Neural correlates of temporality: Default mode variability and temporal awareness

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Abstract

The continual background awareness of duration is an essential structure of consciousness, conferring temporal extension to the many objects of awareness within the evanescent sensory present. Seeking the possible neural correlates of ubiquitous temporal awareness, this article reexamines fMRI data from off-task “default mode” (DM) periods in 25 healthy subjects studied by Grady et al. (“Age-related Changes in Brain Activity across the Adult Lifespan,” Journal of Cognitive Neuroscience 18(2), 2005). “Brain reading” using support vector machines detected information specifying elapsed time, and further analysis specified distributed networks encoding implicit time. These networks fluctuate; none are continuously active during DM. However, the aggregate regions of greatest variability closely resemble the default mode network. It appears that the default mode network has an important role as a state-dependent monitor of temporality.

Section snippets

Introduction: timing and temporality

Time is important in human behavior, cognition, and consciousness. Yet the role of time is differently conceived by different disciplines. In cognitive science, time is a perceptual dimension exploited as needed in various and distinct time-dependent tasks, such as temporal order and simultaneity judgments, or duration estimation and reproduction tasks. Time in these contexts is attended time, time in the spotlighted foreground of perception and behavior. As such time is one prominent stimulus

Encoding temporality

If temporality is a continuous structural component of consciousness then, in some sense, the contents of consciousness at every moment include some form of temporal awareness. This entails (as Husserl realized) that all perception is in continuous flux. Even if a scene is static, the experience of it is always changing as its intentional objects progressively extend their perceived duration. This in turn implies that the information encoding objects of perception is time-inflected. Even if no

Neural correlates of temporality

This result above naturally raises the question of which brain regions might be particularly involved in encoding temporal information, in this case information about elapsed time in each off-task time interval. Something in the distributed pattern of component activity encodes elapsed time. The encoding could be embedded in a single component, activated at a different intensity at each time point, or it could be encoded in distinct patterns involving all components, or it could be somewhere in

Temporal information and the default mode network

The Default Mode network generally encompasses the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), inferior parietal cortex (IPC), lateral temporal cortex (LTC), and hippocampal regions, among others (Buckner et al., 2008, Mason et al., 2007, McKiernan et al., 2003, Raichle et al., 2001, Sheline et al., 2009). What we have called the Dynamic Temporality Network (DTN) overlaps with the “traditional” Default network in several areas, most prominently the anterior DM regions:

Conclusion

The successive analyses in this paper lend support to a new interpretation of the function of the Default Mode Network, relating it to the continuous monitoring of duration, a core structural feature temporal consciousness. The emerging picture of default mode function, and the specific function of the overlapping Dynamic Temporal Network, conforms nicely to the phenomenology of time. In James’ and Husserl’s classic descriptions (and in the phenomenological tradition since), objects are

Acknowledgments

Thanks for Brian Castelluccio for data analysis and to the Trinity College Interdisciplinary Science Program for research assistance support. An earlier draft of this paper was presented at a workshop on temporality sponsored by the European Platform for Life Sciences, Mind Sciences, and the Humanities at the University of Turku, Finland; I thank the workshop hosts and participants for their comments. Thanks also to Goeff Lee, Michal Klincewicz, and Joe Neisser for their extensive and

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