Foundations of human reasoning in the prefrontal cortex

By Maël Donoso, A. Collins, Etienne Koechlin. 2014

396 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, General Decision Sciences

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Citations in the corpus

Abstract context representations in primate amygdala and prefrontal cortex (2015, background): “…contexts, or if a task employs unreliable feedback, immediate switching becomes impossible, and exploration is necessary to figure out the new context (Koechlin, 2014; Yu and Dayan, 2005) (also see Collins and Koechlin, 2012; Donoso et al., 2014) (these papers refer to a context as a ‘‘task set’’).”
Hierarchical Error Representation: A Computational Model of Anterior Cingulate and Dorsolateral Prefrontal Cortex (2015, background): “This training function may be separate from a more general inhibitory function, by which mPFC may suppress or reset the current task set (Donoso et al., 2014; Kolling et al., 2012).”
Active Inference, Curiosity and Insight (2017, background): “The purpose of this article is not to review the large literature in these fields or provide a synthesis of established ideas (e.g., Schmidhuber, 1991; Oaksford & Chater, 2001; Koechlin et al., 2003; Botvinick & An, 2008; Nelson et al., 2010; Navarro & Perfors, 2011; Tenenbaum, Kemp, Griffiths, & Go”
The Importance of Falsification in Computational Cognitive Modeling. (2017, positive): “In the ‘model first' cases, the computational model itself can provide the key insight to identify the behavioral phenomena of interest by, for example, aligning the data to events that are not cued by the task, but are instead predicted by the model [24].”
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives (2018, neutral): “Because the intrinsic dynamic of trafﬁc is changing and open ended, the dimension of the parameter space regarding hidden states in the model is believed to be inﬁnite[15], [22].”
Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation (2017, background): “…learn the model of the task in the first place (corresponding to reduction in uncertainty at the rule or strategy level, Bach & Dolan, 2012; Donoso et al., 2014) or learn beliefs (hyperparameters) about self-ability, but these are likely to be important for understanding the dynamics of…”
Neuroscience-Inspired Artificial Intelligence. (2017, background): “Intriguingly, the proposed architecture bears some resemblance to a successful computational model of sequential task learning in humans (Collins and Koechlin, 2012; Donoso et al., 2014).”
Lateral prefrontal theta oscillations causally drive a computational mechanism underlying conflict expectation and adaptation (2024, unknown): “Neuroimaging studies have underscored the signi ﬁ cance of the lateral prefrontal cortex in situations where organisms require ﬂ exible cognitive control 29 .”