Rapidly learned stimulus expectations alter perception of motion

Chalk, Matthew; Seitz, Aaron R.; Seriès, Peggy

doi:10.1167/10.8.2

Cited by 119 publications

(171 citation statements)

References 54 publications

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“…One might argue that a prior to slow speeds is implemented in the motion-pooling networks in early visual motion processing areas like macaque middle temporal and medial superior temporal areas and that the prior statistical model on object speeds affecting subjects' motor planning is implemented in later stages of neural processing that integrate motion information to make decisions and guide motor behavior. Psychophysical results like the current ones cannot resolve this question; however, it is intriguing to note that, after viewing stimuli with relatively high mean speeds, biases in subjects' percepts of the directions of moving, oriented texture patterns shift away from the direction predicted by a prior peaked at zero to a direction predicted by a prior with a peak at higher speeds (31). This result suggests that even supposedly low-level motion percepts are affected by adaptation to the statistical context of stimuli.…”

Section: Discussioncontrasting

confidence: 64%

The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning

Kwon

Knill

2013

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

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Because of uncertainty and noise, the brain should use accurate internal models of the statistics of objects in scenes to interpret sensory signals. Moreover, the brain should adapt its internal models to the statistics within local stimulus contexts. Consider the problem of hitting a baseball. The impoverished nature of the visual information available makes it imperative that batters use knowledge of the temporal statistics and history of previous pitches to accurately estimate pitch speed. Using a laboratory analog of hitting a baseball, we tested the hypothesis that the brain uses adaptive internal models of the statistics of object speeds to plan hand movements to intercept moving objects. We fit Bayesian observer models to subjects' performance to estimate the statistical environments in which subjects' performance would be ideal and compared the estimated statistics with the true statistics of stimuli in an experiment. A first experiment showed that subjects accurately estimated and used the variance of object speeds in a stimulus set to time hitting behavior but also showed serial biases that are suboptimal for stimuli that were uncorrelated over time. A second experiment showed that the strength of the serial biases depended on the temporal correlations within a stimulus set, even when the biases were estimated from uncorrelated stimulus pairs subsampled from the larger set. Taken together, the results show that subjects adapted their internal models of the variance and covariance of object speeds within a stimulus set to plan interceptive movements but retained a bias to positive correlations.Bayesian inference | motion perception | statistical learning | sequential effects

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Section: Discussioncontrasting

confidence: 64%

The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning

Kwon

Knill

2013

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

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“…A few studies on visual perception reported feedbackinduced rapid adjustments of decision criteria and relative inertia of sensitivity changes (Ahissar and Hochstein 1997;Berniker et al 2010;Chalk et al 2010;Herzog et al 2006). To the best of our knowledge, however, this is the first study to report systematic dissociations between mean accuracy and precision of IT in the two major aspects of PL, time course and specificity.…”

Section: Dissociations Between Timing Accuracy and Precisionmentioning

confidence: 58%

Dichotomy in perceptual learning of interval timing: calibration of mean accuracy and precision differ in specificity and time course

Sohn

Lee

2013

Journal of Neurophysiology

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Sohn H, Lee SH. Dichotomy in perceptual learning of interval timing: calibration of mean accuracy and precision differ in specificity and time course. J Neurophysiol 109: 344 -362, 2013. First published October 17, 2012 doi:10.1152/jn.01201.2011.-Our brain is inexorably confronted with a dynamic environment in which it has to fine-tune spatiotemporal representations of incoming sensory stimuli and commit to a decision accordingly. Among those representations needing constant calibration is interval timing, which plays a pivotal role in various cognitive and motor tasks. To investigate how perceived time interval is adjusted by experience, we conducted a human psychophysical experiment using an implicit interval-timing task in which observers responded to an invisible bar drifting at a constant speed. We tracked daily changes in distributions of response times for a range of physical time intervals over multiple days of training with two major types of timing performance, mean accuracy and precision. We found a decoupled dynamics of mean accuracy and precision in terms of their time course and specificity of perceptual learning. Mean accuracy showed feedback-driven instantaneous calibration evidenced by a partial transfer around the time interval trained with feedback, while timing precision exhibited a long-term slow improvement with no evident specificity. We found that a Bayesian observer model, in which a subjective time interval is determined jointly by a prior and likelihood function for timing, captures the dissociative temporal dynamics of the two types of timing measures simultaneously. Finally, the model suggested that the width of the prior, not the likelihoods, gradually shrinks over sessions, substantiating the important role of prior knowledge in perceptual learning of interval timing. time perception; perceptual learning; mean accuracy and precision; Bayesian observer model; prior and likelihood TO ACT ON OBJECTS in an ever-changing and noisy environment, our brain has to dynamically calibrate its spatiotemporal representations of the objects by interacting with the environment.

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“…These results are in line with a recent monkey electrophysiology study (10), which showed that neurons in the face patch of inferior temporal cortex encode the prior expectation of a face appearing both before and after actual stimulus presentation. When the subsequently presented stimulus is noisy or ambiguous, such a prestimulus template could conceivably bias perception toward the expected stimulus (21)(22)(23)(24).…”

Section: Discussionmentioning

confidence: 99%

Prior expectations induce prestimulus sensory templates

Kok

Mostert

Lange

2017

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

294

331

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Perception can be described as a process of inference, integrating bottom-up sensory inputs and top-down expectations. However, it is unclear how this process is neurally implemented. It has been proposed that expectations lead to prestimulus baseline increases in sensory neurons tuned to the expected stimulus, which in turn, affect the processing of subsequent stimuli. Recent fMRI studies have revealed stimulus-specific patterns of activation in sensory cortex as a result of expectation, but this method lacks the temporal resolution necessary to distinguish pre-from poststimulus processes. Here, we combined human magnetoencephalography (MEG) with multivariate decoding techniques to probe the representational content of neural signals in a time-resolved manner. We observed a representation of expected stimuli in the neural signal shortly before they were presented, showing that expectations indeed induce a preactivation of stimulus templates. The strength of these prestimulus expectation templates correlated with participants' behavioral improvement when the expected feature was task-relevant. These results suggest a mechanism for how predictive perception can be neurally implemented.prediction | perceptual inference | predictive coding | feature-based expectation | feature-based attention P erception is heavily influenced by prior knowledge (1-3). Accordingly, many theories cast perception as a process of inference, integrating bottom-up sensory inputs and top-down expectations (4-6). However, it is unclear how this integration is neurally implemented. It has been proposed that prior expectations lead to baseline increases in sensory neurons tuned to the expected stimulus (7-9), which in turn, leads to improved neural processing of matching stimuli (10, 11). In other words, expectations may induce stimulus templates in sensory cortex before the actual presentation of the stimulus. Alternatively, topdown influences in sensory cortex may exert their influence only after the bottom-up stimulus has been initially processed, and the integration of the two sources of information may become apparent only during later stages of sensory processing (12).The evidence necessary to distinguish between these hypotheses has been lacking. fMRI studies have revealed stimulusspecific patterns of activation in sensory cortex as a result of expectation (9, 13), but this method lacks the temporal resolution necessary to distinguish pre-from poststimulus periods. Here, we combined magnetoencephalography (MEG) with multivariate decoding techniques to probe the representational content of neural signals in a time-resolved manner (14-17). The experimental paradigm was virtually identical to the ones used in our previous fMRI studies that studied how expectations modulate stimulus-specific patterns of activity in the primary visual cortex (9, 11). We trained a forward model to decode the orientation of task-irrelevant gratings from the MEG signal (18,19) and applied this decoder to trials in which participants expected a grating of a pa...

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Rapidly learned stimulus expectations alter perception of motion

Cited by 119 publications

References 54 publications

The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning

The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning

Dichotomy in perceptual learning of interval timing: calibration of mean accuracy and precision differ in specificity and time course

Prior expectations induce prestimulus sensory templates

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