Late Bayesian inference in mental transformations

Remington, Evan D.; Parks, Tiffany V.; Jazayeri, Mehrdad

doi:10.1038/s41467-018-06726-9

Cited by 22 publications

(39 citation statements)

References 39 publications

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“…6) than the model without considering the predictive attention ( F 1,12 = 10.51, p = 0.007; chi-square difference test). The best-fitting value of Weber fraction parameter ( w = 0.11) was in the range of the previously reported Weber fractions for time estimation 37–39 , and the best-fitting value of predictive term was 106 ms.…”

Section: Resultssupporting

confidence: 71%

Temporal Dynamics of Visual Attention Allocation

Moon

Choe

Lee

et al. 2019

Sci Rep

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We often temporally prepare our attention for an upcoming event such as a starter pistol. In such cases, our attention should be properly allocated around the expected moment of the event to process relevant sensory input efficiently. In this study, we examined the dynamic changes of attention levels near the expected moment by measuring contrast sensitivity to a target that was temporally cued by a five-second countdown. We found that the overall attention level decreased rapidly after the expected moment, while it stayed relatively constant before it. Results were not consistent with the predictions of existing explanations of temporal attention such as the hazard rate or the stimulus-driven oscillations. A control experiment ruled out the possibility that the observed pattern was due to biased time perception. In a further experiment with a wider range of cue-stimulus-intervals, we observed that attention level increased until the last 500 ms of the interval range, and thereafter, started to decrease. Based on the performances of a generative computational model, we suggest that our results reflect the nature of temporal attention that takes into account the subjectively estimated hazard rate and the probability of relevant events occurring in the near future.

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Section: Resultssupporting

confidence: 71%

Temporal Dynamics of Visual Attention Allocation

Moon

Choe

Lee

et al. 2019

Sci Rep

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“…For the original pair, participants' t p increased with t s , and exhibited systematic biases toward the mean of the prior (Figure 2d, red). Similar to numerous previous studies 40,41,[43][44][45][46] , this behavior was consistent with predictions of the ideal observer model ( Supplementary Figure 1).…”

Section: Time-interval Reproduction Task: Ready-set-go (Rsg)supporting

confidence: 91%

Metamers of Bayesian computation

Sohn

Jazayeri

2020

Preprint

Self Cite

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There are two sharply debated views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative model-free hypothesis posits that decisions are optimized through trial and error without explicit internal models for priors and cost functions. To distinguish between these possibilities, we introduce a novel paradigm that probes sensitivity of humans to transitions between prior-cost pairs that demand the same optimal policy (metamers) but distinct internal models. We demonstrate the utility of our approach in two experiments that were classically explained by model-based Bayesian theory. Our approach validates the model-based strategy in an interval timing task but not in a visuomotor rotation task. More generally, our work provides a domain-general approach for testing the circumstances under which humans implement model-based Bayesian computations.

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“…To tease these two possibilities apart, we employed a Bayesian Observer-Actor Model previously described by Remington and colleagues ( Remington et al, 2018 ; Jazayeri and Shadlen, 2010 ) (see Materials and methods). In this model, sample durations (t s ) are inferred as draws from noisy measurement distributions (t m ) that scale in width according to the length of the presented interval.…”

Section: Resultsmentioning

confidence: 99%

“…In the reproduction experiment, all participants tended to overestimate durations, but viscosity was related to decreased overestimation and greater central tendency. We utilized a Bayesian Observer Model ( Jazayeri and Shadlen, 2010 ; Remington et al, 2018 ) to verify that this effect was a result of perceptual bias rather than increased noise in the measurement and production processes. Overall, these results suggest that movement distance has a direct influence on perceived interval length, regardless of whether this parameter is modulated by volitional or environmental factors.…”

Section: Introductionmentioning

confidence: 99%

Slowing the body slows down time perception

Kock

Zhou

Joiner

et al. 2021

eLife

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Interval timing is a fundamental component of action, and is susceptible to motor-related temporal distortions. Previous studies have shown that concurrent movement biases temporal estimates, but have primarily considered self-modulated movement only. However, real-world encounters often include situations in which movement is restricted or perturbed by environmental factors. In the following experiments, we introduced viscous movement environments to externally modulate movement and investigated the resulting effects on temporal perception. In two separate tasks, participants timed auditory intervals while moving a robotic arm that randomly applied four levels of viscosity. Results demonstrated that higher viscosity led to shorter perceived durations. Using a drift-diffusion model and a Bayesian observer model, we confirmed these biasing effects arose from perceptual mechanisms, instead of biases in decision making. These findings suggest that environmental perturbations are an important factor in movement-related temporal distortions, and enhance the current understanding of the interactions of motor activity and cognitive processes.

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Late Bayesian inference in mental transformations

Cited by 22 publications

References 39 publications

Temporal Dynamics of Visual Attention Allocation

Temporal Dynamics of Visual Attention Allocation

Metamers of Bayesian computation

Slowing the body slows down time perception

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