scholarly journals NONLINEAR NEURAL NETWORK DYNAMICS ACCOUNTS FOR HUMAN CONFIDENCE IN A SEQUENCE OF PERCEPTUAL DECISIONS

2019 ◽  
Author(s):  
Kevin Berlemont ◽  
Jean-Rémy Martin ◽  
Jérôme Sackur ◽  
Jean-Pierre Nadal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Response Times ◽  
Network Dynamics ◽  
Confidence Judgment ◽  
Orientation Discrimination ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Sequential Effects ◽  
Modeling Framework

ABSTRACTElectrophysiological recordings during perceptual decision tasks in monkeys suggest that the degree of confidence in a decision is based on a simple neural signal produced by the neural decision process. Attractor neural networks provide an appropriate biophysical modeling framework, and account for the experimental results very well. However, it remains unclear whether attractor neural networks can account for confidence reports in humans. We present the results from an experiment in which participants are asked to perform an orientation discrimination task, followed by a confidence judgment. Here we show that an attractor neural network model quantitatively reproduces, for each participant, the relations between accuracy, response times and confidence. We show that the attractor neural network also accounts for confidence-specific sequential effects observed in the experiment (participants are faster on trials following high confidence trials). Remarkably, this is obtained as an inevitable outcome of the network dynamics, without any feedback specific to the previous decision (that would result in, e.g., a change in the model parameters before the onset of the next trial). Our results thus suggest that a metacognitive process such as confidence in one’s decision is linked to the intrinsically nonlinear dynamics of the decision-making neural network.

scholarly journals Qualitative speed-accuracy tradeoff effects can be explained by a diffusion/fast-guess mixture model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Roger Ratcliff ◽  
Inhan Kang
Keyword(s):  
Mixture Model ◽  
Diffusion Model ◽  
Response Times ◽  
Model Fit ◽  
Orientation Discrimination ◽  
Model Parameters ◽  
Stimulus Contrast ◽  
Accuracy Stress ◽  
Speed Accuracy ◽  
Selective Influence

AbstractRafiei and Rahnev (2021) presented an analysis of an experiment in which they manipulated speed-accuracy stress and stimulus contrast in an orientation discrimination task. They argued that the standard diffusion model could not account for the patterns of data their experiment produced. However, their experiment encouraged and produced fast guesses in the higher speed-stress conditions. These fast guesses are responses with chance accuracy and response times (RTs) less than 300 ms. We developed a simple mixture model in which fast guesses were represented by a simple normal distribution with fixed mean and standard deviation and other responses by the standard diffusion process. The model fit the whole pattern of accuracy and RTs as a function of speed/accuracy stress and stimulus contrast, including the sometimes bimodal shapes of RT distributions. In the model, speed-accuracy stress affected some model parameters while stimulus contrast affected a different one showing selective influence. Rafiei and Rahnev’s failure to fit the diffusion model was the result of driving subjects to fast guess in their experiment.

scholarly journals Inter-trial effects in priming of pop-out: Comparison of computational updating models

2021 ◽  
Vol 17 (9) ◽  
pp. e1009332
Author(s):  
Fredrik Allenmark ◽  
Ahu Gokce ◽  
Thomas Geyer ◽  
Artyom Zinchenko ◽  
Hermann J. Müller ◽  
...  
Keyword(s):  
Model Comparison ◽  
Response Times ◽  
Target Position ◽  
Preceding Trial ◽  
Target Color ◽  
Model Parameters ◽  
Target Feature ◽  
Modeling Framework ◽  
Trial Effect ◽  
Starting Point

In visual search tasks, repeating features or the position of the target results in faster response times. Such inter-trial ‘priming’ effects occur not just for repetitions from the immediately preceding trial but also from trials further back. A paradigm known to produce particularly long-lasting inter-trial effects–of the target-defining feature, target position, and response (feature)–is the ‘priming of pop-out’ (PoP) paradigm, which typically uses sparse search displays and random swapping across trials of target- and distractor-defining features. However, the mechanisms underlying these inter-trial effects are still not well understood. To address this, we applied a modeling framework combining an evidence accumulation (EA) model with different computational updating rules of the model parameters (i.e., the drift rate and starting point of EA) for different aspects of stimulus history, to data from a (previously published) PoP study that had revealed significant inter-trial effects from several trials back for repetitions of the target color, the target position, and (response-critical) target feature. By performing a systematic model comparison, we aimed to determine which EA model parameter and which updating rule for that parameter best accounts for each inter-trial effect and the associated n-back temporal profile. We found that, in general, our modeling framework could accurately predict the n-back temporal profiles. Further, target color- and position-based inter-trial effects were best understood as arising from redistribution of a limited-capacity weight resource which determines the EA rate. In contrast, response-based inter-trial effects were best explained by a bias of the starting point towards the response associated with a previous target; this bias appeared largely tied to the position of the target. These findings elucidate how our cognitive system continually tracks, and updates an internal predictive model of, a number of separable stimulus and response parameters in order to optimize task performance.

scholarly journals Generalized Born radii computation using linear models and neural networks

2019 ◽  
Vol 36 (6) ◽  
pp. 1757-1764
Author(s):  
Saida Saad Mohamed Mahmoud ◽  
Gennaro Esposito ◽  
Giuseppe Serra ◽  
Federico Fogolari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Model ◽  
Correlation Coefficient ◽  
Linear Models ◽  
Reference Method ◽  
Supplementary Information ◽  
Model Parameters ◽  
Generalized Born ◽  
Supplementary Material

Abstract Motivation Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples. Results GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of ‘perfect’ GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with ‘perfect’ GB radii of 0.97 and ca. 20% smaller root mean square error. Availability and implementation We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks

2020 ◽  
Vol 10 (10) ◽  
pp. 3358 ◽  
Author(s):  
Jiyuan Song ◽  
Aibin Zhu ◽  
Yao Tu ◽  
Hu Huang ◽  
Muhammad Affan Arif ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Human Motion ◽  
Control Mode ◽  
Multilayer Perceptrons ◽  
Model Parameters ◽  
Motion Pattern ◽  
Accuracy Rate ◽  
Feature Parameters

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.

scholarly journals NEURAL NETWORK INVERSION OF RESISTIVITY DATA FOR DETERMINATION OF DISTANCE TO A BED BOUNDARY

2021 ◽  
Vol 2 (2) ◽  
pp. 95-102
Author(s):  
Dmitry Yu. Kushnir ◽  
Nikolay N. Velker ◽  
Darya V. Andornaya ◽  
Yuriy E. Antonov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Estimation ◽  
Model Parameters ◽  
Formation Model ◽  
Inversion Algorithm ◽  
Resistivity Data ◽  
Neural Network Inversion ◽  
Real Time Estimation

Accurate real-time estimation of a distance to the nearest bed boundary simplifies the steering of directional wells. For estimation of that distance, we propose an approach of pointwise inversion of resistivity data using neural networks based on two-layer resistivity formation model. The model parameters are determined from the tool responses using a cascade of neural networks. The first network calculates the resistivity of the layer containing the tool measure point. The subsequent networks take as input the tool responses and the model parameters determined with the previous networks. All networks are trained on the same synthetic database. The samples of that database consist of the pairs of model parameters and corresponding noisy tool responses. The results of the proposed approach are close to the results of the general inversion algorithm based on the method of the most-probable parameter combination. At the same time, the performance of the proposed inversion is several orders faster.

scholarly journals Global visual confidence

2021 ◽  
Author(s):  
Alan L. F. Lee ◽  
Vincent de Gardelle ◽  
Pascal Mamassian
Keyword(s):  
Response Time ◽  
Model Comparison ◽  
Response Times ◽  
Confidence Judgment ◽  
Orientation Discrimination ◽  
Perceptual Task ◽  
Choice Accuracy ◽  
Sensory Evidence ◽  
Discrimination Judgment ◽  
Time Information

AbstractVisual confidence is the observers’ estimate of their precision in one single perceptual decision. Ultimately, however, observers often need to judge their confidence over a task in general rather than merely on one single decision. Here, we measured the global confidence acquired across multiple perceptual decisions. Participants performed a dual task on two series of oriented stimuli. The perceptual task was an orientation-discrimination judgment. The metacognitive task was a global confidence judgment: observers chose the series for which they felt they had performed better in the perceptual task. We found that choice accuracy in global confidence judgments improved as the number of items in the series increased, regardless of whether the global confidence judgment was made before (prospective) or after (retrospective) the perceptual decisions. This result is evidence that global confidence judgment was based on an integration of confidence information across multiple perceptual decisions rather than on a single one. Furthermore, we found a tendency for global confidence choices to be influenced by response times, and more so for recent perceptual decisions than earlier ones in the series of stimuli. Using model comparison, we found that global confidence is well described as a combination of noisy estimates of sensory evidence and position-weighted response-time evidence. In summary, humans can integrate information across multiple decisions to estimate global confidence, but this integration is not optimal, in particular because of biases in the use of response-time information.

scholarly journals Improved supervised learning methods for EoR parameters reconstruction

2019 ◽  
Vol 490 (1) ◽  
pp. 371-384 ◽  
Author(s):  
Aristide Doussot ◽  
Evan Eames ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bayesian Inference ◽  
Maximum Likelihood ◽  
Supervised Learning ◽  
Confidence Level ◽  
Thermal Noise ◽  
Model Parameters ◽  
Learning Methods ◽  
Parameter Values

ABSTRACT Within the next few years, the Square Kilometre Array (SKA) or one of its pathfinders will hopefully detect the 21-cm signal fluctuations from the Epoch of Reionization (EoR). Then, the goal will be to accurately constrain the underlying astrophysical parameters. Currently, this is mainly done with Bayesian inference. Recently, neural networks have been trained to perform inverse modelling and, ideally, predict the maximum-likelihood values of the model parameters. We build on these by improving the accuracy of the predictions using several supervised learning methods: neural networks, kernel regressions, or ridge regressions. Based on a large training set of 21-cm power spectra, we compare the performances of these methods. When using a noise-free signal generated by the model itself as input, we improve on previous neural network accuracy by one order of magnitude and, using a local ridge kernel regression, we gain another factor of a few. We then reach an accuracy level on the reconstruction of the maximum-likelihood parameter values of a few per cents compared the 1σ confidence level due to SKA thermal noise (as estimated with Bayesian inference). For an input signal affected by an SKA-like thermal noise but constrained to yield the same maximum-likelihood parameter values as the noise-free signal, our neural network exhibits an error within half of the 1σ confidence level due to the SKA thermal noise. This accuracy improves to 10$\, {\rm per\, cent}$ of the 1σ level when using the local ridge kernel. We are thus reaching a performance level where supervised learning methods are a viable alternative to determine the maximum-likelihood parameters values.

scholarly journals A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex

2020 ◽  
Vol 117 (47) ◽  
pp. 29872-29882
Author(s):  
Ben Tsuda ◽  
Kay M. Tye ◽  
Hava T. Siegelmann ◽  
Terrence J. Sejnowski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prefrontal Cortex ◽  
Reinforcement Learning ◽  
Modeling Framework ◽  
Simple Neural Network ◽  
Neuropsychological Impairments ◽  
And Storage ◽  
The Brain

The prefrontal cortex encodes and stores numerous, often disparate, schemas and flexibly switches between them. Recent research on artificial neural networks trained by reinforcement learning has made it possible to model fundamental processes underlying schema encoding and storage. Yet how the brain is able to create new schemas while preserving and utilizing old schemas remains unclear. Here we propose a simple neural network framework that incorporates hierarchical gating to model the prefrontal cortex’s ability to flexibly encode and use multiple disparate schemas. We show how gating naturally leads to transfer learning and robust memory savings. We then show how neuropsychological impairments observed in patients with prefrontal damage are mimicked by lesions of our network. Our architecture, which we call DynaMoE, provides a fundamental framework for how the prefrontal cortex may handle the abundance of schemas necessary to navigate the real world.

scholarly journals Neural Network Dynamics without Minimizing Energy

2013 ◽  
Vol 2013 ◽  
pp. 1-4
Author(s):  
Mau-Hsiang Shih ◽  
Feng-Sheng Tsai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Structure ◽  
Energy Minimization ◽  
Network Dynamics ◽  
Neural Dynamics ◽  
Network Systems ◽  
Energy Functions ◽  
Content Addressable Memory ◽  
Neural Network Dynamics

Content-addressable memory (CAM) has been described by collective dynamics of neural networks and computing with attractors (equilibrium states). Studies of such neural network systems are typically based on the aspect of energy minimization. However, when the complexity and the dimension of neural network systems go up, the use of energy functions might have its own limitations to study CAM. Recently, we have proposed the decirculation process in neural network dynamics, suggesting a step toward the reshaping of network structure and the control of neural dynamics without minimizing energy. Armed with the decirculation process, a sort of decirculating maps and its structural properties are built here, dedicated to showing that circulation breaking taking place in the connections among many assemblies of neurons can collaborate harmoniously toward the completion of network structure that generates CAM.

scholarly journals Global visual confidence

2020 ◽  
Author(s):  
Alan L. F. Lee ◽  
Vincent de Gardelle ◽  
Pascal Mamassian
Keyword(s):  
Response Time ◽  
Model Comparison ◽  
Response Times ◽  
Confidence Judgment ◽  
Orientation Discrimination ◽  
Perceptual Task ◽  
Choice Accuracy ◽  
Sensory Evidence ◽  
Discrimination Judgment ◽  
Time Information

Visual confidence is the observers’ estimate of their precision in one single perceptual decision. Ultimately, however, observers often need to judge their confidence over a task in general rather than merely on one single decision. Here, we measured the global confidence acquired across multiple perceptual decisions. Participants performed a dual task on two series of oriented stimuli. The perceptual task was an orientation-discrimination judgment. The metacognitive task was a global confidence judgment: observers chose the series for which they felt they had performed better in the perceptual task. We found that choice accuracy in global confidence judgments improved as the number of items in the series increased, regardless of whether the global confidence judgment was made before (prospective) or after (retrospective) the perceptual decisions. This result is evidence that global confidence judgment was based on an integration of confidence information across multiple perceptual decisions rather than on a single one. Furthermore, we found a tendency for global confidence choices to be influenced by response times, and more so for recent perceptual decisions than earlier ones in the series of stimuli. Using model comparison, we found that global confidence is well described as a combination of noisy estimates of sensory evidence and position-weighted response-time evidence. In summary, humans can integrate information across multiple decisions to estimate global confidence, but this integration is not optimal, in particular because of biases in the use of response-time information.

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