Activity in perceptual classification networks as a basis for human subjective time perception

Warrick Roseboom,Zafeirios Fountas,David Bhowmik,Anil K. Seth,Murray Shanahan,Kyriacos Nikiforou

doi:10.1038/s41467-018-08194-7

Abstract

Despite being a fundamental dimension of experience, how the human brain generates the perception of time remains unknown. Here, we provide a novel explanation for how human time perception might be accomplished, based on non-temporal perceptual classification processes. To demonstrate this proposal, we build an artificial neural system centred on a feed-forward image classification network, functionally similar to human visual processing. In this system, input videos of natural scenes drive changes in network activation, and accumulation of salient changes in activation are used to estimate duration. Estimates produced by this system match human reports made about the same videos, replicating key qualitative biases, including differentiating between scenes of walking around a busy city or sitting in a cafe or office. Our approach provides a working model of duration perception from stimulus to estimation and presents a new direction for examining the foundations of this central aspect of human experience.

Highlights

Despite being a fundamental dimension of experience, how the human brain generates the perception of time remains unknown
Changes in perceptual content in these networks can be quantified as the collective difference in activation of neurons in the network to successive inputs, such as consecutive frames of a video. We propose that this simple metric provides a sufficient basis for subjective time estimation. Because this metric is based on perceptual classification processes, we hypothesise that the produced duration estimates will exhibit the same content-related biases as characterise human time perception
The dynamic threshold was implemented for each layer, following a decaying exponential corrupted by Gaussian noise and resetting whenever the measured Euclidean distance exceeded it

Summary

Introduction

Despite being a fundamental dimension of experience, how the human brain generates the perception of time remains unknown. We build an artificial neural system centred on a feed-forward image classification network, functionally similar to human visual processing In this system, input videos of natural scenes drive changes in network activation, and accumulation of salient changes in activation are used to estimate duration. Changes in perceptual content in these networks can be quantified as the collective difference in activation of neurons in the network to successive inputs, such as consecutive frames of a video We propose that this simple metric provides a sufficient basis for subjective time estimation. Because this metric is based on perceptual classification processes, we hypothesise that the produced duration estimates will exhibit the same content-related biases as characterise human time perception. We implemented a model of time perception using an image classification network[39] as its core and compared its performance to that of human participants in estimating time for the same natural video stimuli

Methods

Results

Conclusion