Relevant bibliographies by topics / Reinforcement (Psychology) Learning, Psychology of. Schizophrenics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Reinforcement (Psychology) Learning, Psychology of. Schizophrenics'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Reinforcement (Psychology) Learning, Psychology of. Schizophrenics"

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Vanes, Lucy D., Elias Mouchlianitis, Tracy Collier, Bruno B. Averbeck, and Sukhi S. Shergill. "Differential neural reward mechanisms in treatment-responsive and treatment-resistant schizophrenia." Psychological Medicine 48, no. 14 (February 14, 2018): 2418–27. http://dx.doi.org/10.1017/s0033291718000041.

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AbstractBackgroundThe significant proportion of schizophrenia patients refractory to treatment, primarily directed at the dopamine system, suggests that multiple mechanisms may underlie psychotic symptoms. Reinforcement learning tasks have been employed in schizophrenia to assess dopaminergic functioning and reward processing, but these have not directly compared groups of treatment-refractory and non-refractory patients.MethodsIn the current functional magnetic resonance imaging study, 21 patients with treatment-resistant schizophrenia (TRS), 21 patients with non-treatment-resistant schizophrenia (NTR), and 24 healthy controls (HC) performed a probabilistic reinforcement learning task, utilizing emotionally valenced face stimuli which elicit a social bias toward happy faces. Behavior was characterized with a reinforcement learning model. Trial-wise reward prediction error (RPE)-related neural activation and the differential impact of emotional bias on these reward signals were compared between groups.ResultsPatients showed impaired reinforcement learning relative to controls, while all groups demonstrated an emotional bias favoring happy faces. The pattern of RPE signaling was similar in the HC and TRS groups, whereas NTR patients showed significant attenuation of RPE-related activation in striatal, thalamic, precentral, parietal, and cerebellar regions. TRS patients, but not NTR patients, showed a positive relationship between emotional bias and RPE signal during negative feedback in bilateral thalamus and caudate.ConclusionTRS can be dissociated from NTR on the basis of a different neural mechanism underlying reinforcement learning. The data support the hypothesis that a favorable response to antipsychotic treatment is contingent on dopaminergic dysfunction, characterized by aberrant RPE signaling, whereas treatment resistance may be characterized by an abnormality of a non-dopaminergic mechanism – a glutamatergic mechanism would be a possible candidate.
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Vakhrushev, Daniil, and Ivan Gorbunov. "ERP Indicators of the Types of Errors in Reinforcement Learning in Schizophrenic Patients." International Journal of Psychophysiology 168 (October 2021): S105. http://dx.doi.org/10.1016/j.ijpsycho.2021.07.315.

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Bradley, Ellen R., Johanna Brustkern, Lize De Coster, Wouter van den Bos, Samuel M. McClure, Alison Seitz, and Joshua D. Woolley. "Victory is its own reward: oxytocin increases costly competitive behavior in schizophrenia." Psychological Medicine 50, no. 4 (April 4, 2019): 674–82. http://dx.doi.org/10.1017/s0033291719000552.

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AbstractBackgroundAberrant sensitivity to social reward may be an important contributor to abnormal social behavior that is a core feature of schizophrenia. The neuropeptide oxytocin impacts the salience of social information across species, but its effect on social reward in schizophrenia is unknown.MethodsWe used a competitive economic game and computational modeling to examine behavioral dynamics and oxytocin effects on sensitivity to social reward among 39 men with schizophrenia and 54 matched healthy controls. In a randomized, double-blind study, participants received one dose of oxytocin (40 IU) or placebo and completed a 35-trial Auction Game that quantifies preferences for monetary v. social reward. We analyzed bidding behavior using multilevel linear mixed models and reinforcement learning models.ResultsBidding was motivated by preferences for both monetary and social reward in both groups, but bidding dynamics differed: patients initially overbid less compared to controls, and across trials, controls decreased their bids while patients did not. Oxytocin administration was associated with sustained overbidding across trials, particularly in patients. This drug effect was driven by a stronger preference for winning the auction, regardless of monetary consequences. Learning rate and response variability did not differ between groups or drug condition, suggesting that differences in bidding derive primarily from differences in the subjective value of social rewards.ConclusionsOur findings suggest that schizophrenia is associated with diminished motivation for social reward that may be increased by oxytocin administration.
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Botvinick, Matthew, Sam Ritter, Jane X. Wang, Zeb Kurth-Nelson, Charles Blundell, and Demis Hassabis. "Reinforcement Learning, Fast and Slow." Trends in Cognitive Sciences 23, no. 5 (May 2019): 408–22. http://dx.doi.org/10.1016/j.tics.2019.02.006.

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Tomov, Momchil S., Eric Schulz, and Samuel J. Gershman. "Multi-task reinforcement learning in humans." Nature Human Behaviour 5, no. 6 (January 28, 2021): 764–73. http://dx.doi.org/10.1038/s41562-020-01035-y.

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Godoy, J. F., A. Catena, V. E. Caballo, and A. E. Puente. "Auditory discrimination, attention, learning, and memory in paranoid schizophrenics." Archives of Clinical Neuropsychology 5, no. 3 (January 1, 1990): 231–41. http://dx.doi.org/10.1093/arclin/5.3.231.

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LaFreniere, Lucas S., and Michelle G. Newman. "Probabilistic Learning by Positive and Negative Reinforcement in Generalized Anxiety Disorder." Clinical Psychological Science 7, no. 3 (November 19, 2018): 502–15. http://dx.doi.org/10.1177/2167702618809366.

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This experiment examined learning tendencies in generalized anxiety disorder (GAD) using reinforcement feedback for probabilistic outcomes. One hundred sixty-six GAD and 105 non-GAD participants were randomized to a computerized probabilistic learning task that used either negative or positive reinforcement. Participants chose between stimuli with specific probabilities of reinforcement to learn which of each pair had the highest probability. Reinforced choices either removed an angry face (negative reinforcement) or made a happy face appear (positive reinforcement). Results showed that those with GAD learned the correct probabilistic choices at a slower rate over time and to a lesser degree than control participants regardless of reinforcement type. Estimations of the likelihood of receiving a good outcome posttask were also more inaccurate for those with GAD, especially when true likelihoods were high. Furthermore, compared with control participants, those with GAD reported lower perceived reinforcement sensitivity, higher behavioral inhibition sensitivity, and higher undesirable feelings toward probabilistic learning.
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Li, Xiao, Hanchen Xu, Jinming Zhang, and Hua-hua Chang. "Optimal Hierarchical Learning Path Design With Reinforcement Learning." Applied Psychological Measurement 45, no. 1 (August 22, 2020): 54–70. http://dx.doi.org/10.1177/0146621620947171.

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E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.
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Williams, Ben A. "Partial reinforcement effects on discrimination learning." Animal Learning & Behavior 17, no. 4 (December 1989): 418–32. http://dx.doi.org/10.3758/bf03205222.

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Kool, Wouter, Samuel J. Gershman, and Fiery A. Cushman. "Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems." Psychological Science 28, no. 9 (July 21, 2017): 1321–33. http://dx.doi.org/10.1177/0956797617708288.

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Human behavior is sometimes determined by habit and other times by goal-directed planning. Modern reinforcement-learning theories formalize this distinction as a competition between a computationally cheap but inaccurate model-free system that gives rise to habits and a computationally expensive but accurate model-based system that implements planning. It is unclear, however, how people choose to allocate control between these systems. Here, we propose that arbitration occurs by comparing each system’s task-specific costs and benefits. To investigate this proposal, we conducted two experiments showing that people increase model-based control when it achieves greater accuracy than model-free control, and especially when the rewards of accurate performance are amplified. In contrast, they are insensitive to reward amplification when model-based and model-free control yield equivalent accuracy. This suggests that humans adaptively balance habitual and planned action through on-line cost-benefit analysis.
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Dissertations / Theses on the topic "Reinforcement (Psychology) Learning, Psychology of. Schizophrenics"

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Stachenfeld, Kimberly. "Learning Neural Representations that Support Efficient Reinforcement Learning." Thesis, Princeton University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10824319.

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RL has been transformative for neuroscience by providing a normative anchor for interpreting neural and behavioral data. End-to-end RL methods have scored impressive victories with minimal compromises in autonomy, hand-engineering, and generality. The cost of this minimalism in practice is that model-free RL methods are slow to learn and generalize poorly. Humans and animals exhibit substantially improved flexibility and generalize learned information rapidly to new environment by learning invariants of the environment and features of the environment that support fast learning rapid transfer in new environments. An important question for both neuroscience and machine learning is what kind of ``representational objectives'' encourage humans and other animals to encode structure about the world. This can be formalized as ``representation feature learning,'' in which the animal or agent learns to form representations with information potentially relevant to the downstream RL process. We will overview different representational objectives that have received attention in neuroscience and in machine learning. The focus of this overview will be to first highlight conditions under which these seemingly unrelated objectives are actually mathematically equivalent. We will use this to motivate a breakdown of properties of different learned representations that are meaningfully different and can be used to inform contrasting hypotheses for neuroscience. We then use this perspective to motivate our model of the hippocampus. A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity, and policy dependence in place cells suggests that the representation is not purely spatial. We approach the problem of understanding hippocampal representations from a reinforcement learning perspective, focusing on what kind of spatial representation is most useful for maximizing future reward. We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. We go on to argue that entorhinal grid cells encode a low-dimensional basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.

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Chan, Chi-wan Tracey, and 陳緻韻. "Reward learning impairments in patients with first-episode schizophrenia-spectrum disorder." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2015. http://hdl.handle.net/10722/209481.

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Reward learning refers to outcome-based learning that involves selecting optimal response choices from feedback which facilitate adaptive behavior. It is believed that reward learning paradigm represents a promising translational target in schizophrenia research. Previous studies generated relatively consistent evidence of rapid learning deficits but mixed findings on gradual learning deficits. Reward learning impairments were also associated with symptoms as well as antipsychotics treatment. The current study aimed to investigate the reward learning impairments and its longitudinal change in patients with first-episode schizophrenia spectrum disorder. A total of 34 patients and 36 healthy control participants were recruited. Patients and controls were matched in terms of age, sex, and education level. All participants were assessed twice: at baseline and after one year. For each assessment time point, data were collected on demographics, clinical and treatment characteristics. Participants were asked to complete a battery of cognitive assessments and two reward learning tasks: the Gain vs. loss-avoidance task and the Go-NoGo task. Patients and controls were compared in terms of cross-sectional reward learning performance at baseline and follow-up. Correlates of reward deficits were examined, and longitudinal analyses were conducted to investigate change of reward learning performance over time. At baseline, it was found that patients had significant rapid learning deficit in win-stay (learning from positive feedback) and gradual learning deficits in learning from both positive and negative feedback. Reward-driven learning impairments were more robust. At one-year follow-up, patients continued to have significant rapid learning deficit in win-stay and gradual learning deficits in learning from negative feedback. Longitudinal analyses demonstrated that patients had significant decrease in win-stay rate in training phase and significantly lower accuracy for punishment-driven stimuli across assessment time points. No deficits in representing expected reward value of stimuli or Go response bias were demonstrated. Correlations were found between different symptom domains (negative symptoms, positive symptoms) and reward learning impairments. Current findings regarding rapid and gradual learning deficits in patients with first-episode schizophrenia spectrum disorder were partially in keeping with that of previous studies. Discrepant findings across studies may be attributable to different sample characteristics in terms of illness chronicity and symptoms severity. The current study provided valuable information regarding the longitudinal change of reward learning deficits in early psychosis patients.
published_or_final_version
Psychiatry
Master
Master of Philosophy
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Cigales, Maricel. "Vicarious reinforcement is a result of earlier learning." FIU Digital Commons, 1995. http://digitalcommons.fiu.edu/etd/2367.

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The term "vicarious reinforcement" has been used by social-learning theorists to denote imitation that results from the observed reinforcement of behavior performed by a model. This conceptualization is incompatible with that of behavior analysis because it ignores the effect of prior learning on the observer's behavior and violates the definition of reinforcement. Experiment 1 replicated prior findings. Preschool children (N=32) imitated a model's reinforced choice responses, in the absence of direct experience with contingencies. In Experiment 2 (N=48), subjects failed to imitate reinforced modeled behavior when observed behavior contingencies were 'incongruent' with those experienced. The results were interpreted as consistent with the behavior-analytic position that observed reinforcement of a model's behavior functions as a discriminative cue (SD), not reinforcement, for the observer's imitative responses.
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Costa, Daniel S. J. "Maintenance of behaviour when reinforcement becomes delayed." Connect to full text, 2009. http://ses.library.usyd.edu.au/handle/2123/5078.

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Thesis (Ph. D.)--University of Sydney, 2009.
Includes graphs and tables. Title from title screen (viewed June 15, 2009) Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Psychology, Faculty of Science. Includes bibliographical references. Also available in print form.
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Duarte, Myra. "The effects of immediate versus delayed reinforcement on infant operant learning." FIU Digital Commons, 2002. http://digitalcommons.fiu.edu/etd/3089.

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Two experiments examined operant leg kick responses to a panel by six 3-month-old infants under baseline, immediate reinforcement, delayed reinforcement, and yoked-control conditions using a discrete trial procedure and a single-subject repeated-measures design. Two infants participated in the first experiment and four infants participated in the second experiment. The research design of Experiment I was baseline (A), 5s delay of reinforcement (C), yoked-control (A'), and immediate reinforcement (B). There were two experimental orders in the second experiment. The first order consisted of baseline (A), immediate reinforcement (B), yoked-control (A'), and 5s delay of reinforcement (C). The second order consisted of baseline (A), 5s delayed reinforcement (C), yoked-control (A'), and immediate reinforcement (B). The reinforcer was a combination of multicolored holiday lights and music, and a moving hand puppet. Changes in experimental phases were based on the attainment of learning and stability criteria. With the exception of one infant, leg kicks to a to a panel were learned under both immediate and 5s delay of reinforcement conditions, with learning appearing to be attained more rapidly under immediate reinforcement.
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Foo, Chia Mun. "Learning Requires Attention for Binding Affective Reinforcement to Information Content." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/scripps_theses/555.

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Humans are limited in their capacity to process information about the environment; to choose the most salient details to process, we have to make rapid value appraisals and prioritize our attentional resources. In this proposed study, it is expected that attention is required to learn from affective information. Learning is measured by the difference between update (the difference between the first and second estimation) and the estimation error (the difference between the average likelihood and the first estimation). Using a belief-updating paradigm, participants will be asked to estimate their likelihood of encountering a negative event, once before and once after they receive the average likelihood information. By comparing the difference in estimations after being exposed to desirable or undesirable information and a positive or negative reinforcer across three levels of attentional load, the effects of attention on learning from affective reinforcement can be examined. It is proposed that attention mediates learning from affective information. This is demonstrated by the failure to learn differentially from affective information under high attentional load, while in a no load condition participants will learn differentially according to the type of news and affective reinforcer that they receive. The expected result would indicate that attention is a necessity for optimal learning outcomes, especially when learning from affective information. This has implications in the effectiveness of communicating affective information, such as in the health care field.
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Ritter, Samuel. "Meta-reinforcement Learning with Episodic Recall| An Integrative Theory of Reward-Driven Learning." Thesis, Princeton University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13420812.

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Research on reward-driven learning has produced and substantiated theories of model-free and model-based reinforcement learning (RL), which respectively explain how humans and animals learn reflexive habits and build prospective plans. A highly developed line of work has unearthed the role of striatal dopamine in model-free learning, while the prefrontal cortex (PFC) appears to critically subserve model-based learning. The recent theory of meta-reinforcement learning (meta-RL) explained a wide array of findings by positing that the model-free dopaminergic reward prediction error trains the recurrent prefrontal network to execute arbitrary RL algorithms—including model-based RL—in its activations.

In parallel, a nascent understanding of a third reinforcement learning system is emerging: a non-parametric system that stores memory traces of individual experiences rather than aggregate statistics. Research on such episodic learning has revealed its unmistakeable traces in human behavior, developed theory to articulate algorithms underlying that behavior, and pursued the contention that the hippocampus is centrally involved. These developments lead to a set of open questions about (1) how the neural mechanisms of episodic learning relate to those underlying incremental model-free and model-based learning and (2) how the brain arbitrates among the contributions of this abundance of valuation strategies.

This thesis extends meta-RL to provide an account for episodic learning, incremental learning, and the coordination between them. In this theory of episodic meta-RL (EMRL), episodic memory reinstates activations in the prefrontal network based on contextual similarity, after passing them through a learned gating mechanism (Chapters 1 and 2). In simulation, EMRL can solve episodic contextual water maze navigation problems and episodic contextual bandit problems, including those with Omniglot class contexts and others with compositional structure (Chapter 3). Further, EMRL reproduces episodic model-based RL and its coordination with incremental model-based RL on the episodic two-step task (Vikbladh et al., 2017; Chapter 4). Chapter 5 discusses more biologically detailed extensions to EMRL, and Chapter 6 analyzes EMRL with respect to a set of recent empirical findings. Chapter 7 discusses EMRL in the context of various topics in neuroscience.

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Ridley, Elizabeth. "Error-Related Negativity and Feedback-Related Negativity on a Reinforcement Learning Task." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/etd/3714.

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Event-related potentials play a significant role in error processing and attentional processes. Specifically, event-related negativity (ERN), feedback-related negativity (FRN), and the P300 are related to performance monitoring. The current study examined these components in relation to subjective probability, or confidence, regarding response accuracy on a complicated learning task. Results indicated that confidence ratings were not associated with any changes in ERN, FRN, or P300 amplitude. P300 amplitude did not vary according to participants’ subjective probabilities. ERN amplitude and FRN amplitude did not change throughout the task as participants learned. Future studies should consider the relationship between ERN and FRN using a learning task that is less difficult than the one employed in this study.
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Bredthauer, Jennifer Lyn Johnston James M. "The assessment of preference for qualitatively different reinforcers in persons with developmental and learning disabilities a comparison of value using behavioral economic and standard preference assessment procedures /." Auburn, Ala, 2009. http://hdl.handle.net/10415/1809.

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Lee, Coral Em. "Order effects of variability-contingent and variability-independent point delivery: Effects on operant variability and target sequence acquisition." Thesis, University of North Texas, 2004. https://digital.library.unt.edu/ark:/67531/metadc4502/.

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Previous research has shown that variability is a reinforceable dimension of operant behavior. Additionally, it has been demonstrated that learning is facilitated when variability in responding is high. In this research, variability was observed within an operant composed of any sequence of six left and right key presses. Variability was either a requirement for point delivery (VAR conditions) or points were delivered independent of variability (ANY conditions). Two groups of college undergraduates experienced different orders of conditions. One group began the experiment under VAR conditions, and the variability requirement was later removed. The other group began the experiment under ANY conditions, and the variability requirement was later added. A concurrently reinforced target sequence (i.e., an always-reinforced sequence of left and right key presses) was introduced to both groups after these orders of conditions had been experienced. A variety of outcomes resulted. Subjects learned the target sequence when variability was both high and low with non-target points concurrently available. Other subjects learned the target sequence after all non-target point deliveries had been suspended. One subject failed to acquire the target sequence at all. These results were compared to previous findings and possible explanations for the discrepancies were suggested.
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Books on the topic "Reinforcement (Psychology) Learning, Psychology of. Schizophrenics"

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

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

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

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

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

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

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

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Transfer In Reinforcement Learning Domains. Springer, 2009.

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

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

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Book chapters on the topic "Reinforcement (Psychology) Learning, Psychology of. Schizophrenics"

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Berridge, Kent C. "Reward learning: Reinforcement, incentives, and expectations." In Psychology of Learning and Motivation, 223–78. Elsevier, 2000. http://dx.doi.org/10.1016/s0079-7421(00)80022-5.

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Fagg, Ah. "Chapter 14 Reinforcement Learning for Robotic Reaching and Grasping." In Advances in Psychology, 281–308. Elsevier, 1994. http://dx.doi.org/10.1016/s0166-4115(08)61283-2.

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Rachlin, Howard, Jay Brown, and Forest Baker. "Reinforcement and punishment in the prisoner's dilemma game." In Psychology of Learning and Motivation, 327–64. Elsevier, 2000. http://dx.doi.org/10.1016/s0079-7421(00)80024-9.

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Schwartz, Barry. "The Experimental Synthesis of Behavior: Reinforcement, Behavioral Stereotypy, and Problem Solving." In Psychology of Learning and Motivation, 93–138. Elsevier, 1988. http://dx.doi.org/10.1016/s0079-7421(08)60039-0.

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"Learning Negative reinforcement and positive punishment. James V.McConnell An objective and functional matrix for introducing concepts of reinforcement and punishment. 216." In Handbook for Teaching Introductory Psychology, 226–34. Psychology Press, 2001. http://dx.doi.org/10.4324/9781410604927-22.

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Ludvig, Elliot A., Marc G. Bellemare, and Keir G. Pearson. "A Primer on Reinforcement Learning in the Brain." In Computational Neuroscience for Advancing Artificial Intelligence, 111–44. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-021-1.ch006.

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In the last 15 years, there has been a flourishing of research into the neural basis of reinforcement learning, drawing together insights and findings from psychology, computer science, and neuroscience. This remarkable confluence of three fields has yielded a growing framework that begins to explain how animals and humans learn to make decisions in real time. Mastering the literature in this sub-field can be quite daunting as this task can require mastery of at least three different disciplines, each with its own jargon, perspectives, and shared background knowledge. In this chapter, the authors attempt to make this fascinating line of research more accessible to researchers in any of the constitutive sub-disciplines. To this end, the authors develop a primer for reinforcement learning in the brain that lays out in plain language many of the key ideas and concepts that underpin research in this area. This primer is embedded in a literature review that aims not to be comprehensive, but rather representative of the types of questions and answers that have arisen in the quest to understand reinforcement learning and its neural substrates. Drawing on the basic findings in this research enterprise, the authors conclude with some speculations about how these developments in computational neuroscience may influence future developments in Artificial Intelligence.
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Conference papers on the topic "Reinforcement (Psychology) Learning, Psychology of. Schizophrenics"

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WARLAUMONT, ANNE S. "REINFORCEMENT-MODULATED SELF-ORGANIZATION IN INFANT MOTOR SPEECH LEARNING." In Proceedings of the 13th Neural Computation and Psychology Workshop. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814458849_0009.

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Rosenfeld, Ariel, Matthew E. Taylor, and Sarit Kraus. "Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/534.

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Reinforcement Learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort on the human designer's part. To date, human factors are generally not considered in the development and evaluation of possible approaches. In this paper, we propose and evaluate a novel method, based on human psychology literature, which we show to be both effective and efficient, for both expert and non-expert designers, in injecting human knowledge for speeding up tabular RL.
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