Cristina de la Malla

The role of differential delays in integrating transient visual and proprioceptive information

Many actions involve limb movements toward a target. Visual and proprioceptive estimates are available online, and by optimally combining (Ernst and Banks, 2002) both modalities during the movement, the system can increase the precision of the hand estimate. The notion that both sensory modalities are integrated is also motivated by the intuition that we do not consciously perceive any discrepancy between the felt and seen hands positions. This coherence as a result of integration does not necessarily imply realignment between the two modalities (Smeets et al., 2006). For example, the two estimates (visual and proprioceptive) might be different without either of them (e.g., proprioception) ever being adjusted after recovering the other (e.g., vision). The implication that the felt and seen positions might be different has a temporal analog. Because the actual feedback from the hand at a given instantaneous position reaches brain areas at different times for proprioception and vision (shorter for proprioception), the corresponding instantaneous unisensory position estimates will be different, with the proprioceptive one being ahead of the visual one. Based on the assumption that the system integrates optimally and online the available evidence from both senses, we introduce a temporal mechanism that explains the reported overestimation of hand positions when vision is occluded for active and passive movements (Gritsenko et al., 2007) without the need to resort to initial feedforward estimates (Wolpert et al., 1995). We set up hypotheses to test the validity of the model, and we contrast simulation-based predictions with empirical data.

Predictive plus online visual information optimizes temporal precision in interception.

Humans time their interceptive actions with remarkable precision. This daily-life performance is far too good to be explained by reported experimental perceptual estimates of when an object will arrive at the interception location. One option is that people use general principles to reduce variability such as integrating early estimates from predictive mechanisms with late estimates from online vision. Here we explore this possibility by presenting virtual balls that people had to catch and compared 3 conditions: early, late, and full vision of a parabolic path. If people integrate these different estimates, the precision of the timing under full vision should be higher than when only late vision is available. We tested this hypothesis and found a benefit for full vision, but only for those (steeper) trajectories in which early and late estimates are likely based on different cues. Overall, the integration of the different estimates of the impending interceptive event was optimal and can help explain the observed high temporal precision in many daily-life situations. Finally, by revealing the situations in which people do not take into account early predictions and rely on online visual information only we elucidate the theoretical controversy between predictive versus online control of timed actions.

Hitting moving targets with a continuously changing temporal window.

Abstract Hitting a moving target requires that we do not miss the target when it is around the aimed position. The time available for us not to miss the target when it is at the position of interest is usually called the time window and depends on target’s speed and size. These variables, among others, have been manipulated in previous studies but kept constant within the same trial or session. Here, we present results of a hitting task in which targets underwent simple harmonic motion, which is defined by a sinusoidal function. Target velocity changes continuously in this motion and so does the time window which is shorter in the centre (peak velocity) and longer at the turning points (lowest velocity) within a single trial. We studied two different conditions in which the target moved with a reliable (across trials) amplitude displacement or reliable peak velocity, respectively, and subjects were free to decide where and when to hit it. Results show that subjects made a compromise between maximum and minimum target’s speed, so that they did hit the target at intermediate speed values. Interestingly, the reliability of target peak velocity (or displacement) modulated the point of interception. When target’s peak velocity was more reliable, subjects intercepted the target at positions with smaller temporal windows and the reverse was true when displacement was reliable. Subjects adapted the interceptive behaviour to the underlying statistical structure of the targets. Finally, in a control condition in which the temporal window also depended on the instant size and not only on speed, subjects intercepted the target when it moved at similar speeds than when the size was constant. This finding suggests that velocity rather than the temporal window contributed more to controlling the interceptive movements.

Why do movements drift in the dark? Passive versus active mechanisms of error accumulation

When vision of the hand is unavailable, movements drift systematically away from their targets. It is unclear, however, why this drift occurs. We investigated whether drift is an active process, in which people deliberately modify their movements based on biased position estimates, causing the real hand to move away from the real target location, or a passive process, in which execution error accumulates because people have diminished sensory feedback and fail to adequately compensate for the execution error. In our study participants reached back and forth between two targets when vision of the hand, targets, or both the hand and targets was occluded. We observed the most drift when hand vision and target vision were occluded and equivalent amounts of drift when either hand vision or target vision was occluded. In a second experiment, we observed movement drift even when no visual target was ever present, providing evidence that drift is not driven by a visual-proprioceptive discrepancy. The observed drift in both experiments was consistent with a model of passive error accumulation in which the amount of drift is determined by the precision of the sensory estimate of movement error.

How timely can our hand movements be

The temporal variability of our movements is reduced when we move fast. Here we study whether different sensory information (vision and proprioception) or prior knowledge of final position or travelled distance can affect the temporal precision of movements directed to static targets. We attempted to promote the use of either on-line feedback control (providing visual and proprioceptive information of the movement and maintaining the target position predictable trial-to-trial) or forward control (removing visual feedback and maintaining a predictable target position). In a first experiment, the variability of movement times indicates that temporal precision is affected by the predictability of the targets position and by different feedback conditions. In a second experiment we disentangled the question regarding whether it is the targets position estimate, the travelled distance or the velocity that enhanced the temporal performance. In accordance with previous studies, results indicate that velocity is the main factor in controlling temporal precision across different conditions.

Detection of radial motion depends on spatial displacement.

Nakayama and Tyler (1981) disentangled the use of pure motion (speed) information from spatial displacement information for the detection of lateral motion. They showed that when positional cues were removed the contribution of motion or spatial information was dependent on the temporal frequency: for temporal frequencies lower than 1Hz the mechanism used to detect motion relied on speed information while for higher temporal frequencies a mechanism based on displacement information was used. Here we test whether the same dependency is also revealed in radial motion. In order to do so, we adapted the paradigm previously used by Nakayama and Tyler to obtain detection thresholds for lateral and radial motion by using a 2-IFC procedure. Subjects had to report which of the intervals contained the signal stimulus (33% coherent motion). We replicated the temporal frequency dependency for lateral motion but results indicate, however, that the detection of radial is always consistent with detecting a spatial displacement amplitude.