scholarly journals Prior expectations in visual speed perception predict encoding characteristics of neurons in area MT

2021 ◽  
Author(s):  
Ling-Qi Zhang ◽  
Alan A Stocker
Keyword(s):  
Population Coding ◽  
Area Mt ◽  
Efficient Coding ◽  
Psychophysical Data ◽  
Speed Perception ◽  
Quantitative Validation ◽  
Observer Model ◽  
Visual Speed ◽  
Sensory Uncertainty ◽  
Quantitative Account

Bayesian inference provides an elegant theoretical framework for understanding the characteristic biases and discrimination thresholds in visual speed perception. However, the framework is difficult to validate due to its flexibility and the fact that suitable constraints on the structure of the sensory uncertainty have been missing. Here, we demonstrate that a Bayesian observer model constrained by efficient coding not only well fits extensive psychophysical data of human visual speed perception but also provides an accurate quantitative account of the tuning characteristics of neurons known for representing visual speed. Specifically, we found that the population coding accuracy for visual speed in area MT ("neural prior") is precisely predicted by the power-law, slow-speed prior extracted from fitting the Bayesian model to the psychophysical data ("behavioral prior"), to the point that they are indistinguishable in a model cross-validation comparison. Our results demonstrate a quantitative validation of the Bayesian observer model constrained by efficient coding at both the behavioral and neural levels.

scholarly journals Optimal Short-Term Population Coding: When Fisher Information Fails

2002 ◽  
Vol 14 (10) ◽  
pp. 2317-2351 ◽  
Author(s):  
M. Bethge ◽  
D. Rotermund ◽  
K. Pawelzik
Keyword(s):  
Fisher Information ◽  
Dynamic Range ◽  
Mean Squared Error ◽  
Average Rate ◽  
Neuronal Response ◽  
Minimum Mean Square Error ◽  
Population Coding ◽  
Neuronal Firing ◽  
Tuning Curves ◽  
Efficient Coding

Efficient coding has been proposed as a first principle explaining neuronal response properties in the central nervous system. The shape of optimal codes, however, strongly depends on the natural limitations of the particular physical system. Here we investigate how optimal neuronal encoding strategies are influenced by the finite number of neurons N (place constraint), the limited decoding time window length T (time constraint), the maximum neuronal firing rate fmax (power constraint), and the maximal average rate fmax (energy constraint). While Fisher information provides a general lower bound for the mean squared error of unbiased signal reconstruction, its use to characterize the coding precision is limited. Analyzing simple examples, we illustrate some typical pitfalls and thereby show that Fisher information provides a valid measure for the precision of a code only if the dynamic range (fmin T, fmax T) is sufficiently large. In particular, we demonstrate that the optimal width of gaussian tuning curves depends on the available decoding time T. Within the broader class of unimodal tuning functions, it turns out that the shape of a Fisher-optimal coding scheme is not unique. We solve this ambiguity by taking the minimum mean square error into account, which leads to flat tuning curves. The tuning width, however, remains to be determined by energy constraints rather than by the principle of efficient coding.

A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts

2015 ◽  
Vol 18 (10) ◽  
pp. 1509-1517 ◽  
Author(s):  
Xue-Xin Wei ◽  
Alan A Stocker
Keyword(s):  
Efficient Coding ◽  
Observer Model

scholarly journals Choice-induced biases in perception

2016 ◽  
Author(s):  
Long Luu ◽  
Alan A Stocker
Keyword(s):  
Discrimination Task ◽  
Sensory Information ◽  
Motion Direction ◽  
Great Opportunity ◽  
Perceptual Illusion ◽  
Psychophysical Data ◽  
Observer Model ◽  
Discrimination Judgment ◽  
Perceived Motion ◽  
The Brain

AbstractIllusions provide a great opportunity to study how perception is affected by both the observer's expectations and the way sensory information is represented1,2,3,4,5,6. Recently, Jazayeri and Movshon7 reported a new and interesting perceptual illusion, demonstrating that the perceived motion direction of a dynamic random dot stimulus is systematically biased when preceded by a motion discrimination judgment. The authors hypothesized that these biases emerge because the brain predominantly relies on those neurons that are most informative for solving the discrimination task8, but then is using the same neural weighting profile for generating the percept. In other words, they argue that these biases are “mistakes” of the brain, resulting from using inappropriate neural read-out weights. While we were able to replicate the illusion for a different visual stimulus (orientation), our new psychophysical data suggest that the above interpretation is likely incorrect: Biases are not caused by a read-out profile optimized for solving the discrimination task but rather by the specific choices subjects make in the discrimination task on any given trial. We formulate this idea as a conditioned Bayesian observer model and show that it can explain the new as well as the original psychophysical data. In this framework, the biases are not caused by mistake but rather by the brain's attempt to remain ‘self-consistent’ in its inference process. Our model establishes a direct connection between the current perceptual illusion and the well-known phenomena of cognitive consistency and dissonance9,10.

Motion selectivity in macaque visual cortex. III. Psychophysics and physiology of apparent motion

1986 ◽  
Vol 55 (6) ◽  
pp. 1340-1351 ◽  
Author(s):  
W. T. Newsome ◽  
A. Mikami ◽  
R. H. Wurtz
Keyword(s):  
Apparent Motion ◽  
Cortical Neurons ◽  
Striate Cortex ◽  
Preceding Paper ◽  
Direction Selectivity ◽  
Physiological Data ◽  
Area Mt ◽  
Temporal Area ◽  
Psychophysical Data ◽  
Spatial Interval

We have conducted physiological and psychophysical experiments to identify possible neural substrates of the perception of apparent motion. We used identical sequences of flashed stimuli in both sets of experiments to better compare the responses of cortical neurons and psychophysical observers. Physiological data were obtained from two cortical visual areas, striate cortex (V1) and the middle temporal area (MT). In the previous paper we presented evidence that neuronal thresholds for direction selectivity in extrastriate area MT were similar to psychophysical thresholds for motion perception at the largest effective interflash interval, and thus speed, for a given eccentricity. We now examine physiological and psychophysical thresholds for a broad range of speeds to determine whether such a correspondence exists for speeds below the upper threshold considered in the previous paper. Stimuli were presented in stroboscopic motion of constant apparent speed while the spatial and temporal interflash intervals were systematically varied. For each neuron we measured the largest spatial interval that elicited directionally selective responses at each of several apparent speeds. We calculated the composite performance of neurons in both MT and V1 by averaging the spatial interval necessary for direction selectivity at each apparent speed. We employed the same apparent-motion stimuli for psychophysical experiments with human subjects in which we measured the spatial interval necessary for the perception of motion over a similar range of apparent speeds. We obtained a composite profile of psychophysical performance by averaging thresholds across subjects at each apparent speed. For high apparent speeds, physiological data from MT, but not V1, corresponded closely to the psychophysical data as suggested in the preceding paper. For low apparent speeds, however, physiological data from MT and V1 were similar to each other and to the psychophysical data. It would appear, therefore, that neurons in either V1 or MT could mediate the perceptual effect at low speeds, whereas MT is a stronger candidate for this role at high speeds. We suggest that the neuronal substrate for apparent motion may be distributed over multiple cortical areas, depending upon the speed and spatial interval of the stimulus.

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