Access the full text.
Sign up today, get DeepDyve free for 14 days.
永福 智志 (2005)
The Organization of LearningJournal of Cognitive Neuroscience, 3
C. Balkenius, J. Morén (1998)
From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior
(1998)
A neural network model of classical conditioning I: The dynamics of learning, Lund University Cognitive Studies 68
Marius Smith, S. Coleman, I. Gormezano (1969)
Classical conditioning of the rabbit's nictitating membrane response at backward, simultaneous, and forward CS-US intervals.Journal of comparative and physiological psychology, 69 2
B. Balleine (1992)
Instrumental performance following a shift in primary motivation depends on incentive learning.Journal of experimental psychology. Animal behavior processes, 18 3
N. Schmajuk (1990)
Role of the hippocampus in temporal and spatial navigation An adaptive neural networkBehavioural Brain Research, 39
C. Balkenius, J. Morén (1998)
Computational models of classical conditioning: a comparative study
M. Gabriel, JOHN Moore (1990)
Learning and Computational Neuroscience: Foundations of Adaptive Networks
N. Mackintosh (1975)
A Theory of Attention: Variations in the Associability of Stimuli with ReinforcementPsychological Review, 82
M. Minsky, S. Papert (1988)
Perceptrons: expanded edition
A. Dickinson (1981)
Conditioning and associative learning.British medical bulletin, 37 2
M. Hassoun, N. Intrator, S. Mckay, Wolfgang Christian (1996)
Fundamentals of Artificial Neural NetworksProceedings of the IEEE, 84
E. Fischer (1942)
Conditioned ReflexesBritish Medical Journal, 1
R. Rescorla, A. Wagner (1972)
A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement
A. Bills
Inhibition and facilitation.Psychological Bulletin, 24
D. Gaffan (1992)
Amygdala and the memory of reward.
J. Staddon, J. Higa (1996)
Multiple time scales in simple habituation.Psychological review, 103 4
L.J. Kamin (1968)
Miami Symposium on the Prediction of Behavior: Aversive Stimulation
C.J.C.H. Watkins (1992)
Q-learningMachine Learning, 8
R. Sutton, A. Barto (1990)
Time-Derivative Models of Pavlovian Reinforcement
N. Schneiderman, I. Gormezano (1964)
CONDITIONING OF THE NICTITATING MEMBRANE OF THE RABBIT AS A FUNCTION OF CS-US INTERVAL.Journal of comparative and physiological psychology, 57
M.E. Bouton (1991)
Current Topics in Animal Learning: Brain, Emotion and Cognition
N.A. Schmajuk, A.D. Thieme (1992)
Purposive behavior and cognitive mapping: A neural network modelBiological Cybernetics, 67
R.A. Rescorla (1985)
Information Processing in Animals: Conditioned Inhibition
He received his Ph.D. in 1995 on the topic His research involves learning theory, artificial vision and mobile robots
(1990)
Classical conditioning: Fundamental issues for adaptive network models
S. Grossberg (1982)
Classical and Instrumental Learning by Neural Networks
Jack Curtis, O. Mowrer (1960)
Learning Theory and Behavior, 21
P.C. Holland (1992)
The Psychology of Learning and Motivation
A. Barto, R. Sutton, Chris Watkins (1989)
Learning and Sequential Decision Making
S. Grossberg (1974)
Progress in Theoretical Biology
C. Balkenius (1995)
Natural intelligence in artificial creatures
C. Hull (1932)
The goal-gradient hypothesis and maze learning.Psychological Review, 39
L. Goldstein (1992)
The Amygdala: Neurobiological Aspects of Emotion, Memory, and Mental DysfunctionThe Yale Journal of Biology and Medicine, 65
JOHN Moore, June-Seek Choi (1998)
Conditioned stimuli are occasion setters.
B. Balleine, Claire Garner, Felisa González, A. Dickinson (1995)
Motivational control of heterogeneous instrumental chains.Journal of Experimental Psychology: Animal Behavior Processes, 21
L. Kaelbling, M. Littman, A. Moore (1996)
Reinforcement Learning: A SurveyJ. Artif. Intell. Res., 4
J. Aggleton (1992)
The amygdala: Neurobiological aspects of emotion, memory, and mental dysfunction.
L. Kamin (1967)
Attention-like processes in classical conditioning
(1992)
Machine Learning
J.A. Gray (1975)
Elements of a Two-Process Theory of Learning
N. Schneiderman (1966)
Interstimulus interval function of the nictitating membrane response of the rabbit under delay versus trace conditioning.Journal of Comparative and Physiological Psychology, 62
A. Klopf (1988)
A neuronal model of classical conditioningPsychobiology, 16
R.S. Sutton, A.G. Barto (1998)
Reinforcement Learning: An Introduction
P. Holland (1992)
Occasion Setting in Pavlovian ConditioningPsychology of Learning and Motivation, 28
(1982)
Conditioning with serial compound stimuli: Theoretical and empirical issues
I. Mclaren (1998)
Animal Learning and Cognition: A neural network approachTrends in Cognitive Sciences, 2
N. Schmajuk, J. DiCarlo (1992)
Stimulus configuration, classical conditioning, and hippocampal function.Psychological review, 99 2
M. Bouton (1991)
Context and retrieval in extinction and in other examples of interference in simple associative learning.
R.A. Rescorla, A.R. Wagner (1972)
Classical Conditioning II: Current Research and Theory
N. Schmajuk, P. Holland (1998)
Occasion setting: Associative learning and cognition in animals.
A. Mullin, F. Rosenblatt (1962)
Principles of neurodynamics
A. Klopf, S. Weaver, James Morgan (1993)
A Hierarchical Network of Control Systems that Learn: Modeling Nervous System Function During Classical and Instrumental ConditioningAdaptive Behavior, 1
S. Kitzis, S. Grossberg (1990)
The Adaptive BrainAmerican Journal of Psychology, 103
N. Schneiderman, I. Fuentes, I. Gormezano (1962)
Acquisition and Extinction of the Classically Conditioned Eyelid Response in the Albino RabbitScience, 136
A. Machado (1997)
Learning the temporal dynamics of behavior.Psychological review, 104 2
E. Kehoe, B. Marshall-goodell, I. Gormezano (1987)
Differential conditioning of the rabbit's nictitating membrane response to serial compound stimuli.Journal of experimental psychology. Animal behavior processes, 13 1
J. Donahoe, D. Palmer (1993)
Learning and Complex Behavior
C. Balkenius (1996)
Generalization in Instrumental Learning
Balkenius and Morén
Classical conditioning is a basic learning mechanism in animals and can be found in almost all organisms. If we want to construct robots with abilities matching those of their biological counterparts, this is one of the learning mechanisms that needs to be implemented first. This article describes a computational model of classical conditioning where the goal of learning is assumed to be the prediction of a temporally discounted reward or punishment based on the current stimulus situation.
Autonomous Robots – Springer Journals
Published: Oct 28, 2004
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.