Moving in time: Bayesian causal inference explains movement coordination to auditory beats

Elliott, Mark T.; Wing, Alan M.; Welchman, Andrew E.

doi:10.1098/rspb.2014.0751

Cited by 41 publications

(49 citation statements)

References 32 publications

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“…Third, performance was better in 1-2-3-Go condition in which subjects made two measurements, as evidenced by a lower root-mean-square error (RMSE) in 1-2-3-Go compared to 1-2-Go condition ( Fig 2C; permutation test; p-value < 0.01 for all subjects). This observation indicates that subjects combined the two measurements to improve their estimates, corroborating reports from other behavioral paradigms [46][47][48][49][50][51][52][53][54]. Combined with the systematic bias toward the mean of t s , these results indicated that subjects integrated prior information with one or two measurements to improve their performance.…”

Section: Subjects Integrate Interval Measurements With Prior Knowledgesupporting

confidence: 85%

A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

Egger

Jazayeri

2018

Preprint

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Bayesian models of behavior have advanced the idea that humans combine prior beliefs and sensory observations to minimize uncertainty. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent and manipulate probability distributions. An alternative view is that brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property makes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects' behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that inference strategies humans deploy may deviate from Bayes-optimal integration when the computational demands are high.

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Section: Subjects Integrate Interval Measurements With Prior Knowledgesupporting

confidence: 85%

A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

Egger

Jazayeri

2018

Preprint

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“…The central PC claim that the brain uses Bayesian inference when choosing a plausible metrical model for a given rhythmical input was recently supported experimentally. Using a finger‐tapping paradigm, Elliot and colleagues provided evidence suggesting that humans exploit a Bayesian inference process to control movement timing when facing microtemporal differences . They presented two metronomes of equal tempo but differing in phase and temporal regularity to participants and asked participants to synchronize their tapping with the experienced beat.…”

Section: Predictive Coding Of Rhythmmentioning

confidence: 99%

“…Using a finger-tapping paradigm, Elliot and colleagues provided evidence suggesting that humans exploit a Bayesian inference process to control movement timing when facing microtemporal differences. 37 They presented two metronomes of equal tempo but differing in phase and temporal regularity to participants and asked participants to synchronize their tapping with the experienced beat. When participants chose to integrate the two timing cues into a single-event estimate, modeling the behavior as a Bayesian inference process provided a better description of the data than other plausible models.…”

Section: Predictive Coding Of Rhythmmentioning

confidence: 99%

Now you hear it: a predictive coding model for understanding rhythmic incongruity

Vuust

Dietz

Witek

et al. 2018

Annals of the New York Academy of Sciences

119

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Rhythmic incongruity in the form of syncopation is a prominent feature of many contemporary musical styles. Syncopations afford incongruity between rhythmic patterns and the meter, giving rise to mental models of differently accented isochronous beats. Syncopations occur either in isolation or as part of rhythmic patterns, so-called grooves. On the basis of the predictive coding framework, we discuss how brain processing of rhythm can be seen as a special case of predictive coding. We present a simple, yet powerful model for how the brain processes rhythmic incongruity: the model for predictive coding of rhythmic incongruity. Our model proposes that a given rhythm's syncopation and its metrical uncertainty (precision) is at the heart of how the brain models rhythm and meter based on priors, predictions, and prediction error. Our minimal model can explain prominent features of brain processing of syncopation: why isolated syncopations lead to stronger prediction error in the brains of musicians, as evidenced by larger event-related potentials to rhythmic incongruity, and why we all experience a stronger urge to move to grooves with a medium level of syncopation compared with low and high levels of syncopation.

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“…Work by Bolt and Loehr has described a relationship not only between an individual's own performance stability and joint agency but also between reliability of a co-actor's actions and a shared sense of control (Bolt and Loehr, 2017). Previous work has suggested that assessing and attributing agency is dependent on the sensory reliability and distinctiveness of the signal (Elliott, M. T., Wing, A. M., & Welchman, 2014). This issue in particularly germane in choral singing, for example, where individuals adjust the intensity of their vocal output in order to optimize the so-called "self-to-other ratio", which reflects the degree to which an individual can hear their own sounds amongst co-performers' sounds (Ternström, 2003).…”

Section: Self-other Distinction and Self-other Blurringmentioning

confidence: 99%

Distinguishing “self” from “other” in a dynamic synchronization task with an adaptive virtual partner

Fairhurst

Janata²,

Keller

2019

Preprint

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For precise interpersonal coordination, some degree of merging a sense of self with other is required. In group music making, one may want to be in "sync" with one's ensemble and, if playing a similar instrument, one can assume a degree of temporal and acoustic overlap. However, to what extent is selfother merging optimal? An incorrect balance of segregation and integration of self and other information would result in a lack of interpersonal cohesion or a disruption of self-agency. Using an interactive finger-tapping task with a virtual partner and functional MRI, we explored neural differences between self-other merging and distinction. Varying both the level of adaptivity of a virtual partner and the quality of self-related auditory feedback, we show that the predictability of the other and availability of distinguishable, self-related information improve performance and demonstrate how dynamic interactions vary one's sense of agency. From neuroimaging data, we identify regions that are more active when self and other are distinct, including the TPJ. Conversely, we observe activity in the cerebellum, EBA and SMA when self and other blur. These findings suggest that a certain degree of self-other distinction at sensorimotor, experiential, and neurophysiological levels is required to maintain successful interpersonal coordination.

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Moving in time: Bayesian causal inference explains movement coordination to auditory beats

Cited by 41 publications

References 32 publications

A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

Now you hear it: a predictive coding model for understanding rhythmic incongruity

Distinguishing “self” from “other” in a dynamic synchronization task with an adaptive virtual partner

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