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Learning & Behavior Mar 2012A significant problem in the study of Pavlovian conditioning is characterizing the nature of the representations of events that enter into learning. This issue has been... (Review)
Review
A significant problem in the study of Pavlovian conditioning is characterizing the nature of the representations of events that enter into learning. This issue has been explored extensively with regard to the question of what features of the unconditioned stimulus enter into learning, but considerably less work has been directed to the question of characterizing the nature of the conditioned stimulus. This article introduces a multilayered connectionist network approach to understanding how "perceptual" or "conceptual" representations of the conditioned stimulus might emerge from conditioning and participate in various learning phenomena. The model is applied to acquired equivalence/distinctiveness of cue effects, as well as a variety of conditional discrimination learning tasks (patterning, biconditional, ambiguous occasion setting, feature discriminations). In addition, studies that have examined what aspects of the unconditioned stimulus enter into learning are also reviewed. Ultimately, it is concluded that adopting a multilayered connectionist network perspective of Pavlovian learning provides us with a richer way in which to view basic learning processes, but a number of key theoretical problems remain to be solved, particularly as they relate to the integration of what we know about the nature of the representations of conditioned and unconditioned stimuli.
Topics: Animals; Association Learning; Conditioning, Classical; Discrimination Learning
PubMed: 21786019
DOI: 10.3758/s13420-011-0036-4 -
Current Topics in Behavioral... 2013The precise neural substrates of major depressive disorder (MDD) remain elusive, and FDA-approved antidepressants fail at least one-third of treatment-seeking patients.... (Review)
Review
The precise neural substrates of major depressive disorder (MDD) remain elusive, and FDA-approved antidepressants fail at least one-third of treatment-seeking patients. It is imperative, therefore, to identify novel research strategies to tackle the factors impeding progress. In this chapter we propose that the knowledge derived from computational investigations of associative learning might offer new insights into the neurobiology of MDD.
Topics: Animals; Association Learning; Depressive Disorder, Major; Fear; Humans; Reward
PubMed: 23299804
DOI: 10.1007/7854_2012_236 -
Annual Review of Psychology 1990
Review
Topics: Association Learning; Attention; Brain; Humans; Learning; Memory; Models, Neurological; Models, Theoretical; Nerve Net; Nervous System Physiological Phenomena
PubMed: 2407168
DOI: 10.1146/annurev.ps.41.020190.000545 -
Methods in Cell Biology 2011The zebrafish has been one of the primary study species utilized in developmental biology. However, it is also gaining increasing amount of interest in other disciplines... (Review)
Review
The zebrafish has been one of the primary study species utilized in developmental biology. However, it is also gaining increasing amount of interest in other disciplines of biology including behavioral neuroscience; the numerous genetic tools developed and the large amount of genetic information accumulated for this species by now make it an excellent tool for the analysis of the mechanisms of complex central nervous system characteristics. Although several studies have investigated the biological and genetic underpinnings of associative learning (and memory), given the complexity of these phenomena, much remains to be discovered. In the past, the zebrafish has been employed particularly successfully in screening applications where a large number of mutations or drug effects had to be analyzed. Briefly, the practical simplicity and system complexity of the zebrafish may make this species an excellent tool also for the analysis of the mechanisms of associative learning. Screening, however, requires appropriate phenotypical (in this case behavioral) paradigms. A step in this direction is the characterization of learning abilities of zebrafish. The number of studies focused on cognitive and/or mnemonic characteristics of zebrafish is orders of magnitude smaller than those with rats or mice, but recently zebrafish has also started to be utilized in this research. The current chapter reviews these most recent developments. It also discusses certain unique features of zebrafish that must be taken into account when designing an associative learning task and how these tasks may be made high throughput.
Topics: Animals; Association Learning; Behavior, Animal; Central Nervous System; Humans; Zebrafish
PubMed: 21550448
DOI: 10.1016/B978-0-12-387036-0.00012-8 -
Behavioural Processes Jan 2014The evidence reviewed in this paper suggests that when two events occur in spatiotemporal proximity to one another, an association between the two events is formed which... (Review)
Review
The evidence reviewed in this paper suggests that when two events occur in spatiotemporal proximity to one another, an association between the two events is formed which encodes the timing of the events in relation to one another (including duration, order, and interval). The primary evidence supporting the view that temporal relationships are encoded is that subsequent presentation of one event ordinarily elicits behavior indicative of an expectation of the other event at a specific time. Thus, temporal relationships appear to be one of several attributes encoded at acquisition.
Topics: Animals; Association Learning; Conditioning, Classical; Cues; Humans; Models, Psychological; Time Factors; Time Perception
PubMed: 23751257
DOI: 10.1016/j.beproc.2013.05.015 -
The Journal of Physiology May 2024
Topics: Animals; Humans; Association Learning
PubMed: 38652560
DOI: 10.1113/JP286472 -
PloS One 2017Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit...
Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories of simple stimuli (bar graph heights) presented in a 3:1 base-rate ratio. Participants learned either from "observational" training to disrupt implicit learning or "response" training which supports implicit learning. Category label feedback on each trial was followed either immediately or after a 2.5 second delay by onset of a working memory task intended to disrupt explicit reasoning about category membership feedback. Decision criterion values were significantly larger following response training, suggesting that implicit learning underlies base-rate sensitivity. Disrupting explicit processing had no effect on base-rate learning as long as implicit learning was supported. These results suggest base-rate sensitivity develops from experience primarily through implicit learning, consistent with separate learning systems accounts of categorization.
Topics: Association Learning; Concept Formation; Discrimination Learning; Feedback; Humans; Practice, Psychological; Reaction Time
PubMed: 28632779
DOI: 10.1371/journal.pone.0179256 -
Neuroscience and Biobehavioral Reviews May 2011The 'action observation network' (AON), which is thought to translate observed actions into motor codes required for their execution, is biologically tuned: it responds... (Review)
Review
The 'action observation network' (AON), which is thought to translate observed actions into motor codes required for their execution, is biologically tuned: it responds more to observation of human, than non-human, movement. This biological specificity has been taken to support the hypothesis that the AON underlies various social functions, such as theory of mind and action understanding, and that, when it is active during observation of non-human agents like humanoid robots, it is a sign of ascription of human mental states to these agents. This review will outline evidence for biological tuning in the AON, examining the features which generate it, and concluding that there is evidence for tuning to both the form and kinematic profile of observed movements, and little evidence for tuning to belief about stimulus identity. It will propose that a likely reason for biological tuning is that human actions, relative to non-biological movements, have been observed more frequently while executing corresponding actions. If the associative hypothesis of the AON is correct, and the network indeed supports social functioning, sensorimotor experience with non-human agents may help us to predict, and therefore interpret, their movements.
Topics: Association Learning; Humans; Movement; Neurons; Robotics; Theory of Mind
PubMed: 21396398
DOI: 10.1016/j.neubiorev.2011.03.004 -
Biology Letters Jun 2011Despite nearly two decades of research on mirror neurons, there is still much debate about what they do. The most enduring hypothesis is that they enable 'action...
Despite nearly two decades of research on mirror neurons, there is still much debate about what they do. The most enduring hypothesis is that they enable 'action understanding'. However, recent critical reviews have failed to find compelling evidence in favour of this view. Instead, these authors argue that mirror neurons are produced by associative learning and therefore that they cannot contribute to action understanding. The present opinion piece suggests that this argument is flawed. We argue that mirror neurons may both develop through associative learning and contribute to inferences about the actions of others.
Topics: Association Learning; Comprehension; Humans; Models, Neurological; Neurons
PubMed: 21084333
DOI: 10.1098/rsbl.2010.0850 -
International Journal of Neural Systems Aug 2012Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the...
Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN - a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow-Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.
Topics: Action Potentials; Algorithms; Animals; Artificial Intelligence; Association Learning; Models, Neurological; Nerve Net; Neurons; Time Factors
PubMed: 22830962
DOI: 10.1142/S0129065712500128