Efficient coding explains the universal law of generalization in human perception

Science ◽  
2018 ◽  
Vol 360 (6389) ◽  
pp. 652-656 ◽  
Author(s):  
Chris R. Sims
Keyword(s):  
Human Perception ◽  
Efficient Coding ◽  
Universal Law

A Computational Model of Human Perception With Prior Expectation: Bayesian Integration and Efficient Coding

2015 ◽  
Author(s):  
Hideyoshi Yanagisawa
Keyword(s):  
Computational Model ◽  
Human Perception ◽  
External Noise ◽  
Physical Variable ◽  
Sensory Stimuli ◽  
Efficient Coding ◽  
Psychological Phenomena ◽  
Bayesian Integration ◽  
Physical Variables ◽  
Expectation Effect

Human perception of sensory stimuli is affected by prior prediction of the sensory experience. For example, perception of weight of an object changes depending on weight predicted with size of the object appearance. We call such psychological phenomena expectation effect. The expectation effect is a key factor to explain a gap between physical variables and their perceptions. In this paper, we propose a novel computational model of human perception involving the expectation effect. We hypothesized that perceived physical variable was estimated using a Bayesian integration of prior prediction and sensory likelihood of a physical variable. We applied efficient coding hypothesis to form a shape of sensory likelihood. We formalized the expectation effect as a function of three factors: expectation error (difference between predicted and actual physical variables), prediction uncertainty (variance of prior distributions), and external noise (variance of noise distributions convolved with likelihood). Using the model, we conducted computer simulations to analyze the behavior of two opposite patterns of expectation effect, that is, assimilation and contrast. The results of the simulation revealed that 1) the pattern of expectation effect shifted from assimilation to contrast as the prediction error increased, 2) uncertainty decreased the extent of the expectation effect, 3) and external noise increased the assimilation.

scholarly journals Efficient coding of numbers explains decision bias and noise

2020 ◽  
Author(s):  
Arthur Prat-Carrabin ◽  
Michael Woodford
Keyword(s):  
Maximum Likelihood ◽  
Prior Distribution ◽  
Alternative Hypothesis ◽  
Human Subjects ◽  
Human Perception ◽  
Stimulus Space ◽  
Maximum Likelihood Decoding ◽  
Decision Bias ◽  
Efficient Coding

AbstractHuman subjects differentially weight different stimuli in averaging tasks. This has been interpreted as reflecting biased stimulus encoding, but an alternative hypothesis is that stimuli are encoded with noise, then optimally decoded. Moreover, with efficient coding, the amount of noise should vary across stimulus space, and depend on the statistics of stimuli. We investigate these predictions through a task in which participants are asked to compare the averages of two series of numbers, each sampled from a prior distribution that differs across blocks of trials. We show that subjects encode numbers with both a bias and a noise that depend on the number. Infrequently occurring numbers are encoded with more noise. A maximum-likelihood decoding model captures subjects’ behaviour and indicates efficient coding. Finally, our model predicts a relation between the bias and variability of estimates, thus providing a statistically-founded, parsimonious derivation of Wei and Stocker’s “law of human perception”.

Dogs’ (Canis familiaris) attention to human perception: Influence of breed groups and life experiences.

2017 ◽  
Vol 131 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Marianne T. E. Heberlein ◽  
Dennis C. Turner ◽  
Marta B. Manser
Keyword(s):  
Life Experiences ◽  
Canis Familiaris ◽  
Human Perception

Augmented Reality--Implications toward Virtual Reality, Human Perception and Performance

2012 ◽  
Author(s):  
R. A. Grier ◽  
H. Thiruvengada ◽  
S. R. Ellis ◽  
P. Havig ◽  
K. S. Hale ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Human Perception ◽  
And Performance

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

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