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Ong, Jia Hoong; Liu, Fang – Journal of Autism and Developmental Disorders, 2023
According to Bayesian/predictive coding models of autism, autistic individuals may have difficulties learning probabilistic cue-outcome associations, but empirical evidence has been mixed. The target cues used in previous studies were often straightforward and might not reflect real-life learning of such associations which requires learners to…
Descriptors: Autism Spectrum Disorders, Probability, Cues, Associative Learning
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Rey, Arnaud; Fagot, Joël; Mathy, Fabien; Lazartigues, Laura; Tosatto, Laure; Bonafos, Guillem; Freyermuth, Jean-Marc; Lavigne, Frédéric – Cognitive Science, 2022
The extraction of cooccurrences between two events, A and B, is a central learning mechanism shared by all species capable of associative learning. Formally, the cooccurrence of events A and B appearing in a sequence is measured by the transitional probability (TP) between these events, and it corresponds to the probability of the second stimulus…
Descriptors: Animals, Learning Processes, Associative Learning, Serial Learning
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Maxwell, Nicholas P.; Perry, Trevor; Huff, Mark J. – Metacognition and Learning, 2022
Judgments of learning (JOL) are often used to assess memory monitoring at encoding. Participants study a cue-target word pair (e.g., mouse-cheese) and are asked to rate the probability of correctly recalling the target in the presence of the cue at test (e.g., mouse -?). Prior research has shown that JOL accuracy is sensitive to perceptual cues.…
Descriptors: Metacognition, Layout (Publications), Decision Making, Memory
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Yu-Chin, Chiu – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
Recent context-control learning studies have shown that switch costs are reduced in a particular context predicting a high probability of switching as compared to another context predicting a low probability of switching. These context-specific switch probability effects suggest that control of task sets, through experience, can become associated…
Descriptors: Learning Processes, Prior Learning, Task Analysis, Cognitive Ability
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Stevens, Jon Scott; Gleitman, Lila R.; Trueswell, John C.; Yang, Charles – Cognitive Science, 2017
We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed "Pursuit," uses an associative…
Descriptors: Semantics, Associative Learning, Probability, Computational Linguistics
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Faraut, Mailys C. M.; Procyk, Emmanuel; Wilson, Charles R. E. – Learning & Memory, 2016
Unexpected outcomes can reflect noise in the environment or a change in the current rules. We should ignore noise but shift strategy after rule changes. How we learn to do this is unclear, but one possibility is that it relies on learning to learn in uncertain environments. We propose that acquisition of latent task structure during learning to…
Descriptors: Learning, Cognitive Processes, Animals, Error Patterns
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Ashby, F. Gregory; Vucovich, Lauren E. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2016
Feedback is highly contingent on behavior if it eventually becomes easy to predict, and weakly contingent on behavior if it remains difficult or impossible to predict even after learning is complete. Many studies have demonstrated that humans and nonhuman animals are highly sensitive to feedback contingency, but no known studies have examined how…
Descriptors: Feedback (Response), Classification, Learning Processes, Associative Learning