Perceptual Decision-Making: Biases in Post-Error Reaction Times Explained by Attractor Network Dynamics

@article{Berlemont2019PerceptualDB,
  title={Perceptual Decision-Making: Biases in Post-Error Reaction Times Explained by Attractor Network Dynamics},
  author={Kevin Berlemont and Jean-Pierre Nadal},
  journal={The Journal of Neuroscience},
  year={2019},
  volume={39},
  pages={833 - 853}
}
Perceptual decision-making is the subject of many experimental and theoretical studies. Most modeling analyses are based on statistical processes of accumulation of evidence. In contrast, very few works confront attractor network models' predictions with empirical data from continuous sequences of trials. Recently however, numerical simulations of a biophysical competitive attractor network model have shown that such a network can describe sequences of decision trials and reproduce repetition… 
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References

SHOWING 1-10 OF 126 REFERENCES
Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits
TLDR
Given that neural responses that switch stochastically between discrete states can “masquerade” as integration in single-neuron and trial-averaged data, the results suggest that such networks should be considered as plausible alternatives to the integrator model.
Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models
TLDR
It is found that decaying tail activity from the previous trial caused choice hysteresis, especially during difficult trials, and accurately predicted human perceptual choices, in an established, biophysically informed model of a competitive attractor network for decision making.
Neural Circuit Dynamics Underlying Accumulation of Time-Varying Evidence During Perceptual Decision Making
TLDR
A recurrent neural circuit model is used to simulate an experiment in which monkeys performed a direction-discrimination task on a visual motion stimulus, and shows further support for an attractor network model of time integration in perceptual decision making.
Probabilistic Decision Making by Slow Reverberation in Cortical Circuits
Coupled Decision Processes Update and Maintain Saccadic Priors in a Dynamic Environment
TLDR
This study shows how sensory-motor decision processes, typically studied in isolation, interact via functional information-processing loops in the brain to produce complex, adaptive behaviors.
Can Post-Error Dynamics Explain Sequential Reaction Time Patterns?
TLDR
The results suggest that error-based parameter adjustments are critical to modeling sequential effects, and it is shown that simple, sequential updates to the initial condition and thresholds of a pure drift diffusion model can account for the trends in RT for correct and error trials.
Confidence estimation as a stochastic process in a neurodynamical system of decision making.
TLDR
A biologically realistic spiking network model is examined and it is found that it reproduced salient behavioral observations and single-neuron activity data from a monkey experiment designed to study confidence about a decision under uncertainty, predicting changes of mind can occur in a mnemonic delay when confidence is low.
Probabilistic Population Codes for Bayesian Decision Making
Dynamic Excitatory and Inhibitory Gain Modulation Can Produce Flexible, Robust and Optimal Decision-making
TLDR
The model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated, and can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range.
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