scholarly journals Decision-making through integration of sensory evidence at prolonged timescales

2018 ◽  
Author(s):  
Michael L. Waskom ◽  
Roozbeh Kiani
Keyword(s):  
Decision Making ◽  
Biological Networks ◽  
Network Models ◽  
Neuronal Responses ◽  
Neural Basis ◽  
Neural Network Models ◽  
Memory Leak ◽  
Sensory Evidence ◽  
Long Timescales ◽  
Evidence Integration

SummaryWhen multiple pieces of information bear on a decision, the best approach is to combine the evidence provided by each one. Evidence integration models formalize the computations underlying this process [1–3], explain human perceptual discrimination behavior [4–11], and correspond to neuronal responses elicited by discrimination tasks [12–17]. These findings indicate that evidence integration is key to understanding the neural basis of decision-making [18–21]. Evidence integration has most often been studied with simple tasks that limit the timescale of deliberation to hundreds of milliseconds, but many natural decisions unfold over much longer durations. Because neural network models imply acute limitations on the timescale of evidence integration [22–26], it is unknown whether current computational insights can generalize beyond rapid judgments. Here, we introduce a new psychophysical task and report model-based analyses of human behavior that demonstrate evidence integration at long timescales. Our task requires probabilistic inference using brief samples of visual evidence that are separated in time by long and unpredictable gaps. We show through several quantitative assays how decision-making can approximate a normative integration process that extends over tens of seconds without accruing significant memory leak or noise. These results support the generalization of evidence integration models to a broader class of behaviors while posing new challenges for models of how these computations are implemented in biological networks.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

Models of Decision-Making Operators of Socio-Technical System

2017 ◽  
pp. 33-75 ◽  
Author(s):  
Tetiana Shmelova ◽  
Yuliya Sikirda
Keyword(s):  
Decision Making ◽  
Stochastic Models ◽  
Network Models ◽  
Technical System ◽  
Behavioral Activity ◽  
Neural Network Models ◽  
Markov Network ◽  
The Neural Network ◽  
Tree Models ◽  
Aerial Vehicle

In this chapter, the authors present Air Navigation System (ANS) as a Socio-technical System (STS). The authors present models of decision making (DM) operators of STS, such as the deterministic models obtained for using network planning; the stochastic models obtained for using decision-tree; models in uncertainty obtained for using criteria Vald, Laplace, Savage, Hurwicz and other. The authors presented also DM models of operators in ANS, such as the neural network models, fuzzy models, the Markov network models, GERT-models for modelling and forecasting of behavioral activity of ANS's Human-operator (H-O) in flight emergencies situation. The scenarios of developing a flight situation in case of selecting either the positive or negative pole in accordance with the reflexive theory have been obtained. They demonstrate some examples with DM's deterministic and stochastic models for engineers, pilots, air traffic controllers, Unmanned Aerial Vehicle (UAV) operators, managers etc. In addition, the chapter presents some examples of DM models developed by the author and students at National Aviation University.

scholarly journals Inhibition of Pre–Supplementary Motor Area by Continuous Theta Burst Stimulation Leads to More Cautious Decision-making and More Efficient Sensory Evidence Integration

2017 ◽  
Vol 29 (8) ◽  
pp. 1433-1444 ◽  
Author(s):  
Tuğçe Tosun ◽  
Dilara Berkay ◽  
Alexander T. Sack ◽  
Yusuf Ö. Çakmak ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Decision Threshold ◽  
Theta Burst Stimulation ◽  
Burst Stimulation ◽  
Sensory Evidence ◽  
Speed Accuracy ◽  
Theta Burst ◽  
Continuous Theta Burst Stimulation ◽  
Accuracy Tradeoff ◽  
Evidence Integration

Decisions are made based on the integration of available evidence. The noise in evidence accumulation leads to a particular speed–accuracy tradeoff in decision-making, which can be modulated and optimized by adaptive decision threshold setting. Given the effect of pre-SMA activity on striatal excitability, we hypothesized that the inhibition of pre-SMA would lead to higher decision thresholds and an increased accuracy bias. We used offline continuous theta burst stimulation to assess the effect of transient inhibition of the right pre-SMA on the decision processes in a free-response two-alternative forced-choice task within the drift diffusion model framework. Participants became more cautious and set higher decision thresholds following right pre-SMA inhibition compared with inhibition of the control site (vertex). Increased decision thresholds were accompanied by an accuracy bias with no effects on post-error choice behavior. Participants also exhibited higher drift rates as a result of pre-SMA inhibition compared with the vertex inhibition. These results, in line with the striatal theory of speed–accuracy tradeoff, provide evidence for the functional role of pre-SMA activity in decision threshold modulation. Our results also suggest that pre-SMA might be a part of the brain network associated with the sensory evidence integration.

scholarly journals A Neural Network Auction For Group Decision Making Over a Continuous Space

2021 ◽  
Author(s):  
Yoram Bachrach ◽  
Ian Gemp ◽  
Marta Garnelo ◽  
Janos Kramar ◽  
Tom Eccles ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Group Decision ◽  
Network Models ◽  
Individual Utility ◽  
Neural Network Models ◽  
Continuous Space ◽  
Total Utility ◽  
Pricing Rule ◽  
True Preferences

We propose a system for conducting an auction over locations in a continuous space. It enables participants to express their preferences over possible choices of location in the space, selecting the location that maximizes the total utility of all agents. We prevent agents from tricking the system into selecting a location that improves their individual utility at the expense of others by using a pricing rule that gives agents no incentive to misreport their true preferences. The system queries participants for their utility in many random locations, then trains a neural network to approximate the preference function of each participant. The parameters of these neural network models are transmitted and processed by the auction mechanism, which composes these into differentiable models that are optimized through gradient ascent to compute the final chosen location and charged prices.

scholarly journals Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity

2013 ◽  
Vol 109 (10) ◽  
pp. 2542-2559 ◽  
Author(s):  
Nicholas Cain ◽  
Andrea K. Barreiro ◽  
Michael Shadlen ◽  
Eric Shea-Brown
Keyword(s):  
Decision Making ◽  
Sequential Analysis ◽  
Basic Function ◽  
Fine Tuning ◽  
Decay Modes ◽  
Decision Performance ◽  
Recurrent Excitation ◽  
Dot Motion ◽  
Long Timescales ◽  
Evidence Integration

A key step in many perceptual decision tasks is the integration of sensory inputs over time, but a fundamental questions remain about how this is accomplished in neural circuits. One possibility is to balance decay modes of membranes and synapses with recurrent excitation. To allow integration over long timescales, however, this balance must be exceedingly precise. The need for fine tuning can be overcome via a “robust integrator” mechanism in which momentary inputs must be above a preset limit to be registered by the circuit. The degree of this limiting embodies a tradeoff between sensitivity to the input stream and robustness against parameter mistuning. Here, we analyze the consequences of this tradeoff for decision-making performance. For concreteness, we focus on the well-studied random dot motion discrimination task and constrain stimulus parameters by experimental data. We show that mistuning feedback in an integrator circuit decreases decision performance but that the robust integrator mechanism can limit this loss. Intriguingly, even for perfectly tuned circuits with no immediate need for a robustness mechanism, including one often does not impose a substantial penalty for decision-making performance. The implication is that robust integrators may be well suited to subserve the basic function of evidence integration in many cognitive tasks. We develop these ideas using simulations of coupled neural units and the mathematics of sequential analysis.

scholarly journals Prediction of weld quality using intelligent decision making tools

2012 ◽  
Vol 1 (2) ◽  
pp. 131 ◽  
Author(s):  
Edwin Raja Dhas ◽  
Somasundaram Kumanan ◽  
C.P. Jesuthanam
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Regression Model ◽  
Back Propagation ◽  
Network Models ◽  
Multiple Regression Model ◽  
Decision Making Process ◽  
Weld Quality ◽  
Neural Network Models ◽  
Intelligent Decision Making

Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design anddemand of quality products. To make decision making process online, effective and efficient artificial intelligent tools likeneural networks are being attempted. This paper proposes the development of neural network models for prediction ofweld quality in Submerged Arc Welding (SAW). Experiments are designed according to Taguchi’s principles andmathematical equations are developed using multiple regression model. Proposed neural network models are developedusing experimental data, supported with the data generated by regression model. The performances of the developedmodels are compared in terms of computational speed and prediction accuracy. It is found that Neural Network trainedwith Particle Swarm Optimization (NNPSO) performs better than Neural Network trained with Back Propagation (BPNN)algorithm, Radial Basis Functional Neural Network (RBFNN) and Neural Network trained with Genetic Algorithm(NNGA). The developed scheme for weld quality prediction is flexible, competent, and accurate than existing models andit scopes better online monitoring system. Finally the developed models are validated. The proposed and developedtechnique finds a good scope and a better future in the relevant field where human can avoid unwanted risks duringoperations with the deployment of robots.

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