Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-26T20:18:57.209Z Has data issue: false hasContentIssue false

21 - Computational Models of Animal and Human Associative Learning

from Part III - Computational Modeling of Basic Cognitive Functionalities

Published online by Cambridge University Press:  21 April 2023

By
Evan J. Livesey
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
Get access

Summary

This chapter provides a selective review of the issues that have dominated computational models of associative learning in recent decades. Associative learning research concerns the simplest and most fundamental processes by which humans and other animals come to predict events in their environment based on past experience. It has far-reaching implications for understanding adaptive and maladaptive human behavior. With a focus on Pavlovian conditioning and adjacent subdisciplines, this chapter explores how the prediction error learning algorithm has shaped understanding of competitive learning, selective attention, stimulus representation, and learning about absent events. A number of alternative computational approaches will be introduced, along with some remaining challenges in the computational modeling of human and animal associative learning.

Keywords

associative learningPavlovian conditioninginstrumental conditioningprediction errorattentionstimulus representation
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 703 - 738
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, C. D. (1982) Variations in the sensitivity of instrumental responding to reinforcer devaluation. Quarterly Journal of Experimental Psychology, 34B, 77–98.CrossRefGoogle Scholar
Aitken, M. R., & Dickinson, A. (2005). Simulations of a modified SOP model applied to retrospective revaluation of human causal learning. Learning & Behavior, 33, 147–159.Google Scholar
Atkinson, R. C., & Estes, W. K. (1963). Stimulus sampling theory. In Luce, R. D., Bush, R. R., & Galanter, E. (Eds.), Handbook of Mathematical Psychology (vol. 2, pp. 121–268). New York, NY: Wiley.Google Scholar
Baetu, I., Burns, N. R., Yu, E., & Baker, A. G. (2018). Fluid abilities and rule learning: patterning and biconditional discriminations. Journal of Intelligence, 6, 7.CrossRefGoogle ScholarPubMed
Balleine, B. W., Dickinson, A. (1998). Goal-directed instrumental action: contingency and incentive learning and their cortical substrates. Neuropharmacology, 37, 407–419.Google Scholar
Balleine, B. W., & Ostlund, S. B. (2007). Still at the choice‐point: action selection and initiation in instrumental conditioning. Annals of the New York Academy of Sciences, 1104, 147–171.Google Scholar
Beckers, T., Miller, R. R., De Houwer, J., & Urushihara, K. (2006). Reasoning rats: forward blocking in Pavlovian animal conditioning is sensitive to constraints of causal inference. Journal of Experimental Psychology: General, 135(1), 92–102.Google Scholar
Behrens, T. E., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 1214–1221.Google Scholar
Bellingham, W. P., Gillette-Bellingham, K., & Kehoe, E. J. (1985). Summation and configuration 2016 schedules with the rat and rabbit. Animal Learning & Behavior, 13, 152–164.CrossRefGoogle Scholar
Blough, D. S. (1975). Steady state data and a quantitative model of operant generalization and discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 1, 3–21.Google Scholar
Boakes, R. A. (1977). Performance on learning to associate a stimulus with positive reinforcement. In Davis, H. & Hurwitz, H. M. B. (Eds.), Operant–Pavlovian Interactions (pp. 67–101). Hillsdale, NJ: Erlbaum.Google Scholar
Bouton, M. E. (1994). Conditioning, remembering, and forgetting. Journal of Experimental Psychology: Animal Behavior Processes, 20, 219–231.Google Scholar
Bouton, M. E. (2004). Context and behavioral processes in extinction. Learning & Memory, 11, 485–494.Google Scholar
Bouton, M. E., & Bolles, R. C. (1979). Contextual control of the extinction of conditioned fear. Learning and Motivation, 10, 445–466.Google Scholar
Bouton, M., Doyle-Burr, C. & Vurbic, D. (2012). Asymmetrical generalization of conditioning and extinction from compound to element and element to compound. Journal of Experimental Psychology: Animal Behavior Processes, 38, 381–393.Google Scholar
Bouton, M. E., & King, D. A. (1983). Contextual control of the extinction of conditioned fear: tests for the associative value of the context. Journal of Experimental Psychology: Animal Behavior Processes, 9, 248–265.Google Scholar
Bouton, M. E., & Swartzentruber, D. (1986). Analysis of the associative and occasion setting properties of contexts participating in a Pavlovian discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 12, 333–350.Google Scholar
Brogden, W. J. (1939). Sensory pre-conditioning. Journal of Experimental Psychology, 25(4), 323–332.Google Scholar
Bush, R. R., & Mosteller, F. (1951). A model for stimulus generalization and discrimination. Psychological Review, 58, 413–423.Google Scholar
Byrom, N. C., & Murphy, R. A. (2014). Sampling capacity underlies individual differences in human associative learning. Journal of Experimental Psychology: Animal Learning and Cognition, 40, 133–143.Google ScholarPubMed
Cartoni, E., Puglisi-Allegra, S., Baldassarre, G. (2013). The three principles of action: a Pavlovian-instrumental transfer hypothesis. Frontiers in Behavioral Neuroscience, 7, 153.Google Scholar
Dayan, P., Kakade, S., & Montague, P. R. (2000). Learning and selective attention. Nature Neuroscience, 3, 1218–1223.Google Scholar
Delamater, A. R., Sosa, W., & Katz, M. (1999). Elemental and configural processes in patterning discrimination learning. The Quarterly Journal of Experimental Psychology, 52B, 97–124.Google Scholar
Delamater, A. R., & Westbrook, R. F. (2014). Psychological and neural mechanisms of experimental extinction: a selective review. Neurobiology of Learning and Memory, 108, 38–51.Google Scholar
Denniston, J. C., Savastano, H. I., & Miller, R. R. (2001). The extended comparator hypothesis: learning by contiguity, responding by relative strength. In Mowrer, R. R. & Klein, S. B. (Eds.), Handbook of Contemporary Learning Theories (pp. 65–117). Mahwah, NJ: Erlbaum.Google Scholar
Dickinson, A., & Balleine, B. W. (1993). Actions and responses: the dual psychology of behaviour. In Eilan, N., McCarthy, R., & Brewer, M. W., (Eds.), Spatial Representation (pp. 277–293). Oxford: Blackwells.Google Scholar
Dickinson, A., & Burke, J. (1996). Within-compound associations mediate the retrospective revaluation of causality judgements. Quarterly Journal of Experimental Psychology, 49B, 60–80.CrossRefGoogle Scholar
Dickinson, A., Hall, G., & Mackintosh, N. J. (1976). Surprise and the attenuation of blocking. Journal of Experimental Psychology: Animal Behavior Processes, 2, 313–322.Google Scholar
Dickinson, A., Squire, S., Varga, Z., & Smith, J. W. (1998). Omission learning after instrumental pretraining. Quarterly Journal of Experimental Psychology, 51B, 271–286.Google Scholar
Don, H. J., Beesley, T., & Livesey, E. J. (2019). Learned predictiveness models predict opposite attention biases in the inverse base-rate effect. Journal of Experimental Psychology: Animal Learning & Cognition, 45, 143–162.Google Scholar
Don, H. J., Goldwater, M. B., Greenaway, J. K., Hutchings, R., & Livesey, E. J. (2020) Relational rule discovery in complex discrimination learning. Journal of Experimental Psychology: Learning, Memory & Cognition, 46, 1807–1827.Google Scholar
Don, H. J., Goldwater, M. B., Otto, R., & Livesey, E. J. (2016). Rule abstraction, model-based choice and cognitive reflection. Psychonomic Bulletin & Review, 23, 1615–1623.Google Scholar
Don, H. J., Worthy, D. A., & Livesey, E. J. (2021). Hearing hooves, thinking zebras: a review of the inverse base-rate effect. Psychonomic Bulletin & Review, 28, 1142–1163.Google Scholar
Esber, G. R., & Haselgrove, M. (2011). Reconciling the influence of predictiveness and uncertainty on stimulus salience: a model of attention in associative learning. Proceedings of the Royal Society B: Biological Sciences, 278(1718), 2553–2561.Google Scholar
Estes, W. K. (1943). Discriminative conditioning I. A discriminative property of conditioned anticipation. Journal of Experimental Psychology, 32, 150–155.Google Scholar
Estes, W. K. (1948). Discriminative conditioning II. Effects of a Pavlovian conditioned stimulus upon a subsequently established operant response. Journal of Experimental Psychology, 38, 173–177.Google Scholar
Estes, W. K. (1950). Towards a statistical theory of learning. Psychological Review, 57, 94–107.CrossRefGoogle Scholar
Flagel, S. B., Akil, H., & Robinson, T. E. (2009). Individual differences in the attribution of incentive salience to reward-related cues: implications for addiction. Neuropharmacology, 56, 139–148.Google Scholar
Fletcher, P. C., Anderson, J. M., Shanks, D. R., et al. (2001). Responses of human frontal cortex to surprising events are predicted by formal associative learning theory. Nature Neuroscience, 4, 1043–1048.Google Scholar
Fraser, K. M., & Holland, P. C. (2019). Occasion setting. Behavioral Neuroscience, 133, 145–175.Google Scholar
Frost, R., Armstrong, B. C., & Christiansen, M. H. (2019). Statistical learning research: a critical review and possible new directions. Psychological Bulletin, 145(12), 1128–1153.Google Scholar
George, D. N., & Pearce, J. M. (2012). A configural theory of attention and associative learning. Learning & Behavior, 40, 241–254.Google Scholar
Gershman, S. J. (2015). A unifying probabilistic view of associative learning. PLoS Computational Biology, 11, e1004567.Google Scholar
Gershman, S. J., Blei, D. M., & Niv, Y. (2010). Context, learning, and extinction. Psychological Review, 117, 197–209.Google Scholar
Ghirlanda, S. (2015). On elemental and configural models of associative learning. Journal of Mathematical Psychology, 64–65, 8–16.Google Scholar
Ghirlanda, S., & Enquist, M. (1998). Artificial neural networks as models of stimulus control. Animal Behaviour, 56, 1383–1389.Google Scholar
Gibson, E. J., & Walk, R. D. (1956). The effect of prolonged exposure to visually presented patterns on learning to discriminate them. Journal of Comparative and Physiological Psychology, 49, 239–242.Google Scholar
Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: an adaptive network model. Journal of Experimental Psychology: General, 117(3), 227.Google Scholar
Goldwater, M. B., Don, H. J., Krusche, M., & Livesey, E. J. (2018). Relational discovery in category learning. Journal of Experimental Psychology: General, 147, 1–35.Google Scholar
Hall, G., & Rodriguez, G. (2010). Associative and nonassociative processes in latent inhibition: an elaboration of the Pearce-Hall model. In Lubow, R. E. & Weiner, I. (Eds.), Latent Inhibition: Data, Theories, and Applications to Schizophrenia (pp. 114–136). Cambridge: Cambridge University Press.Google Scholar
Hanson, H. M. (1957). Discrimination training effect on stimulus generalization gradient for spectrum stimuli. Science, 125, 888–889.Google Scholar
Harris, J. A. (2006). Elemental representations of stimuli in associative learning. Psychological Review, 113, 584–605.Google Scholar
Harris, J. A. (2011). The acquisition of conditioned responding. Journal of Experimental Psychology: Animal Behavior Processes, 37(2), 151–164.Google Scholar
Harris, J. A., & Livesey, E. J. (2008). Comparing patterning and biconditional discriminations in humans. Journal of Experimental Psychology: Animal Behavior Processes, 34, 144–154.Google Scholar
Harris, J. A., & Livesey, E. J. (2010). An attention-modulated associative network. Learning & Behavior, 38, 1–26.Google Scholar
Harris, J. A., Livesey, E. J., Ghareai, S., & Westbrook, R. F. (2008). Negative patterning is easier than a biconditional discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 34, 494–500.Google ScholarPubMed
Haselgrove, M. (2010). Reasoning rats or associative animals? A common-element analysis of the effects of additive and subadditive pretraining on blocking. Journal of Experimental Psychology: Animal Behavior Processes, 36(2), 296–306.Google Scholar
Heyes, C. (2012). Simple minds: a qualified defence of associative learning. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1603), 2695–2703.Google Scholar
Holland, P. C. (1983). Occasion setting in Pavlovian feature positive discriminations. In Commons, M. L., Herrnstein, R. J., & Wagner, A. R. (Eds.), Quantitative Analyses of Behavior: Volume 4. Discrimination Processes (pp. 183–206). New York, NY: Ballinger.Google Scholar
Holmes, N. M., Chan, Y. Y., & Westbrook, R. F. (2020). An application of Wagner’s standard operating procedures or sometimes opponent processes (SOP) model to experimental extinction. Journal of Experimental Psychology: Animal Learning and Cognition, 46(3), 215–234.Google Scholar
Honey, R. C., Dwyer, D. M., & Iliescu, A. F. (2020). HeiDI: a model for Pavlovian learning and performance with reciprocal associations. Psychological Review, 127(5), 829–852.Google Scholar
Hull, C. L. (1943). Principles of Behavior: An Introduction to Behavior Theory. New York, NY: Appleton-Century.Google Scholar
Hume, D. (1741/1978). A Treatise of Human Nature, edited by L. A. Selby-Bigge, 2nd ed. revised by P. H. Nidditch. Oxford: Clarendon Press.Google Scholar
Inman, R. A., & Pearce, J. M. (2018). The discrimination of magnitude: a review and theoretical analysis. Neurobiology of Learning and Memory, 153, 118–130.Google Scholar
Kamin, L. J. (1968). “Attention-like” processes in classical conditioning. In Jones, M. R. (Ed.), Miami Symposium on the Prediction of Behavior: Aversive Stimulation (pp. 9–31). Miami, FL: University of Miami Press.Google Scholar
Kehoe, E. J. 1988. A layered network model of associative learning: learning to learn and configuration. Psychological Review, 95, 411–433.Google Scholar
Kehoe, E. J., 1998. Can the whole be something other than the sum of its parts? In Wynne, C. D. L. & Staddon, J. E. R., (Eds.), Models of Action: Mechanisms for Adaptive Behavior (pp. 87–126). Mahwah, NJ: Erlbaum.Google Scholar
Kehoe, E. J., Horne, A. J., Horne, P. S., & Macrae, M. (1994). Summation and configuration between and within sensory modalities in classical conditioning of the rabbit. Animal Learning & Behavior, 22, 19–26.CrossRefGoogle Scholar
Kehoe, E. J., Ludvig, E. A., Dudeney, J. E., Neufeld, J., & Sutton, R. S. (2008). Magnitude and timing of nictitating membrane movements during classical conditioning of the rabbit (Oryctolagus cuniculus). Behavioral Neuroscience, 122, 471–476.Google Scholar
Kinder, A., & Lachnit, H. (2003). Similarity and discrimination in human Pavlovian conditioning. Psychophysiology, 40(2), 226–234.Google Scholar
Konorski, J. (1967). Integrative Activity of the Brain. Chicago, IL: University of Chicago Press.Google Scholar
Kremer, E. F. (1978). The Rescorla-Wagner model: losses in associative strength in compound conditioned stimuli. Journal of Experimental Psychology: Animal Behavior Processes, 4(1), 22–36.Google Scholar
Kruschke, J. K. (2001). Toward a unified model of attention in associative learning. Journal of Mathematical Psychology, 45, 812–863.Google Scholar
Lashley, K. S. (1929). Brain Mechanisms and Intelligence. Chicago, IL: University of Chicago Press.Google Scholar
Le Pelley, M. E. (2004). The role of associative history in models of associative learning: a selective review and a hybrid model. The Quarterly Journal of Experimental Psychology, 57B, 193–243.Google Scholar
Le Pelley, M. E. (2012). Metacognitive monkeys or associative animals? Simple reinforcement learning explains uncertainty in nonhuman animals. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(3), 686–708.Google Scholar
Le Pelley, M. E., & McLaren, I. P. L. (2003). Learned associability and associative change in human causal learning. The Quarterly Journal of Experimental Psychology, 56B, 68–79.Google Scholar
Le Pelley, M. E., Mitchell, C. J., Beesley, T., George, D. N., & Wills, A. J. (2016). Attention and associative learning in humans: an integrative review. Psychological Bulletin, 142, 1111–1140.Google Scholar
Le Pelley, M. E., Oakeshott, S. M., & McLaren, I. P. L. (2005). Blocking and unblocking in human causal learning. Journal of Experimental Psychology: Animal Behavior Processes, 31, 56–70.Google Scholar
Le Pelley, M. E., Schmidt-Hansen, M., Harris, N. J., Lunter, C. M., & Morris, C. S. (2010). Disentangling the attentional deficit in schizophrenia: pointers from schizotypy. Psychiatry Research, 176(2–3), 143–149.Google Scholar
Livesey, E. J., Don, H. J., Uengoer, M., & Thorwart, A. (2019). Transfer of associability and relational structure in human associative learning. Journal of Experimental Psychology: Animal Learning & Cognition, 45, 125–142.Google Scholar
Livesey, E. J., Greenaway, J., Schubert, S., & Thorwart, A. (2019). Testing the deductive inferential account of blocking in causal learning. Memory & Cognition, 47, 1120–1132.Google Scholar
Livesey, E. J. & McLaren, I. P. L. (2011). An elemental model of associative learning and memory. In Pothos, E. & Wills, A. J. (Eds.), Formal Approaches in Categorization (pp. 153–172). Cambridge: Cambridge University Press.Google Scholar
Livesey, E. J. & McLaren, I. P. L. (2019). Revisiting peak shift on an artificial dimension: effects of stimulus variability on generalization. Quarterly Journal of Experimental Psychology, 72, 132–150.Google Scholar
Livesey, E. J., Thorwart, A., & Harris, J. A. (2011). Comparing positive and negative patterning in human learning. Quarterly Journal of Experimental Psychology, 64, 2316–2333.Google Scholar
Lochmann, T., & Wills, A. J. (2003). Predictive history in an allergy prediction task. In Schmalhofer, F., Young, R. M., & Katz, G. (Eds.), Proceedings of EuroCogSci: The European Conference of the Cognitive Science Society (pp. 217–222). Mahwah, NJ: Erlbaum.Google Scholar
Lotz, A., Uengoer, M., Koenig, S., Pearce, J. M., & Lachnit, H. (2012). An exploration of the feature-positive effect in adult humans. Learning & Behavior, 40, 222–230.Google Scholar
Lovibond, P. F., Been, S. L., Mitchell, C. J., Bouton, M. E., & Frohardt, R. (2003). Forward and backward blocking of causal judgment is enhanced by additivity of effect magnitude. Memory & Cognition, 31(1), 133–142.Google Scholar
Lubow, R. E., & Moore, A. U. (1959). Latent inhibition: the effect of nonreinforced pre-exposure to the conditional stimulus. Journal of Comparative and Physiological Psychology, 52, 415–419.Google Scholar
Luce, R. D. (1959). Individual Choice Behavior. New York, NY: Wiley.Google Scholar
Luzardo, A., Alonso, E., & Mondragón, E. (2017). A Rescorla-Wagner drift-diffusion model of conditioning and timing. PLOS Computational Biology, 13(11), e1005796.Google Scholar
Mackintosh, N. (1975). A theory of attention: variations in the associability of stimuli with reinforcement. Psychological Review, 82, 276–298. https://doi.org/10.1037/h0076778Google Scholar
Mackintosh, N. J., & Turner, C. (1971). Blocking as a function of novelty of CS and predictability of UCS. The Quarterly Journal of Experimental Psychology, 23(4), 359–366.Google Scholar
Maes, E., Boddez, Y., Alfei, J. M., et al. (2016). The elusive nature of the blocking effect: 15 failures to replicate. Journal of Experimental Psychology: General, 145(9), e49–e71.Google Scholar
Maes, E., Vanderoost, E., D’Hooge, R., De Houwer, J., & Beckers, T. (2017). Individual difference factors in the learning and transfer of patterning discriminations. Frontiers in Psychology, 8, 1262.Google Scholar
McDaniel, M. A., Cahill, M. J., Robbins, M., & Wiener, C. (2014). Individual differences in learning and transfer: stable tendencies for learning exemplars versus abstracting rules. Journal of Experimental Psychology: General, 143, 668.Google Scholar
McLaren, I. P. L., Kaye, H., & Mackintosh, N. J. (1989). An associative theory of the representation of stimuli: applications to perceptual learning and latent inhibition. In Morris, R. G. M. (Ed.), Parallel Distributed Processing: Implications for Psychology and Neurobiology (pp. 102–130). Oxford: Oxford University Press.Google Scholar
McLaren, I. P. L., & Mackintosh, N. J. (2000). An elemental model of associative learning: I. Latent inhibition and perceptual learning. Animal Learning & Behavior, 28, 211–246.Google Scholar
McLaren, I. P. L., & Mackintosh, N. J. (2002). Associative learning and elemental representation: II. Generalization and discrimination. Animal Learning & Behavior, 30, 177–200.Google Scholar
Medin, D. L., & Edelson, S. M. (1988). Problem structure and the use of base-rate information from experience. Journal of Experimental Psychology: General, 1, 68–85.Google Scholar
Melchers, K. G., Shanks, D. R., & Lachnit, H. (2008). Stimulus coding in human associative learning: flexible representations of parts and wholes. Behavioural Processes, 77, 413–427.Google Scholar
Miller, R. R., & Matzel, L. D. (1988). The comparator hypothesis: a response rule for the expression of associations. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation (vol. 22, pp. 51–92). San Diego, CA: Academic Press.Google Scholar
Mitchell, C. J., De Houwer, J., & Lovibond, P. F. (2009). The propositional nature of human associative learning. Behavioral and Brain Science, 32, 183–246.Google Scholar
Paskewitz, S., & Jones, M. (2020). Dissecting EXIT. Journal of Mathematical Psychology, 97, 102371.Google Scholar
Patitucci, E., Nelson, A. J. D., Dwyer, D. M., & Honey, R. C. (2016). The origins of individual differences in how learning is expressed in rats: a general-process perspective. Journal of Experimental Psychology: Animal Learning and Cognition, 42, 313–324.Google Scholar
Pavlov, I. P. (1927). Conditioned Reflexes. London: Oxford University Press.Google Scholar
Pearce, J. M. (1987). A model for stimulus generalization in Pavlovian conditioning. Psychological Review, 94, 61–73. https://doi.org/10.1037/0033-295X.94.1.61Google Scholar
Pearce, J. M. (1994). Similarity and discrimination: a selective review and a connectionist model. Psychological Review, 101, 587–607. https://doi.org/10.1037/0033-295X.101.4.587CrossRefGoogle Scholar
Pearce, J. M. (2002). Evaluation and development of a connectionist theory of configural learning. Animal Learning & Behavior, 30, 73–95.Google Scholar
Pearce, J. M., Dopson, J. C., Haselgrove, M., & Esber, G. R. (2012). The fate of redundant cues during blocking and a simple discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 38, 167–179. https://doi.org/10.1037/a0027662Google Scholar
Pearce, J. M., & Hall, G. (1980). A model for Pavlovian learning: variations in the effectiveness of conditioned but not of unconditioned stimuli. Psychological Review, 87, 532–552. https://doi.org/10.1037/0033-295x.87.6.532Google Scholar
Pearce, J. M., & Mackintosh, N. J. (2010). Two theories of attention: a review and a possible integration. In Mitchell, C. J. & Le Pelley, M. E. (Eds.), Attention and Associative Learning: From Brain to Behaviour (pp. 11–40). Oxford: Oxford University Press.Google Scholar
Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: one phenomenon, two approaches. Trends in Cognitive Sciences, 10, 233–238.Google Scholar
Polack, C. W., Laborda, M. A., & Miller, R. R. (2012). Extinction context as a conditioned inhibitor. Learning & Behavior, 40, 24–33.CrossRefGoogle ScholarPubMed
Redish, A., Jensen, S., Johnson, A., & Kurth-Nelson, A. (2007). Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling. Psychological Review, 114, 784–805.Google Scholar
Relkin, E. M., & Doucet, J. R. (1997). Is loudness simply proportional to the auditory nerve spike count? The Journal of the Acoustical Society of America, 101, 2735–2740.Google Scholar
Rescorla, R. A. (1967). Pavlovian conditioning and its proper control procedures. Psychological Review, 74, 71–81.Google Scholar
Rescorla, R. A. (1968). Probability of shock in the presence and absence of CS in fear conditioning. Journal of Comparative and Physiological Psychology, 66, 1–5.Google Scholar
Rescorla, R. A. (1969). Pavlovian conditioned inhibition. Psychological Bulletin, 72, 77–94.Google Scholar
Rescorla, R. A. (1970). Reduction in the effectiveness of reinforcement after prior excitatory conditioning. Learning and Motivation, 1(4), 372–381.Google Scholar
Rescorla, R. A. (1972). “ Configural” conditioning in discrete-trial bar pressing. Journal of Comparative and Physiological Psychology, 79(2), 307–317.Google Scholar
Rescorla, R. A. (1988). Pavlovian conditioning: it’s not what you think it is. American Psychologist, 43(3), 151–160.Google Scholar
Rescorla, R. A. (2006). Deepened extinction from compound stimulus presentation. Journal of Experimental Psychology: Animal Behavior Processes, 32(2), 135–144.Google Scholar
Rescorla, R. A., & Solomon, R. L. (1967). Two-process learning theory: relationships between Pavlovian conditioning and instrumental learning. Psychological Review, 74, 151–182.Google Scholar
Rescorla, R. A., & Wagner, A. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and non-reinforcement. In Black, A., & Prokasy, W. (Eds.), Classical Conditioning. II. Current Research and Theory (pp. 64–99). New York, NY: Appleton-Century-Crofts.Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, G. E. (1986). Learning internal representations by error propagation. In Rumelhart, D. E. & McClelland, J. L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (vol. 1). Cambridge, MA: MIT Press.Google Scholar
Saavedra, M. A. (1975). Pavlovian compound conditioning in the rabbit. Learning and Motivation, 6, 314–326.Google Scholar
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926–1928.Google Scholar
Schmajuk, N. A., Di Carlo, J. J., (1992). Stimulus configuration, classical conditioning, and hippocampal function. Psychological Review, 99, 268–305.Google Scholar
Schmajuk, N. A., Lamoureux, J. A., & Holland, P. C., 1998. Occasion setting: a neural network approach. Psychological Review, 105, 3–32.Google Scholar
Schultz, W. Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599.Google Scholar
Sewell, D. K., Jach, H. K., Boag, R. J., & Van Heer, C. A. (2019). Combining error-driven models of associative learning with evidence accumulation models of decision-making. Psychonomic Bulletin & Review, 26(3), 868–893.Google Scholar
Shanks, D. R. (1985). Forward and backward blocking in human contingency judgement. The Quarterly Journal of Experimental Psychology, 37B, 1–21.Google Scholar
Shanks, D. R. (1987). Acquisition functions in contingency judgment. Learning and Motivation, 18(2), 147–166.Google Scholar
Soto, F. A. (2018). Contemporary associative learning theory predicts failures to obtain blocking: comment on Maes et al. (2016). Journal of Experimental Psychology: General, 147(4), 597–602.Google Scholar
Soto, F. A., & Wasserman, E. A. (2010). Error-driven learning in visual categorization and object recognition: a common-elements model. Psychological Review, 117(2), 349–381.Google Scholar
Spence, K. W. (1956). Behavior Theory and Conditioning. New Haven, CT: Yale University Press.Google Scholar
Stout, S. C., & Miller, R. R. (2007). Sometimes-competing retrieval (SOCR): a formalization of the comparator hypothesis. Psychological Review, 114(3), 759–783.CrossRefGoogle ScholarPubMed
Sutherland, N. S., & Mackintosh, N. J. (1971). Mechanisms of Animal Discrimination Learning. New York, NY: Academic Press.Google Scholar
Sutton, R. S. (1992). Gain adaptation beats least squares? In Proceedings of the Seventh Annual Yale Workshop on Adaptive and Learning Systems (pp. 161–166). New Haven, CT: Yale University Press.Google Scholar
Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: expectation and prediction. Psychological Review, 88, 135–171.Google Scholar
Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning. Cambridge, MA: MIT Press.Google Scholar
Thein, T., Westbrook, R. F., & Harris, J. A. (2008). How the associative strengths of stimuli combine in compound: summation and overshadowing. Journal of Experimental Psychology: Animal Behavior Processes, 34, 155–166.Google Scholar
Thorndike, E. L. (1898). Animal intelligence: an experimental study of the associative processes in animals. The Psychological Review: Monograph Supplements, 2(4), i.Google Scholar
Thorwart, A., & Lachnit, H. (2020). Inhibited elements model—implementation of an associative learning theory. Journal of Mathematical Psychology, 94, 102310.Google Scholar
Thorwart, A., & Livesey, E. J. (2016). Three ways that non-associative knowledge may affect associative learning processes. Frontiers in Psychology, 7, 2024. https://doi.org/10.3389/fpsyg.2016.02024Google Scholar
Thorwart, A., Livesey, E. J., & Harris, J. A. (2012). Normalisation between stimulus elements in a model of Pavlovian conditioning: showjumping on an elemental horse. Learning & Behavior, 40, 334–346.Google Scholar
Thorwart, A., Uengoer, M., Livesey, E. J., & Harris, J. A. (2017). Summation effects in human learning: evidence from patterning discriminations in goal-tracking. Quarterly Journal of Experimental Psychology, 70, 1366–1379.Google Scholar
Tobler, P. N., O’Doherty, J. P., Dolan, R. J., & Schultz, W. (2006). Human neural learning depends on reward prediction errors in the blocking paradigm. Journal of Neurophysiology, 95, 301–310.Google Scholar
Urushihara, K., & Miller, R. R. (2010). Backward blocking in first-order conditioning. Journal of Experimental Psychology: Animal Behavior Processes, 36(2), 281–295.Google Scholar
Van Hamme, L. J., & Wasserman, E. A. (1994). Cue competition in causality judgments: the role of nonpresentation of compound stimulus elements. Learning & Motivation, 25, 127–151.Google Scholar
Waelti, P., Dickinson, A., & Schultz, W. (2001). Dopamine responses comply with basic assumptions of formal learning theory. Nature, 412, 43–48.Google Scholar
Wagner, A. R. (1978). Expectancies and the priming of STM. In Hulse, S. H., Fowler, H., & Honig, W. K. (Eds.), Cognitive Processes in Animal Behavior (pp. 177–209). Hillsdale, NJ: Erlbaum.Google Scholar
Wagner, A. R. (1981). SOP: a model of automatic memory processing in animal behavior. In Spear, N. E. & Miller, R. R. (Eds.), Information Processing in Animals: Memory Mechanisms (pp. 5–47). Hillsdale, NJ: Erlbaum.Google Scholar
Wagner, A. R. (2003). Context-sensitive elemental theory. Quarterly Journal of Experimental Psychology, 56B, 7–29.Google Scholar
Wagner, A. R., & Brandon, S. E. (2001). A componential theory of Pavlovian conditioning. In Mowrer, R. R. & Klein, S. B. (Eds.), Handbook of Contemporary Learning Theories (pp. 23–64). Mahwah, NJ: Erlbaum.Google Scholar
Wagner, A. R., Logan, F. A., Haberlandt, K., & Price, T. (1968). Stimulus selection in animal discrimination learning. Journal of Experimental Psychology, 76, 171–180.Google Scholar
Wagner, A. R., & Rescorla, R. A. (1972). Inhibition in Pavlovian conditioning: applications of a theory. In Boakes, R. A. & Halliday, M. S. (Eds.), Inhibition and Learning (pp. 301–336). New York, NY: Academic Press.Google Scholar
Whitlow Jr, J. W., & Wagner, A. R. (1972). Negative patterning in classical conditioning: summation of response tendencies to isolable and configural components. Psychonomic Science, 27, 299–301.Google Scholar
Widrow, G., & Hoff, M. E. (1960). Adaptive switching circuits. Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record, 4, 96–194.Google Scholar
Williams, D. A., Overmier, J. B., & LoLordo, V. M. (1992). A reevaluation of Rescorla’s early dictums about Pavlovian conditioned inhibition. Psychological Bulletin, 111, 275–290.CrossRefGoogle Scholar
Wills, S., & Mackintosh, N. J. (1998). Peak shift on an artificial dimension. The Quarterly Journal of Experimental Psychology Section B: Comparative and Physiological Psychology, 51, 1–32.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×