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Books on the topic 'Reinforcement (Psychology) Discrimination learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Domjan, Michael. The essentials of conditioning and learning. 3rd ed. Southbank, Vic., Australia ; Belmont, CA: Thomson/Wadsworth, 2005.

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2

The essentials of conditioning and learning. 2nd ed. Belmont, CA: Wadsworth, 2000.

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3

The essentials of conditioning and learning. Pacific Grove: Brooks/Cole Pub. Co., 1996.

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4

John, Lutz. Introduction to learning & memory. Pacific Grove, Calif: Brooks/Cole Pub. Co., 1994.

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5

Lutz, John. Introduction to learning & memory. Pacific Grove,Calif: Brooks-Cole, 1994.

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6

Hammerl, Marianne. Effekte signalisierter Verstärkung: Ein experimenteller Beitrag zu den Grundlagen der Lernpsychologie. Regensburg: Roderer, 1991.

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7

The teacher's book of affective instruction: A competency based approach. Lanham, MD: University Press of America, 1987.

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8

Glennon, Richard A. Drug discrimination: Applications to medicinal chemistry and drug studies. Hoboken, New Jersey: Wiley, 2011.

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9

Drug discrimination: Applications to medicinal chemistry and drug studies. Hoboken, New Jersey: Wiley, 2011.

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10

Böhm, Winfried. Pedagogía masculina - educación femenina? Washington, D.C: Organization of American States, 1993.

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11

Higa, Jennifer J. The effects of stimulus class on dimensional contrast. 1987.

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12

Transfer In Reinforcement Learning Domains. Springer, 2009.

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13

Gureckis, Todd M., and Bradley C. Love. Computational Reinforcement Learning. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.5.

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Reinforcement learning (RL) refers to the scientific study of how animals and machines adapt their behavior in order to maximize reward. The history of RL research can be traced to early work in psychology on instrumental learning behavior. However, the modern field of RL is a highly interdisciplinary area that lies that the intersection of ideas in computer science, machine learning, psychology, and neuroscience. This chapter summarizes the key mathematical ideas underlying this field including the exploration/exploitation dilemma, temporal-difference (TD) learning, Q-learning, and model-based versus model-free learning. In addition, a broad survey of open questions in psychology and neuroscience are reviewed.

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14

Positive Reinforcement: Activities and Strategies for Creating Confident Learners. Crown House Publishing, 2010.

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15

Learning and Memory, Second Edition. 2nd ed. Waveland Press, 2004.

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16

Dougan, James D. Response-independent reinforcement: An examination of the superstition and autoshaping paradigms. 1985.

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17

Grossberg, Stephen. The Adaptive Brain I: Cognition, Learning, Reinforcement, and Rhythmn (Advances in Psychology, No 42). Elsevier Science, 1987.

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18

The effect of competitive anxiety and reinforcement on the performance of collegiate student-athletes. 1990.

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19

The effect of competitive anxiety and reinforcement on the performance of collegiate student-athletes. 1991.

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20

The contextual interference effect in learning an open motor skill. 1986.

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21

The contextual interference effect in learning an open motor skill. 1988.

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22

Effects of cognitive learning strategies and reinforcement on the acquisition of closed motor skills in older adults. 1990.

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23

Effects of cognitive learning strategies and reinforcement on the acquisition of closed motor skills in older adults. 1991.

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24

Effects of cognitive learning strategies and reinforcement on the acquisition of closed motor skills in older adults. 1991.

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25

Effects of cognitive learning strategies and reinforcement: On the acquisition of closed motor skills in older adults. 1991.

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26

Elizabeth, Sarah, and Spender Dale, eds. Learning to lose: Sexism and education. 2nd ed. London: Women's Press, 1988.

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27

Herreros, Ivan. Learning and control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0026.

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This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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28

Learning Difference: Race and Schooling in the Multiracial Metropolis. Stanford University Press, 2006.

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29

Learning Difference: Race and Schooling in the Multiracial Metropolis. Stanford University Press, 2006.

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30

Intelligent control systems research. [Washington, DC: National Aeronautics and Space Administration, 1994.

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31

Honig, Werner K., W. K. Honig, and J. Gregor Fetterman. Cognitive Aspects of Stimulus Control. Taylor & Francis Group, 2016.

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32

Honig, Werner K., W. K. Honig, and J. Gregor Fetterman. Cognitive Aspects of Stimulus Control. Taylor & Francis Group, 2018.

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33

K, Honig Werner, and Fetterman J. Gregor, eds. Cognitive aspects of stimulus control. Hillsdale, N.J: L. Erlbaum Associates, 1992.

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34

Honig, Werner K., W. K. Honig, and J. Gregor Fetterman. Cognitive Aspects of Stimulus Control. Taylor & Francis Group, 2018.

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35

Pre- and post-knowledge of results intervals and motor performance of mentally retarded individuals. 1990.

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36

Yun, Chi-Hong. Pre- and post-knowledge of results intervals and motor performance of mentally retarded individuals. 1989.

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37

An examination of the effect(s) of specified teacher behaviors on student ALT-PE. 1987.

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38

An examination of the effect(s) of specified teacher behaviors on student ALT-PE. 1987.

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39

An examination of the effect(s) of specified teacher behaviors on student ALT-PE. 1985.

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40

Anne, Rehfeldt Ruth, and Barnes-Holmes Yvonne, eds. Derived relational responding: Applications for learners with autism and other developmental disabilities. Oakland: New Harbinger Publications, 2009.

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41

The relative effectiveness of three forms of visual knowledge of results on maximal strength output in an isokinetic extension flexion of the knee. 1991.

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42

The relative effectiveness of three forms of visual knowledge of results on maximal strength output in an isokinetic extension/flexion of the knee. 1989.

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43

Chan, Tammy Y. Work satisfaction and perceived job skills: Impact on motivation to learn among ethnic minority women. 2003.

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44

Busemeyer, Jerome R., Zheng Wang, James T. Townsend, and Ami Eidels, eds. The Oxford Handbook of Computational and Mathematical Psychology. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.001.0001.

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A comprehensive and authoritative review on most important developments in computational and mathematical psychology that have impacted many other fields in past decades. Written in tutorial style by leading scientists in each topic area, with an emphasis on examples and applications. Each chapter is self-contained and aims to engage readers with various levels of modeling experience. The Handbook covers the key developments in elementary cognitive mechanisms (e.g., signal detection, information processing, reinforcement learning), basic cognitive skills (e.g., perceptual judgment, categorization, episodic memory), higher-level cognition (e.g., Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (e.g., Bayesian estimation and other new model comparison methods), and emerging new directions (e.g., neurocognitive modeling, applications to clinical psychology, quantum cognition) in computation and mathematical psychology. The chapters were written for a typical graduate student in virtually any area of psychology, cognitive science, and related social and behavioral sciences, such as consumer behavior and communication. We also expect it to be useful for readers ranging from advanced undergraduate students to experienced faculty members and researchers. Beyond being a handy reference book, it should be beneficial as a textbook for self-teaching, and for graduate level (or advanced undergraduate level) courses in computational and mathematical psychology.

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45

Butz, Martin V., and Esther F. Kutter. Behavior is Reward-oriented. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0005.

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Delving further into development, adaptation, and learning, this chapter considers the potential of reward-oriented optimization of behavior. Reinforcement learning (RL) is motivated from the Rescorla–Wagner model in psychology and behaviorism. Next, a detailed introduction to RL in artificial systems is provided. It is shown when and how RL works, but also current shortcomings and challenges are discussed. In conclusion, the chapter emphasizes that behavioral optimization and reward-based behavioral adaptations can be well-accomplished with RL. However, to be able to solve more challenging planning problems and to enable flexible, goal-oriented behavior, hierarchically and modularly structured models about the environment are necessary. Such models then also enable the pursuance of abstract reasoning and of thoughts that are fully detached from the current environmental state. The challenge remains how such models may actually be learned and structured.

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46

McNamara, John M., and Olof Leimar. Game Theory in Biology. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198815778.001.0001.

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Game theory in biology seeks to predict social behaviour and other traits that influence how individuals interact. It does this by tentatively assuming that current traits are stable endpoints of evolution by natural selection. The theory is used to model aggressive behaviour, cooperation, negotiation, and signalling, as well as phenotypic attributes like an individual’s sex and mating type. This book covers the basic concepts and the traditional examples of biological game theory. It expands the frontiers of the field, emphasizing the importance of the co-evolution of traits and the implications of variation for reputation, markets, negotiation, and other social phenomena. It also highlights that it can be important to embed game interactions in the environment and an individual’s life. A major new direction developed in the book is that game theory can be extended by incorporating behavioural mechanisms, including mechanisms of reinforcement learning. By doing this the theory can successfully describe important phenomena like social dominance in group-living animals that previously have been difficult to model. By focusing on behavioural mechanisms, game theory can also make closer contact with empirical observation and with current research in fields like animal psychology and neuroscience.

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47

Staub, Michael E. The Mismeasure of Minds. University of North Carolina Press, 2018. http://dx.doi.org/10.5149/northcarolina/9781469643595.001.0001.

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The 1954 Brown v. Board of Education decision required desegregation of America’s schools, but it also set in motion an agonizing multidecade debate over race, class, and IQ. In this innovative book, Michael E. Staub investigates neuropsychological studies published between Brown and the controversial 1994 book The Bell Curve. In doing so, he illuminates how we came to view race and intelligence today. In tracing how research and experiments around such concepts as learned helplessness, deferred gratification, hyperactivity, and emotional intelligence migrated into popular culture and government policy, Staub reveals long-standing and widespread dissatisfaction—not least among middle-class whites—with the metric of IQ. He also documents the devastating consequences—above all for disadvantaged children of color—as efforts to undo discrimination and create enriched learning environments were recurrently repudiated and defunded. By connecting psychology, race, and public policy in a single narrative, Staub charts the paradoxes that have emerged and that continue to structure investigations of racism even into the era of contemporary neuroscientific research.

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