Auditory Time-Interval Perception as Causal Inference on Sound Sources

Sawai, Ken ichi; Satō, Yoshiyuki; Aihara, Kazuyuki

doi:10.3389/fpsyg.2012.00524

Cited by 12 publications

(7 citation statements)

References 29 publications

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“…finding is broadly in agreement with previous work revealing how temporal context can bias the perceived duration of events (Burr et al, 2013;Jazayeri & Shadlen, 2010;Nakajima et al, 1991;Sawai et al, 2012). Critically, though, we showed here that the temporal proximity of two objects can bias the perceived time of a single brief event.…”

Section: Discussionsupporting

confidence: 94%

“…The longer the duration between the distractor and the first event, the stronger the bias to perceive the duration between the two attended events as longer. The attraction toward the duration of the distractor interval is explained by a tendency to regularize the sequence of the three events constituting the intervals (Burr et al, 2013;Remijn et al, 1999;Sawai, Sato, & Aihara, 2012). Although it is implicit in the regularization hypothesis, we do not know whether the perceived time of a single event is affected by its context.…”

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confidence: 88%

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Temporal context affects the perceived time of visual events

Jovanović

Mamassian

2019

Psychon Bull Rev

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We investigated whether the moment at which an event is perceived depends on its temporal context. Participants learned a mapping between time and space by watching the hand of a clock rotating a full revolution in a fixed duration. Then the hand was removed, and a target disc was flashed within a fixed-interval duration. Participants were to indicate where the hand would have been at the time of the target. In three separate experiments, we estimated the disruption from a distractor disc that was presented before or after the target disc, with a variable time between them. The target was either revealed at the end of the trial or cued beforehand, and in the latter case, was cued by either color or temporal order. We found an attraction to the presentation time of the distractor when both events were attended equally (target revealed at the end). When the target was cued beforehand, the reported time was under-or overestimated, depending on whether the nature of distractor had to be decoded (precued by color) or not (precued by order). In summary, the perceived time of an event is always affected by other events in temporal proximity, but the nature of this effect depends on how each event is attended.

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Section: Discussionsupporting

confidence: 94%

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confidence: 88%

Temporal context affects the perceived time of visual events

Jovanović

Mamassian

2019

Psychon Bull Rev

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“…Bayesian causal inference—inference of the causal relationship between observed cues, based on the inversion of the statistical model of the task—has been proposed as the decision strategy adopted by the brain to address the problem of integration vs. segregation of sensory cues [ 18 , 19 ]. Such a decision strategy has described human performance in spatial localization [ 18 – 27 ], orientation judgment [ 28 ], oddity detection [ 29 ], speech perception [ 30 ], time-interval perception [ 31 ], simple perceptual organization [ 32 ], and heading perception [ 33 , 34 ]. In recent years, interest in the Bayesian approach to causal inference has further increased as neural imaging has identified a hierarchy of brain areas involved in neural processing while observers implemented a Bayesian strategy to perform a causal inference task [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception

et al. 2018

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The precision of multisensory perception improves when cues arising from the same cause are integrated, such as visual and vestibular heading cues for an observer moving through a stationary environment. In order to determine how the cues should be processed, the brain must infer the causal relationship underlying the multisensory cues. In heading perception, however, it is unclear whether observers follow the Bayesian strategy, a simpler non-Bayesian heuristic, or even perform causal inference at all. We developed an efficient and robust computational framework to perform Bayesian model comparison of causal inference strategies, which incorporates a number of alternative assumptions about the observers. With this framework, we investigated whether human observers’ performance in an explicit cause attribution and an implicit heading discrimination task can be modeled as a causal inference process. In the explicit causal inference task, all subjects accounted for cue disparity when reporting judgments of common cause, although not necessarily all in a Bayesian fashion. By contrast, but in agreement with previous findings, data from the heading discrimination task only could not rule out that several of the same observers were adopting a forced-fusion strategy, whereby cues are integrated regardless of disparity. Only when we combined evidence from both tasks we were able to rule out forced-fusion in the heading discrimination task. Crucially, findings were robust across a number of variants of models and analyses. Our results demonstrate that our proposed computational framework allows researchers to ask complex questions within a rigorous Bayesian framework that accounts for parameter and model uncertainty.

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“…Bayesian causal inference -inference of the causal relationship between observed cues, based on the inversion of the statistical model of the task -has been proposed as the decision strategy adopted by the CNS to address the problem of integration vs. segregation of sensory cues (Körding et al, 2007). Such a decision strategy has 15 described human performance in spatial localization (Beierholm, Quartz, & Shams, 2009;Bejjanki, Knill, & Aslin, 2016;Körding et al, 2007;Odegaard & Shams, 2016;Odegaard, Wozny, & Shams, 2015;Rohe & Noppeney, 2015a, 2015bSato, Toyoizumi, & Aihara, 2007;Wozny, Beierholm, & Shams, 2010;Wozny & Shams, 2011), orientation judgment van den Berg, Vogel, Josić, and Ma (2012), oddity detection (Hospedales & 20 Vijayakumar, 2009), speech perception (Magnotti, Ma, & Beauchamp, 2013) and time-interval perception (Sawai, Sato, & Aihara, 2012). Over the years, Bayesian models have become more complex as they include more precise descriptions of the sensory noise (de Winkel, Katliar, & Bülthoff, 2015Odegaard et al, 2015) and alternative Bayesian decision strategies (Rohe & Noppeney, 2015b;Wozny et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Comparison of Explicit and Implicit Causal Inference Strategies in Multisensory Heading Perception

Acerbi

Dokka

Angelaki

et al. 2017

Preprint

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The precision of multisensory heading perception improves when visual and vestibular cues arising from the same cause, namely motion of the observer through a stationary environment, are integrated. Thus, in order to determine how the cues should be processed, the brain must infer the causal relationship underlying the multisensory cues.In heading perception, however, it is unclear whether observers follow the Bayesian strategy, a simpler non-Bayesian heuristic, or even perform causal inference at all. We developed an efficient and robust computational framework to perform Bayesian model comparison of causal inference strategies, which incorporates a number of alternative assumptions about the observers. With this framework, we investigated whether human observers' performance in an explicit cause attribution and an implicit heading discrimination task can be modeled as a causal inference process. In the explicit inference task, all subjects accounted for cue disparity when reporting judgments of common cause, although not necessarily all in a Bayesian fashion. By contrast, but in agreement with previous findings, data from the heading discrimination task only could not rule out that several of the same observers were adopting a forced-fusion strategy, whereby cues are integrated regardless of disparity. Only when we combined evidence from both tasks we were able to rule out forced-fusion in the heading discrimination task. Crucially, findings were robust across a number of variants of models and analyses. Our results demonstrate that our proposed computational framework allows researchers to ask complex questions within a rigorous Bayesian framework that accounts for parameter and model uncertainty.

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Auditory Time-Interval Perception as Causal Inference on Sound Sources

Cited by 12 publications

References 29 publications

Temporal context affects the perceived time of visual events

Temporal context affects the perceived time of visual events

Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception

Bayesian Comparison of Explicit and Implicit Causal Inference Strategies in Multisensory Heading Perception

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