A Bayesian perspective on magnitude estimation

Petzschner, Frederike H.; Glasauer, Stefan; Stephan, Klaas Ε.

doi:10.1016/j.tics.2015.03.002

Cited by 265 publications

(347 citation statements)

References 103 publications

Supporting

Mentioning

319

Contrasting

Order By: Relevance

“…Such a framework has been successfully applied to several perceptual domains6061626364, including interval estimation484965 and reproduction7814, but here for the first time we propose a descriptive model that captures changes in the perceived timing of individual stimuli.…”

Section: Resultsmentioning

confidence: 99%

Optimal Perceived Timing: Integrating Sensory Information with Dynamically Updated Expectations

Luca

Rhodes

2016

Sci Rep

View full text Add to dashboard Cite

The environment has a temporal structure, and knowing when a stimulus will appear translates into increased perceptual performance. Here we investigated how the human brain exploits temporal regularity in stimulus sequences for perception. We find that the timing of stimuli that occasionally deviate from a regularly paced sequence is perceptually distorted. Stimuli presented earlier than expected are perceptually delayed, whereas stimuli presented on time and later than expected are perceptually accelerated. This result suggests that the brain regularizes slightly deviant stimuli with an asymmetry that leads to the perceptual acceleration of expected stimuli. We present a Bayesian model for the combination of dynamically-updated expectations, in the form of a priori probability of encountering future stimuli, with incoming sensory information. The asymmetries in the results are accounted for by the asymmetries in the distributions involved in the computational process.

show abstract

Section: Resultsmentioning

confidence: 99%

Optimal Perceived Timing: Integrating Sensory Information with Dynamically Updated Expectations

Luca

Rhodes

2016

Sci Rep

View full text Add to dashboard Cite

show abstract

“…To test this hypothesis, future studies could take a similar approach to characterize the selectivity of rapidly acquired priors in nontemporal tasks. Central tendency biases provide a particularly useful testbed for examining these issues; robust effects have been reported across a wide range of magnitude estimation tasks, including length, distance, and loudness (16,39).…”

Section: Discussionmentioning

confidence: 99%

Generalization of prior information for rapid Bayesian time estimation

Roach

McGraw

Whitaker

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

103

151

View full text Add to dashboard Cite

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...

show abstract

“…This is an extremely general concept which not only underlies common models in statistics (Kass and Steffey, 1989), but provides a key metaphor for models of brain function (e.g., Rao and Ballard, 1999; Lee and Mumford, 2003; Friston, 2005, 2008; Petzschner et al, 2015), such as predictive coding described below.…”

Section: Teleological Brain Theories As Fundament For Understanding Fmentioning

confidence: 99%

Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression

Stephan

Manjaly

Mathys

et al. 2016

Front. Hum. Neurosci.

Self Cite

310

437

View full text Add to dashboard Cite

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.

show abstract

A Bayesian perspective on magnitude estimation

Cited by 265 publications

References 103 publications

Optimal Perceived Timing: Integrating Sensory Information with Dynamically Updated Expectations

Optimal Perceived Timing: Integrating Sensory Information with Dynamically Updated Expectations

Generalization of prior information for rapid Bayesian time estimation

Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression

Contact Info

Product

Resources

About