control requires methods that accurately identify the onset of corrective action. We developed a modified regression approach and a template-based approach to identify the online reaction time of single-reaching movements. Both outperform previous methods when challenged by temporal jitter in the response onset and increased background noise.

Learning of object-in-context sequences in freely-moving macaques

Flexible learning is a hallmark of primate cognition, which arises through interactions with changing environments. Studies of the neural basis for this flexibility are typically limited by laboratory settings that use minimal environmental cues and restrict interactions with the environment, including active sensing and exploration. To address this, we constructed a 3-D enclosure containing touchscreens on its walls, for studying cognition in freely moving macaques. To test flexible learning, two monkeys completed trials consisting of a regular sequence of object selections across four touchscreens. On each screen, the monkeys had to select by touching the sole correct object item ('target') among a set of four items, irrespective of their positions on the screen. Each item was the target on exactly one screen of the sequence, making correct performance conditioned on the spatiotemporal sequence rule across screens. Both monkeys successfully learned multiple 4-item sets (N=14 and 22 sets), totaling over 50 and 80 unique, conditional item-context memoranda, with no indication of capacity limits. The enclosure allowed freedom of movements leading up to and following the touchscreen interactions. To determine whether movement economy changed with learning, we reconstructed 3D position and movement dynamics using markerless tracking software and gyroscopic inertial measurements. Whereas general body positions remained consistent across repeated sequences, fine head movements varied as monkeys learned, within and across sequence sets, demonstrating learning set or "learning to learn". These results demonstrate monkeys' rapid, capacious, and flexible learning within an integrated, multisensory 3-D space. Furthermore, this approach enables the measurement of continuous behavior while ensuring precise experimental control and behavioral repetition of sequences over time. Overall, this approach harmonizes the design features that are needed for electrophysiological studies with tasks that showcase fully situated, flexible cognition.

Capacity Limits Lead to Information Bottlenecks in Ongoing Rapid Motor Behaviors

Studies of ongoing, rapid motor behaviors have often focused on the decision-making implicit in the task. Here, we instead study how decision-making integrates with the perceptual and motor systems and propose a framework of limited-capacity, pipelined processing with flexible resources to understand rapid motor behaviors. Results from three experiments show that human performance is consistent with our framework: participants perform objectively worse as task difficulty increases, and, surprisingly, this drop in performance is largest for the most skilled performers. As well, our analysis shows that the worst-performing participants can perform equally well under increased task demands, which is consistent with flexible neural resources being allocated to reduce bottleneck effects and improve overall performance. We conclude that capacity limits lead to information bottlenecks and that processes like attention help reduce the effects that these bottlenecks have on maximal performance.