DOI: 10.25338/b8fd0n
title: The role of consciously timed movements in shaping and improving auditory timing

description: Our subjective sense of time is intertwined with a plethora of perceptual,
 cognitive, and motor functions, and likewise, the brain is equipped to
 expertly filter, weight, and combine these signals for seamless
 interactions with a dynamic world. Until relatively recently, the
 literature on time perception has excluded the influence of motor
 activity, yet, it has been found that motor circuits in the brain are at
 the core of most timing functions. Several studies have now identified
 that concurrent movements exert robust effects on perceptual timing
 estimates, but critically, have not assessed how humans consciously judge
 the duration of their own movements. This creates a gap in our
 understanding of the mechanisms driving movement-related effects on
 sensory timing. We sought to address this gap by administering a
 sensorimotor timing task in which we explicitly compared the timing of
 isolated auditory tones and arm movements, or both simultaneously. We
 contextualized our findings within a Bayesian cue combination framework,
 in which separate sources of temporal information are weighted by their
 reliability and integrated into a unitary time estimate that is more
 precise than either unisensory estimate. Our results revealed differences
 in accuracy between auditory, movement, and combined trials, and
 crucially, that combined trials were the most accurately timed. Under the
 Bayesian framework, we found that participants’ combined estimates were
 more precise than isolated estimates in a way that trended towards
 optimality, while being overall less optimal than the model’s prediction.
 These findings elucidate previously unknown qualities of conscious motor
 timing and propose computational mechanisms that can describe how
 movements combine with perceptual signals to create unified, multimodal
 experiences of time.
description: Participants performed the experiment using a robotic arm
 manipulandum (KINARM End-Point Lab, BKIN Technologies; Nguyen et al. 2019;
 Hosseini et al. 2017) that allowed movement along a flat workspace using
 the right arm. Direct viewing of the robotic arm was occluded by a flat
 display that allowed viewing of targets and cues via a downward-facing
 monitor mounted above the workspace. Motor output was sampled at 1000 Hz.
 Participants were free to adjust the chair so they could comfortably view
 the full display.  Trials were divided into encoding
 and reproduction phases, and were structured as follows (see Fig. 1):
 first, the robotic arm guided participants to one of 16 locations in a
 grid-like array. Then, they experienced one of three trial conditions. In
 “movement” trials, subjects began moving until interrupted by an imposed
 brake (a 100 ms linear increase in resistive force from 0 to 50 N). In
 “auditory” trials, the robotic arm was locked in the random location and
 the participant heard an auditory tone. In “combined” trials, subjects
 were cued to move while timing a concurrent auditory tone; in this
 condition, the tone began as soon as the apparatus detected movement at
 the velocity threshold of 5 cm/s, and the brake was applied synchronously
 with the auditory tone offset. After the encoding phase, they were guided
 to a central target for the reproduction phase. When this target turned
 green, subjects reproduced the encoded duration by holding and releasing a
 button attached to the handle. The tested durations were 1000, 1500, 2000,
 2500, 3000, 3500, and 4000 ms. Trial conditions were experienced in blocks
 of 14 trials (for a total of 210 trials) in a pseudorandomized order such
 that no condition was experienced twice in a row.
 Robotic arm manipulandum data were sampled at 1000 Hz to produce
 vectors for position, velocity, force, and other movement parameters over
 the course of time for each trial. Trials were excluded if reproduced
 times fell outside three deviations from the mean (&lt;1% of trials
 excluded). Additionally, trials with movement (movement-only and combined)
 were excluded if the stop latency after the brake was applied fell outside
 three scaled absolute deviations from the median (2.9% of trials
 excluded).  Our first goal was to investigate the
 relationship between duration and reproduced time to determine timing
 accuracy. Importantly, movement-only and combined trials were analyzed
 with respect to time spent moving rather than the pre-specified duration.
 This is because in general, participants exhibited a short delay to
 respond to the movement brake, usually adding up to a few hundred
 milliseconds on most trials. We directly assessed constant error as a
 measure of accuracy, defined as the difference between the reproduced
 duration using the button press and the actual target duration.  To
 measure performance, we applied a linear mixed model (LMM) design, in
 which response error was the predicted variable, encoded duration and
 condition were fixed effects, and subject was treated as a random
 effect.     Additionally, we
 examined the relationships between movement parameters and timing
 performance. Previous work has shown that arm movements covering a greater
 distance lead to longer perceived durations (De Kock et
 al., 2021; Wiener et al., 2019). Here, we extracted the movement distance for each trial (defined as the Euclidean distance traveled between duration onset and offset) and performed a Spearman partial correlation test between movement distance and reproduced time, controlling for target duration.  Partial correlation values were calculated for each subject separately for movement and combined trial types; each was assessed with a one-sample t-test against a baseline of zero.   Statistical analyses were performed using R, JASP (http://www.jasp-stats.org), and Matlab. For accuracy and CV analyses, we report results from linear mixed models with subject as a random effect. Results are reported at a significance level of 0.05. This dataset includes trials for all participants. 
description: The is file is in comma separated values (CSV) format, and can be
 imported into most data analysis software including R, Matlab, and
 JASP.