scholarly journals Policies or knowledge: priors differ between a perceptual and sensorimotor task

2019 ◽  
Vol 121 (6) ◽  
pp. 2267-2275 ◽  
Author(s):  
Claire Chambers ◽  
Hugo Fernandes ◽  
Konrad Paul Kording
Keyword(s):  
Probability Distributions ◽  
Perceptual Task ◽  
Comparison Task ◽  
Estimation Task ◽  
Model And Simulation ◽  
Simulation Based ◽  
Central Tenet ◽  
Internal States ◽  
Perceptual Comparison ◽  
The Brain

If the brain abstractly represents probability distributions as knowledge, then the modality of a decision, e.g., movement vs. perception, should not matter. If, on the other hand, learned representations are policies, they may be specific to the task where learning takes place. Here, we test this by asking whether a learned spatial prior generalizes from a sensorimotor estimation task to a two-alternative-forced choice (2-Afc) perceptual comparison task. A model and simulation-based analysis revealed that while participants learn prior distribution in the sensorimotor estimation task, measured priors are consistently broader than sensorimotor priors in the 2-Afc task. That the prior does not fully generalize suggests that sensorimotor priors are more like policies than knowledge. In disagreement with standard Bayesian thought, the modality of the decision has a strong influence on the implied prior distributions. NEW & NOTEWORTHY We do not know whether the brain represents abstract and generalizable knowledge or task-specific policies that map internal states to actions. We find that learning in a sensorimotor task does not generalize strongly to a perceptual task, suggesting that humans learned policies and did not truly acquire knowledge. Priors differ across tasks, thus casting doubt on the central tenet of many Bayesian models, that the brain’s representation of the world is built on generalizable knowledge.

scholarly journals Policies or Knowledge: Priors differ between perceptual and sensorimotor tasks

2017 ◽  
Author(s):  
Claire Chambers ◽  
Hugo Fernandes ◽  
Konrad Paul Kording
Keyword(s):  
Prior Distribution ◽  
Probability Distributions ◽  
Perceptual Task ◽  
Comparison Task ◽  
Estimation Task ◽  
Model And Simulation ◽  
Central Tenet ◽  
Internal States ◽  
Perceptual Comparison ◽  
The Brain

ABSTRACTIf the brain abstractly represents probability distributions as knowledge, then the modality of a decision, e.g. movement vs perception, should not matter. If on the other hand, learned representations are policies, they may be specific to the task where learning takes place. Here, we test this by asking if a learned spatial prior generalizes from a sensorimotor estimation task to a two-alternative-forced choice (2-Afc) perceptual comparison task. A model and simulation-based analysis revealed that while participants learn the experimentally-imposed prior distribution in the sensorimotor estimation task, measured priors are consistently broader than expected in the 2-Afc task. That the prior does not fully generalize suggests that sensorimotor priors strongly resemble policies. In disagreement with standard Bayesian thought, the modality of the decision has a strong influence on the implied prior distribution.NEW AND NOTEWORTHYWe do not know if the brain represents abstract and generalizable knowledge or task-specific policies that map internal states to actions. We find that learning in a sensorimotor task does not generalize strongly to a perceptual task, suggesting that humans learned policies and did not truly acquire knowledge. Priors differ across tasks, thus casting doubt on the central tenet of may Bayesian models, that the brain’s representation of the world is built on generalizable knowledge.

W-Peakedness: A Simulation based Study

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Suboohi Safdar ◽  
Dr. Ejaz Ahmed
Keyword(s):  
Probability Distributions ◽  
Extensive Study ◽  
Descriptive Statistics ◽  
Target Point ◽  
Density Functions ◽  
Central Value ◽  
Distribution Shape ◽  
Simulation Based ◽  
Different Shapes ◽  
Kurtosis Coefficient

Kurtosis is a commonly used descriptive statistics. Kurtosis “Coefficient of excess” is critically reviewed in different aspects and is called as, measuring the fatness of the tails of the density functions, concentration towards the central value, scattering away from the target point or degree of peakedness of probability distribution. Kurtosis is referred to the shape of the distribution but many distributions having same kurtosis value may have different shapes while Kurtosis may exist when peak of a distribution is not in existence. Through extensive study of kurtosis on several distributions, Wu (2002) introduced a new measure called “W-Peakedness” that offers a fine capture of distribution shape to provide an intuitive measure of peakedness of the distribution which is inversely proportional to the standard deviation of the distribution. In this paper the work is extended for different others continuous probability distributions. Empirical results through simulation illustrate the proposed method to evaluate kurtosis by W-peakedness

scholarly journals Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink Sheikh Md. Rabiul Islam1, Md. Shakibul Islam2

2021 ◽  
pp. 1-7
Author(s):  
Sheikh Md. Rabiul Islam ◽  
◽  
Md. Shakibul Islam ◽  
Keyword(s):  
Signal Generation ◽  
Neural Mass Model ◽  
Pathophysiological Mechanism ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Mass Model ◽  
Detailed Model ◽  
Simulation Based ◽  
Neural Mass ◽  
The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

scholarly journals A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

2018 ◽  
Author(s):  
Seth W. Egger ◽  
Mehrdad Jazayeri
Keyword(s):  
Probability Distributions ◽  
Nonlinear Function ◽  
Alternative View ◽  
Time Interval ◽  
Prior Beliefs ◽  
Optimal Behavior ◽  
Multiple Measurements ◽  
The Brain ◽  
Full Probability ◽  
Sequential Measurements

AbstractBayesian models of behavior have advanced the idea that humans combine prior beliefs and sensory observations to minimize uncertainty. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent and manipulate probability distributions. An alternative view is that brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property makes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects’ behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that inference strategies humans deploy may deviate from Bayes-optimal integration when the computational demands are high.

scholarly journals Latent learning drives sleep-dependent plasticity in distinct CA1 subpopulations

2020 ◽  
Author(s):  
Wei Guo ◽  
Jie J. Zhang ◽  
Jonathan P. Newman ◽  
Matthew A. Wilson
Keyword(s):  
Hippocampal Neurons ◽  
Dimensional Manifold ◽  
Latent Learning ◽  
Neural Ensembles ◽  
Correlated Activity ◽  
Place Fields ◽  
Internal States ◽  
Low Dimensional ◽  
Low Dimensional Manifold ◽  
The Brain

AbstractLatent learning allows the brain the transform experiences into cognitive maps, a form of implicit memory, without reinforced training. Its mechanism is unclear. We tracked the internal states of the hippocampal neural ensembles and discovered that during latent learning of a spatial map, the state space evolved into a low-dimensional manifold that topologically resembled the physical environment. This process requires repeated experiences and sleep in-between. Further investigations revealed that a subset of hippocampal neurons, instead of rapidly forming place fields in a novel environment, remained weakly tuned but gradually developed correlated activity with other neurons. These ‘weakly spatial’ neurons bond activity of neurons with stronger spatial tuning, linking discrete place fields into a map that supports flexible navigation.

Tonsil tie simulator

2013 ◽  
Vol 127 (9) ◽  
pp. 924-926 ◽  
Author(s):  
J Duodu ◽  
T H J Lesser
Keyword(s):  
Blood Vessels ◽  
Low Pressure ◽  
Realistic Model ◽  
Surgical Skills ◽  
Operating Theatre ◽  
Active Bleeding ◽  
Surgical Trainee ◽  
Model And Simulation ◽  
Simulation Based ◽  
Training Opportunities

AbstractBackground:The surgical trainee has to acquire surgical skills in an era of reduced training hours and greater demands for efficient use of operating theatre time. Many surgical specialties are utilising model and simulation-based training to provide safe, low-pressure training opportunities for today's trainee.Method and results:This paper describes a simple, relatively inexpensive tonsillectomy model that enables the practice of tonsil removal and ligation of bleeding vessels. The model is beneficial for the patient, trainee and trainer.Conclusion:The pseudo mouth and active bleeding components of this model provide the trainee with a relatively inexpensive, realistic model with which to gain confidence and competence in the skill of ligating tonsillar blood vessels with a tonsil tie.

scholarly journals Conservation law for self-paced movements

2016 ◽  
Vol 113 (31) ◽  
pp. 8831-8836 ◽  
Author(s):  
Dongsung Huh ◽  
Terrence J. Sejnowski
Keyword(s):  
Optimal Control ◽  
Scaling Law ◽  
Control Variable ◽  
Level Control ◽  
Internal Motivation ◽  
Internal States ◽  
Motivation Level ◽  
High Level ◽  
The Brain ◽  
Biological Motor

Optimal control models of biological movements introduce external task factors to specify the pace of movements. Here, we present the dual to the principle of optimality based on a conserved quantity, called “drive,” that represents the influence of internal motivation level on movement pace. Optimal control and drive conservation provide equivalent descriptions for the regularities observed within individual movements. For regularities across movements, drive conservation predicts a previously unidentified scaling law between the overall size and speed of various self-paced hand movements in the absence of any external tasks, which we confirmed with psychophysical experiments. Drive can be interpreted as a high-level control variable that sets the overall pace of movements and may be represented in the brain as the tonic levels of neuromodulators that control the level of internal motivation, thus providing insights into how internal states affect biological motor control.

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