scholarly journals Perceptual insensitivity to higher-order statistical moments of coherent random dot motion

2018 ◽  
Vol 18 (6) ◽  
pp. 9 ◽  
Author(s):  
Michael L. Waskom ◽  
Janeen Asfour ◽  
Roozbeh Kiani
Keyword(s):  
Higher Order ◽  
Statistical Moments ◽  
Dot Motion ◽  
Random Dot Motion

scholarly journals Perceptual insensitivity to higher-order statistical moments of coherent random dot motion

2018 ◽  
Author(s):  
Michael L. Waskom ◽  
Janeen Asfour ◽  
Roozbeh Kiani
Keyword(s):  
Visual Processing ◽  
Probability Distributions ◽  
Higher Order ◽  
Statistical Moments ◽  
Coherent Motion ◽  
It Use ◽  
Higher Order Moments ◽  
Dynamic Stimuli ◽  
Dot Motion ◽  
The Individual

ABSTRACTWhen the visual system analyses distributed patterns of sensory inputs, what features of those distributions does it use? It has been previously demonstrated that higher-order statistical moments of luminance distributions influence perception of static surfaces and textures. Here, we tested whether the brain also represents higher-order moments of dynamic stimuli. We constructed random dot kinematograms where dots moved according to probability distributions that selectively differed in terms of their mean, variance, skewness, or kurtosis. When viewing these stimuli, human observers were sensitive to the mean direction of coherent motion and to the variance of the individual dot displacement angles, but they were insensitive to skewness and kurtosis. Observer behavior accorded with a model of directional motion energy, suggesting that information about higher-order moments is discarded early in the visual processing hierarchy. These results demonstrate that use of higher-order moments is not a general property of visual perception.

scholarly journals A Hierarchical Attractor Network Model of perceptual versus intentional decision updates

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anne Löffler ◽  
Anastasia Sylaidi ◽  
Zafeirios Fountas ◽  
Patrick Haggard
Keyword(s):  
Network Model ◽  
Higher Order ◽  
Motion Direction ◽  
Attractor Network ◽  
Movement Trajectories ◽  
Internal Action ◽  
Perceptual Evidence ◽  
Dot Motion ◽  
Action Tendencies ◽  
Random Dot Motion

AbstractChanges of Mind are a striking example of our ability to flexibly reverse decisions and change our own actions. Previous studies largely focused on Changes of Mind in decisions about perceptual information. Here we report reversals of decisions that require integrating multiple classes of information: 1) Perceptual evidence, 2) higher-order, voluntary intentions, and 3) motor costs. In an adapted version of the random-dot motion task, participants moved to a target that matched both the external (exogenous) evidence about dot-motion direction and a preceding internally-generated (endogenous) intention about which colour to paint the dots. Movement trajectories revealed whether and when participants changed their mind about the dot-motion direction, or additionally changed their mind about which colour to choose. Our results show that decision reversals about colour intentions are less frequent in participants with stronger intentions (Exp. 1) and when motor costs of intention pursuit are lower (Exp. 2). We further show that these findings can be explained by a hierarchical, multimodal Attractor Network Model that continuously integrates higher-order voluntary intentions with perceptual evidence and motor costs. Our model thus provides a unifying framework in which voluntary actions emerge from a dynamic combination of internal action tendencies and external environmental factors, each of which can be subject to Change of Mind.

scholarly journals The basis of area and dot number effects in random dot motion perception

1982 ◽  
Vol 22 (10) ◽  
pp. 1253-1259 ◽  
Author(s):  
Curtis L. Baker ◽  
Oliver J. Braddick
Keyword(s):  
Motion Perception ◽  
Dot Motion ◽  
Random Dot Motion

scholarly journals Sensitivity of Neurons in the Middle Temporal Area of Marmoset Monkeys to Random Dot Motion

2017 ◽  
Author(s):  
Tristan A. Chaplin ◽  
Benjamin J. Allitt ◽  
Maureen A. Hagan ◽  
Nicholas S. Price ◽  
Ramesh Rajan ◽  
...  
Keyword(s):  
Primate Species ◽  
Neural Basis ◽  
Temporal Area ◽  
Middle Temporal ◽  
Mt Neurons ◽  
Wide Range ◽  
Middle Temporal Area ◽  
Dot Motion ◽  
Marmoset Monkeys ◽  
Random Dot Motion

AbstractNeurons in the Middle Temporal area (MT) of the primate cerebral cortex respond to moving visual stimuli. The sensitivity of MT neurons to motion signals can be characterized by using random-dot stimuli, in which the strength of the motion signal is manipulated by adding different levels of noise (elements that move in random directions). In macaques, this has allowed the calculation of “neurometric” thresholds. We characterized the responses of MT neurons in sufentanil/nitrous oxide anesthetized marmoset monkeys, a species which has attracted considerable recent interest as an animal model for vision research. We found that MT neurons show a wide range of neurometric thresholds, and that the responses of the most sensitive neurons could account for the behavioral performance of macaques and humans. We also investigated factors that contributed to the wide range of observed thresholds. The difference in firing rate between responses to motion in the preferred and null directions was the most effective predictor of neurometric threshold, whereas the direction tuning bandwidth had no correlation with the threshold. We also showed that it is possible to obtain reliable estimates of neurometric thresholds using stimuli that were not highly optimized for each neuron, as is often necessary when recording from large populations of neurons with different receptive field concurrently, as was the case in this study. These results demonstrate that marmoset MT shows an essential physiological similarity to macaque MT, and suggest that its neurons are capable of representing motion signals that allow for comparable motion-in-noise judgments.New and NoteworthyWe report the activity of neurons in marmoset MT in response to random-dot motion stimuli of varying coherence. The information carried by individual MT neurons was comparable to that of the macaque, and that the maximum firing rates were a strong predictor of sensitivity. Our study provides key information regarding the neural basis of motion perception in the marmoset, a small primate species that is becoming increasingly popular as an experimental model.

scholarly journals Neuronal correlations in MT and MST impair population decoding of opposite directions of random dot motion

2018 ◽  
Author(s):  
Tristan A. Chaplin ◽  
Maureen A. Hagan ◽  
Benjamin J. Allitt ◽  
Leo L. Lui
Keyword(s):  
Population Coding ◽  
Correlation Structure ◽  
Single Neurons ◽  
Electrode Arrays ◽  
Preferred Direction ◽  
Dot Motion ◽  
Random Dot Patterns ◽  
Stimulus Features ◽  
Random Dot Motion ◽  
Spiking Activity

AbstractThe study of neuronal responses to random-dot motion patterns has provided some of the most valuable insights into how the activity of neurons is related to perception. In the opposite directions of motion paradigm, the motion signal strength is decreased by manipulating the coherence of random dot patterns to examine how well the activity of single neurons represents the direction of motion. To extend this paradigm to populations of neurons, studies have used modelling based on data from pairs of neurons, but several important questions require further investigation with larger neuronal datasets. We recorded neuronal populations in the middle temporal (MT) and medial superior temporal (MST) areas of anaesthetized marmosets with electrode arrays, while varying the coherence of random dot patterns in two opposite directions of motion (left and right). Using the spike rates of simultaneously recorded neurons, we decoded the direction of motion at each level of coherence with linear classifiers. We found that the presence of correlations had a detrimental effect to decoding performance, but that learning the correlation structure produced better decoding performance compared to decoders that ignored the correlation structure. We also found that reducing motion coherence increased neuronal correlations, but decoders did not need to be optimized for each coherence level. Finally, we showed that decoder weights depend of left-right selectivity at 100% coherence, rather than the preferred direction. These results have implications for understanding how the information encoded by populations of neurons is affected by correlations in spiking activity.Significance StatementMany studies have examined how the spiking activity of single neurons can encode stimulus features, such the direction of motion of visual stimuli. However, majority of such studies to date have only recorded from a small number of neurons at the same time, meaning that one cannot adequately account for the trial-to-trial correlations in spiking activity between neurons. Using multi-channel recordings, we were able to measure the neuronal correlations, and their effects on population coding of stimulus features. Our results have implications on the way which neural populations must be readout in order to maximize information.

scholarly journals Dynamic expressions of confidence within an evidence accumulation framework

2020 ◽  
Author(s):  
Kobe Desender ◽  
Tobias H. Donner ◽  
Tom Verguts
Keyword(s):  
Dynamic Model ◽  
Temporal Evolution ◽  
Human Behaviour ◽  
Evidence Accumulation ◽  
Decision Confidence ◽  
Confidence Judgments ◽  
Evidence Strength ◽  
Dot Motion ◽  
Over Time ◽  
Random Dot Motion

AbstractHuman observers can reliably report their confidence in the choices they make. An influential framework conceptualizes decision confidence as the probability of a decision being correct, given the choice made and the evidence on which it was based. This framework accounts for three diagnostic signatures of human confidence reports, including an opposite dependence of confidence on evidence strength for correct and error trials. However, the framework does not account for the temporal evolution of these signatures, because it only describes the transformation of a static representation of evidence into choice and the associated confidence. Here, we combine this framework with another influential framework: dynamic accumulation of evidence over time, and build on the notion that confidence reflects the probability of being correct, given the choice and accumulated evidence up until that point. Critically, we show that such a dynamic model predicts that the diagnostic signatures of confidence depend on time; most critically, it predicts a stronger opposite dependence of confidence on evidence strength and choice correctness as a function of time. We tested, and confirmed, these predictions in human behaviour during random dot motion discrimination, in which confidence judgments were queried at different points in time. We conclude that human confidence reports reflect the dynamics of the probability of being correct given the accumulated evidence and choice.

scholarly journals The Effects of Gridding Algorithms on the Statistical Moments and Their Trends of Daily Surface Air Temperature*

2015 ◽  
Vol 28 (23) ◽  
pp. 9188-9205 ◽  
Author(s):  
Nicholas R. Cavanaugh ◽  
Samuel S. P. Shen
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Climate Models ◽  
Decadal Variability ◽  
Surface Air Temperature ◽  
Higher Order ◽  
Statistical Moments ◽  
Weighted Sums ◽  
Higher Order Moments ◽  
Station Data

Abstract This paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network–Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT’s higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations.

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