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Dedicated and intrinsic models of time perception

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Two general frameworks have been articulated to describe how the passage of time is perceived. One emphasizes that the judgment of the duration of a stimulus depends on the operation of dedicated neural mechanisms specialized for representing the temporal relationships between events. Alternatively, the representation of duration could be ubiquitous, arising from the intrinsic dynamics of nondedicated neural mechanisms. In such models, duration might be encoded directly through the amount of activation of sensory processes or as spatial patterns of activity in a network of neurons. Although intrinsic models are neurally plausible, we highlight several issues that must be addressed before we dispense with models of duration perception that are based on dedicated processes.

Section snippets

Perceiving the passage of time

Cognition is dynamic, with our perceptions, actions and comprehension of the world unfolding over time. A generation ago, research on timing was limited, emphasizing the study of behaviors marked by temporal regularities [1]. More recently, a renaissance has taken hold in the study of time perception, with researchers addressing a broad range of temporal phenomena. Behavioral studies have revealed a host of puzzling effects in which our perception of time is far from veridical [2].

Dedicated models of temporal processing

Dedicated models of time perception are, at their core, modular. As vision scientists speak of dedicated mechanisms for color or motion perception, modular models of time perception entail some sort of specialized mechanism that represents the temporal relationship between events. The pacemaker-counter model is one example of a modular system [12]. These two components define a clock with an interval specified by the accumulation of inputs from a pacemaker. Spectral models of timing constitute

Intrinsic models of temporal processing

A spate of recent studies has promoted a more generic view of timing, which we will refer to as ‘intrinsic models’. Intrinsic models offer a radically different perspective on the perception of time. These models assume that there is no specialized brain system for representing temporal information, asserting that time is inherent in neural dynamics (Figure 1c,d). In one class of models, this property might be limited to neural regions that are capable of sustaining their activity in the

Evaluating the evidence for modality specificity in intrinsic timing

Some of the most compelling evidence for intrinsic timing comes from physiological studies that emphasize local representations that are, at least implicitly, modality specific. In one study neurons in the lateral inferior parietal region LIP were recorded during a visual duration discrimination task [5]. Two lights, the first of a fixed duration (e.g. 316 ms) and the second a variable duration, were presented at fixation. The animal judged the relative duration of the second by making a saccade

The role of nontemporal factors on perceived duration

Performance on time-perception tasks entails several component processes, many of which are not specific to time. These include attention, working memory and long-term or reference memory [55]. To date few studies of intrinsic timing have asked which of these processes are affected by training. Perceptual studies of generalization have reported that benefits are interval specific 56, 57, similar to the results observed by Meegen et al.[53]. Although this would rule out training effects related

Future directions

Following a modular paradigm, neuropsychological research generally has promoted models in which time is represented by dedicated neural systems. An appealing feature of these models is that they account for supramodal features of time perception and provide a principled basis for linking temporal processing in action, perception and cognition. By contrast, recent physiological and computational studies have highlighted how temporal information is reflected in the intrinsic dynamics of neural

Acknowledgements

The authors are grateful to Gerald Westheimer and Nola Klemfuss for their comments on this paper. This work was supported by grants from the National Institute of Health, NS30256, and National Science Foundation, BCS 0726685.

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