The Profiles of Basic Cognitive Functioning for Children with Mild Mental Retardation (MMR)

1. The Profiles of Basic Cognitive Functioning for Children with Mild Mental Retardation (MMR) Relative Importance of Working Memory, Associative Learning, and Processing Speed to MMR Dasen Luo Indiana University of Pennsylvania

2. Background: Importance of Ostensibly Basic Cognitive Capacities (OBCC), General • Processing Speed correlated with intelligence, particularly related to its age variation; • Working Memory strongly correlated with intelligence and scholastic achievement.

3. Y 1 f -2 -1 0 1 2 -1 -2 Nonlinear Relationship between OBCC (Processing Speed & Working Memory) and Intelligence

4. Importance of OBCC to MMR(Chinese Sample) Index of Separation: Area Under Operating Characteristic Curve (AUROC) 1.00 Perfect .97-.99 Excellent, Conclusive .92-.96 Very Good .75-.91 Fair .50-.74 Poor Observed AUROC=0.96 Hit: 78% False Alarm: 5% Hit: 59% False Alarm: 1% Non-MMR MMR

5. Importance of OBCC to MMR(US Sample) US WJ-III Standardization Sample Hit: 91% False Alarm: 5% Hit: 74% False Alarm: 1% Observed AUROC=0.97 Non-MMR MMR

6. Associative Learning—Additional OBCC for Intelligence? • Old to Psychology, But New to Intelligence Research; • Both a Cognitive Capacity and a Learning Paradigm; • Substantial Correlation with Reasoning Found in a College Sample (Williams & Pearlberg, 2006)

7. Objectives of Presentation • Demonstrate the general importance of associative learning to intelligence • Illustrate relative roles of associative learning in different ranges of intelligence • Provide cognitive profiles of MMR based on Working Memory, Associative Learning, and Processing Speed

8. Method—Participants Chinese primary school children Age 8-10 140 with MMR, 800 without MMR Second Wave (Year Two) Testing

9. Method—Measures Working memory: Letter Number Sequencing (LNS) Counting Span (CS) WISC Digit Span (DS); Processing Speed: Stimulus Discrimination (SD) Feature Detection (FD) WISC Coding (CO)

10. Method—Measures: Associative Learning Visual-Auditory Learning (VAL) e.g., Word Pairs e.g., Star—Ladder Star tree horse and police police and horse Ladder

11. .62 .03 .60 .87 .40 .78 Structural Equation Models Indicating Importance of Working Memory, Associative Learning, and Processing Speed in the Full Ability Range Coding StimDisc FeatDetc Inform Simi Vocab Compre -.57 .46 .76 .77 .79 .68 .57 Proc Speed PictC .85 Verbal .64 DigSpan .68 .65 PictA .45 .48 .80 G R2=.85 .52 .70 Perform CSpan WM BlockD .74 .62 .70 LNSeq Assoc Learn .83 ObectA Achieve .67 .91 .78 .54 VALrn WrdPair Chinese Math Model Fit Indexes χ2=488, df=126; RMSEA=0.057

12. AUROC Based on OBCC Predictors for MMR, IQ<100, and for IQ≥115 Subgroups Processing Working Assoc. OBCC Subgroup Speed Memory Learn. Composite MMR (N=122) 0.84 0.95* 0.85 0.97 IQ<100 (N=520) 0.76* 0.82* 0.76* 0.85 IQ≥115 (N=206) 0.73* 0.77* 0.77* 0.83 Symbol * indicates significant unique contribution.

13. Illustration of K-Means clustering – Step 1 Cluster 1 Cluster 2 Y2 Y1

14. Step 2: Cases Assigned to Clusters Y2 Y1

15. Step 3: Clusters are Repositioned Y2 Y1

16. Steps 2 and 3 are Repeated Y2 Y1

17. Cases are Assigned to Repositioned Clusters, and Algorithm Continues Y2 Y1

18. OBCC Profiles Indicating Two Subtypes of MMR

19. Conclusions • Associative learning appears to play a substantial general role in intelligence independent of working memory and processing speed; • Its unique role seems to be the most pronounced in the average and above-average ability ranges; • A sizable subgroup of MMR appears to be almost normal in associative learning.

20. Discussion • A cross-validation in the US population is direly needed; • Compensation based on associative learning strength may be possible for a subgroup of children with MMR ; • Factors affecting associative learning (e.g., reinforcement, between- vs within-modality associations, etc) should be investigated.

21. Question 1A: What do you think is the most needed for the next phase of the research? • cross-validate the general role of associative learning in intelligence in the US population. • cross-validate the observed cognitive profiles of MMR in the US population. • design associative-learning-based compensation measures and apply them to the accessible Chinese sample with MMR. • none of the above. e. no opinion.

22. Question 1B: What do you think is the most needed for the next phase of the research? • cross-validate the general role of associative learning in intelligence in the US population. • cross-validate the observed cognitive profiles of MMR in the US population. • design associative-learning-based compensation measures and apply them to the accessible Chinese sample with MMR. • none of the above. e. no opinion.

23. Question 2A: How do you think about your possibility of engaging in the related research in the near future? a. research about the general role of associative learning likely. b. research about the cognitive profile of MMR likely. c. research about a profile-based compensation strategy likely. d. would like to partake in any of the above areas of research. e. unlikely to engage in this type of research.

24. Question 2B: How do you think about your possibility of engaging in the related research in the near future? a. research about the general role of associative learning likely. b. research about the cognitive profile of MMR likely. c. research about a profile-based compensation strategy likely. d. would like to partake in any of the above areas of research. e. unlikely to engage in this type of research.