@article {46900,
	title = {Confidence estimation as a stochastic process in a neurodynamical system of decision making.},
	journal = {Journal of Neurophysiology},
	volume = {114},
	year = {2015},
	month = {2015 Jul},
	pages = {99-113},
	abstract = {<p>Evaluation of confidence about one{\textquoteright}s knowledge is key to the brain{\textquoteright}s ability to monitor cognition. To investigate the neural mechanism of confidence assessment, we examined a biologically realistic spiking network model and 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. Interestingly, the model predicts that changes of mind can occur in a mnemonic delay when confidence is low; the probability of changes of mind increases (decreases) with task difficulty in correct (error) trials. Furthermore, a so-called "hard-easy effect" observed in humans naturally emerges, i.e., behavior shows underconfidence (underestimation of correct rate) for easy or moderately difficult tasks and overconfidence (overestimation of correct rate) for very difficult tasks. Importantly, in the model, confidence is computed using a simple neural signal in individual trials, without explicit representation of probability functions. Therefore, even a concept of metacognition can be explained by sampling a stochastic neural activity pattern.</p>
},
	issn = {1522-1598},
	doi = {10.1152/jn.00793.2014},
	author = {Wei, Ziqiang and Wang, Xiao-Jing}
}
@article {5078,
	title = {From distributed resources to limited slots in multiple-item working memory: a spiking network model with normalization.},
	journal = {Journal of Neuroscience},
	volume = {32},
	year = {2012},
	month = {2012 Aug 15},
	pages = {11228-40},
	abstract = {<p>Recent behavioral studies have given rise to two contrasting models for limited working memory capacity: a "discrete-slot" model in which memory items are stored in a limited number of slots, and a "shared-resource" model in which the neural representation of items is distributed across a limited pool of resources. To elucidate the underlying neural processes, we investigated a continuous network model for working memory of an analog feature. Our model network fundamentally operates with a shared resource mechanism, and stimuli in cue arrays are encoded by a distributed neural population. On the other hand, the network dynamics and performance are also consistent with the discrete-slot model, because multiple objects are maintained by distinct localized population persistent activity patterns (bump attractors). We identified two phenomena of recurrent circuit dynamics that give rise to limited working memory capacity. As the working memory load increases, a localized persistent activity bump may either fade out (so the memory of the corresponding item is lost) or merge with another nearby bump (hence the resolution of mnemonic representation for the merged items becomes blurred). We identified specific dependences of these two phenomena on the strength and tuning of recurrent synaptic excitation, as well as network normalization: the overall population activity is invariant to set size and delay duration; therefore, a constant neural resource is shared by and dynamically allocated to the memorized items. We demonstrate that the model reproduces salient observations predicted by both discrete-slot and shared-resource models, and propose testable predictions of the merging phenomenon.</p>
},
	issn = {1529-2401},
	doi = {10.1523/​JNEUROSCI.0735-12.2012},
	author = {Wei, Ziqiang and Wang, Xiao-Jing and Wang, Da-Hui}
}
