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Journal articles on the topic 'Associative learning in spiders'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

Su, Yong-Chao, Cheng-Yu Wu, Cheng-Hong Yang, Bo-Sheng Li, Sin-Hua Moi, and Yu-Da Lin. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider." Mathematics 9, no. 4 (2021): 415. http://dx.doi.org/10.3390/math9040415.

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Cost–benefit analysis is widely used to elucidate the association between foraging group size and resource size. Despite advances in the development of theoretical frameworks, however, the empirical systems used for testing are hindered by the vagaries of field surveys and incomplete data. This study developed the three approaches to data imputation based on machine learning (ML) algorithms with the aim of rescuing valuable field data. Using 163 host spider webs (132 complete data and 31 incomplete data), our results indicated that the data imputation based on random forest algorithm outperfor

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2

Haselgrove, Mark. "Overcoming associative learning." Journal of Comparative Psychology 130, no. 3 (2016): 226–40. http://dx.doi.org/10.1037/a0040180.

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3

Shanks, David R. "Bayesian associative learning." Trends in Cognitive Sciences 10, no. 11 (2006): 477–78. http://dx.doi.org/10.1016/j.tics.2006.09.004.

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4

Shanks, David R. "The associative nature of human associative learning." Behavioral and Brain Sciences 32, no. 2 (2009): 225–26. http://dx.doi.org/10.1017/s0140525x09001149.

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AbstractThe extent to which human learning should be thought of in terms of elementary, automatic versus controlled, cognitive processes is unresolved after nearly a century of often fierce debate. Mitchell et al. provide a persuasive review of evidence against automatic, unconscious links. Indeed, unconscious processes seem to play a negligible role in any form of learning, not just in Pavlovian conditioning. But a modern connectionist framework, in which “cognitive” phenomena are emergent properties, is likely to offer a fuller account of human learning than the propositional framework Mitch

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5

Volbrecht, Vicki J. "Perceptual learning meets associative learning." New Ideas in Psychology 11, no. 2 (1993): 285–86. http://dx.doi.org/10.1016/0732-118x(93)90042-c.

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6

Moore, John W., and Geoffrey Hall. "Perceptual and Associative Learning." American Journal of Psychology 107, no. 3 (1994): 465. http://dx.doi.org/10.2307/1422887.

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7

Balsam, Peter, Michael Drew, and C. Gallistel. "Time and Associative Learning." Comparative Cognition & Behavior Reviews 5 (2010): 1–22. http://dx.doi.org/10.3819/ccbr.2010.50001.

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8

Hummel, John E. "Symbolic Versus Associative Learning." Cognitive Science 34, no. 6 (2010): 958–65. http://dx.doi.org/10.1111/j.1551-6709.2010.01096.x.

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9

Hawkins, Robert D., and John H. Byrne. "Associative Learning in Invertebrates." Cold Spring Harbor Perspectives in Biology 7, no. 5 (2015): a021709. http://dx.doi.org/10.1101/cshperspect.a021709.

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10

Bennet, Alex, and David Bennet. "Learning as associative patterning." VINE 36, no. 4 (2006): 371–76. http://dx.doi.org/10.1108/03055720610716638.

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11

Glanzman, David L. "Associative Learning: Hebbian Flies." Current Biology 15, no. 11 (2005): R416—R419. http://dx.doi.org/10.1016/j.cub.2005.05.028.

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12

Rachman, S. "Conditioning and associative learning." Behaviour Research and Therapy 23, no. 2 (1985): 229. http://dx.doi.org/10.1016/0005-7967(85)90036-1.

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13

Kirkpatrick, Kimberly, and Peter D. Balsam. "Associative learning and timing." Current Opinion in Behavioral Sciences 8 (April 2016): 181–85. http://dx.doi.org/10.1016/j.cobeha.2016.02.023.

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14

Andreae, John H., Shaun W. Ryan, Mark L. Tomlinson, and Peter M. Andreae. "Structure from associative learning." International Journal of Man-Machine Studies 39, no. 6 (1993): 1031–50. http://dx.doi.org/10.1006/imms.1993.1094.

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15

Degonda, Nadia, Christian R. A. Mondadori, Simone Bosshardt, et al. "Implicit Associative Learning Engages the Hippocampus and Interacts with Explicit Associative Learning." Neuron 46, no. 3 (2005): 505–20. http://dx.doi.org/10.1016/j.neuron.2005.02.030.

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16

Liedtke, Jannis, and Jutta M. Schneider. "Association and reversal learning abilities in a jumping spider." Behavioural Processes 103 (March 2014): 192–98. http://dx.doi.org/10.1016/j.beproc.2013.12.015.

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17

Dickinson, Anthony. "Associative learning and animal cognition." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1603 (2012): 2733–42. http://dx.doi.org/10.1098/rstb.2012.0220.

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Associative learning plays a variety of roles in the study of animal cognition from a core theoretical component to a null hypothesis against which the contribution of cognitive processes is assessed. Two developments in contemporary associative learning have enhanced its relevance to animal cognition. The first concerns the role of associatively activated representations, whereas the second is the development of hybrid theories in which learning is determined by prediction errors, both directly and indirectly through associability processes. However, it remains unclear whether these developme

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18

Ghirlanda, Stefano. "Studying associative learning without solving learning equations." Journal of Mathematical Psychology 85 (August 2018): 55–61. http://dx.doi.org/10.1016/j.jmp.2018.07.003.

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19

Rose, Michael, Rolf Verleger, and Edmund Wascher. "ERP correlates of associative learning." Psychophysiology 38, no. 3 (2001): 440–50. http://dx.doi.org/10.1111/1469-8986.3830440.

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20

VERGINI, EDUARDO G., and MARCELO G. BLATT. "LEARNING RULES FOR ASSOCIATIVE MEMORIES." Modern Physics Letters B 05, no. 30 (1991): 1963–72. http://dx.doi.org/10.1142/s0217984991002367.

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We discuss some of the most popular learning rules that can be used to construct Neural Networks that act as associative memories. The Hebb’s rule, perceptron type algorithms and the projector rule with local versions are included.

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21

Dickinson, Anthony, and I. P. L. McLaren. "Associative Learning and Representation: Introduction." Quarterly Journal of Experimental Psychology Section B 56, no. 1b (2003): 3–6. http://dx.doi.org/10.1080/02724990244000250.

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22

Mukhopadhyay, S., and M. A. L. Thathachar. "Associative learning of Boolean functions." IEEE Transactions on Systems, Man, and Cybernetics 19, no. 5 (1989): 1008–15. http://dx.doi.org/10.1109/21.44015.

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23

Behrens, Timothy E. J., Laurence T. Hunt, Mark W. Woolrich, and Matthew F. S. Rushworth. "Associative learning of social value." Nature 456, no. 7219 (2008): 245–49. http://dx.doi.org/10.1038/nature07538.

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24

Wasserman, Edward A., and Ralph R. Miller. "WHAT'S ELEMENTARY ABOUT ASSOCIATIVE LEARNING?" Annual Review of Psychology 48, no. 1 (1997): 573–607. http://dx.doi.org/10.1146/annurev.psych.48.1.573.

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25

Spiegel, Rainer, and I. P. L. McLaren. "Associative sequence learning in humans." Journal of Experimental Psychology: Animal Behavior Processes 32, no. 2 (2006): 156–63. http://dx.doi.org/10.1037/0097-7403.32.2.150.

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26

Waddell, Jaylyn, and Tracey J. Shors. "Neurogenesis, learning and associative strength." European Journal of Neuroscience 27, no. 11 (2008): 3020–28. http://dx.doi.org/10.1111/j.1460-9568.2008.06222.x.

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27

Gandhi, Nikhil, Gonen Ashkenasy, and Emmanuel Tannenbaum. "Associative learning in biochemical networks." Journal of Theoretical Biology 249, no. 1 (2007): 58–66. http://dx.doi.org/10.1016/j.jtbi.2007.07.004.

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28

Gewirtz, Jonathan C. "Novelty value in associative learning." Behavioral and Brain Sciences 14, no. 1 (1991): 29. http://dx.doi.org/10.1017/s0140525x0006516x.

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29

Tsodyks, Misha, Yael Adini, and Dov Sagi. "Associative learning in early vision." Neural Networks 17, no. 5-6 (2004): 823–32. http://dx.doi.org/10.1016/j.neunet.2004.03.004.

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30

Andreae, John H., and Shaun W. Ryan. "Associative learning and task complexity." Behavioral and Brain Sciences 17, no. 2 (1994): 357–58. http://dx.doi.org/10.1017/s0140525x0003497x.

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31

Mondragón, Esther, Eduardo Alonso, and Niklas Kokkola. "Associative Learning Should Go Deep." Trends in Cognitive Sciences 21, no. 11 (2017): 822–25. http://dx.doi.org/10.1016/j.tics.2017.06.001.

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32

Roberts, L. E., R. J. Racine, and P. J. Durlach. "A macroarchitecture for associative learning." International Journal of Psychophysiology 14, no. 2 (1993): 144–45. http://dx.doi.org/10.1016/0167-8760(93)90233-f.

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33

Vriends, Noortje, Tanja Michael, Bettina Schindler, and Jürgen Margraf. "Associative learning in flying phobia." Journal of Behavior Therapy and Experimental Psychiatry 43, no. 2 (2012): 838–43. http://dx.doi.org/10.1016/j.jbtep.2011.11.003.

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34

Benz, Anton. "Partial blocking and associative learning." Linguistics and Philosophy 29, no. 5 (2006): 587–615. http://dx.doi.org/10.1007/s10988-006-9005-3.

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35

Niemi, Maj-Britt, Gustavo Pacheco-López, Wei Kou, et al. "Murine taste-immune associative learning." Brain, Behavior, and Immunity 20, no. 6 (2006): 527–31. http://dx.doi.org/10.1016/j.bbi.2006.02.004.

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36

Urushihara, Kouji, Sadahiko Nakajima, Takatoshi Nagaishi, Takayuki Tanno, and Kazuhiro Goto. "Stimulus competitions in associative learning." Proceedings of the Annual Convention of the Japanese Psychological Association 83 (September 11, 2019): SS—012—SS—012. http://dx.doi.org/10.4992/pacjpa.83.0_ss-012.

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37

Byrne, J. H. "Cellular analysis of associative learning." Physiological Reviews 67, no. 2 (1987): 329–439. http://dx.doi.org/10.1152/physrev.1987.67.2.329.

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38

Menzel, R. "Associative learning in honey bees." Apidologie 24, no. 3 (1993): 157–68. http://dx.doi.org/10.1051/apido:19930301.

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39

Harvey, John A. "Serotonergic regulation of associative learning." Behavioural Brain Research 73, no. 1-2 (1995): 47–50. http://dx.doi.org/10.1016/0166-4328(96)00068-x.

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40

Rosas, Juan M., Travis P. Todd, and Mark E. Bouton. "Context change and associative learning." Wiley Interdisciplinary Reviews: Cognitive Science 4, no. 3 (2013): 237–44. http://dx.doi.org/10.1002/wcs.1225.

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41

Zentall, Thomas R., Edward A. Wasserman, and Peter J. Urcuioli. "Associative concept learning in animals." Journal of the Experimental Analysis of Behavior 101, no. 1 (2013): 130–51. http://dx.doi.org/10.1002/jeab.55.

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42

Chechik, Gal, Isaac Meilijson, and Eytan Ruppin. "Effective Neuronal Learning with Ineffective Hebbian Learning Rules." Neural Computation 13, no. 4 (2001): 817–40. http://dx.doi.org/10.1162/089976601300014367.

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In this article we revisit the classical neuroscience paradigm of Hebbian learning. We find that it is difficult to achieve effective associative memory storage by Hebbian synaptic learning, since it requires network-level information at the synaptic level or sparse coding level. Effective learning can yet be achieved even with nonsparse patterns by a neuronal process that maintains a zero sum of the incoming synaptic efficacies. This weight correction improves the memory capacity of associative networks from an essentially bounded one to a memory capacity that scales linearly with network siz

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43

Vega-Trejo, Regina, Annika Boussard, Lotta Wallander, et al. "Artificial selection for schooling behaviour and its effects on associative learning abilities." Journal of Experimental Biology 223, no. 23 (2020): jeb235093. http://dx.doi.org/10.1242/jeb.235093.

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ABSTRACTThe evolution of collective behaviour has been proposed to have important effects on individual cognitive abilities. Yet, in what way they are related remains enigmatic. In this context, the ‘distributed cognition’ hypothesis suggests that reliance on other group members relaxes selection for individual cognitive abilities. Here, we tested how cognitive processes respond to evolutionary changes in collective motion using replicate lines of guppies (Poecilia reticulata) artificially selected for the degree of schooling behaviour (group polarization) with >15% difference in schooling

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44

Lind, Johan. "What can associative learning do for planning?" Royal Society Open Science 5, no. 11 (2018): 180778. http://dx.doi.org/10.1098/rsos.180778.

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There is a new associative learning paradox. The power of associative learning for producing flexible behaviour in non-human animals is downplayed or ignored by researchers in animal cognition, whereas artificial intelligence research shows that associative learning models can beat humans in chess. One phenomenon in which associative learning often is ruled out as an explanation for animal behaviour is flexible planning. However, planning studies have been criticized and questions have been raised regarding both methodological validity and interpretations of results. Due to the power of associ

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45

Lotem, Arnon, and Oren Kolodny. "Reconciling genetic evolution and the associative learning account of mirror neurons through data-acquisition mechanisms." Behavioral and Brain Sciences 37, no. 2 (2014): 210–11. http://dx.doi.org/10.1017/s0140525x13002392.

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AbstractAn associative learning account of mirror neurons should not preclude genetic evolution of its underlying mechanisms. On the contrary, an associative learning framework for cognitive development should seek heritable variation in the learning rules and in the data-acquisition mechanisms that construct associative networks, demonstrating how small genetic modifications of associative elements can give rise to the evolution of complex cognition.

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46

Hall, Geoffrey. "Learning in simple systems." Behavioral and Brain Sciences 32, no. 2 (2009): 210–11. http://dx.doi.org/10.1017/s0140525x09000983.

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AbstractStudies of conditioning in simple systems are best interpreted in terms of the formation of excitatory links. The mechanisms responsible for such conditioning contribute to the associative learning effects shown by more complex systems. If a dual-system approach is to be avoided, the best hope lies in developing standard associative theory to deal with phenomena said to show propositional learning.

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47

ANDRECUT, MIRCEA. "RANDOM WALK LEARNING MACHINE." International Journal of Modern Physics B 14, no. 08 (2000): 869–76. http://dx.doi.org/10.1142/s0217979200000704.

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A simple random walk learning algorithm for associative memories is described. The Hebbian memory matrix optimized by the random walk algorithm leads to a perfect learning in associative memories. Also, in the special case of a binary memory matrix, the random walk learning algorithm leads to an increase of the critical storage density from α c = 0.102 to α c = 0.25.

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48

Le Pelley, M. E., and I. P. L. McLaren. "Learned Associability and Associative Change in Human Causal Learning." Quarterly Journal of Experimental Psychology Section B 56, no. 1b (2003): 68–79. http://dx.doi.org/10.1080/02724990244000179.

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The Mackintosh (1975) model of associative learning specifies that processing of both the cues presented on a trial and the outcome of that trial will interact to determine the amount of associative change undergone by a given cue. Experiments looking at the distribution of associative change among the elements of a reinforced compound in animal conditioning studies indicate that processing of the outcome of a trial does indeed influence associative change. The work reported here investigates the distribution of associative change among the elements of a reinforced compound in a human causal j

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49

Ceccarelli, Fadia Sara. "Ant-Mimicking Spiders: Strategies for Living with Social Insects." Psyche: A Journal of Entomology 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/839181.

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Mimicry is a fascinating topic, in particular when viewed in terms of selective forces and evolutionary strategies. Mimicry is a system involving a signaller, a signal receiver, and a model and has evolved independently many times in plants and animals. There are several ways of classifying mimicry based on the interactions and cost-benefit scenarios of the parties involved. In this review, I briefly outline the dynamics of the most common types of mimicry to then apply it to some of the spider-ant associative systems known to date. In addition, this review expands on the strategies that ant-a

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50

Kawai, Nobuyuki. "Towards a new study on associative learning in human fetuses: fetal associative learning in primates." Infant and Child Development 19, no. 1 (2010): 55–59. http://dx.doi.org/10.1002/icd.654.

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