There’s more to timing than time: P-centers, beat bins and groove in musical microrhythm
How does the dynamic shape of a sound affect its perceived microtiming? In the TIME project, we studied basic aspects of musical microrhythm, exploring both stimulus features and the participants’ enculturated expertise via perception experiments, observational studies of how musicians produce particular microrhythms, and ethnographic studies of musicians’ descriptions of microrhythm. Collectively, we show that altering the microstructure of a sound (“what” the sound is) changes its perceived temporal location (“when” it occurs). Specifically, there are systematic effects of core acoustic factors (duration, attack) on perceived timing. Microrhythmic features in longer and more complex sounds can also give rise to different perceptions of the same sound. Our results shed light on conflicting results regarding the effect of microtiming on the “grooviness” of a rhythm.
Dynamic dopaminergic signaling probabilistically controls the timing of self-timed movements
Human movement disorders and pharmacological studies have long suggested molecular dopamine modulates the pace of the internal clock. But how does the endogenous dopaminergic system influence the timing of our movements? We examined the relationship between dopaminergic signaling and the timing of reward-related, self-timed movements in mice. Animals were trained to initiate licking after a self-timed interval following a start cue; reward was delivered if the animal’s first lick fell within a rewarded window (3.3-7 s). The first-lick timing distributions exhibited the scalar property, and we leveraged the considerable variability in these distributions to determine how the activity of the dopaminergic system related to the animals’ timing. Surprisingly, dopaminergic signals ramped-up over seconds between the start-timing cue and the self-timed movement, with variable dynamics that predicted the movement/reward time, even on single trials. Steeply rising signals preceded early initiation, whereas slowly rising signals preceded later initiation. Higher baseline signals also predicted earlier self-timed movement. Optogenetic activation of dopamine neurons during self-timing did not trigger immediate movements, but rather caused systematic early-shifting of the timing distribution, whereas inhibition caused late-shifting, as if dopaminergic manipulation modulated the moment-to-moment probability of unleashing the planned movement. Consistent with this view, the dynamics of the endogenous dopaminergic signals quantitatively predicted the moment-by-moment probability of movement initiation. We conclude that ramping dopaminergic signals, potentially encoding dynamic reward expectation, probabilistically modulate the moment-by-moment decision of when to move. (Based on work from Hamilos et al., eLife, 2021).
Timing errors and decision making
Error monitoring refers to the ability to monitor one's own task performance without explicit feedback. This ability is studied typically in two-alternative forced-choice (2AFC) paradigms. Recent research showed that humans can also keep track of the magnitude and direction of errors in different magnitude domains (e.g., numerosity, duration, length). Based on the evidence that suggests a shared mechanism for magnitude representations, we aimed to investigate whether metric error monitoring ability is commonly governed across different magnitude domains. Participants reproduced/estimated temporal, numerical, and spatial magnitudes after which they rated their confidence regarding first order task performance and judged the direction of their reproduction/estimation errors. Participants were also tested in a 2AFC perceptual decision task and provided confidence ratings regarding their decisions. Results showed that variability in reproductions/estimations and metric error monitoring ability, as measured by combining confidence and error direction judgements, were positively related across temporal, spatial, and numerical domains. Metacognitive sensitivity in these metric domains was also positively associated with each other but not with metacognitive sensitivity in the 2AFC perceptual decision task. In conclusion, the current findings point at a general metric error monitoring ability that is shared across different metric domains with limited generalizability to perceptual decision-making.
How to simulate and analyze drift-diffusion models of timing and decision making
My talk will discuss the use of some of these four, simple Matlab functions to simulate models of timing, and to fit models to empirical data. Feel free to examine the code and the relatively brief book chapter that explains the code before the talk if you would like to learn more about computational/mathematical modeling.