Time as a fundamental aspect of consciousness
Time is the only property of experience which is ubiquitous across modalities and contents of experience. Its study offers a basis for grounding mental representations, understanding phenomenological universals, and linking disparate fields of inquiry of experience. This talk will first cover the distinct temporal properties which pervade across our experiences. Specifically, the hierarchical, nested, and mirrored nature of time in our experience. I will then present empirical evidence in support of these phenomenal regularities. A case will be made for temporal partitioning of experiences into moments or ‘psychological nows’. These “nows” offer a scaffolding over which we can gauge seemingly private and inaccessible experiences. To demonstrate this, I will present results from a recently concluded project on the dynamics of imagination. Here, participants recreated the speed, smoothness, and persistence of their imagined contents. Results showed that while there was immense individual variability in these abilities across participants, the dynamics of mental imagery were constrained by principles of “now” moments. In the final part of this talk, I will extend the idea of temporal regularities beyond the case of human experience. I will speculate on a possible comparative investigation of “nows” across species. We know already that experience partitions reality in different umwelts across species, with varying sensitivities to audio-visual spectra, distinct navigational capabilities and so on. The same may also be true for how elements of experience are structured in time. While there is already work on comparing temporal sensitivities of the retina across species, this talk will extend this discussion to temporal regularities of attention, pain, and perception.
Bio:
Ishan Singhal has recently joined the Sussex Centre for Consciousness Science as a postdoctoral research fellow. He will be working with Anil Seth under an ERC grant which involves putting phenomenological constraints in predictive processing models.
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