A distributed representation of internal time.

Howard, Marc W.; Shankar, Karthik H.; Aue, William R.; Criss, Amy H.

doi:10.1037/a0037840

Cited by 134 publications

(178 citation statements)

References 152 publications

(348 reference statements)

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“…Various versions of the theory exist (e.g., Howard & Kahana, 2002;Howard, Shankar, Aue, & Criss, 2015), but they all invoke a concept of temporal context, which is assumed to change in a regular way with the passage of time. When a word is studied, it is assumed to be associated with the prevailing context.…”

Section: The Resurrection Of Contiguity and Temporal Organizationmentioning

confidence: 99%

“…A recent article advocating one version of temporal-context theory (Howard et al, 2015) placed great emphasis on a different kind of recency judgment experiment that uses short, rapidly presented lists. The prime example is Experiment 2 of Hacker (1980).…”

Section: Empirical Evidencementioning

confidence: 99%

“…These results are as predicted by models that assume backward parallel scanning for the first-encountered probe. Howard et al (2015) proposed that a backward search from the present moment is the general basis of recency judgments, including forced-choice and numerical judgments in very long lists. This ignores the prospective nature of Hacker's experiment.…”

Section: Empirical Evidencementioning

confidence: 99%

“…A savvy experimental subject will actively prepare for an anticipated test. Free recall, as it is almost always done, is a case in point: If you accept that free-recall subjects develop encoding strategies, that the strategies are often effective, and that they are likely to influence the order in which words are recalled, then you cannot use the temporal ordering of free recall to measure an automatic or obligatory process, such as the one central to temporal context theory (Howard & Kahana, 2002;Howard et al, 2015). If strategic effects and other artifacts could somehow be removed from statistics such as the lag-CRP, what remains would surely be smaller than the literature makes it appear.…”

Section: Concluding Commentsmentioning

confidence: 99%

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Is memory organized by temporal contiguity?

Hintzman

2015

Mem Cogn

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The hypotheses that memories are ordered according to time and that contiguity is central to learning have recently reemerged in the human memory literature. This article reviews some of the key empirical findings behind this revival and some of the evidence against it, and finds the evidence for temporal organization unconvincing. A central problem is that, as many memory experiments are done, they have a prospective, as well as a retrospective, component. That is, if subjects can anticipate how they will be tested, they encode the to-be-remembered material in a way that they believe will facilitate performance on the anticipated test. Experiments that avoid this confounding factor have shown little or no evidence of organization by contiguity.

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Section: The Resurrection Of Contiguity and Temporal Organizationmentioning

confidence: 99%

Section: Empirical Evidencementioning

confidence: 99%

Section: Empirical Evidencementioning

confidence: 99%

Section: Concluding Commentsmentioning

confidence: 99%

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Is memory organized by temporal contiguity?

Hintzman

2015

Mem Cogn

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“…French et al [36 ] suggest an attentional resource-sharing mechanism that allows prospective and retrospective timing to be accounted for in a single model. Moreover, this model, GAMIT [36 ], is currently the only computational model to account for this interaction.Most models simply do not consider attentional effects on interval time perception [34,38,53]. One simple proposal is that attention might modulate clock speed directly [25 ].…”

mentioning

confidence: 99%

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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A neural microcircuit model for a scalable scale‐invariant representation of time

et al. 2018

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Scale‐invariant timing has been observed in a wide range of behavioral experiments. The firing properties of recently described time cells provide a possible neural substrate for scale‐invariant behavior. Earlier neural circuit models do not produce scale‐invariant neural sequences. In this article, we present a biologically detailed network model based on an earlier mathematical algorithm. The simulations incorporate exponentially decaying persistent firing maintained by the calcium‐activated nonspecific (CAN) cationic current and a network structure given by the inverse Laplace transform to generate time cells with scale‐invariant firing rates. This model provides the first biologically detailed neural circuit for generating scale‐invariant time cells. The circuit that implements the inverse Laplace transform merely consists of off‐center/on‐surround receptive fields. Critically, rescaling temporal sequences can be accomplished simply via cortical gain control (changing the slope of the f–I curve).

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A distributed representation of internal time.

Cited by 134 publications

References 152 publications

Is memory organized by temporal contiguity?

Is memory organized by temporal contiguity?

Computational models of interval timing

A neural microcircuit model for a scalable scale‐invariant representation of time

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