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

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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8

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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9

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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10

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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11

Martínez, Lourdes, Angeles F. Estévez, Luis J. Fuentes, and J. Bruce Overmier. "Improving conditional discrimination learning and memory in five-year-old children: Differential outcomes effect using different types of reinforcement." Quarterly Journal of Experimental Psychology 62, no. 8 (August 2009): 1617–30. http://dx.doi.org/10.1080/17470210802557827.

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Previous studies have demonstrated that discriminative learning is facilitated when a particular outcome is associated with each relation to be learned. When this training procedure is applied (the differential outcomes procedure; DOP), learning is faster and better than when the typical common outcomes procedure or nondifferential outcomes (NDO) is used. Our primary purpose in the two experiments reported here was to assess the potential advantage of DOP in 5-year-old children using three different strategies of reinforcement in which (a) children received a reinforcer following a correct choice (“ + ”), (b) children lost a reinforcer following an incorrect choice (“ − ”), or (c) children received a reinforcer following a correct choice and lost one following an incorrect choice (“ + / − ”). In Experiment 1, we evaluated the effects of the presence of DOP and different types of reinforcement on learning and memory of a symbolic delayed matching-to-sample task using secondary and primary reinforcers. Experiment 2 was similar to the previous one except that only primary reinforcers were used. The results from these experiments indicated that, in general, children learned the task faster and showed higher performance and persistence of learning whenever differential outcomes were arranged independent of whether it was differential gain, loss, or combinations. A novel finding was that they performed the task better when they lost a reinforcer following an incorrect choice (type of training “ − ”) in both experiments. A further novel finding was that the advantage of the DOP over the nondifferential outcomes training increased in a retention test.

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12

Melsbach, Gudrun, Martina Siemann, and Juan D. Delius. "Right or Wrong, Familiar or Novel in Pictorial List Discrimination Learning." Experimental Psychology 50, no. 4 (October 2003): 285–97. http://dx.doi.org/10.1026//1618-3169.50.4.285.

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Abstract. The interaction between nonassociative learning (presentation frequencies) and associative learning (reinforcement rates) in stimulus discrimination performance was investigated. Subjects were taught to discriminate lists of visual pattern pairs. When they chose the stimulus designated as right they were symbolically rewarded and when they chose the stimulus designated as wrong they were symbolically penalised. Subjects first learned one list and then another list. For a “right” group the pairs of the second list consisted of right stimuli from the first list and of novel wrong stimuli. For a “wrong” group it was the other way round. The right group transferred some discriminatory performance from the first to the second list while the control and wrong groups initially only performed near chance with the second list. When the first list involved wrong stimuli presented twice as frequently as right stimuli, the wrong group exhibited a better transfer than the right group. In a final experiment subjects learned lists which consisted of frequent right stimuli paired with scarce wrong stimuli and frequent wrong stimuli paired with scarce right stimuli. In later test trials these stimuli were shown in new combinations and additionally combined with novel stimuli. Subjects preferred to choose the most rewarded stimuli and to avoid the most penalised stimuli when the test pairs included at least one frequent stimulus. With scarce/scarce or scarce/novel stimulus combinations they performed less well or even chose randomly. A simple mathematical model that ascribes stimulus choices to a Cartesian combination of stimulus frequency and stimulus value succeeds in matching all these results with satisfactory precision.

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Montare, Alberto. "Knowledge Acquired from Learning: Procedural Cognition and its Declarative Cognizance." Perceptual and Motor Skills 74, no. 1 (February 1992): 243–57. http://dx.doi.org/10.2466/pms.1992.74.1.579.

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62 college students articulated the procedural cognition acquired during successful learning of both original and reversal-shifts of the discrimination-reversal learning task. Articulations formed a four-level hierarchy of “declarative cognizance” (defined as correct articulation of reinforcement contingencies) as follows: Level 1 having no declarative cognizance, Level 2 of perceptually based cognizance, Level 3 of concrete-rule-based cognizance, and Level 4 of abstract-rule-based cognizance. The plausibility of this cognitive hierarchy is enhanced by observations that increasingly higher levels of declarative cognizance are associated with increasingly faster learning. Mon-tare's 1983 and 1988 concepts of primary and secondary signalization are invoked to account for the learning processes underlying these examples of procedural cognition and the hierarchy of declarative cognizance.

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Ploog, B. O., and B. A. Williams. "Serial discrimination reversal learning in pigeons as a function of intertrial interval and delay of reinforcement." Learning & Behavior 38, no. 1 (January 11, 2010): 96–102. http://dx.doi.org/10.3758/lb.38.1.96.

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15

De Meyer, Hasse, Gail Tripp, Tom Beckers, and Saskia van der Oord. "Conditional Learning Deficits in Children with ADHD can be Reduced Through Reward Optimization and Response-Specific Reinforcement." Research on Child and Adolescent Psychopathology 49, no. 9 (April 1, 2021): 1165–78. http://dx.doi.org/10.1007/s10802-021-00781-5.

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AbstractWhen children with ADHD are presented with behavioral choices, they struggle more than Typically Developing [TD] children to take into account contextual information necessary for making adaptive choices. The challenge presented by this type of behavioral decision making can be operationalized as a Conditional Discrimination Learning [CDL] task. We previously showed that CDL is impaired in children with ADHD. The present study explores whether this impairment can be remediated by increasing reward for correct responding or by reinforcing correct conditional choice behavior with situationally specific outcomes (Differential Outcomes). An arbitrary Delayed Matching-To-Sample [aDMTS] procedure was used, in which children had to learn to select the correct response given the sample stimulus presented (CDL). We compared children with ADHD (N = 45) and TD children (N = 49) on a baseline aDMTS task and sequentially adapted the aDMTS task so that correct choice behavior was rewarded with a more potent reinforcer (reward manipulation) or with sample-specific (and hence response-specific) reinforcers (Differential Outcomes manipulation). At baseline, children with ADHD performed significantly worse than TD children. Both manipulations (reward optimization and Differential Outcomes) improved performance in the ADHD group, resulting in a similar level of performance to the TD group. Increasing the reward value or the response-specificity of reinforcement enhances Conditional Discrimination Learning in children with ADHD. These behavioral techniques may be effective in promoting the learning of adaptive behavioral choices in children with ADHD.

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Cousens, Graham A., and Tim Otto. "Neural substrates of olfactory discrimination learning with auditory secondary reinforcement. I. Contributions of the basolateral amygdaloid complex and orbitofrontal cortex." Integrative Physiological & Behavioral Science 38, no. 4 (October 2003): 272–94. http://dx.doi.org/10.1007/bf02688858.

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Lozano, J. H., J. M. Hernández, V. J. Rubio, and J. Santacreu. "Fluid intelligence and discriminative operant learning of reinforcement contingencies in a fixed ratio 3 schedule." Learning and Individual Differences 21, no. 5 (October 2011): 585–89. http://dx.doi.org/10.1016/j.lindif.2011.07.009.

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Prével, Arthur, Vinca Rivière, Jean-Claude Darcheville, Gonzalo P. Urcelay, and Ralph R. Miller. "Excitatory second-order conditioning using a backward first-order conditioned stimulus: A challenge for prediction error reduction." Quarterly Journal of Experimental Psychology 72, no. 6 (August 21, 2018): 1453–65. http://dx.doi.org/10.1177/1747021818793376.

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Prével and colleagues reported excitatory learning with a backward conditioned stimulus (CS) in a conditioned reinforcement preparation. Their results add to existing evidence of backward CSs sometimes being excitatory and were viewed as challenging the view that learning is driven by prediction error reduction, which assumes that only predictive (i.e., forward) relationships are learned. The results instead were consistent with the assumptions of both Miller’s Temporal Coding Hypothesis and Wagner’s Sometimes Opponent Processes (SOP) model. The present experiment extended the conditioned reinforcement preparation developed by Prével et al. to a backward second-order conditioning preparation, with the aim of discriminating between these two accounts. We tested whether a second-order CS can serve as an effective conditioned reinforcer, even when the first-order CS with which it was paired is a backward CS that elicits no responding. Evidence of conditioned reinforcement was found, despite no conditioned response (CR) being elicited by the first-order backward CS. The evidence of second-order conditioning in the absence of excitatory conditioning to the first-order CS is interpreted as a challenge to SOP. In contrast, the present results are consistent with the Temporal Coding Hypothesis and constitute a conceptual replication in humans of previous reports of excitatory second-order conditioning in rodents with a backward CS. The proposal is made that learning is driven by “discrepancy” with prior experience as opposed to “ prediction error.”

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Barnes, Marcia A., Claire Davis, Paulina Kulesz, and David Francis. "Effects of semantic reinforcement, semantic discrimination, and affix frequency on new word learning in skilled and less skilled readers in Grades 6 to 12." Journal of Experimental Child Psychology 205 (May 2021): 105083. http://dx.doi.org/10.1016/j.jecp.2020.105083.

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20

Pérez, Vicente, and Eduardo Polín. "Simple discrimination training and conditional discrimination response." Anales de Psicología 32, no. 1 (December 25, 2015): 250. http://dx.doi.org/10.6018/analesps.32.1.189471.

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<span lang="EN-US">The conditional discrimination is a procedure the use of which is widely extended in the EAB, especially those known as “Matching to Sample”. Although it has been used with a wide variety of species, the behavior of humans with verbal skills in these kinds of tasks may involve other control variables which are different from the scheduled contingencies of four terms. The aim of this work was to verify if conditional discriminations could be acquired, although reinforcement contingencies did not involve the sample. 109 psychology students, who were divided into three conditions, participated in the study. All of them were exposed to two blocks of training (A and B), with one sample and three comparisons, however, the sample did not really function as a conditional stimulus in 75% of the trials in block B. Simultaneity between sample and comparisons, as well as the requirement of a sample observation response, were manipulated resulting in three different conditions. The results showed no differences between acquisition speed in block A and block B in any condition, which suggests that the behavior of the participants was more controlled by the stimuli configuration than by the reinforcement contingencies.</span>

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Avarguès-Weber, Aurore, Maria G. de Brito Sanchez, Martin Giurfa, and Adrian G. Dyer. "Aversive Reinforcement Improves Visual Discrimination Learning in Free-Flying Honeybees." PLoS ONE 5, no. 10 (October 15, 2010): e15370. http://dx.doi.org/10.1371/journal.pone.0015370.

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22

Coenders, CarlaJ H., and JoM H. Vossen. "Random reinforcement probability history affects irrelevant stimulus strategies in discrimination learning." Behavioural Processes 29, no. 1-2 (April 1993): 125–26. http://dx.doi.org/10.1016/0376-6357(93)90046-t.

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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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Nelson, Deborah G. Kemler. "Cognitive Development Meets Discrimination Learning." Contemporary Psychology: A Journal of Reviews 41, no. 11 (November 1996): 1109–10. http://dx.doi.org/10.1037/003205.

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Kastanis, Iason, and Mel Slater. "Reinforcement learning utilizes proxemics." ACM Transactions on Applied Perception 9, no. 1 (March 2012): 1–15. http://dx.doi.org/10.1145/2134203.2134206.

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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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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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Armus, Harvard L. "Secondary Reinforcement Strength and Primary Reinforcement Schedule: Single Group Design and Successive Discrimination Testing." Psychological Record 45, no. 2 (April 1995): 319–23. http://dx.doi.org/10.1007/bf03395936.

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Kaelbling, L. P., M. L. Littman, and A. W. Moore. "Reinforcement Learning: A Survey." Journal of Artificial Intelligence Research 4 (May 1, 1996): 237–85. http://dx.doi.org/10.1613/jair.301.

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This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

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Frick, Robert W., and Yuh-Shiow Lee. "Implicit Learning and Concept Learning." Quarterly Journal of Experimental Psychology Section A 48, no. 3 (August 1995): 762–82. http://dx.doi.org/10.1080/14640749508401414.

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In Experiments 1 and 2, subjects were exposed to letter strings that followed a pattern—the second letter was always the same. This exposure was disguised as a test of immediate memory. Following this training, subjects could discriminate new letter strings following the pattern from letter strings not following the pattern more often than would be expected by chance, which is the traditional evidence for concept learning. Discrimination was also better than would be predicted from subjects’ explicit report of the pattern, demonstrating the co-occurrence of concept learning and implicit learning. In Experiment 3, rules were learned explicitly. Discrimination was worse than would be predicted from subjects’ explicit report, validating the implicit learning paradigm. In Experiment 4, deviations from a prototypical pattern were presented during training. In the test of discrimination, prototypes were as familiar as old deviations and more familiar than new deviations, even when considering only implicit knowledge. Experiment 5 found implicit knowledge of a familiar concept. These results are consistent with the hypothesis that the distinguishing features of a concept can be learned implicitly, and that one type of implicit learning is concept learning.

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Zentall, Thomas R., and Tricia S. Clement. "Simultaneous discrimination learning: Stimulus interactions." Animal Learning & Behavior 29, no. 4 (November 2001): 311–25. http://dx.doi.org/10.3758/bf03192898.

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Capaldi, E. J., Daniel J. Miller, Suzan Alptekin, Kimberly Barry, and Steven J. Haggbloom. "Memory retrieval and discrimination learning." Learning and Motivation 22, no. 4 (November 1991): 439–52. http://dx.doi.org/10.1016/0023-9690(91)90006-t.

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Williams, Ben A., and Roger Dunn. "CONTEXT SPECIFICITY OF CONDITIONED-REINFORCEMENT EFFECTS ON DISCRIMINATION ACQUISITION." Journal of the Experimental Analysis of Behavior 62, no. 2 (September 1994): 157–67. http://dx.doi.org/10.1901/jeab.1994.62-157.

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Pearce, John M., and Paul N. Wilson. "Feature-positive discrimination learning." Journal of Experimental Psychology: Animal Behavior Processes 16, no. 4 (1990): 315–25. http://dx.doi.org/10.1037/0097-7403.16.4.315.

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VALLURI, ANNAPURNA, MICHAEL J. NORTH, and CHARLES M. MACAL. "REINFORCEMENT LEARNING IN SUPPLY CHAINS." International Journal of Neural Systems 19, no. 05 (October 2009): 331–44. http://dx.doi.org/10.1142/s0129065709002063.

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Effective management of supply chains creates value and can strategically position companies. In practice, human beings have been found to be both surprisingly successful and disappointingly inept at managing supply chains. The related fields of cognitive psychology and artificial intelligence have postulated a variety of potential mechanisms to explain this behavior. One of the leading candidates is reinforcement learning. This paper applies agent-based modeling to investigate the comparative behavioral consequences of three simple reinforcement learning algorithms in a multi-stage supply chain. For the first time, our findings show that the specific algorithm that is employed can have dramatic effects on the results obtained. Reinforcement learning is found to be valuable in multi-stage supply chains with several learning agents, as independent agents can learn to coordinate their behavior. However, learning in multi-stage supply chains using these postulated approaches from cognitive psychology and artificial intelligence take extremely long time periods to achieve stability which raises questions about their ability to explain behavior in real supply chains. The fact that it takes thousands of periods for agents to learn in this simple multi-agent setting provides new evidence that real world decision makers are unlikely to be using strict reinforcement learning in practice.

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Tadashi Tusda, Liu^ Yong Zhen, Aiko Sugaya, Kazumi Katoh, Koji Hori, Sanae Tanaka, Masahiko Nomur, and Eiichi Sugaya. "Reinforcement effects of Boschniakia rossica on discrimination learning in cholinergic lesions of rats." Journal of Ethnopharmacology 44, no. 2 (October 1994): 67–71. http://dx.doi.org/10.1016/0378-8741(94)90070-1.

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Thompson, Laura A., Jeanne Malmberg, and Tracy S. Kendler. "Discrimination Learning for the 1990s?" American Journal of Psychology 111, no. 4 (1998): 626. http://dx.doi.org/10.2307/1423554.

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Stone, Peter, Richard S. Sutton, and Gregory Kuhlmann. "Reinforcement Learning for RoboCup Soccer Keepaway." Adaptive Behavior 13, no. 3 (September 2005): 165–88. http://dx.doi.org/10.1177/105971230501300301.

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Trobalon, J. B., J. Sansa, V. D. Chamizo, and N. J. Mackintos. "Perceptual Learning in Maze Discriminations." Quarterly Journal of Experimental Psychology Section B 43, no. 4b (November 1991): 389–402. http://dx.doi.org/10.1080/14640749108401276.

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In Experiment 1, rats were trained on a discrimination between rubber- and sandpaper-covered arms of a maze after one group had been pre-exposed to these intra-maze cues. Pre-exposure facilitated subsequent discrimination learning, unless the discrimination was made easier by adding further discriminative stimuli, when it now significantly retarded learning. In Experiment 2, rats were trained on an extra-maze spatial discrimination, again after one group, but not another, had been pre-exposed to the extra-maze landmarks. Here too, pre-exposure facilitated subsequent discrimination learning, unless the discrimination was made substantially easier by arranging that the two arms between which rats had to choose were always separated by 135°. The results of both experiments can be explained by supposing that perceptual learning depends on the presence of features common to S+ and S-.

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Dillien, Tineke, Kris Goethals, Bernard Sabbe, and Inti A. Brazil. "Reinforcement learning in child molesters." Criminal Behaviour and Mental Health 29, no. 1 (November 26, 2018): 31–42. http://dx.doi.org/10.1002/cbm.2097.

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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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Mutter, Sharon A., Steven J. Haggbloom, Leslie F. Plumlee, and Amy R. Schirmer. "Aging, Working Memory, and Discrimination Learning." Quarterly Journal of Experimental Psychology 59, no. 9 (September 2006): 1556–66. http://dx.doi.org/10.1080/17470210500343546.

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Reynolds, Jeremy R., and Randall C. O’Reilly. "Developing PFC representations using reinforcement learning." Cognition 113, no. 3 (December 2009): 281–92. http://dx.doi.org/10.1016/j.cognition.2009.05.015.

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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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Santucci, Anthony C., and F. Robert Treichler. "Concurrent object-discrimination learning in rats." Animal Learning & Behavior 18, no. 3 (September 1990): 295–301. http://dx.doi.org/10.3758/bf03205289.

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Lachnit, Harald, Ira Ludwig, and Günter Reinhard. "Responding in Configural Discrimination Problems Depends on Density of Reinforcement in Time." Experimental Psychology 54, no. 4 (January 2007): 281–88. http://dx.doi.org/10.1027/1618-3169.54.4.281.

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Abstract. Previous research has shown that conditioned responding in differential skin conductance conditioning increased for reinforced stimuli (CSs+) but remained constant for nonreinforced stimuli (CSs-) due to decreasing reinforcement density. The present two experiments (Experiment 1: Negative patterning; Experiment 2: Positive patterning) were designed to disentangle a possible confound of reinforcement density with stimulus frequency. In order to achieve this, we varied the intertrial interval (18 s, 24 s, or 48 s) and held constant the numbers of CSs+ and of CSs- in each of both discrimination problems. With increasing intertrial intervals, we found higher responding both to CSs+ and to CSs- as well as increased response differentiation. We discuss these results with respect to two mechanisms offered by Wagner's SOP model and conclude that the observed effects are due to variations in density of reinforcement in time.

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Rodríguez-Gironés, Miguel A., Alejandro Trillo, and Guadalupe Corcobado. "Long Term Effects of Aversive Reinforcement on Colour Discrimination Learning in Free-Flying Bumblebees." PLoS ONE 8, no. 8 (August 12, 2013): e71551. http://dx.doi.org/10.1371/journal.pone.0071551.

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Yamada, K., K. Ookura, and K. Ueda. "2P1-L5 Segmentation of the State Space based on Bayesian Discrimination for Reinforcement Learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2001 (2001): 64. http://dx.doi.org/10.1299/jsmermd.2001.64_1.

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Graham, Robert B. "A Computer Tutorial for Psychology of Learning Courses." Teaching of Psychology 21, no. 2 (April 1994): 115–16. http://dx.doi.org/10.1207/s15328023top2102_16.

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A Macintosh computer tutorial that teaches the fundamentals discrimination learning is described. Concepts covered includes stimulus discrimination, extinction, S+, S−, and differential reinforcen Questions are repeated until the correct answer is given, but the repetitions are spaced to maximize retention. Student records are automatically saved. The program-is-free.

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Pearce, John M., and Paul N. Wilson. "Configural associations in discrimination learning." Journal of Experimental Psychology: Animal Behavior Processes 16, no. 3 (1990): 250–61. http://dx.doi.org/10.1037/0097-7403.16.3.250.

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