Difficult rule-based category learning benefits from massed practice

2009 ◽  
Author(s):  
Michael A. Garcia ◽  
Nate Kornell ◽  
Robert A. Bjork
2014 ◽  
Author(s):  
Shawn Ell ◽  
Steve Hutchinson ◽  
Lauren Hawthorne ◽  
Lauren Szymula ◽  
Shannon K. McCoy

2020 ◽  
Author(s):  
Bailey Brashears ◽  
John Paul Minda

This study intended to investigate the effects of varying factors on the use of verbal and implicit classification systems when learning novel categories in an interactive video game environment by measuring the effects of feature type (easy vs difficult to describe verbally). Verbal and implicit classification were operationalized by measuring rule-based and family resemblance strategy use respectively. This experiment found that participants presented with stimuli that were easy to describe verbally were more likely to use rule- based classification, while participants presented with stimuli that were difficult to describe verbally showed no preference for one form of classification. The results of this study open up a novel field of research within category learning, further exploring the effects of feature verbalizablity.


2017 ◽  
Author(s):  
Rahel Rabi ◽  
Marc F Joanisse ◽  
Tianshu Zhu ◽  
John Paul Minda

PreprintWhen learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule (“easy” stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule (“difficult” stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared to easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning


2010 ◽  
Vol 48 (10) ◽  
pp. 2998-3008 ◽  
Author(s):  
W. Todd Maddox ◽  
Jennifer Pacheco ◽  
Maia Reeves ◽  
Bo Zhu ◽  
David M. Schnyer

2005 ◽  
Vol 28 (1) ◽  
pp. 15-16 ◽  
Author(s):  
Gregory Ashby ◽  
Michael B. Casale

The target article postulates that rule-based and similarity-based categorization are best described by a unitary process. A number of recent empirical dissociations between rule-based and similarity-based categorization severely challenge this view. Collectively, these new results provide strong evidence that these two types of category learning are mediated by separate systems.


Author(s):  
W. Todd Maddox ◽  
J. Vincent Filoteo ◽  
J. Scott Lauritzen ◽  
Emily Connally ◽  
Kelli D. Hejl

2018 ◽  
Vol 18 (5) ◽  
pp. 1034-1048
Author(s):  
Rahel Rabi ◽  
Marc F. Joanisse ◽  
Tianshu Zhu ◽  
John Paul Minda

Author(s):  
B. Allyson Phillips ◽  
Frances A. Conners ◽  
Edward Merrill ◽  
Mark R. Klinger

Abstract Rule-based category learning was examined in youths with Down syndrome (DS), youths with intellectual disability (ID), and typically developing (TD) youths. Two tasks measured category learning: the Modified Card Sort task (MCST) and the Concept Formation test of the Woodcock–Johnson-III (Woodock, McGrew, & Mather, 2001). In regression-based analyses, DS and ID groups performed below the level expected for their nonverbal ability. In cross-sectional developmental trajectory analyses, results depended on the task. On the MCST, the DS and ID groups were similar to the TD group. On the Concept Formation test, the DS group had slower cross-sectional change than the other 2 groups. Category learning may be an area of difficulty for those with ID, but task-related factors may affect trajectories for youths with DS.


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