Production system models of learning and development
Production system models of learning and development
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Fast planning through planning graph analysis
Artificial Intelligence
A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling
Machine Learning - Special issue on inductive transfer
Learning to learn: introduction and overview
Learning to learn
Understanding strategy selection
International Journal of Human-Computer Studies
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
The Architecture of Cognition
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Actor-critic models of the basal ganglia: new anatomical and computational perspectives
Neural Networks - Computational models of neuromodulation
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Human Problem Solving
SAL: an explicitly pluralistic cognitive architecture
Journal of Experimental & Theoretical Artificial Intelligence - Pluralism and the Future of Cognitive Science
Building portable options: skill transfer in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Constraint Based Planning with Composable Substate Graphs
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
From an executive network to executive control: A computational model of the n-back task
Journal of Cognitive Neuroscience
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We address strategic cognitive sequencing, the "outer loop" of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or "self-instruction"). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a "bridging" state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.