2019
DOI: 10.1038/s41467-018-08194-7
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Activity in perceptual classification networks as a basis for human subjective time perception

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… Show more

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Cited by 85 publications
(153 citation statements)
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References 63 publications
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“…On each trial a video of duration 8, 12, 16, 20 or 24 seconds was presented. For each participant, videos of the appropriate duration and scene category were constructed by randomly sampling continuous frames from the stimuli built for Roseboom et al (2019). These videos depicted either an office scene or a city scene.…”
Section: Experimental Design and Trial Sequencementioning
confidence: 99%
See 1 more Smart Citation
“…On each trial a video of duration 8, 12, 16, 20 or 24 seconds was presented. For each participant, videos of the appropriate duration and scene category were constructed by randomly sampling continuous frames from the stimuli built for Roseboom et al (2019). These videos depicted either an office scene or a city scene.…”
Section: Experimental Design and Trial Sequencementioning
confidence: 99%
“…Artificial classification network-based modelling. Frames from each video presented during the experiment were fed into the model presented in Roseboom et al (48). Instead of accumulating events based on changes in BOLD amplitude, salient events in the video frames themselves were detected by an artificial image classification network (Alexnet) (26).…”
Section: Robustness Analysis (Exploratory)mentioning
confidence: 99%
“…Despite being a core topic of interest since the very beginning of psychology, the cognitive and neural processes that underlie human time perception remain largely unknown. Human perception of time is affected by many factors; experience on the scale of seconds is most prominently influenced by the content, complexity, and rate of change of experience [1][2][3][4] and whether attention can and is being directed toward the task of monitoring time [5][6][7] . Specifically, increasing cognitive load (by requiring attention to additional tasks beyond tracking time) has been reported to decrease the apparent duration of an interval when a person is simultaneously and actively attending to time (prospective time), while apparent duration is increased by cognitive load when reflecting on the duration of an interval after it has occurred (retrospective time; comprehensively reviewed in 7 and Figure 1).…”
Section: Introductionmentioning
confidence: 99%
“…Alternatively, we find a more complex relationship where a signed prediction-error that is processed in timing-related circuits, cannot be accounted by absolute (attention-like) prediction-error signal. It is possible that an integrative mechanism that combines opposite patterns of attention due to saliency (56), valence (57) and/or amount of information passing (58) together with unpredictability (7,8), could eventually account for our findings.…”
Section: Discussionmentioning
confidence: 88%
“…Time perception in the sub-second range is essential for human behaviors such as speech, movement, attention, memory and other functions (1-5). The subjective perception of time is affected by physical attributes of the stimulus such as spatial frequency, statistics, target type, saliency and intensity (6)(7)(8)(9), but also by motivational states and emotional content (10). Mainly, studies have observed that the duration of a stimulus with an emotional content, such as a face with an emotional expression or an arousing image, is perceived as longer compared to other neutral stimuli (11)(12)(13)(14).…”
mentioning
confidence: 99%