Generalization of prior information for rapid Bayesian time estimation

Abstract

To enable effective interaction with the environment, the brain combines noisy sensory information with expectations based on prior experience. There is ample evidence showing that humans can learn statistical regularities in sensory input and exploit this knowledge to improve perceptual decisions and actions. However, fundamental questions remain regarding how priors are learned and how they generalize to different sensory and behavioral contexts. In principle, maintaining a large set of highly specific priors may be inefficient and restrict the speed at which expectations can be formed and updated in response to changes in the environment. However, priors formed by generalizing across varying contexts may not be accurate. Here, we exploit rapidly induced contextual biases in duration reproduction to reveal how these competing demands are resolved during the early stages of prior acquisition. We show that observers initially form a single prior by generalizing across duration distributions coupled with distinct sensory signals. In contrast, they form multiple priors if distributions are coupled with distinct motor outputs. Together, our findings suggest that rapid prior acquisition is facilitated by generalization across experiences of different sensory inputs but organized according to how that sensory information is acted on.Bayesian inference | time perception | sensorimotor learning L ike all complex animals, humans rely on their senses to extract information about the environment and guide decision making and behavior. Often, however, sensory information is ambiguous. Signals transmitted to the senses can be weak or degraded, such as patterns of reflected light under low illumination or speech sounds in noisy environments. Moreover, sensory representations of even the most high-fidelity signals tend to be variable (1) and are insufficient to completely disambiguate different distal causes (2). Mounting empirical evidence indicates that, when forming decisions and planning actions, the brain combines uncertain sensory information with expectations based on prior knowledge (3-6). For example, a variety of biases in visual perception have been shown to be consistent with reliance on prior knowledge regarding statistical regularities in the environment, such as the distribution of local orientations (7) and speeds (8) in natural scenes and the positioning of light sources (9). In many studies, perception and behavior have been shown to be well-described by near-optimal integration of sensory evidence and prior knowledge according to the principles of statistical decision theory.Prior knowledge can be acquired over a range of different timescales. Priors specifying stable statistical characteristics of the environment are typically thought to be either innate or the consequence of life-long implicit learning (5, 7). However, context-specific priors can also be formed based on recent experiences. Studies using simple sensorimotor tasks suggest that human participants are adept at learning the distribution of set...

Temporal context actively shapes EEG signatures of time perception

“…The general overestimation we found in the behavioral results might potentially be explained by the integration of these visual stimuli in temporal estimation (Shi & Burr, 2016). Future studies might look further into potential modality differences in contextual calibration and their neural underpinnings (Rhodes, Seth, & Roseboom, 2018;Roach et al, 2017). Furthermore, we found no significant decoding corresponding to the windows of CNV differences.…”

“…Although there is abundant behavioral evidence for Bayesian integration in human time perception (Acerbi, Wolpert, & Vijayakumar, 2012;Cicchini, Arrighi, Cecchetti, Giusti, & Burr, 2012;Gu, Jurkowski, Lake, Malapani, & Meck, 2015;Hallez, Damsma, Rhodes, van Rijn, & Droit-Volet, 2019;Jazayeri & Shadlen, 2010;Maaß, Riemer, Wolbers, & van Rijn, 2019;Maaß, Schlichting, & van Rijn, 2019;Roach, McGraw, Whitaker, & Heron, 2017;Schlichting et al, 2018;Shi, Church, & Meck, 2013), its temporal locus and neural underpinnings are not yet understood.…”

Spectral memory: Illumination-evoked plasticity

“…Consistent with previous reports under normal conditions [16,17], it is the combination of these two priors the human visual system appears to recruit, as it attempts to stabilize the chromaticity of the retinal image of real-world scenes under extremely ill-defined conditions [1,2, see Figure. Depending on the illumination the visual system's most basic architecture was preferentially tuned under, during its development -λ , an illumination prior emerges and is stored -PriorL+M or PriorS. Because the mathematical operation employed by the visual system for the geometric scenes-the first rectangular box, is subtractive in nature, perception will most likely shift towards that prior's opponent color.…”

Multiple Duration Priors Within and Across the Senses

“…More recently, central tendency effects have been interpreted within a Bayesian framework [4,7,8,11,[18][19][20][21][22] with the context of recent experience (prior distribution) combined with current sensory evidence (likelihood function) to generate estimations of duration (posterior). Previous investigations of central tendency in duration perception have found evidence for a single temporal prior: participants acquire and maintain information about durations that is shaped by all durations presented, regardless of context or stimulus characteristics [8,15,23].…”

“…Much research in time and timing perception suggests that temporal dimensions of perception follow the same principles [8,9,[11][12][13][14][15]. It has long been known that temporal perception is shaped by the context of recent experiences:…”