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

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

doi:10.1038/s41467-018-08194-7

Cited by 82 publications

(153 citation statements)

References 63 publications

(61 reference statements)

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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%

“…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%

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Trial-by-trial predictions of subjective time from human brain activity

Sherman

Fountas

Seth

et al. 2020

Preprint

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Author ContributionsWR conceived of the study. MTS and WR designed and pre-registered the experiments and analyses. MTS collected, analyzed, and constructed models of human behavioral and neuroimaging data. ZF constructed the artificial network model and analyzed the data. MTS and WR wrote the manuscript. AKS and ZF provided critical revisions on the manuscript. AbstractHuman experience of time exhibits systematic, context-dependent deviations from objective clock time. For example, time is experienced differently at work than on holiday. The cognitive and neural bases of how time perception interacts with the content of experience remain unclear, and leading explanations of human time perception are not equipped to explain this interaction. We propose an alternative account of human time perception, based on the dynamics of sensory processing. Our approach naturally links content of experience with time perception through a common foundation in basic sensory processing. We provide evidence for this proposal in model-based analyses of the dynamics of perceptual processing in an artificial neural network and in the activity of human sensory cortex. Healthy human participants watched naturalistic, silent videos and estimated their duration while fMRI was acquired. The same videos were used as stimuli for the artificial network.We constructed a computational model that predicted video durations from salient events in the activity of the artificial network, or in participants' visual cortex. The artificial network model reproduced human-like duration estimates, including biases in estimation depending on the content of a given video. Most importantly, the model based on human visual cortex activity reproduced trial-by-trial biases in our human participants' subjective reports, whereas control models trained on auditory or somatosensory activity did not. Together, our results reveal that human subjective time is based on information arising during the processing of our dynamic sensory environment, providing a computational basis for an end-to-end account of time perception. Significance StatementOur perception of time depends on the contents of experience, reflected in expressions such as "a watched pot never boils". Prevailing accounts of human time perception cannot provide good explanations for this. We tested a new explanation: time perception arises from the processing of our dynamic sensory environment. Supporting this theory, human participants' duration reports of silent videos correlated with estimates we reconstructed from activity in the visual cortex of their brain while they watched those videos. This was not a brain-wide phenomenon; reconstructions based on other brain regions did not correlate with subjective reports of duration. Our results validate our new theory, linking content of experience and perception of time through a common foundation in basic sensory processing.

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Section: Experimental Design and Trial Sequencementioning

confidence: 99%

Section: Robustness Analysis (Exploratory)mentioning

confidence: 99%

Trial-by-trial predictions of subjective time from human brain activity

Sherman

Fountas

Seth

et al. 2020

Preprint

Self Cite

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“…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%

A predictive processing model of episodic memory and time perception

Fountas

Sylaidi²,

Nikiforou

et al. 2020

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Human perception and experience of time is strongly affected by environmental context. When paying close attention to time, time experience seems to expand; when distracted from time, experience of time seems to contract. Contrasts in experiences like these are common enough to be exemplified in sayings like "time flies when you're having fun". Similarly, experience of time depends on the content of perceptual experience -more rapidly changing or complex perceptual scenes seem longer in duration than less dynamic ones. The complexity of interactions among stimulation, attention, and memory that characterise time experience is likely the reason that a single overarching theory of time perception has been difficult to achieve. In the present study we propose a framework that reconciles these interactions within a single model, built using the principles of the predictive processing approach to perception. We designed a neural hierarchical Bayesian system, functionally similar to human perceptual processing, making use of hierarchical predictive coding, short-term plasticity, spatio-temporal attention, and episodic memory formation and recall. A large-scale experiment with ∼ 13, 000 human participants investigated the effects of memory, cognitive load, and stimulus content on duration reports of natural scenes up to ∼ 1 minute long. Model-based estimates matched human reports, replicating key qualitative biases including differences by cognitive load, scene type, and judgement (prospective or retrospective). Our approach provides an end-to-end model of duration perception from natural stimulus processing to estimation and from current experience to recalling the past, providing a new understanding of this central aspect of human experience. Predictive processing neural architecture for perceptionAccording to (Bayesian) predictive processing theories, the brain is constantly updating its internal representations of the state of the world with new sensory information x 0 t (at time t) through the process of hierarchical probabilistic inference 14,17,19,20 . The resulting 'top-down' generative model is used to predict new states and simulate the outcome of prospective actions 21 . Comparing predicted against actual sensory signals gives rise to prediction errors ξ n t which, in most instantiations of predictive processing, are assumed to flow in a 'bottom-up' direction from the sensory periphery (n = 0), towards higher hierarchical levels n ∈ {1, 2, ..}. Numerous connections with neurophysiology have been made over the years, including proposals viewing predictive processing as the fundamental function of canonical microcircuits in the cortex 22 , or as the result of the interplay between competing oscillatory bands 23, 24 , as well as providing theoretical explanations for a wide range of cognitive and perceptual effects such as hallucinations 25 , autism 26 and schizophrenia 27 . In addition, this framework is uniquely qualified for studying the relation between perceptual change and human perception of time 4 , as i...

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“…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).…”

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confidence: 99%

Right on time: prediction-errors bidirectionally bias time perception

Toren

Aberg

Paz

2019

Preprint

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The brain updates internal representation of the environment by using the mismatch between the predicted state/outcome and the actual one, termed prediction-error. In parallel, time perception in the sub-second range is crucial for many behaviors such as movement, learning, memory, attention and speech. Both time-perception and prediction-errors are essential for everyday life function of an organism, and interestingly, the striatum was shown to be independently involved in both functions. We therefore hypothesized that the putative shared circuitry might induce behavioral interaction, namely that prediction-errors might bias time perception. To examine this, participants performed a time-duration discrimination task in the presence of positive and negative prediction-errors that were irrelevant and independent of the main task. We find that positive/negative prediction-errors induce a bias in time perception by increasing/decreasing the perceived time, respectively. Using functional imaging, we identify an interaction in Putamen activity between encoding of prediction-error and performance in the discrimination task. A model that accounts for the behavioral and physiological observations confirms that the interaction in regional activations for prediction-errors and time-estimation underlies the observed bias. Our results demonstrate that these two presumably independent roles of the striatum can actually interfere or aid one another in specific scenarios. Prediction-errors interact with time discriminationWe first grouped outcomes into one positive and one negative group, and a three-way repeated measures ANOVA was conducted with factors Trial-type (LS, SL), PE-type (PE-, PE+, PE0), and Δt (0-26.6% corresponding to 0-133ms). . This main result was robust for each group separately (behavior-only and fMRI), and also when dividing the PE into different magnitudes (Figs.S1,S2,S3; see Supp.Info. for detailed statistics).In sum, we found that prediction error interacts with time perception to influence discrimination judgments. An opposite effect of PE+ and PE-on perceived durationWe next examined what drives this interaction. In our design the PE occurs only when the second stimulus appears. Therefore, in SL trials when the value induces a PE+ or a PE-(PE+/PE-), if the image is perceived as longer/shorter, it would lead to a perceived larger/smaller Δt between the two stimuli resulting in easier/harder discrimination and hence better/worse performance. For LS trials, PE+/PE-would induce the opposite change in perceived Δt thus resulting in an opposite effect on performance.Accordingly, we observed better performance in SL trials for PE+ vs. PE-(proportion of discrimination errors; mean PE+ = 0.32, mean PE-= 0.49, post-hoc Tukey, p < 10 *. ) and compared to PE0 (mean PE0 = 0.43, post-hoc Tukey, p = 0.0001). Correspondingly, trials with PE-showed worse performance compared to PE0 (post-hoc Tukey, p = 0.02). The exact opposite was observed in LS trials: performance was worse in PE+ compared to PE-trials (mean PE+ = 0.3...

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

Cited by 82 publications

References 63 publications

Trial-by-trial predictions of subjective time from human brain activity

Trial-by-trial predictions of subjective time from human brain activity

A predictive processing model of episodic memory and time perception

Right on time: prediction-errors bidirectionally bias time perception

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