Relevant bibliographies by topics / Reinforcement (Psychology) Discrimination learning

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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) Discrimination learning'

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

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Journal articles on the topic "Reinforcement (Psychology) Discrimination learning"

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Keith, Kenneth D. "Peak Shift Phenomenon: A Teaching Activity for Basic Learning Theory." Teaching of Psychology 29, no. 4 (October 2002): 298–300. http://dx.doi.org/10.1207/s15328023top2904_09.

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Stimulus discrimination is a standard subject in undergraduate courses presenting basic principles of learning, and a particularly interesting aspect of discrimination is the peak shift phenomenon. Peak shift occurs in generalization tests following intradimensional discrimination training as a displacement of peak responding away from the S+ (a stimulus signaling availability of reinforcement) in a direction opposite the S– (a stimulus signaling lack of reinforcement). This activity allows students to develop intradimensional discriminations that enable firsthand observation of the peak shift phenomenon. Evaluation of the activity suggests that it produces improved understanding of peak shift and that undergraduate students can demonstrate peak shift in simple discrimination tasks.
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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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Eldevik, Sigmund, Hege Aarlie, Kristine B. Titlestad, Ellie Kazemi, and Greg Elsky. "Effects of Functional Discrimination Training on Initial Receptive Language in Individuals With Autism Spectrum Disorder." Behavior Modification 44, no. 5 (April 9, 2019): 670–97. http://dx.doi.org/10.1177/0145445519841052.

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The success of behavior-analytic procedures to teach language to individuals with autism spectrum disorder (ASD) has been well established in the literature. Nevertheless, some individuals may not learn any receptive or expressive language following extensive teaching efforts. We examined the effects of two reinforcement contingencies, functional and arbitrary, on increasing the level of auditory–visual conditional discriminations in children with ASD with a history of having difficulty learning discriminations. We evaluated the effects of the reinforcement contingencies by comparing the number of trials needed to establish discriminations in an adapted alternating treatment design. We found that five out of eight participants showed more rapid acquisition and demonstrated discrimination between more items in the functional reinforcement condition. The remaining three participants did not exhibit any discrimination in either condition within the allotted 500 trials/20 days. These findings suggest that using functional reinforcement procedures may be a helpful alternative for individuals who do not learn discriminations through traditional procedures.
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Graham, Robert B. "A Computer Tutorial on the Principles of Stimulus Generalization." Teaching of Psychology 25, no. 2 (April 1998): 149–51. http://dx.doi.org/10.1207/s15328023top2502_21.

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In this article, I describe a computer tutorial that teaches the fundamentals of stimulus generalization in operant learning. The content is appropriate for courses in general psychology, learning, and behavioral programming. Concepts covered include reinforcement, discrimination learning, stimulus continua, generalization, generalization gradients, and peak shift. The tutorial also reviews applications in animal and human situations. Student reaction to this form of presentation was very favorable.
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Bertolino, Margot, Vinca Riviere, and Lanny Fields. "Two Reinforcement Contingencies that Influence Discrimination Learning in Stimulus-Fading." Psychological Record 70, no. 2 (February 19, 2020): 187–203. http://dx.doi.org/10.1007/s40732-020-00387-1.

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Miskovsky, Charles, Brittney Becker, Alleah Hilker, and Charles I. Abramson. "The Fish Stick: An Easy-to-Use Classroom Training Apparatus for Fish." Psychological Reports 106, no. 1 (February 2010): 135–46. http://dx.doi.org/10.2466/pr0.106.1.135-146.

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The fish stick is an easy-to-use device for conditioning fish under classroom conditions. It is constructed from inexpensive plastic pipe available at most hardware stores and uses electronic components available at retail electronics outlets. Fish press a nipple for baby food which can be brought under stimulus control using lights, vibratory cues, or both. The fish stick is suitable for inquiry-based experiences in courses on the psychology of learning or comparative psychology. Data are presented using a continuous reinforcement schedule and discrimination learning. Students report that the fish are easy to train and enjoy the hands-on experience.
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Minto de Sousa, Naiara, Lucas Tadeu Garcia, and Maria Stella Coutinho de Alcantara Gil. "Differential Reinforcement in Simple Discrimination Learning in 10- to 20-Month-Old Toddlers." Psychological Record 65, no. 1 (June 18, 2014): 31–40. http://dx.doi.org/10.1007/s40732-014-0081-4.

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Garcia, Andres, Fatima Pérez González, Rocío Martín Vera, Mayte Gutiérrez Domínguez, Jesús Gómez Bujedo, Vicente Pérez Fernández, and Santiago Benjumea Rodriguez. "Effect of age and type of reinforcer in the equivalence – equivalence by a partition procedure." International Journal of Psychological Research 4, no. 1 (June 30, 2011): 7–15. http://dx.doi.org/10.21500/20112084.788.

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Equivalence – Equivalence responding (Barnes et al., 1997), based on derived or non-explicitly trained relational responding, supports a behaviour-analytic model of analogical reasoning. Conditional discriminations are the most common procedure used to train its prerequisites. In this exploratory work we test Vaughan’s (1988) simple discrimination procedure instead to derive Eq-Eq responses in children. Two factors were assessed: type of reinforcer used (primary or secondary) and age of participants (9-10 or 12-13 years). The procedure successfully leaded to the derivation of equivalence – equivalence responses, and both factors influenced the results: selecting older children and applying primary reinforcement leaded to faster learning and better results in the equivalence – equivalence test. No interaction between factors was found. This training procedure can provide a new way to investigate the behavioural prerequisites of this important ability
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Blair, C. A. J., Pam Blundell, Tiffany Galtress, Geoffrey Hall, and Simon Killcross. "Discrimination between Outcomes in Instrumental Learning: Effects of Preexposure to the Reinforcers." Quarterly Journal of Experimental Psychology Section B 56, no. 3b (August 2003): 253–65. http://dx.doi.org/10.1080/02724990244000241.

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In two experiments rats received instrumental training with two response levers, one response being reinforced by sucrose solution and the other by sucrose pellets. Prior to a test session, on which both levers were made available in the absence of reinforcement, the rats were given free access to one of the reinforcers, a procedure known to reduce its value. It was found that the rats responded at a lower rate on the lever that had produced the now-devalued reinforcer, but that this effect was substantial only in rats that had received preexposure to the two reinforcers before instrumental training was begun (Experiment 1). Experiment 2 demonstrated that this effect was obtained only when presentations of the two reinforcers were presented according to an inter-mixed schedule during preexposure. It is suggested that this result constitutes an instance of the perceptual learning effect in which intermixed preexposure to similar events enhances their discriminability.
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Hall, Geoffrey, and R. C. Honey. "Poststimulus Events in Discrimination Learning with Delayed Reinforcement: Role of Distraction and Implications for "Marking"." Learning and Motivation 24, no. 3 (August 1993): 242–54. http://dx.doi.org/10.1006/lmot.1993.1014.

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Dissertations / Theses on the topic "Reinforcement (Psychology) Discrimination learning"

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Guerrero, Luis Fernando. "Disruption of conditional discrimination and its effects on equivalence /." abstract and full text PDF (free order & download UNR users only), 2005. http://0-wwwlib.umi.com.innopac.library.unr.edu/dissertations/fullcit/3198197.

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Thesis (Ph. D.)--University of Nevada, Reno, 2005.
"May 2005." Includes bibliographical references (leaves 64-72). Online version available on the World Wide Web. Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2005]. 1 microfilm reel ; 35 mm.
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Delgado, Diana. "Subsitution of stimulus functions as a means to distinguish among different types of functional classes /." abstract and full text PDF (free order & download UNR users only), 2005. http://0-wwwlib.umi.com.innopac.library.unr.edu/dissertations/fullcit/1430443.

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Thesis (M.A.)--University of Nevada, Reno, 2005.
"May, 2005." Includes bibliographical references (leaves 47-49). Online version available on the World Wide Web. Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2005]. 1 microfilm reel ; 35 mm.
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Koelker, Rachel Lee Ellis Janet. "Comparing a discriminative stimulus procedure to a pairing procedure conditioning neutral social stimuli to function as conditioned reinforcers /." [Denton, Tex.] : University of North Texas, 2009. http://digital.library.unt.edu/ark:/67531/metadc12143.

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Stancato, Stefanie S. "On the Further Exploration of Interactions between Equivalence Classes and Analytic Units." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc849665/.

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Sidman's (2000) theory of stimulus equivalence predicts an interaction between the development of analytic units and the development of equivalence relations. Previous research has documented these interactions (stewart, Barnes-Holmes, Roche, & Smeets, 2002; Vaidya & Brackney, 2014), therefore the current study attempted to replicate the effects seen in Vaidya & Brackney, 2014 (Experiment 2). Baseline conditional discriminations were trained for two sets of three, three-member classes, while participants simply observed stimuli in the third set which was arranged identical to those of Sets 1 and 2. Following equivalence tests where performance met the accuracy criterion of 85% for Sets 1 and 2, participants then entered a simple successive discrimination training phase where common responses were then trained with an equivalence class (pressing the Q key in the presence of A1, B1, or C1), cross equivalence classes (pressing the R key in the presence of A4, A5, or A6), or for stimuli where the participants had experience with them, but the contingencies were never arranged to facilitate equivalence class formation. Results showed a facilitative effect for common responses drawn from within equivalence classes (Set 1), and a retardation effect for common responses drawn from across equivalence classes (Set 2), for three of the five participants. Results for Set 3 showed an acquisition that fell intermediate to that of Sets 1 and 2, respectively, suggesting an interaction occurring between existing equivalence relations and the development of analytic units.
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Silguero, Russell V. "Do contingency-conflicting elements drop out of equivalence classes? Re-testing Sidman's (2000) theory." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc848078/.

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Sidman's (2000) theory of stimulus equivalence states that all positive elements in a reinforcement contingency enter an equivalence class. The theory also states that if an element from an equivalence class conflicts with a programmed reinforcement contingency, the conflicting element will drop out of the equivalence class. Minster et al. (2006) found evidence suggesting that a conflicting element does not drop out of an equivalence class. In an effort to explain maintained accuracy on programmed reinforcement contingencies, the authors seem to suggest that participants will behave in accordance with a particular partitioning of the equivalence class which continues to include the conflicting element. This hypothesis seems to explain their data well, but their particular procedures are not a good test of the notion of "dropping out" due to the pre-establishment of equivalence classes before the conflicting member entered the class. The current experiment first developed unpartitioned equivalence classes and only later exposed participants to reinforcement contingencies that conflicted with pre-established equivalence classes. The results are consistent with the notion that a partition developed such that the conflicting element had dropped out of certain subclasses of the original equivalence class. The notion of a partitioning of an equivalence class seems to provide a fuller description of the phenomenon Sidman (1994, 2000) described as "dropping out" of an equivalence class.
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Koelker, Rachel Lee. "Comparing a discriminative stimulus procedure to a pairing procedure: Conditioning neutral social stimuli to function as conditioned reinforcers." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc12143/.

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Social stimuli that function as reinforcers for most children generally do not function as reinforcers for children diagnosed with autism. These important social stimuli include smiles, head nods, thumb-ups, and okay signs. It should be an important goal of therapy for children with autism to condition these neutral social stimuli to function as reinforcers for children diagnosed with autism. There is empirical evidence to support both a pairing procedure (classical conditioning) and a discriminative stimulus procedure to condition neutral stimuli to function as reinforcers. However, there is no clear evidence as to the superiority of effectiveness for either procedure. Despite this most textbooks and curriculum guides for children with autism state only the pairing procedure to condition neutral stimuli to function as reinforcers. Recent studies suggest that the discriminative stimulus procedure may in fact be more effective in conditioning neutral stimuli to function as reinforcers for children diagnosed with autism. The present research is a further comparison of these two procedures. Results from one participant support recent findings that suggest the discriminative stimulus procedure is more effective in conditioning neutral stimuli to function as reinforcers. But the results from the other participant show no effects from either procedure, suggesting future research into conditions necessary to condition neutral social stimuli to function as reinforcers for children with autism.
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Sansing, Elizabeth M. "Teaching Observational Learning to Children with Autism: An In-vivo and Video-Model Assessment." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1062891/.

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Observational learning (OL) occurs when an individual contacts reinforcement as a direct result of discriminating the observed consequences of other individuals' responses. Individuals with autism spectrum disorder (ASD) may have deficits in observational learning and previous research has demonstrated that teaching a series of prerequisite skills (i.e., attending, imitation, delayed imitation, and consequence discrimination) can result in observational learning. We sequentially taught these prerequisite skills for three young children with ASD across three play-based tasks. We assessed the direct and indirect effects of training by assessing OL before and after instruction across tasks and task variations (for two participants) during both in-vivo and video-model probes using a concurrent multiple-probe design. All participants acquired the prerequisite skills and demonstrated observational learning during probes of directly-trained tasks. Generalization results varied across participants. Observational learning generalized to one untrained task for one participant. For the other two participants, observational learning generalized to variations of the trained tasks but not to untrained tasks. Generalization additionally occurred during the in-vivo probes for both participants for whom we assessed this response. Implications of these findings, as well as directions for future research, are discussed.
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Wiist, Catherine E. C. "The Effects of Differential Outcomes on Audio-Visual Conditional Discriminations in Children with ASD." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157625/.

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The differential outcomes effect (DOE) refers to an observed increase in rates of acquisition of simple or conditional relations when the contingencies of reinforcement arrange for reinforcers to be uniquely correlated with a particular stimulus or response relative to conditions where the reinforcers are not uniquely correlated with either stimulus or response. This effect has been robustly documented in the literature with nonhuman subjects. This study asked whether the DOE would be observed with children with autism spectrum disorder (ASD) learning audio-visual conditional relations. Two participants learned two sets of 3 audio-visual conditional relations. For one set, the training conditions arranged for each of the three conditional relations to be uniquely correlated with a particular reinforcing stimulus (the DO condition). For the second set, the training conditions arranged for the same reinforcer to be used for all three audio-visual conditional relations (the NDO condition). Early results show that audio-visual conditional relations were acquired faster under the DO condition relative to the NDO outcomes condition (accuracy in DO condition was 30.8% higher on average than in NDO condition). These data suggest that differential outcomes should be more thoroughly investigated with children with diagnoses of ASD.
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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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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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Books on the topic "Reinforcement (Psychology) Discrimination learning"

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

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

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

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Book chapters on the topic "Reinforcement (Psychology) Discrimination learning"

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Viney, Deb. "The Disability Discrimination Act and Lifelong Learning? Students with Disabilities and Higher Education." In Disability and Psychology, 42–70. London: Macmillan Education UK, 2006. http://dx.doi.org/10.1007/978-1-137-12098-4_4.

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"Discrimination Learning Set and Transfer." In Comparative Psychology, 578–81. Routledge, 1998. http://dx.doi.org/10.4324/9780203826492-79.

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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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Wasserman, Edward A., and Leyre Castro. "Categorical Discrimination in Humans and Animals." In Psychology of Learning and Motivation, 145–84. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-12-394393-4.00005-4.

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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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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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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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"A Criterion Setting Theory Of Discrimination Learning That Accounts For Anisotropies And Context Effects." In Fechner's Legacy in Psychology, 121–54. BRILL, 2011. http://dx.doi.org/10.1163/ej.9789004192201.i-214.54.

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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) Discrimination learning"

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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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He, Xiangteng, Yuxin Peng, and Junjie Zhao. "StackDRL: Stacked Deep Reinforcement Learning for Fine-grained Visual Categorization." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/103.

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Fine-grained visual categorization (FGVC) is the discrimination of similar subcategories, whose main challenge is to localize the quite subtle visual distinctions between similar subcategories. There are two pivotal problems: discovering which region is discriminative and representative, and determining how many discriminative regions are necessary to achieve the best performance. Existing methods generally solve these two problems relying on the prior knowledge or experimental validation, which extremely restricts the usability and scalability of FGVC. To address the "which" and "how many" problems adaptively and intelligently, this paper proposes a stacked deep reinforcement learning approach (StackDRL). It adopts a two-stage learning architecture, which is driven by the semantic reward function. Two-stage learning localizes the object and its parts in sequence ("which"), and determines the number of discriminative regions adaptively ("how many"), which is quite appealing in FGVC. Semantic reward function drives StackDRL to fully learn the discriminative and conceptual visual information, via jointly combining the attention-based reward and category-based reward. Furthermore, unsupervised discriminative localization avoids the heavy labor consumption of labeling, and extremely strengthens the usability and scalability of our StackDRL approach. Comparing with ten state-of-the-art methods on CUB-200-2011 dataset, our StackDRL approach achieves the best categorization accuracy.
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