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Journal articles on the topic 'Discrimination learning'

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Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

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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2

Pérez-González, Luis Antonio, and Héctor Martínez. "Emergence of Third-Order Conditional Discriminations from Learning Discriminations with Unrelated Stimuli." Psychological Record 72, no. 1 (November 17, 2021): 75–88. http://dx.doi.org/10.1007/s40732-021-00461-2.

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AbstractThis study explored learning and generalization of a third-order conditional discrimination. Two 8-year-old children learned two auditory–visual conditional discriminations in which they selected visual Japanese syllabic symbols in response to syllables spoken by the experimenter. Then, they learned a third-order conditional discrimination in which they selected between two visual symbols after being exposed to two spoken syllables and one visual symbol. Thereafter, we probed generalization with novel symbols and names by teaching two additional conditional discriminations with Nahuatl symbols and spoken words and probing without reinforcement a new third-order conditional discrimination in which they had to select between two visual Nahuatl symbols after being exposed to two spoken Nahuatl words and one visual Nahuatl symbol. The two children responded in a predicted way to the novel third-order conditional discrimination. The emergent performance was possible because the set of relations established among the stimuli of the third-order conditional discrimination with Japanese syllables was analogous to the set of relations established among the stimuli of the third-order conditional discriminations with Nahuatl words. These results demonstrated a novel type of emergent responding in third-order conditional discrimination with arbitrary relations.

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3

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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4

Zheng, Hao, and Dapeng Tao. "Discriminative dictionary learning via Fisher discrimination K-SVD algorithm." Neurocomputing 162 (August 2015): 9–15. http://dx.doi.org/10.1016/j.neucom.2015.03.071.

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5

Byrom, Nicola C., and Robin A. Murphy. "Cue competition influences biconditional discrimination." Quarterly Journal of Experimental Psychology 72, no. 2 (January 1, 2018): 182–92. http://dx.doi.org/10.1080/17470218.2017.1363256.

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When multiple cues are presented in compound and trained to predict an outcome, the cues may compete for association with an outcome. However, if both cues are necessary for solution of the discrimination, then competition might be expected to interfere with the solution of the discrimination. We consider how unequal stimulus salience influences learning in configural discriminations, where no individual stimulus predicts the outcome. We compared two hypotheses: (1) salience modulation minimises the initial imbalance in salience and (2) unequal stimulus salience will impair acquisition of configural discriminations. We assessed the effect of varying stimulus salience in a biconditional discrimination (AX+, AY−, BX−, BY+). Across two experiments, we found stronger discrimination when stimuli had matched, rather than mismatched, salience, supporting our second hypothesis. We discuss the implications of this finding for Mackintosh’s model of selective attention, modified elemental models and configural models of learning.

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6

Ren, Qiubing, Mingchao Li, Shuai Han, Ye Zhang, Qi Zhang, and Jonathan Shi. "Basalt Tectonic Discrimination Using Combined Machine Learning Approach." Minerals 9, no. 6 (June 22, 2019): 376. http://dx.doi.org/10.3390/min9060376.

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Geochemical discrimination of basaltic magmatism from different tectonic settings remains an essential part of recognizing the magma generation process within the Earth’s mantle. Discriminating among mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB) is that matters to geologists because they are the three most concerned basalts. Being a supplement to conventional discrimination diagrams, we attempt to utilize the machine learning algorithm (MLA) for basalt tectonic discrimination. A combined MLA termed swarm optimized neural fuzzy inference system (SONFIS) was presented based on neural fuzzy inference system and particle swarm optimization. Two geochemical datasets of basalts from GEOROC and PetDB served as to test the classification performance of SONFIS. Several typical discrimination diagrams and well-established MLAs were also used for performance comparisons with SONFIS. Results indicated that the classification accuracy of SONFIS for MORB, OIB and IAB in both datasets could reach over 90%, superior to other methods. It also turns out that MLAs had certain advantages in making full use of geochemical characteristics and dealing with datasets containing missing data. Therefore, MLAs provide new research tools other than discrimination diagrams for geologists, and the MLA-based technique is worth extending to tectonic discrimination of other volcanic rocks.

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7

Maddess, T., D. Coy, J. C. Herrington, C. F. Carle, F. Sabeti, and M. S. Barbosa. "Learning complex texture discrimination." Journal of the Optical Society of America A 38, no. 3 (March 1, 2021): 449. http://dx.doi.org/10.1364/josaa.413065.

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8

Blume, Lawrence E. "Learning and Statistical Discrimination." American Economic Review 95, no. 2 (April 1, 2005): 118–21. http://dx.doi.org/10.1257/000282805774670257.

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9

Herrington, Jessica, Ted Maddess, Dominique Coy, Corinne Carle, Faran Sabeti, and Marconi Barbosa. "Learning Complex Texture Discrimination." Journal of Vision 18, no. 10 (September 1, 2018): 260. http://dx.doi.org/10.1167/18.10.260.

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10

Jain, A. K., and K. Karu. "Learning texture discrimination masks." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 2 (1996): 195–205. http://dx.doi.org/10.1109/34.481543.

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11

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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12

Järbe, Torbjörn U. C., Arto J. Hiltunen, and Michael D. B. Swedberg. "Compound drug discrimination learning." Drug Development Research 16, no. 2-4 (1989): 111–22. http://dx.doi.org/10.1002/ddr.430160205.

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13

Lages, Martin, and Michel Treisman. "A Criterion Setting Theory of Discrimination Learning that Accounts for Anisotropies and Context Effects." Seeing and Perceiving 23, no. 5 (2010): 401–34. http://dx.doi.org/10.1163/187847510x541117.

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AbstractWe can discriminate departures from the vertical or horizontal more accurately than from other orientations. This may reflect perceptual learning, but the mechanisms behind such learning are not well understood. Here we derive a theory of discrimination learning based on criterion setting theory (CST; Treisman and Williams, 1984), an extension of signal detection theory in which judgment of the current stimulus is partly determined by previous discriminations and context. The CST-based theory of discrimination learning (CST-DL) describes mechanisms which use information from previous acts of discrimination to improve current decision making. CST-DL distinguishes between types of decision criteria and provides an account of anisotropies and context effects affecting discrimination. Predictions from this model are tested in experiments on anisotropies in orientation and depth perception. The results obtained support CST-DL. They also support the conclusion that the account of the retention of sensory information in delayed discrimination provided by CST is superior to the traditional belief that information retention relies on a fixed memory trace or representation of the stimulus.

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14

Glautier, Steven, Tamaryn Menneer, Hayward J. Godwin, Nick Donnelly, and José A. Aristizabal. "Flexible Configural Learning of Non-Linear Discriminations and Detection of Stimulus Compounds." Experimental Psychology 63, no. 4 (July 2016): 215–36. http://dx.doi.org/10.1027/1618-3169/a000331.

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Abstract. Previous work showed that prior experience with discriminations requiring configural solutions (e.g., biconditional discrimination) confers an advantage for the learning of new configural discriminations (e.g., negative patterning) in comparison to prior experience with elemental discriminations. This effect is well established but its mechanism is not well understood. In the studies described below we assessed whether the saliences of configural and element cues were affected by prior training. We observed positive transfer to a new configural discrimination after configural pre-training but we were unable to find evidence for changes in cue salience using a signal-detection task. Our results confirm previous work by demonstrating experience-dependent flexibility in cue processing but they also suggest that this flexibility occurs at a point in the stimulus processing pipeline later than 1–2 s after the presentation of stimulus inputs. (138 words)

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15

Maeda, Yoshiaki, Yui Sugiyama, Atsushi Kogiso, Tae-Kyu Lim, Manabu Harada, Tomoko Yoshino, Tadashi Matsunaga, and Tsuyoshi Tanaka. "Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches." Sensors 18, no. 9 (August 24, 2018): 2789. http://dx.doi.org/10.3390/s18092789.

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Detection and discrimination of bacteria are crucial in a wide range of industries, including clinical testing, and food and beverage production. Staphylococcus species cause various diseases, and are frequently detected in clinical specimens and food products. In particular, S. aureus is well known to be the most pathogenic species. Conventional phenotypic and genotypic methods for discrimination of Staphylococcus spp. are time-consuming and labor-intensive. To address this issue, in the present study, we applied a novel discrimination methodology called colony fingerprinting. Colony fingerprinting discriminates bacterial species based on the multivariate analysis of the images of microcolonies (referred to as colony fingerprints) with a size of up to 250 μm in diameter. The colony fingerprints were obtained via a lens-less imaging system. Profiling of the colony fingerprints of five Staphylococcus spp. (S. aureus, S. epidermidis, S. haemolyticus, S. saprophyticus, and S. simulans) revealed that the central regions of the colony fingerprints showed species-specific patterns. We developed 14 discriminative parameters, some of which highlight the features of the central regions, and analyzed them by several machine learning approaches. As a result, artificial neural network (ANN), support vector machine (SVM), and random forest (RF) showed high performance for discrimination of theses bacteria. Bacterial discrimination by colony fingerprinting can be performed within 11 h, on average, and therefore can cut discrimination time in half compared to conventional methods. Moreover, we also successfully demonstrated discrimination of S. aureus in a mixed culture with Pseudomonas aeruginosa. These results suggest that colony fingerprinting is useful for discrimination of Staphylococcus spp.

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16

Kurmi, Vinod K., Rishabh Sharma, Yash Vardhan Sharma, and Vinay P. Namboodiri. "Gradient Based Activations for Accurate Bias-Free Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7255–62. http://dx.doi.org/10.1609/aaai.v36i7.20687.

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Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial learning. A discriminator is used to identify the bias attributes such as gender, age or race in question. This discriminator is used adversarially to ensure that it cannot distinguish the bias attributes. The main drawback in such a model is that it directly introduces a trade-off with accuracy as the features that the discriminator deems to be sensitive for discrimination of bias could be correlated with classification. In this work we solve the problem. We show that a biased discriminator can actually be used to improve this bias-accuracy tradeoff. Specifically, this is achieved by using a feature masking approach using the discriminator's gradients. We ensure that the features favoured for the bias discrimination are de-emphasized and the unbiased features are enhanced during classification. We show that this simple approach works well to reduce bias as well as improve accuracy significantly. We evaluate the proposed model on standard benchmarks. We improve the accuracy of the adversarial methods while maintaining or even improving the unbiasness and also outperform several other recent methods.

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17

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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18

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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19

Portegys, Thomas E. "Discrimination learning guided by instinct." International Journal of Hybrid Intelligent Systems 10, no. 3 (July 4, 2013): 129–36. http://dx.doi.org/10.3233/his-130171.

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20

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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21

Altonji, J. G., and C. R. Pierret. "Employer Learning and Statistical Discrimination." Quarterly Journal of Economics 116, no. 1 (February 1, 2001): 313–50. http://dx.doi.org/10.1162/003355301556329.

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22

Kuc, K. A., B. M. Gregersen, K. S. Gannon, and J. C. Dodart. "Holeboard discrimination learning in mice." Genes, Brain and Behavior 5, no. 4 (June 2006): 355–63. http://dx.doi.org/10.1111/j.1601-183x.2005.00168.x.

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23

Yu, C., S. A. Klein, and D. M. Levi. "Perceptual learning of contrast discrimination." Journal of Vision 3, no. 9 (March 16, 2010): 161. http://dx.doi.org/10.1167/3.9.161.

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24

Halliday, Lorna F., Jenny L. Taylor, A. Mark Edmondson-Jones, and David R. Moore. "Frequency discrimination learning in children." Journal of the Acoustical Society of America 123, no. 6 (June 2008): 4393–402. http://dx.doi.org/10.1121/1.2890749.

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25

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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26

Jiang, Rui, Hong Qiao, and Bo Zhang. "Efficient Fisher Discrimination Dictionary Learning." Signal Processing 128 (November 2016): 28–39. http://dx.doi.org/10.1016/j.sigpro.2016.03.013.

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27

Flannery, Barbara. "Relational discrimination learning in horses." Applied Animal Behaviour Science 54, no. 4 (November 1997): 267–80. http://dx.doi.org/10.1016/s0168-1591(97)00006-3.

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28

Liu, Z., H. Lu, and N. Qian. "Learning motion discrimination without MT." Journal of Vision 1, no. 3 (March 14, 2010): 27. http://dx.doi.org/10.1167/1.3.27.

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29

Demany, Laurent. "Perceptual learning in frequency discrimination." Journal of the Acoustical Society of America 78, no. 3 (September 1985): 1118–20. http://dx.doi.org/10.1121/1.393034.

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30

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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31

Dantendorfer, K., D. Maierhofer, I. Daum, M. Schugens, P. Anderer, H. V. Semlitsch, and H. Katschnig. "Conditional discrimination learning in schizophrenia." International Journal of Psychophysiology 25, no. 1 (January 1997): 76–77. http://dx.doi.org/10.1016/s0167-8760(97)85559-6.

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32

Wesley, Frank, and F. D. Klopfer. "Visual Discrimination Learning in Swine1." Zeitschrift für Tierpsychologie 19, no. 1 (April 26, 2010): 93–104. http://dx.doi.org/10.1111/j.1439-0310.1962.tb00764.x.

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33

Zentall, Thomas R. "Temporal discrimination learning by pigeons." Behavioural Processes 74, no. 2 (February 2007): 286–92. http://dx.doi.org/10.1016/j.beproc.2006.09.011.

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34

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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35

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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36

Cruz, Fátima, and Robert E. Stake. "Teaching for Equity, Learning about Discrimination in a Meritocratic Society." Qualitative Research in Education 1, no. 2 (October 30, 2012): 112–34. http://dx.doi.org/10.4471/qre.2012.07.

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In this paper, we will examine key points for research attention in the effort to commit educational systems to equity education. We will examine the concepts of equity, equality and discrimination. We will give specific attention to the role of teacher educators. Teachers need to understand and to be able to see social discrimination in educational systems and policies and in classroom relationships. We will claim that equity education holds low priority even in those countries making the strongest efforts at social equity and protection of human rights. And the reason for low priority as we see it is the almost universal demand for discriminating among students on narrow academic grounds.

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37

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 (February 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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38

Pang, Jing, Xuwen Zhang, Xiaojun Lin, Jianghui Liu, Xinwu Du, and Jiangang Han. "Tillage-Depth Verification Based on Machine Learning Algorithms." Agriculture 13, no. 1 (January 4, 2023): 130. http://dx.doi.org/10.3390/agriculture13010130.

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In an analysis of the penetration resistance and tillage depth of post-tillage soil, four surface-layer discrimination methods, specifically, three machine learning algorithms—Kmeans, DBSCAN, and GMM—and a curve-fitting method, were used to analyze data collected from the cultivated and uncultivated layers. Among them, the three machine learning algorithms found the boundary between the tilled and untilled layers by analyzing which data points belonged to which layer to determine the depth of the soil in the tilled layer. The curve-fitting method interpreted the intersection among data from the fitted curves of the ploughed layer and the un-ploughed layer as the tillage depth. The three machine learning algorithms were used to process a standard data set for model evaluation. DBSCAN’s discrimination accuracy of this data set reached 0.9890 and its F1 score reached 0.9934, which were superior to those of the other two algorithms. Under standard experimental conditions, the ability of DBSCAN clustering to determine the soil depth was the best among the four discrimination methods, and the discrimination accuracy reached 90.63% when the error was 15 mm. During field-test verification, the discriminative effect of DBSCAN clustering was still the best among the four methods. However, the soil blocks encountered in the field test affected the test data, resulting in large errors in the processing results. Therefore, the combined RANSCA robust regression and DBSCAN clustering algorithm, which can eliminate interference from soil blocks in the cultivated layer and can solve the problem of large depth errors caused by soil blocks in the field, was used to process the data. After testing, when the RANSCA and DBSCAN combined method was used to process all samples in the field and the error was less than 20mm, the accuracy rate reached 82.69%. This combined method improves the applicability of discrimination methods and provides a new method of determining soil depth.

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39

ZHENG, Sirui, Bo HUANG, Jin LIU, Guohui ZENG, Ling YIN, Zhi LI, and Tie SUN. "Fu-Rec: Multi-Task Learning Recommendation Model Fusing Neighbor-Discrimination and Self-Discrimination." Wuhan University Journal of Natural Sciences 29, no. 2 (April 2024): 134–44. http://dx.doi.org/10.1051/wujns/2024292134.

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In recent years, self-supervised learning has achieved great success in areas such as computer vision and natural language processing because it can mine supervised signals from unlabeled data and reduce the reliance on manual labels. However, the currently generated self-supervised signals are either neighbor discrimination or self-discrimination, and there is no model to integrate neighbor discrimination and self-discrimination. Based on this, this paper proposes Fu-Rec that integrates neighbor-discrimination contrastive learning and self-discrimination contrastive learning, which consists of three modules: (1) neighbor-discrimination contrastive learning, (2) self-discrimination contrastive learning, and (3) recommendation module. The neighbor-discrimination contrastive learning and self-discrimination contrastive learning tasks are used as auxiliary tasks to assist the recommendation task. The Fu-Rec model effectively utilizes the respective advantages of neighbor-discrimination and self-discrimination to consider the information of the user's neighbors as well as the user and the item itself for the recommendation, which results in better performance of the recommendation module. Experimental results on several public datasets demonstrate the effectiveness of the Fu-Rec proposed in this paper.

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40

Steullet, Pascal, Dana R. Krützfeldt, Gemma Hamidani, Tanya Flavus, Vivian Ngo, and Charles D. Derby. "Dual antennular chemosensory pathways mediate odor-associative learning and odor discrimination in the Caribbean spiny lobsterPanulirus argus." Journal of Experimental Biology 205, no. 6 (March 15, 2002): 851–67. http://dx.doi.org/10.1242/jeb.205.6.851.

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SUMMARYChemosensory neurons in the antennular flagella of lobsters mediate long-range responses to chemicals. These neurons are part of two parallel chemosensory pathways with different peripheral and central components. Aesthetasc sensilla on the lateral flagella are innervated by chemosensory neurons that project to the olfactory lobes. A diversity of other ‘non-aesthetasc’ sensilla on both lateral and medial flagella are innervated by mechano- and chemosensory neurons, and most of these non-aesthetasc neurons project to the lateral antennular neuropils. We investigated the roles of these two pathways in odor-associative learning and odor discrimination by selectively removing either aesthetasc or non-aesthetasc sensilla from the spiny lobster Panulirus argus. Lobsters lacking both aesthetasc and non-aesthetasc antennular sensilla show very reduced or no odor-mediated searching behavior. We associatively conditioned lobsters using two paradigms: aversive conditioning with generalization testing (which reveals the similarity in the lobsters’ perception of odorants) and discrimination conditioning (which reveals the lobsters’ ability to discriminate odorants). Sham-control intact lobsters performed these tasks well, as did lobsters lacking either aesthetascs or non-aesthetasc setae. There was a strong but statistically non-significant trend that lobsters lacking either aesthetascs or non-aesthetasc setae generalized more between complex odor mixtures than did intact lobsters. After aversive conditioning with generalization testing, aesthetasc-ablated lobsters had more difficulty discriminating among the most closely related complex mixtures than did intact or non-aesthetasc-ablated lobsters. However, after discrimination conditioning, aesthetasc-ablated lobsters were as proficient as intact animals in discriminating highly similar mixtures. These results indicate overlap and redundancy in the function of these two chemosensory pathways in odor-associative learning and odor discrimination, but these pathways also complement each other to enable better discrimination. This study presents the first evidence for a role of non-aesthetasc chemosensory neurons in complex odor-mediated behaviors such as learning and discrimination.

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41

Roddick, Kyle M., Heather M. Schellinck, and Richard E. Brown. "Serial reversal learning in an olfactory discrimination task in 3xTg-AD mice." Learning & Memory 30, no. 12 (November 17, 2023): 310–19. http://dx.doi.org/10.1101/lm.053840.123.

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Male and female 3xTg-AD mice between 5 and 24 mo of age and their B6129F2/J wild-type controls were tested on a series of 18 olfactory discrimination and reversal tasks in an operant olfactometer. All mice learned the odor discriminations and reversals to a criterion of 85% correct, but the 3xTg-AD mice made fewer errors than the B6129F2/J mice in the odor discriminations and in the first six reversal learning tasks. Many mice showed evidence of near errorless learning, and on the reversal tasks the 3xTg-AD mice showed more instances of near errorless learning than the B6129F2/J mice. There was no evidence of an age effect on odor discrimination, but there was a decrease in errorless reversal learning in aged B6129F2/J mice. In long-term memory tests, there was an increase in the number of errors made but no genotype difference. The high level of performance indicates that the mice were able to develop a “learning to learn” strategy. The finding that the 3xTg-AD mice outperformed their littermate controls provides an example of paradoxical functional facilitation in these mice.

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42

George, David N., Jasper Ward-Robinson, and John M. Pearce. "Discrimination of structure: I. Implications for connectionist theories of discrimination learning." Journal of Experimental Psychology: Animal Behavior Processes 27, no. 3 (2001): 206–18. http://dx.doi.org/10.1037/0097-7403.27.3.206.

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Wang, Gerui, and Sheng Tang. "Generalized Zero-Shot Image Classification via Partially-Shared Multi-Task Representation Learning." Electronics 12, no. 9 (May 3, 2023): 2085. http://dx.doi.org/10.3390/electronics12092085.

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Generalized Zero-Shot Learning (GZSL) holds significant research importance as it enables the classification of samples from both seen and unseen classes. A prevailing approach for GZSL is learning transferable representations that can generalize well to both seen and unseen classes during testing. This approach encompasses two key concepts: discriminative representations and semantic-relevant representations. “Semantic-relevant” facilitates the transfer of semantic knowledge using pre-defined semantic descriptors, while “discriminative” is crucial for accurate category discrimination. However, these two concepts are arguably inherently conflicting, as semantic descriptors are not specifically designed for image classification. Existing methods often struggle with balancing these two aspects and neglect the conflict between them, leading to suboptimal representation generalization and transferability to unseen classes. To address this issue, we propose a novel partially-shared multi-task representation learning method, termed PS-GZSL, which jointly preserves complementary and sharable knowledge between these two concepts. Specifically, we first propose a novel perspective that treats the learning of discriminative and semantic-relevant representations as optimizing a discrimination task and a visual-semantic alignment task, respectively. Then, to learn more complete and generalizable representations, PS-GZSL explicitly factorizes visual features into task-shared and task-specific representations and introduces two advanced tasks: an instance-level contrastive discrimination task and a relation-based visual-semantic alignment task. Furthermore, PS-GZSL employs Mixture-of-Experts (MoE) with a dropout mechanism to prevent representation degeneration and integrates a conditional GAN (cGAN) to synthesize unseen features for estimating unseen visual features. Extensive experiments and more competitive results on five widely-used GZSL benchmark datasets validate the effectiveness of our PS-GZSL.

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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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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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Mellgren, Roger L., and Steven W. Brown. "DISCRIMINATION LEARNING IN A FORAGING SITUATION." Journal of the Experimental Analysis of Behavior 50, no. 3 (November 1988): 493–503. http://dx.doi.org/10.1901/jeab.1988.50-493.

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Stanton, Mark E., and Michelle M. Nicolle. "Taste discrimination learning in preweanling rats." Bulletin of the Psychonomic Society 28, no. 4 (October 1990): 319–22. http://dx.doi.org/10.3758/bf03334033.

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Han, Sang-Il. "Speech-Music Discrimination Using Deep Learning." Journal of the Korea Academia-Industrial cooperation Society 22, no. 10 (October 31, 2021): 552–57. http://dx.doi.org/10.5762/kais.2021.22.10.552.

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Milgram, Norton W., Beth Adams, Heather Callahan, Elizabeth Head, Bill Mackay, Celeste Thirlwell, and Carl W. Cotman. "Landmark Discrimination Learning in the Dog." Learning & Memory 6, no. 1 (January 1, 1999): 54–61. http://dx.doi.org/10.1101/lm.6.1.54.

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Allocentric spatial memory was studied in dogs of varying ages and sources using a landmark discrimination task. The primary goal of this study was to develop a protocol to test landmark discrimination learning in the dog. Using a modified version of a landmark test developed for use in monkeys, we successfully trained dogs to make a spatial discrimination on the basis of the position of a visual landmark relative to two identical discriminanda. Task performance decreased, however, as the distance between the landmark and the “discriminandum” was increased. A subgroup of these dogs was also tested on a delayed nonmatching to position spatial memory task (DNMP), which relies on egocentric spatial cues. These findings suggest that dogs can acquire both allocentric and egocentric spatial tasks. These data provide a useful tool for evaluating the ability of canines to use allocentric cues in spatial learning.

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Taniuchi, Tohru, Junko Sugihara, Mariko Wakashima, and Makiko Kamijo. "Abstract numerical discrimination learning in rats." Learning & Behavior 44, no. 2 (January 28, 2016): 122–36. http://dx.doi.org/10.3758/s13420-016-0209-2.

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