Computational models of time perception

Anamalamudi, Komala; Surampudi, Bapi Raju; Maganti, Madhavilatha

doi:10.1109/cnsc.2014.6906670

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Since we consider emergent models of time perception in this review, we accepted the definition of McClelland et al (2010) to include neurocomputational models of time perception. For cognitive models of time perception, you may refer to Anamalamudi, Surampudi, and Maganti (2014) and Komosinski and Kups (2015).…”

Section: Computational and Robotic Models Of Time Perceptionmentioning

confidence: 99%

Time Perception: A Review on Psychological, Computational and Robotic Models

Basgol¹,

Ayhan²,

Uğur³

2020

Preprint

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Animals exploit time to survive in the world. Temporal information is required for higher-level cognitive abilities such as planning, decision making, communication and effective cooperation. Since time is an inseparable part of cognition, there is a growing interest in the artificial intelligence approach to subjective time, which has a possibility of advancing the field. The current survey study aims to provide researchers with an interdisciplinary perspective on time perception. Firstly, we introduce a brief background from the psychology and neuroscience literature, covering the characteristics and models of time perception and the related abilities. Secondly, we summarize the emergent computational and robotic models of time perception. A general overview to the literature reveals that a substantial amount of timing models are based on a dedicated time processing like the emergence of a clock-like mechanism from the neural network dynamics and reveal a relationship between the embodiment and time perception. We also notice that most models of timing are developed for either sensory timing (i.e. the ability of assessment of an interval) or motor timing (i.e. ability to reproduce an interval). The number of timing models capable of retrospective timing, which is the ability to track time without paying attention, is insufficient. In this light, we discuss the possible research directions to promote interdisciplinary collaboration for time perception.

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Section: Computational and Robotic Models Of Time Perceptionmentioning

confidence: 99%

Time Perception: A Review on Psychological, Computational and Robotic Models

Basgol¹,

Ayhan²,

Uğur³

2020

Preprint

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“…Although there are numerous ways in which computational models of interval timing can be classified, we have chosen to group these models into four major, although sometimes overlapping, classes: firstly, pacemaker-accumulator models (PA models), secondly, multiple-oscillator-coincidence detection models (also sometimes called timestamp models), thirdly, memory or neural process models and, finally, fourthly random-process (or drift-diffusion) models. For alternative classification schemes, see, for example [1,2 ].In what follows we will suggest that computational models of interval timing be judged on the basis of the following criteria: the scalar property, prospective and retrospective timing, and the effects of attention and neuropharmacological manipulations.Extensive empirical evidence [3][4][5][6] suggests that timeestimation errors in interval timing grow approximately linearly with the size of the estimate. Known as the scalar property of time estimation, this fact sets a hard constraint on the nature of the underlying processes involved in time estimation [7].…”

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Computational models of interval timing

Addyman

French

Thomas

2016

Current Opinion in Behavioral Sciences

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In recent years great progress has been made in the computational modeling of interval timing. A wide range of models capturing different aspects of interval timing now exist. These models can be seen as constituting four, sometimes overlapping, general classes of models: pacemakeraccumulator models, multiple-oscillator models, memorytrace models, and drift-diffusion (or random-process) models. We suggest that computational models should be judged based on their performance on a number of criteria -namely, the scalar property, their ability to reproduce retrospective and prospective timing effects, and their sensitivity to attentional and neurochemical manipulations. Future challenges will involve building integrated models and sharing model code to allow direct comparisons against a battery of empirical data. Although there are numerous ways in which computational models of interval timing can be classified, we have chosen to group these models into four major, although sometimes overlapping, classes: firstly, pacemaker-accumulator models (PA models), secondly, multiple-oscillator-coincidence detection models (also sometimes called timestamp models), thirdly, memory or neural process models and, finally, fourthly random-process (or drift-diffusion) models. For alternative classification schemes, see, for example [1,2 ].In what follows we will suggest that computational models of interval timing be judged on the basis of the following criteria: the scalar property, prospective and retrospective timing, and the effects of attention and neuropharmacological manipulations.Extensive empirical evidence [3][4][5][6] suggests that timeestimation errors in interval timing grow approximately linearly with the size of the estimate. Known as the scalar property of time estimation, this fact sets a hard constraint on the nature of the underlying processes involved in time estimation [7]. This effect has been widely replicated in humans, pigeons, and rodents (see [8][9][10]. Similar behavioral responses to time scales can even be found in ratedependent habituation in Caenorhabditis elegans [11]. Even though the scalar property has not been found to hold under all conditions [12], modeling it has proved to be a significant challenge for a number of existing models of interval-time judgments [7,13]. In a recent paper, Hass and Hermann [7] use information theoretic arguments to show how the scalar property places several important restrictions on the nature of any interval timing mechanism. Crucially, they argue that, in order to display scalar error profiles, the neural process underlying time perception must be based on a measure of growing variance in the system.Secondly, it has been established that the perceived passage of time by human adults differs according to whether they are forewarned that they will need to make a timing judgment, and are therefore actively attending to its passage ( prospective time estimation), or whether they are required to make an unexpected, after-the-fact judgment of the passage of time (retr...

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