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Thomas, Sujith; Srinivasan, Narayanan – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
In classification learning of artificial stimuli, participants learn the perfectly diagnostic dimension better than the partially diagnostic dimensions. Also, there is a strong preference for a unidimensional categorization based on the perfectly diagnostic dimension. In a different experimental procedure, called array-based classification task,…
Descriptors: Classification, Bayesian Statistics, Observational Learning, Preferences
Hayes, William M.; Wedell, Douglas H. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
In reinforcement learning (RL) tasks, decision makers learn the values of actions in a context-dependent fashion. Although context dependence has many advantages, it can lead to suboptimal preferences when choice options are extrapolated beyond their original encoding contexts. Here, we tested whether we could manipulate context dependence in RL…
Descriptors: Reinforcement, Learning Processes, Attention, Context Effect
Wang, Felix Hao; Mintz, Toben H. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2018
The structure of natural languages give rise to many dependencies in the linear sequences of words, and within words themselves. Detecting these dependencies is arguably critical for young children in learning the underlying structure of their language. There is considerable evidence that human adults and infants are sensitive to the statistical…
Descriptors: Artificial Languages, Sentences, Second Language Learning, Undergraduate Students
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
Wynton, Sarah K. A.; Anglim, Jeromy – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
While researchers have often sought to understand the learning curve in terms of multiple component processes, few studies have measured and mathematically modeled these processes on a complex task. In particular, there remains a need to reconcile how abrupt changes in strategy use can co-occur with gradual changes in task completion time. Thus,…
Descriptors: Learning Strategies, Learning Processes, Bayesian Statistics, Computer Assisted Instruction
Bramley, Neil R.; Lagnado, David A.; Speekenbrink, Maarten – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
Interacting with a system is key to uncovering its causal structure. A computational framework for interventional causal learning has been developed over the last decade, but how real causal learners might achieve or approximate the computations entailed by this framework is still poorly understood. Here we describe an interactive computer task in…
Descriptors: Intervention, Memory, Cognitive Processes, Models
Staels, Eva; Van den Broeck, Wim – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
This article reports on 2 studies that attempted to replicate the findings of a study by Szmalec, Loncke, Page, and Duyck (2011) on Hebb repetition learning in dyslexic individuals, from which these authors concluded that dyslexics suffer from a deficit in long-term learning of serial order information. In 2 experiments, 1 on adolescents (N = 59)…
Descriptors: Dyslexia, Repetition, Sequential Learning, Neurological Impairments