Optimizing Schedules for Category Learning:
When Should We Block or Interleave Category Exemplars?
Sharon M. Noh1, Veronica X. Yan2, Tyson K. Kerr2, Robert A. Bjork2 & W. Todd Maddox1
1The
INTRODUCTION
University of Texas at Austin; 2University of California, Los Angeles
METHODS
Dual-Learning Systems
• Two competing, neurobiologically-grounded learning systems
exist, and learning in each system is optimized under different
training conditions4.
• Rule-based (RB) Learning
• Hypothesis-testing system, uses verbalizable rules
• Frontally mediated
• Information-Integration (II) Learning
• Associative learning by trial-and-error, rules are not easily
verbalizable
• Striatally mediated
EXPERIMENT 2 (N=149)
Procedure
• Study Phase: 16 stimuli per category (total 64)
• Test Phase: 16 new stimuli per category (total 64)
EXPERIMENT 1 (N=159)
Procedure
• Training Phase: 32 stimuli per category (total 128)
• Test Phase: 24 new stimuli per category (total 96)
Materials
• Same relevant dimensions as Exp 1
• Addition of 2 irrelevant, varying dimensions
Materials
• Lines w/ 2 relevant, varying dimensions
RB Category Structure
Sample Stimulus
•
II Category Structure
Ellipse length (height fixed) and stimulus position
II Category Structure
RB Category Structure
Schedules that Optimize Learning
• Recent research on category learning has shown that
interleaving (I) exemplars from different categories, rather than
blocking (B) examples by category leads to better inductive
learning1,2
Artificial stimuli, highly rule-based
• Generally, category structures and dimensionality are not well
controlled, and no studies have considered the dual-systems
framework with optimal learning schedules.
RESEARCH QUESTIONS
1. Are optimal schedules different for rulebased vs. information-integration learning?
2. Does increasing stimulus dimensionality
differentially affect optimal schedules for
learning RB vs. II categories?
GENERAL METHODS
Design
• 2 x 2 between-subjects:
• Category Structure (RB vs. II)
• Study Schedule (blocked vs. interleaved)
Schedules
• Blocked Presentation: AAAABBBBCCCCDDDD
• Interleaved Presentation: BADCACBDADCBCDAB
Category Structures
Rule-Based
Information-Integration
DISCUSSION
Experiment 1
Experiment 1: Low-dimensional Stimuli
0.75
Blocked
Experiment 2
0.75
Interleaved
0.65
Blocked
• I > B, regardless of stimulus type
Interleaved
0.65
Accuracy
•
RESULTS
0.55
0.45
Experiment 2: High-dimensional Stimuli
0.45
• B > I for RB stimuli
0.35
0.25
0.25
RB
II
Category Structure
• Blocking facilitates hypothesis-testing and rule-discovery,
when rules are more difficult to notice
• I > B for II stimuli
RB
II
Category Structure
MODELING RESULTS
1
Blocked
Accuracy in Optimal Strategy Users
0.65
Interleaved
0.8
0.6
0.4
•
Interleaving makes it difficult (high WM load) to hold and test
multiple rules across categories
Interleaved
1. Yes, optimal schedules differ for RB and II
category learning:
•
•
0.45
0.35
0
0.25
II
Blocked
0.55
0.2
RB
• Interleaving discourages the use of rule-based strategies
CONCLUSIONS
Modeling results of participants’ accuracy and strategy use in Experiment 2
Proportion of Optimal Strategy Use
• Stimuli seems simple enough that interleaving/spacing
doesn’t tax working memory
0.55
0.35
Accuracy
• Some research, however, has found that dimensionality can
affect optimal schedules3:
Accuracy
High dimensional stimuli, information-integration based
Proportion Optimal
•
Blocking facilitates hypothesis-testing
process for RB learning
Interleaving helps II learning by
discouraging rule-based strategies
2. Yes, but for RB stimuli only:
•
•
RB
II
REFERENCES
1. Kornell, N., & Bjork, R. A. (2008). Learning concepts and categories is spacing the “enemy of induction”?. Psych Sci.
2. Kang, S. H., & Pashler, H. (2012). Learning painting styles: Spacing is advantageous when it promotes discriminative
contrast. Applied Cognitive Psychology.
3. Zulkiply, N., & Burt, J. S. (2013). The exemplar interleaving effect in inductive learning: Moderation by the difficulty of
category discriminations. Memory & cognition.
4. Ashby, F.G. & Maddox, W.T. (2011). Human category learning 2.0. Annals of the NY Academy of Sciences.
Int > Block for low-dimension stimuli
Block > Int for high-dimension stimuli
Acknowledgments:
University of Texas Diversity Mentoring
Fellowship to SMN
This research was funded by the James S.
McDonnell Foundation Grant to RAB & ELB
Contact:
Sharon Noh smnoh@utexas.edu
http://homepage.psy.utexas.edu/HomePage/Group/Mad
doxLAB/index.htm
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