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

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Dickinson, Anthony, and Sanne de Wit. "The Interaction between Discriminative Stimuli and Outcomes during Instrumental Learning." Quarterly Journal of Experimental Psychology Section B 56, no. 1b (2003): 127–39. http://dx.doi.org/10.1080/02724990244000223.

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Rats were trained on a biconditional discrimination in which the delivery of a food pellet stimulus signalled that pressing on one of two levers would be reinforced, whereas the delivery of a sucrose solution stimulus signalled that the reward was contingent on pressing the other lever. The outcome was the same food type as the discriminative stimulus in the congruent group but the other food type in the incongruent group. Both responses were rewarded with the same outcome in the same group. All the three groups learned the discrimination at statistically indistinguishable rates. Prefeeding one of the outcomes selectively reduced the associated response thereby demonstrating that responding was mediated by a representation of the outcome. Moreover, the outcome of one trial controlled responding on the next trial in accord with the stimulus function of the food type. These results are discussed in relation to the associative structures mediating the discriminative control of instrumental performance.

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2

Metzger, Mary Ann. "Reward Context: Influence on Hypotheses during Learning Set Formation by Preschool Children." Psychological Reports 58, no. 3 (1986): 879–84. http://dx.doi.org/10.2466/pr0.1986.58.3.879.

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Previous studies have shown that, compared to separate reward, a reward sticker attached to the underside of the positive stimulus facilitates solution of discrimination problems by preschool children. It is established here that the context of reward also affects the formation of learning set over a series of discrimination problems and that the improvement is characterized by a reduced frequency (from 19% to 7%) of incorrect position-related hypotheses (error factors) and an increased frequency (from 34% to 65%) of object-related hypotheses.

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3

Capaldi, E. J., and Kimberly M. Birmingham. "Reward produced memories regulate memory-discrimination learning, extinction, and other forms of discrimination learning." Journal of Experimental Psychology: Animal Behavior Processes 24, no. 3 (1998): 254–64. http://dx.doi.org/10.1037/0097-7403.24.3.254.

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4

White, K. Geoffrey. "Psychophysics of Remembering: The Discrimination Hypothesis." Current Directions in Psychological Science 11, no. 4 (2002): 141–45. http://dx.doi.org/10.1111/1467-8721.00187.

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In a psychophysical approach to remembering, the events to be remembered are discriminated from other possibilities at the time of remembering, and not at the time of encoding or learning. The discrimination is specific to the retention interval at which remembering occurs, as shown by experiments demonstrating that discriminability and response bias are delay–specific. This article discusses a discrimination model for remembering that emphasizes the individual's history of learning about reward payoffs in similar experiences in the past. This model predicts the two characteristics of forgetting functions, initial discriminability and rate of forgetting.

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5

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 (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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6

Hampton, Robert R. "Monkey Perirhinal Cortex is Critical for Visual Memory, but not for Visual Perception: Reexamination of the Behavioural Evidence from Monkeys." Quarterly Journal of Experimental Psychology Section B 58, no. 3-4b (2005): 283–99. http://dx.doi.org/10.1080/02724990444000195.

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Overdependence on discrimination learning paradigms to assess the function of perirhinal cortex has complicated understanding of the cognitive role of this structure. Impairments in discrimination learning can result from at least two distinct causes: (a) failure to accurately apprehend and represent the relevant stimuli, or (b) failure to form and remember associations between stimulus representations and reward. Thus, the results of discrimination learning experiments do not readily differentiate deficits in perception from deficits in learning and memory. Here I describe studies that do dissociate learning and memory from perception and show that perirhinal cortex damage impairs learning and/or memory, but not perception. Reanalysis and reconsideration of other published data call into further question the hypothesis that the monkey perirhinal cortex plays a critical role in visual perception.

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7

SWAINSON, R., D. SENGUPTA, T. SHETTY, et al. "Impaired dimensional selection but intact use of reward feedback during visual discrimination learning in Parkinson's disease." Neuropsychologia 44, no. 8 (2006): 1290–304. http://dx.doi.org/10.1016/j.neuropsychologia.2006.01.028.

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8

Melsbach, Gudrun, Martina Siemann, and Juan D. Delius. "Right or Wrong, Familiar or Novel in Pictorial List Discrimination Learning." Experimental Psychology 50, no. 4 (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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9

Capaldi, E. J., Suzan Alptekin, Daniel J. Miller, and Kimberly M. Birmingham. "Is Discriminative Responding in Reward Outcome Serial Learning Mediated by Item Memories or by Position Cues?" Learning and Motivation 28, no. 2 (1997): 153–69. http://dx.doi.org/10.1006/lmot.1996.0964.

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10

Malet-Karas, Aurore, Marion Noulhiane, and Valérie Doyère. "Dynamics of Spatio-Temporal Binding in Rats." Timing & Time Perception 7, no. 1 (2019): 27–47. http://dx.doi.org/10.1163/22134468-20181124.

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Time and space are commonly approached as two distinct dimensions, and rarely combined together in a single task, preventing a comparison of their interaction. In this project, using a version of a timing task with a spatial component, we investigate the learning of a spatio-temporal rule in animals. To do so, rats were placed in front of a five-hole nose-poke wall in a Peak Interval (PI) procedure to obtain a reward, with two spatio-temporal combination rules associated with different to-be-timed cues and lighting contexts. We report that, after successful learning of the discriminative task, a single Pavlovian session was sufficient for the animals to learn a new spatio-temporal association. This was seen as evidence for a beneficial transfer to the new spatio-temporal rule, as compared to control animals that did not experience the new spatio-temporal association during the Pavlovian session. The benefit was observed until nine days later. The results are discussed within the framework of adaptation to a change of a complex associative rule involving interval timing processes.

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Becker, Susanne, Martin Löffler, and Ben Seymour. "Reward Enhances Pain Discrimination in Humans." Psychological Science 31, no. 9 (2020): 1191–99. http://dx.doi.org/10.1177/0956797620939588.

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The notion that reward inhibits pain is a well-supported observation in both humans and animals, allowing suppression of pain reflexes to acquired rewarding stimuli. However, a blanket inhibition of pain by reward would also impair pain discrimination. In contrast, early counterconditioning experiments implied that reward might actually spare pain discrimination. To test this hypothesis, we investigated whether discriminative performance was enhanced or inhibited by reward. We found in adult human volunteers ( N = 25) that pain-based discriminative ability is actually enhanced by reward, especially when reward is directly contingent on discriminative performance. Drift-diffusion modeling shows that this relates to an augmentation of the underlying sensory signal strength and is not merely an effect of decision bias. This enhancement of sensory-discriminative pain-information processing suggests that whereas reward can promote reward-acquiring behavior by inhibition of pain in some circumstances, it can also facilitate important discriminative information of the sensory input when necessary.

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12

Galtress, Tiffany, and Kimberly Kirkpatrick. "Reward magnitude effects on temporal discrimination." Learning and Motivation 41, no. 2 (2010): 108–24. http://dx.doi.org/10.1016/j.lmot.2010.01.002.

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13

Brembs, Björn. "Operant Reward Learning inAplysia." Current Directions in Psychological Science 12, no. 6 (2003): 218–21. http://dx.doi.org/10.1046/j.0963-7214.2003.01265.x.

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14

Hernández, Mireia, María-Ángeles Palomar-García, Benito Nohales-Nieto, et al. "Separate Contribution of Striatum Volume and Pitch Discrimination to Individual Differences in Music Reward." Psychological Science 30, no. 9 (2019): 1352–61. http://dx.doi.org/10.1177/0956797619859339.

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Individual differences in the level of pleasure induced by music have been associated with the response of the striatum and differences in functional connectivity between the striatum and the auditory cortex. In this study, we tested whether individual differences in music reward are related to the structure of the striatum and the ability to discriminate pitch. We acquired a 3-D magnetization-prepared rapid-acquisition gradient-echo image for 32 musicians and 26 nonmusicians who completed a music-reward questionnaire and a test of pitch discrimination. The analysis of both groups together showed that sensitivity to music reward correlated negatively with the volume of both the caudate and nucleus accumbens and correlated positively with pitch-discrimination abilities. Moreover, musicianship, pitch discrimination, and caudate volume significantly predicted individual differences in music reward. These results are consistent with the proposal that individual differences in music reward depend on the interplay between auditory abilities and the reward network.

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15

Bernhard, Sarah M., Jiseok Lee, Mo Zhu, et al. "An automated homecage system for multiwhisker detection and discrimination learning in mice." PLOS ONE 15, no. 12 (2020): e0232916. http://dx.doi.org/10.1371/journal.pone.0232916.

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Automated, homecage behavioral training for rodents has many advantages: it is low stress, requires little interaction with the experimenter, and can be easily manipulated to adapt to different experimental conditions. We have developed an inexpensive, Arduino-based, homecage training apparatus for sensory association training in freely-moving mice using multiwhisker air current stimulation coupled to a water reward. Animals learn this task readily, within 1–2 days of training, and performance progressively improves with training. We examined the parameters that regulate task acquisition using different stimulus intensities, directions, and reward valence. Learning was assessed by comparing anticipatory licking for the stimulus compared to the no-stimulus (blank) trials. At high stimulus intensities (>9 psi), animals showed markedly less participation in the task. Conversely, very weak air current intensities (1–2 psi) were not sufficient to generate rapid learning behavior. At intermediate stimulus intensities (5–6 psi), a majority of mice learned that the multiwhisker stimulus predicted the water reward after 24–48 hrs of training. Both exposure to isoflurane and lack of whiskers decreased animals’ ability to learn the task. Following training at an intermediate stimulus intensity, mice were able to transfer learning behavior when exposed to a lower stimulus intensity, an indicator of perceptual learning. Mice learned to discriminate between two directions of stimulation rapidly and accurately, even when the angular distance between the stimuli was <15 degrees. Switching the reward to a more desirable reward, aspartame, had little effect on learning trajectory. Our results show that a tactile association task in an automated homecage environment can be monitored by anticipatory licking to reveal rapid and progressive behavioral change. These Arduino-based, automated mouse cages enable high-throughput training that facilitate analysis of large numbers of genetically modified mice with targeted manipulations of neural activity.

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16

Lucon-Xiccato, Tyrone, and Angelo Bisazza. "Discrimination reversal learning reveals greater female behavioural flexibility in guppies." Biology Letters 10, no. 6 (2014): 20140206. http://dx.doi.org/10.1098/rsbl.2014.0206.

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Behavioural flexibility allows an animal to adapt its behaviour in response to changes in the environment. Research conducted in primates, rodents and domestic fowl suggests greater behavioural persistence and reduced behavioural flexibility in males. We investigated sex differences in behavioural flexibility in fish by comparing male and female guppies ( Poecilia reticulata ) in a reversal learning task. Fish were first trained on a colour discrimination, which was learned equally rapidly by males and females. However, once the reward contingency was reversed, females were better at inhibiting the previous response and reached criterion twice as fast as males. When reward reversing was repeated, males gradually reduced the number of errors, and the two sexes had a comparable performance after four reversals. We suggest that sex differences in behavioural flexibility in guppies can be explained in terms of the different roles that males and females play in reproduction.

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17

Çoşkun, Filiz, Zeynep Ceyda Sayalı, Emine Gürbüz, and Fuat Balcı. "Optimal time discrimination." Quarterly Journal of Experimental Psychology 68, no. 2 (2015): 381–401. http://dx.doi.org/10.1080/17470218.2014.944921.

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In the temporal bisection task, participants categorize experienced stimulus durations as short or long based on their similarity to previously acquired reference durations. Reward maximization in this task requires integrating endogenous timing uncertainty as well as exogenous probabilities of the reference durations into temporal judgements. We tested human participants on the temporal bisection task with different short and long reference duration probabilities (exogenous probability) in two separate test sessions. Incorrect categorizations were not penalized in Experiment 1 but were penalized in Experiment 2, leading to different levels of stringency in the reward functions that participants tried to maximize. We evaluated the judgements within the framework of optimality. Our participants adapted their choice behaviour in a nearly optimal fashion and earned nearly the maximum possible expected gain they could attain given their level of endogenous timing uncertainty and exogenous probabilities in both experiments. These results point to the optimality of human temporal risk assessment in the temporal bisection task. The long categorization response times (RTs) were overall faster than short categorization RTs, and short but not long categorization RTs were modulated by reference duration probability manipulations. These observations suggested an asymmetry between short and long categorizations in the temporal bisection task.

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18

Metzger, Mary Ann. "Cue selection in discrimination learning by preschool children: Reward context effects." Bulletin of the Psychonomic Society 24, no. 2 (1986): 135–37. http://dx.doi.org/10.3758/bf03330526.

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19

Rose, Jonas, Robert Schmidt, Marco Grabemann, and Onur Güntürkün. "Theory meets pigeons: The influence of reward-magnitude on discrimination-learning." Behavioural Brain Research 198, no. 1 (2009): 125–29. http://dx.doi.org/10.1016/j.bbr.2008.10.038.

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20

Hadland, K. A., M. F. S. Rushworth, D. Gaffan, and R. E. Passingham. "The Anterior Cingulate and Reward-Guided Selection of Actions." Journal of Neurophysiology 89, no. 2 (2003): 1161–64. http://dx.doi.org/10.1152/jn.00634.2002.

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Macaques were taught a reward-conditional response selection task; they learned to associate each of two different actions to each of two different rewards and to select actions that were appropriate for particular rewards. They were also taught a visual discrimination learning task. Cingulate lesions significantly impaired selection of responses associated with different rewards but did not interfere with visual discrimination learning or performance. The results suggest that 1) the cingulate cortex is concerned with action reward associations and not limited to just detecting when actions lead to errors and 2) that the cingulate cortex's function is limited to action reinforcer associations and it is not concerned with stimulus reward associations.

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21

Don, Hilary J., A. Ross Otto, Astin C. Cornwall, Tyler Davis, and Darrell A. Worthy. "Learning reward frequency over reward probability: A tale of two learning rules." Cognition 193 (December 2019): 104042. http://dx.doi.org/10.1016/j.cognition.2019.104042.

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22

Kim, Dongho, Sam Ling, and Takeo Watanabe. "Dual mechanisms governing reward-driven perceptual learning." F1000Research 4 (September 10, 2015): 764. http://dx.doi.org/10.12688/f1000research.6853.1.

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In this review, we explore how reward signals shape perceptual learning in animals and humans. Perceptual learning is the well-established phenomenon by which extensive practice elicits selective improvement in one’s perceptual discrimination of basic visual features, such as oriented lines or moving stimuli. While perceptual learning has long been thought to rely on ‘top-down’ processes, such as attention and decision-making, a wave of recent findings suggests that these higher-level processes are, in fact, not necessary. Rather, these recent findings indicate that reward signals alone, in the absence of the contribution of higher-level cognitive processes, are sufficient to drive the benefits of perceptual learning. Here, we will review the literature tying reward signals to perceptual learning. Based on these findings, we propose dual underlying mechanisms that give rise to perceptual learning: one mechanism that operates ‘automatically’ and is tied directly to reward signals, and another mechanism that involves more ‘top-down’, goal-directed computations.

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23

Spencer, Christopher C., Joshua D. Foster, and Jeffrey S. Bedwell. "Narcissistic neuroticism and elevated reward learning." Personality and Individual Differences 141 (April 2019): 235–40. http://dx.doi.org/10.1016/j.paid.2019.01.002.

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24

Dwyer, Dominic Michael, Jaime Figueroa, Patricia Gasalla, and Matías López. "Reward Adaptation and the Mechanisms of Learning: Contrast Changes Reward Value in Rats and Drives Learning." Psychological Science 29, no. 2 (2017): 219–27. http://dx.doi.org/10.1177/0956797617729825.

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Formal theories of learning suggest that associations between events are determined by the internal representations of those events. Thus, learning should depend on perceived reward value—even when perceptions differ from objective values. We examined this prediction in flavor-preference learning in rats. In two experiments, simultaneous contrast either increased perceived reward value, which was paired with a distinctive flavor cue (the positive conditioned stimulus, CS+), or decreased the perceived value of the same reward, which was then paired with a second flavor (the negative conditioned stimulus, CS–). Even though the CS+ and CS– were paired with the same objective reward, there was a preference for the CS+ in subsequent tests. Moreover, the size of contrast-produced changes in reward value during training predicted the preference for the CS+ at test. This contrast-produced learning effect illustrates the mechanisms by which associations, which normally track veridical relationships between events in the world, are formed.

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25

Bechtel, William, and Adele Abrahamsen. "Learning, reward, and cognitive differences." Behavioral and Brain Sciences 11, no. 03 (1988): 448. http://dx.doi.org/10.1017/s0140525x00058283.

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26

Czaczkes, T. J., and P. Kumar. "Very rapid multi-odour discrimination learning in the ant Lasius niger." Insectes Sociaux 67, no. 4 (2020): 541–45. http://dx.doi.org/10.1007/s00040-020-00787-0.

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AbstractInsects can be very good learners. For example, they can form associations between a cue and a reward after only one exposure. Discrimination learning, in which multiple cues are associated with different outcomes, is critical for responding correctly complex environments. However, the extent of such discrimination learning is not well explored. Studies concerning discrimination learning within one valence are also rare. Here we ask whether Lasius niger ants can form multiple concurrent associations to different reward levels, and how rapidly such associations can be learned. We allowed individual workers to sequentially feed on up to four different food qualities, each associated with a different odour cue. Using pairwise preference tests, we found that ants can successfully learn at least two, and likely three, odour/quality associations, requiring as little as one exposure to each combination in order for learning to take place. By testing preference between two non-extreme values (i.e. between 0.4 M and 0.8 M having been trained to the qualities 0.2, 0.4, 0.8 and 1.6) we exclude the possibility that ants are only memorising the best and worst values in a set. Such rapid learning of multiple associations, within one valence and one modality, is impressive, and makes Lasius niger a very tractable model for complex training paradigms.

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Paret, Christian, and Florian Bublatzky. "Threat rapidly disrupts reward reversal learning." Behaviour Research and Therapy 131 (August 2020): 103636. http://dx.doi.org/10.1016/j.brat.2020.103636.

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28

Galaj, Ewa, and Robert Ranaldi. "Neurobiology of reward-related learning." Neuroscience & Biobehavioral Reviews 124 (May 2021): 224–34. http://dx.doi.org/10.1016/j.neubiorev.2021.02.007.

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29

Armus, Harvard L. "Startle and Reward-Based Avoidance-Avoidance Conflict." Psychological Reports 59, no. 2 (1986): 483–86. http://dx.doi.org/10.2466/pr0.1986.59.2.483.

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Using 30 male rats in a within-subjects design, the hypothesis was tested that avoidance-avoidance conflict based on the omission of food reward in a two-choice discrimination task would result in a more intense acoustic startle reaction than would the absence of such conflict. To maintain a high level of conflict, training days were interspersed between test days. Data showed no differences between conflict and nonconflict conditions.

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30

Gil, M., R. J. De Marco, and R. Menzel. "Learning reward expectations in honeybees." Learning & Memory 14, no. 7 (2007): 491–96. http://dx.doi.org/10.1101/lm.618907.

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31

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

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32

Gutierrez, R., S. A. Simon, and M. A. L. Nicolelis. "Licking-Induced Synchrony in the Taste-Reward Circuit Improves Cue Discrimination during Learning." Journal of Neuroscience 30, no. 1 (2010): 287–303. http://dx.doi.org/10.1523/jneurosci.0855-09.2010.

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33

Grelat, Anne, Laura Benoit, Sébastien Wagner, Carine Moigneu, Pierre-Marie Lledo, and Mariana Alonso. "Adult-born neurons boost odor–reward association." Proceedings of the National Academy of Sciences 115, no. 10 (2018): 2514–19. http://dx.doi.org/10.1073/pnas.1716400115.

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Olfaction is an important sensory modality driving fundamental behaviors. During odor-dependent learning, a positive value is commonly assigned to an odorant, and multiple forms of plasticity are involved when such odor–reward associations are formed. In rodents, one of the mechanisms underlying plasticity in the olfactory bulb consists in recruiting new neurons daily throughout life. However, it is still unknown whether adult-born neurons might participate in encoding odor value. Here, we demonstrate that exposure to reward-associated odors specifically increases activity of adult-born neurons but not preexisting neurons. Remarkably, adult-born neuron activation during rewarded odor presentation heightens discrimination learning and enhances the ability to update the odor value during reversal association. Moreover, in some cases, activation of this interneuron population can trigger olfactory learning without sensory stimulation. Taken together, our results show a specific involvement of adult-born neurons in facilitating odor–reward association during adaptive learning.

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Schaffner, Paul E. "Specious learning about reward and punishment." Journal of Personality and Social Psychology 48, no. 6 (1985): 1377–86. http://dx.doi.org/10.1037/0022-3514.48.6.1377.

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35

Song, Yanlong, Siyuan Lu, and Ann L. Smiley-Oyen. "Differential motor learning via reward and punishment." Quarterly Journal of Experimental Psychology 73, no. 2 (2019): 249–59. http://dx.doi.org/10.1177/1747021819871173.

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Visuomotor adaptation involves multiple processes such as explicit learning, implicit learning from sensory prediction errors, and model-free mechanisms like use-dependent plasticity. Recent findings show that reward and punishment differently affect visuomotor adaptation. This study examined whether punishment and reward had distinct effects on explicit learning. When participants practised adapting to a large, abrupt visual rotation during reaching for a virtual visual target, visual feedback of the cursor was not provided. Only performance-based scalar reward or punishment feedback (money gained or lost) was used, thereby emphasising explicit processes during adaptation. The results revealed that punishment, compared with reward, induced faster adaptation and greater variability of reaching in the initial phase of adaptation. We interpret these findings as reflecting enhanced explicit learning, likely due to loss aversion.

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Pearson, Daniel, and Mike E. Le Pelley. "Learning to avoid looking: Competing influences of reward on overt attentional selection." Psychonomic Bulletin & Review 27, no. 5 (2020): 998–1005. http://dx.doi.org/10.3758/s13423-020-01770-3.

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Abstract Pairing a stimulus with large reward increases the likelihood that it will capture attention and eye-gaze, even when such capture has negative consequences. This suggests that a stimulus’s signalling relationship with reward (the co-occurrence of that stimulus and reward) has a powerful influence on attentional selection. In the present study, we demonstrate that a stimulus’s response relationship with reward (the reward-related consequences of attending to that stimulus) can also exert an independent, competing influence on selection. Participants completed a visual search task in which they made a saccade to a target shape to earn reward. The colour of a distractor signalled the magnitude of reward available on each trial. For one group of participants, there was a negative response relationship between making a saccade to the distractor and reward delivery: looking at the distractor caused the reward to be cancelled. For a second group, there was no negative response relationship, but an equivalent distractor–reward signalling relationship was maintained via a yoking procedure. Participants from both groups were more likely to have their gaze captured by the distractor that signalled high reward versus low reward, demonstrating an influence of the signalling relationship on attention. However, participants who experienced a negative response relationship showed a reduced influence of signal value on capture, and specifically less capture by the high-reward distractor. These findings demonstrate that reward can have a multifaceted influence on attentional selection through different, learned stimulus-reward relationships, and thus that the relationship between reward and attention is more complex than previously thought.

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Gorman, Prue, Luke Jones, and Jacqueline Holman. "Effects Of Free Reward On Learning And Motivation." Australian Journal of Psychology 38, no. 1 (1986): 81–83. http://dx.doi.org/10.1080/00049538608256419.

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38

Frick, Robert W., and Yuh-Shiow Lee. "Implicit Learning and Concept Learning." Quarterly Journal of Experimental Psychology Section A 48, no. 3 (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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Carvalheiro, Joana, Vasco A. Conceição, Ana Mesquita, and Ana Seara-Cardoso. "Acute stress impairs reward learning in men." Brain and Cognition 147 (February 2021): 105657. http://dx.doi.org/10.1016/j.bandc.2020.105657.

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Zentall, Thomas R., and Tricia S. Clement. "Simultaneous discrimination learning: Stimulus interactions." Animal Learning & Behavior 29, no. 4 (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 (1991): 439–52. http://dx.doi.org/10.1016/0023-9690(91)90006-t.

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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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Roche, Daniel P., Katie E. McGhee, and Alison M. Bell. "Maternal predator-exposure has lifelong consequences for offspring learning in threespined sticklebacks." Biology Letters 8, no. 6 (2012): 932–35. http://dx.doi.org/10.1098/rsbl.2012.0685.

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Learning is an important form of phenotypic plasticity that allows organisms to adjust their behaviour to the environment. An individual's learning performance can be affected by its mother's environment. For example, mothers exposed to stressors, such as restraint and forced swimming, often produce offspring with impaired learning performance. However, it is unclear whether there are maternal effects on offspring learning when mothers are exposed to ecologically relevant stressors, such as predation risk. Here, we examined whether maternal predator-exposure affects adult offsprings’ learning of a discrimination task in threespined sticklebacks ( Gasterosteus aculeatus ). Mothers were either repeatedly chased by a model predator (predator-exposed) or not (unexposed) while producing eggs. Performance of adult offspring from predator-exposed and unexposed mothers was assessed in a discrimination task that paired a particular coloured chamber with a food reward. Following training, all offspring learned the colour-association, but offspring of predator-exposed mothers located the food reward more slowly than offspring of unexposed mothers. This pattern was not driven by initial differences in exploratory behaviour. These results demonstrate that an ecologically relevant stressor (predation risk) can induce maternal effects on offspring learning, and perhaps behavioural plasticity more generally, that last into adulthood.

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Poulton, Antoinette, and Robert Hester. "Transition to substance use disorders: impulsivity for reward and learning from reward." Social Cognitive and Affective Neuroscience 15, no. 10 (2019): 1182–91. http://dx.doi.org/10.1093/scan/nsz077.

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Abstract Substance dependence constitutes a profound societal burden. Although large numbers of individuals use licit or illicit substances, few transition to dependence. The specific factors influencing this transition are not well understood. Substance-dependent individuals tend to be swayed by the immediate rewards of drug taking, but are often insensitive to delayed negative consequences of their behavior. Dependence is consequently associated with impulsivity for reward and atypical learning from feedback. Behavioral impulsivity is indexed using tasks measuring spontaneous decision-making and capacity to control impulses. While evidence indicates drug taking exacerbates behavioral impulsivity for reward, animal and human studies of drug naïve populations demonstrate it might precede any drug-related problems. Research suggests dependent individuals are also more likely to learn from rewarding (relative to punishing) feedback. This may partly explain why substance-dependent individuals fail to modify their behavior in response to negative outcomes. This enhanced learning from reward may constitute a further pre-existing risk factor for substance dependence. Although impulsivity for reward and preferential learning from rewarding feedback are both underpinned by a compromised dopaminergic system, few studies have examined the relationship between these two mechanisms. The interplay of these processes may help enrich understanding of why some individuals transition to substance dependence.

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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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Liverant, Gabrielle I., Denise M. Sloan, Diego A. Pizzagalli, et al. "Associations Among Smoking, Anhedonia, and Reward Learning in Depression." Behavior Therapy 45, no. 5 (2014): 651–63. http://dx.doi.org/10.1016/j.beth.2014.02.004.

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Mukherjee, Dahlia, Alexandre L. S. Filipowicz, Khoi Vo, Theodore D. Satterthwaite, and Joseph W. Kable. "Reward and punishment reversal-learning in major depressive disorder." Journal of Abnormal Psychology 129, no. 8 (2020): 810–23. http://dx.doi.org/10.1037/abn0000641.

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Liao, Ming-Ray, and Brian A. Anderson. "Reward learning biases the direction of saccades." Cognition 196 (March 2020): 104145. http://dx.doi.org/10.1016/j.cognition.2019.104145.

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Weiler, Julia A., Christian Bellebaum, Martin Brüne, Georg Juckel, and Irene Daum. "Impairment of probabilistic reward-based learning in schizophrenia." Neuropsychology 23, no. 5 (2009): 571–80. http://dx.doi.org/10.1037/a0016166.

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Schintu, Selene, Michael Freedberg, Zaynah M. Alam, Sarah Shomstein, and Eric M. Wassermann. "Left-shifting prism adaptation boosts reward-based learning." Cortex 109 (December 2018): 279–86. http://dx.doi.org/10.1016/j.cortex.2018.09.021.

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