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1

Hagman, Joseph D. Cooperative learning: Effects of task, reward, and group size on individual achievement. U.S. Army Research Institute for the Behavioral and Social Sciences, 1986.

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2

Frustration theory: An analysis of dispositional learning and memory. Cambridge University Press, 1992.

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3

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

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4

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

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5

Böhm, Winfried. Pedagogía masculina - educación femenina? Organization of American States, 1993.

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6

Chang, Sheng-Chei. The effect of group reward on student motivation, interaction, emotion, and achievement in cooperative learning small groups. 1993.

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7

Intrinsic motivation, cooperative learning and perceived competence. 1989.

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8

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

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9

Kane, Keelan Darren. Symbolic mediation and preschoolers' performance on prudent decision-making tasks. 2005.

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10

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

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11

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

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12

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

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13

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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14

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

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

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16

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

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17

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

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18

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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19

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

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20

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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