Tapping on a target: dealing with uncertainty about its position and motion

Abstract

Reaching movements are guided by estimates of the target object’s location. Since the precision of instantaneous estimates is limited, one might accumulate visual information over time. However, if the object is not stationary, accumulating information can bias the estimate. How do people deal with this trade-off between improving precision and reducing the bias? To find out, we asked participants to tap on targets. The targets were stationary or moving, with jitter added to their positions. By analysing the response to the jitter, we show that people continuously use the latest available information about the target’s position. When the target is moving, they combine this instantaneous target position with an extrapolation based on the target’s average velocity during the last several hundred milliseconds. This strategy leads to a bias if the target’s velocity changes systematically. Having people tap on accelerating targets showed that the bias that results from ignoring systematic changes in velocity is removed by compensating for endpoint errors if such errors are consistent across trials. We conclude that combining simple continuous updating of visual information with the low-pass filter characteristics of muscles, and adjusting movements to compensate for errors made in previous trials, leads to the precise and accurate human goal-directed movements.

How the timing of visual feedback influences goal-directed arm movements: delays and presentation rates

“…The cursor being visible more of the time even seems to be more important than the number of cursor positions that are shown. This is consistent with the idea that people continuously use visual information to guide their movements Brenner et al 2023). In the current experiments, the task was to move a cursor to a target by moving one's hand in a different plane, so participants had to rely on visual information about the cursor as well as the target.…”

“…We expect having an additional delay to make it more difficult to guide movements, because it increases the duration of the last part of the movement during which newly acquired information cannot be used to guide the limb Communicated by Bill J. Yates. (Brenner and Smeets 2018;de la Malla et al 2018;Brenner et al 2023). This increased difficulty will manifest itself as either a reduced precision or longer movement times (Brenner and Smeets 2011;Fitts 1954).…”

Digital behavioural tests as diagnostic aid for psychosis

“…24,27 Additionally, using mechanistic (differential equations) models 59 to combine eye-movements, reaction time and movement data could help to identify people ’ s cognitive strategies e.g., employed to complete neuropsychological tasks. 60,61 Identification of the cognitive strategies and understanding their causal mechanisms would elucidate the role of pathophysiology in perturbed information processing and allow the development of new methodologies for risk and treatment stratification.…”

Spatial contextual cues that help predict how a target will accelerate can be used to guide interception

“…For a target attached to a rolling disk, the direction of the target's instantaneous acceleration is determined by the phase of the rotation. As already mentioned, if targets accelerate in the same way across multiple trials, people learn to compensate for the systematic errors that arise from ignoring the acceleration by simply aiming further to one side ( Brenner et al, 2016 ; Brenner et al, 2023 ). If one tries to hit the target when it reaches the same phase on every trial, neglecting the target's acceleration will always lead to the same error, so correcting for the error on the next trial will be effective.…”

“…This might suggest that people learn to anticipate certain accelerations of the target and control the timing of their interception accordingly (e.g., de Rugy, Marinovic, & Wallis, 2012 ; Diaz, Cooper, Rothkopf, & Hayhoe, 2013 ; Fialho & Tresilian, 2017 ; Mann, Nakamoto, Logt, Sikkink, & Brenner, 2019 ; Zago, McIntyre, Senot, & Lacquaniti, 2009 ). However, the improvement in performance could simply result from people adjusting their movements to feedback: compensating for the error on the previous movement rather than learning to anticipate a certain acceleration ( Brenner, de la Malla, & Smeets, 2023 ). When participants intercepted targets moving across images of surfaces that normally have different friction coefficients, and that indeed indicated how quickly the targets would decelerate, they continued to make systematic errors in line with those seen when no contextual information about the friction coefficient was provided ( Brenner et al, 2016 ).…”