The Log-likelihood of the models increased over runs indicating that later runs showed less noisy behavior that was more in line with the computational reinforcement learning model.</p
(A) The accuracy that the agent achieves at different levels of signal strength (i.e. 100—% noise). ...
<p>The learning trend versus trial number for the conditions of Experiment 2 and Experiment 3 plus a...
Understanding how an animal’s ability to learn relates to neural activity or is altered by lesions, ...
a. Learning rate for wins, αwin, does not change over runs (b = -0.006, p = .19). b. Learning rate f...
Dots depict the rank correlation of parameter estimates in one run with the mean across all other ru...
System behavior models are highly useful for the developers of the system as they aid in system comp...
<p>The graph shows that, on average, spiking rate predictions are better on the full model than on t...
<p>(<b>a</b>) Plotted are the effective learning rates for potentiation () and depression () events ...
Model comparison. AIC, Akaike Information Criterion (computed with nLLmax); BIC, Bayesian Informatio...
Estimates of the rate of change of response accuracy for humans, measured in log-odds units of proba...
The rise of computational modeling in the past decade has led to a substantial increase in the numbe...
Much experimental evidence suggests that during decision making neural circuits accumulate evidence ...
<p>The columns correspond to constant, normal, log-normal and power-law ability distributions with a...
(a) Activity of a readout unit after learning a chunk at different noise levels: σ = 0 (black), 0.25...
This simulation is inspired by a previous study by Behrens et al [2], in which the reward probabilit...
(A) The accuracy that the agent achieves at different levels of signal strength (i.e. 100—% noise). ...
<p>The learning trend versus trial number for the conditions of Experiment 2 and Experiment 3 plus a...
Understanding how an animal’s ability to learn relates to neural activity or is altered by lesions, ...
a. Learning rate for wins, αwin, does not change over runs (b = -0.006, p = .19). b. Learning rate f...
Dots depict the rank correlation of parameter estimates in one run with the mean across all other ru...
System behavior models are highly useful for the developers of the system as they aid in system comp...
<p>The graph shows that, on average, spiking rate predictions are better on the full model than on t...
<p>(<b>a</b>) Plotted are the effective learning rates for potentiation () and depression () events ...
Model comparison. AIC, Akaike Information Criterion (computed with nLLmax); BIC, Bayesian Informatio...
Estimates of the rate of change of response accuracy for humans, measured in log-odds units of proba...
The rise of computational modeling in the past decade has led to a substantial increase in the numbe...
Much experimental evidence suggests that during decision making neural circuits accumulate evidence ...
<p>The columns correspond to constant, normal, log-normal and power-law ability distributions with a...
(a) Activity of a readout unit after learning a chunk at different noise levels: σ = 0 (black), 0.25...
This simulation is inspired by a previous study by Behrens et al [2], in which the reward probabilit...
(A) The accuracy that the agent achieves at different levels of signal strength (i.e. 100—% noise). ...
<p>The learning trend versus trial number for the conditions of Experiment 2 and Experiment 3 plus a...
Understanding how an animal’s ability to learn relates to neural activity or is altered by lesions, ...