Does visual working memory capacity predict intelligence in 7-11 year old children? Laura Jenkins, and Dr Colin Hamilton Department of Psychology, Northumbria University

Figure 1: Logie’s (2011) Multi-Component Model of Working Memory. Background Figure 1: Logie’s (2011) Multi-Component Model of Working Memory. Figure 2: Baddeley’s (2000, 2012) modification of the original working memory model. Initially thought to be a domain specific model, however filtering using the Episodic Buffer would suggest a more domain general approach A domain specific account. Information is derived through perceptual input directly into the visuospatial sketch pad. No initially episodic influences

Can this now be questioned with the suggestions made about the VPT? Visual Patterns Test Representation Logie (2011) would suggest that the Visual Patterns Test uses the visual cache component only. This information would have to be inputted through the episodic buffer on an object level, a more domain general approach. Other research would suggest Non-visual interference effects with the VPT, suggesting the use of visual and verbal information or executive resources (Hamilton et al., 2003; Brown and Wesley, 2013). What about change detection tasks? A domain specific approach by Luck and Vogel (1997, 2013) with perceptually derived representation. Luck and Vogel (2013), P392. “First, to qualify as VWM, it is not sufficient that the information was acquired through the visual modality; the representation of the information must be visual in nature. If the observer stores a verbal or amodal conceptual representation of the sensory input, we no longer consider it to be a visual memory.” Can this now be questioned with the suggestions made about the VPT? Briefly state hamlton and brow and Wesley methods

Previous PhD Study Results 3 previous studies used a quantitative change detection task (large, colour changes) and a qualitative change detection task (small, size changes). Quantitative dual task paradigm – demonstrated interference from a visuospatial and verbal interference tasks = domain general?? Qualitative dual task paradigm – demonstrated interference form dynamic visual noise and verbal interference tasks = domain general?? Electrophysiological Study – activation of N400 and N200. Both tasks had activated N200 in parietal occipital brain areas and N400 in frontal brain areas = domain general?? Next step = Does working memory capacity predict intelligence in children? If the tasks can predict verbal and non-verbal intelligence then they can be seen as domain general.

Rationale for Current Study Change detection tasks have been used successfully with children aged 5-10 years old (Riggs, McTaggart, Simpson & Freeman, 2006). Fukuda, Vogel, Mayr and Awh (2010) suggested that quantitative measures of capacity have stronger links to intelligence than qualitative measures. Heyes, Zokaei, van der Staaij, Bays, & Husain (2012) contrasted Fukuda, suggesting that qualitative measures also have a strong links to intelligence. The current research hopes to discover whether quantitative or qualitative measures of working memory capacity have stronger links to intelligence. No research, that we are aware of, has used these two change detection tasks simultaneously in a developmental context. Wechsler's Abbreviated Scale of Intelligence – assesses both verbal and non-verbal intelligence.

Predictions Due to the results of the previous PhD studies and also the results of Brown and Wesley (2013) and Hamilton et al. (2003) about the VPT … The Quantitative change detection will predict both verbal and non verbal intelligence. The Qualitative change detection task will predict both verbal and non verbal intelligence.

Method Design Repeated measures design 2 working memory capacity tasks (quantitative and qualitative) 2 intelligence tasks (vocabulary and matrix reasoning task, WASI-II) Participants 33 year 6 children took part (13 males, 20 females). They had a mean age of 10.09 (.291). 30 year 3 children took part (14 males, 16 females). They had a mean age of 7.13 (.345). Children were recruited from 4 schools within the North East of England after the school had volunteered to take part.

Quantitative (number) Qualitative (resolution) Method - Working Memory Capacity Tasks Quantitative (number) Is the retrieval square colour the same? Year 6: Array sizes 2 and 4. Year 3: Array sizes 1 and 2. Qualitative (resolution) Is the retrieval shape bigger or smaller? Year 6: Array sizes 1 and 2. Year 3: Array size 1 only. Encoding Array (500 ms) Maintenance (900 ms) Retrieval Array (3000 ms) Encoding Array (500 ms) Maintenance (900 ms) Retrieval Array (3000 ms) Figure 3: Example of the quantitative visual change detection task (Luck and Vogel, 1997). This is in line with the Discrete Slot Model from Luck and Vogel (1997) which looks at large changes. Figure 4: Example of the qualitative visual change detection task (Bae and Flombaum, 2013). This is in line with the Shared Resource Model from Bays et al. (2009) which looks at small changes – 5, 10, 15, 20% changes.

Wechsler’s Abbreviated Scale of Intelligence, second edition (WASI-II) Method – Intelligence Tasks Wechsler’s Abbreviated Scale of Intelligence, second edition (WASI-II) Verbal Intelligence (Reasoning) Vocabulary Task Ask the child to describe the word Both abstract (enthusiastic) and non-abstract (fish) words. Non-Verbal Intelligence (Reasoning) Matrix Patterns Task Child is shown a large image and is asked to fill the gap (and complete the pattern) with the correct missing image. Working memory capacity tasks and intelligence measures were all counterbalanced to reduce order and fatigue effects.

Standardised Score (Z Score) Results – Z Score ANOVAs Table 1: Z Scores scores of the change detection conditions and the reasoning (intelligence) measures.   Quantitative K Z Score Qualitative Vocabulary Matrix Year 3 -.89(.35) -.49(1,03 -1.24(.43) -.85(.55) Year 6 .06(.65) .19(.88) .48(.38) .29(.79) 2 x 2 Mixed ANOVA No main effects of task type F(1,61)=3.317, p=.073, ηp2 =.052. Main effect of age F(1,61)=39.258, p<.001. ηp2 =.392. No interaction between age and task type F(1,61)=.828, p=.336, ηp2 =.013. Standardised Score (Z Score) Task Type Figure 5: Graph showing effects of age for the Year 3 and Year 6 data

Results Year 3 and 6 data sets were collated and age was then used as a separate factor. Four Hierarchical regression analyses were conducted and took age into consideration. For each regression, model 1 included age only and model 2 included age plus the memory task score. Does age and the qualitative score predict verbal intelligence? YES, positive link. F(2,62) = 28.735, P<.001, R²=.489. In model 2, both age and the qualitative task score can predict verbal intelligence. Does age and the qualitative score predict non-verbal intelligence? YES, negative link. F(2,62) = 135.018, P<.001, R²=.818. In model 2, both age and the qualitative task score can predict non-verbal intelligence. Note: The change detection tasks need the factor of age to be able to predict intelligence.

meaning age and the quantitative do not predict intelligence. Results Year 3 and 6 data sets were collated and age was then used as a separate factor. Four Hierarchical regression analyses were conducted and took age into consideration. For each regression, model 1 included age only and model 2 included age plus the memory task score. Does age and the quantitative score predict verbal intelligence? YES, negative link. F(2,62) = 133.957, P<.001, R²=.817. In model 2, both age and the quantitative task score can predict verbal intelligence. Does age and the quantitative score predict non-verbal intelligence? NO The R² change of .003 when adding in the quantitative scores is not significant (p=.547) meaning age and the quantitative do not predict intelligence. Note: The change detection tasks need the factor of age to be able to predict intelligence. Why did the quantitative change detection task not predict non-verbal intelligence?

Discussion The quantitative change detection task predicted verbal intelligence and the qualitative task predicted verbal and non-verbal intelligence. Effects of age – year 6 had larger (positive) z scores. No support for Fukuda et al. (2010) as the quantitative task did not predict non-verbal intelligence. The qualitative measures demonstrated strong links instead. Support for the research from Heyes et al. (2012) as the qualitative task was shown to have links to intelligence. However, we used the WASI-II and Heyes had used scholastic achievement intelligence measures. Suggests that the links between working memory capacity and intelligence are simply not due to the amount of items that can be stored. Future ideas = collect the scholastic achievement scores for the current data set and collect data from year 8 students. Does the quantitative change detection task use a higher proportion of verbal to visual resources, therefore meaning that there are not enough visual resources to predict non-verbal intelligence alone. The qual task could use equal verbal and visual resources, or could use the verbal resources as a support mechanism for the visual information.

Conclusion 1) Does working memory capacity predict intelligence? Yes – both qualitative and quantitative tasks can predict intelligence in some way. 2) Are the change detection tasks domain general or domain specific?? Qualitative change detection task – domain general – evidence from all studies. Quantitative change detection task – unsure until further data collection has been done. Is the quantitative change detection task context dependent? Potentially during the ages of 7-11, children have not developed the ability to allocate visual and verbal resources and attentional control processes (Engle et al., 1999).

Thank you! Any Questions?? laura.jenkins@northumbria.ac.uk

Data including Year 8

3*2 mixed ANOVA Results Main effect associated with age F(2, 87) = 36.212, p < .001, ηp2 = .454 No main effect associated with task, F(1, 87) = 0.038, p = .846, ηp2 = .000 Interaction Effect age * task F(2, 87) = 5.344, p = .006, ηp2 = .109

Unique Variance of Visual Memory Task Regression Model Model Statistics Unique Variance of Visual Memory Task Quantitative memory task score upon Full IQ controlling for age F(2,89) = 79.497, p < .001 Adjusted R2 = .638 Fchange(1,87) = 0.568, p = .453 R2change = .002 (0.2%) Quantitative memory task score upon Non-Verbal (Matrix) IQ controlling for age F(2,89) = 23.689, p < .001 Adjusted R2 = .338 Fchange(1,87) = 0.001, p = .970 R2change = .000 (0%) Quantitative memory task score upon Verbal (Vocabulary) IQ controlling for age F(2,89) = 104.156, p < .001 Adjusted R2 = .699 Fchange(1,87) = 2.006, p = .160 R2change = .007 (0.7%)

Unique Variance of Visual Memory Task Regression Model Model Statistics Unique Variance of Visual Memory Task Qualitative memory task score upon Full IQ controlling for age F(2,89) = 87.998, p < .001 Adjusted R2 = .662 Fchange(1,87) = 6.621, p = .012 R2change = .025 (2.5%) Qualitative memory task score upon Non-Verbal (Matrix) IQ controlling for age F(2,89) = 33.512, p < .001 Adjusted R2 = .422 Fchange(1,87) = 12.720, p < .001 R2change = .083 (8.3%) Qualitative memory task score upon Verbal (Vocabulary) IQ controlling for age F(2,89) = 100.839, p < .001 Adjusted R2 = .692 Fchange(1,87) = .006, p = .936 R2change = .000 (0%)