Large-scale citizen science reveals predictors of sensorimotor adaptation

Tsay, Jonathan S.; Asmerian, Hrach; Germine, Laura T.; Wilmer, Jeremy; Ivry, Richard B.; Nakayama, Ken

doi:10.1038/s41562-023-01798-0

Cited by 10 publications

(9 citation statements)

References 129 publications

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“…That is, gradual learning paradigms reduce the size of error, which in turn limits cognitive awareness of the perturbation and associated strategic re-aiming strategies 22,23,35 . We also anticipate larger strategy use in the Kitago et al’s study due to the utilization of a single target (as opposed to the 4 targets used herein), which also appears to attenuate procedural aftereffects 36,37 . Accordingly, savings in gradual-then-abrupt learning paradigms have been linked to increases in implicit learning rates 23 , as opposed to overt strategies that tend to dominate savings responses in abrupt-then-abrupt protocols 7,12 .…”

Section: Discussionmentioning

confidence: 79%

Protection of savings by reducing the salience of opposing errors

Javidialsaadi,

Albert,

Al Mutairi

et al. 2024

Preprint

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When humans encounter the same disturbance twice, they adapt to it faster during the second exposure. To examine how subconscious learning systems contribute to this savings process, previous studies have suppressed explicit awareness of the perturbation by gradually increasing its magnitude during initial learning. This has produced mixed effects, with some studies demonstrating faster relearning, and others observing no acceleration during relearning. Here we examined whether these differences might be due to the nature of a de-adaptation period that separates two learning periods. To test this idea, we manipulated the magnitude of washout errors by de-adapting participants abruptly, gradually, or by removing feedback entirely. Empirical analyses indicated that the different classes of washout errors had a profound effect on savings: large washout errors nullified the ability to save, whereas small errors or the absence of error protected savings. Model-based analyses suggested that changes in learning rates were mediated by an increase in sensitivity to error that could be reversed by experience with oppositely-oriented washout errors. This suggests that the experience of error produces both a facilitation of learning for similar errors and a reduction in learning for dissimilar errors. The latter can abolish the expression of savings following gradual adaptation.

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Section: Discussionmentioning

confidence: 79%

Protection of savings by reducing the salience of opposing errors

Javidialsaadi,

Albert,

Al Mutairi

et al. 2024

Preprint

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“…Movements involved joint rotations of the arm, wrist, and/or finger, depending on whether the trackpad or mouse was used. In previous validation work with this online interface and procedure, the specific movement and device used did not influence learning in visuomotor adaptation tasks (Tsay et al, 2024(Tsay et al, , 2021.…”

Section: Methodsmentioning

confidence: 78%

Differential Aging Effects on Implicit and Explicit Sensorimotor Learning

Cisneros,

Karny,

Ivry

et al. 2024

Preprint

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Deterioration in motor control is a hallmark of aging, significantly contributing to a decline in quality of life. More controversial is the question of whether and how aging impacts sensorimotor learning. We hypothesized that the inconsistent picture observed in the current literature can be attributed to at least two factors. First, aging studies tend to be underpowered. Second, the learning assays used in these experiments tend to reflect, to varying degrees, the operation of multiple learning processes, making it difficult to make inferences across studies. We took a two-pronged approach to address these issues. We first performed a meta-analysis of the sensorimotor adaptation literature focusing on outcome measures that provide estimates of explicit and implicit components of adaptation. We then conducted two well-powered experiments to re-examine the effect of aging on sensorimotor adaptation, using behavioral tasks designed to isolate explicit and implicit processes. Convergently, both approaches revealed a striking dissociation: Older individuals exhibited a marked impairment in their ability to discover an explicit strategy to counteract a visuomotor perturbation. However, they exhibited enhanced implicit recalibration. We hypothesize that the effect of aging on explicit learning reflects an age-related decline in reasoning and problem solving, and the effect of aging on implicit learning reflects age-related changes in multisensory integration. Taken together, these findings deepen our understanding of the impact of aging on sensorimotor learning.

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“…Generally one cannot control the input device used when recruiting from online participant pools or citizen science websites, with a fairly equal split between users (e.g. Tsay et al, 2024). As such, experimenters should collect information on the input device used by participants in online experiments and account for input device driven differences in behaviour.…”

Section: Discussionmentioning

confidence: 99%

“…In certain cases, these differences may not be important. For example, where assessed, the input device does not appear to significantly influence visuomotor adaptation (Kim et al, 2022;Tsay et al, 2021;Tsay et al, 2024;Warburton et al, 2023b;Watral et al, 2023). In other areas, these differences may be critically important, and the onus will be on experimenters to account for this potentially confounding variable within their specific experiments.…”

Section: Discussionmentioning

confidence: 99%

“…This area of inquiry has seen widespread adoption of online experiments to supplement or replace small-scale laboratorybased research (Avraham et al, 2022;Barradas et al, 2024;Cesanek et al, 2021;Coltman et al, 2021;Kim et al, 2022;Shyr & Joshi, 2024;Tsay et al, 2023;Wang et al, 2024;Warburton et al, 2023a;Watral et al, 2023;Weightman et al, 2022). Some have also harnessed the promise of large-scale data collection, identifying predictors of visuomotor adaptation (Tsay et al, 2024) and investigating the longterm trajectory of learning in a first-person shooter game (Listman et al, 2021). This ready adoption is somewhat surprising because the movements executed by participants tend to be different to those measured in the laboratory.…”

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confidence: 99%

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Input device matters for measures of behaviour in online experiments

Warburton,

Campagnoli,

Mon-Williams

et al. 2024

Preprint

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Studies of perception, cognition, and action increasingly rely on measures derived from the movements of a cursor to investigate how psychological processes unfold over time. This method is one of the most sensitive measures available for remote experiments conducted online, but experimenters have little control over the input device used by participants, typically a mouse or trackpad. These two devices require biomechanically distinct movements to operate, so measures extracted from cursor tracking data may differ between input devices. We investigated this in two online experiments requiring participants to execute goal-directed movements. We identify several measures that are critically influenced by the choice of input device using a kinematic decomposition of the recorded cursor trajectories. This applied to both temporal and spatial variables, for example trackpad users showed greater reaction times but lower variability in movement directions when compared to mouse users. We believe there is a substantial risk that behavioural disparities caused by the input device used could be misidentified as differences in psychological processes. We urge researchers to collect data on input devices in online experiments and carefully consider and account for the effect they may have on their experimental data.

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Large-scale citizen science reveals predictors of sensorimotor adaptation

Cited by 10 publications

References 129 publications

Protection of savings by reducing the salience of opposing errors

Protection of savings by reducing the salience of opposing errors

Differential Aging Effects on Implicit and Explicit Sensorimotor Learning

Input device matters for measures of behaviour in online experiments

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