Cognitive Science 38 (3):580-598 (2014)
AbstractSuccessfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid-navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data
Added to PP
Historical graph of downloads
References found in this work
How Can the Human Mind Occur in the Physical Universe?John R. Anderson - 2007 - Oup Usa.
The Neural Basis of Human Error Processing: Reinforcement Learning, Dopamine, and the Error-Related Negativity.Clay B. Holroyd & Michael G. H. Coles - 2002 - Psychological Review 109 (4):679-709.
Model-Based Influences on Humans' Choices and Striatal Prediction Errors.Nathaniel D. Daw, Samuel J. Gershman, Ben Seymour, Peter Dayan & Raymond J. Dolan - 2011 - Neuron 69 (6):1204-1215.
Contextual Cueing of Visual Attention.Marvin M. Chun - 2000 - Trends in Cognitive Sciences 4 (5):170-178.
Similar books and articles
Computational Models for the Combination of Advice and Individual Learning.Guido Biele, Jörg Rieskamp & Richard Gonzalez - 2009 - Cognitive Science 33 (2):206-242.
Goal‐Proximity Decision‐Making.Vladislav D. Veksler, Wayne D. Gray & Michael J. Schoelles - 2013 - Cognitive Science 37 (4):757-774.
The Computational Nature of Associative Learning.N. A. Schmajuk & G. M. Kutlu - 2009 - Behavioral and Brain Sciences 32 (2):223-224.
Beyond Simple Rule Extraction: The Extraction of Planning Knowledge From Reinforcement Learners.Ron Sun - unknown
The Propositional Nature of Human Associative Learning.Chris J. Mitchell, Jan De Houwer & Peter F. Lovibond - 2009 - Behavioral and Brain Sciences 32 (2):183-198.
When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition.Christian P. Janssen & Wayne D. Gray - 2012 - Cognitive Science 36 (2):333-358.
Associative Learning Without Reason or Belief.James D. Miles, Robert W. Proctor & E. J. Capaldi - 2009 - Behavioral and Brain Sciences 32 (2):217-218.
The Truth and Value of Theories of Associative Learning.Tom Beckers & Bram Vervliet - 2009 - Behavioral and Brain Sciences 32 (2):200-201.
The Interaction of the Explicit and the Implicit in Skill Learning: A Dual-Process Approach.Ron Sun - 2005 - Psychological Review 112 (1):159-192.
Bottom-Up Skill Learning in Reactive Sequential Decision Tasks.Ron Sun, Todd Peterson & Edward Merrill - unknown