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CJ Bucklin, B.A.

Discerning the physiological and behavioral substrates of the relationship between fluid reasoning and working memory. CJ Bucklin, B.A. Overview theories of the intelligence construct, with particular emphasis on Raymond Cattell’s fluid-crystallized ( Gf-Gc ) model

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CJ Bucklin, B.A.

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  1. Discerning the physiological and behavioral substrates of the relationship between fluid reasoning and working memory CJ Bucklin, B.A.

  2. Overview theories of the intelligence construct, with particular emphasis on Raymond Cattell’s fluid-crystallized (Gf-Gc) model • Operationalize Gf and Gc • Introduce the Raven’s Advanced Progressive Matrices (RAPM) as a flagship measure of fluid intelligence (Gf) • Discuss extant issues with the instrument, clinical alternatives (WAIS-IV Matrix Reasoning) • Overview theories and measures of the working memory construct, with special attention to Alan Baddeley’s multicomponent model • Demonstrate its complementary status w/ Gf-Gc model • Highlight differences between n-back and complex span WMC measurement paradigms in terms of correlation with Gf • Summarize findings from seminal and contemporary eye movement studies on RAPM/geometric analogies • Explore suggested relation between strategy use and Gf/WMC, as well as between learning and Gf/WMC • Briefly describe proposed role of processing speed (Gs) Presentation goals

  3. Our textbook’s definition: • “[T]he capacity for learning, reasoning, and understanding…” (Gluck, Mercado, & Myers, 2013, p. 358) • Heitz, Unsworth, and Engle (2004, p. 62) delineate three main theories in the psychometric study of intelligence: • George Spearman’s “positive manifold” (1904, 1927) Concludes, based on “the observation that individuals who score high on one intelligence test tend to score high on other intelligence tests,” that “there is a single intellectual ability on which people differ.” 2. Louis Thurstone’s “primary mental abilities” (1938) Diametrically opposed to Spearman’s conception; posits “no single psychometric factor but instead a variety of specific intelligences,” including perceptual speed. 3. The “intermediate” perspectives (e.g., Cattell, 1941, 1957) Operationalizing constructs: Intelligence

  4. “The fluid-crystallized theory argues that the primary abilities which can be said to involve intelligence…are organized at a general level into two principal classes or dimensions. One of these, referred to as fluid intelligence (Gf) is said to be the major measurable outcome of the influence of biological factors on intellectual development– that is, heredity, injury to the central nervous system,…etc. The other broad dimension, designated crystallized intelligence (Gc), is said to be the principal manifestation of a unitariness in...experiential-educative-acculturation influences” (Horn & Cattell, 1966, p. 253-4). • To summarize, Gf= adaptive reasoning (with novel stimuli/environments), and Gc = factual reasoning Raymond Cattell’s GF-gc theory

  5. Declared as the “gold standard” of Gf testing (Engle, 2010) • Typically, r 0.3 with most major working memory capacity (WMC) measures (Unsworth & Engle, 2005) • …but it is it truly free from cultural bias? • Good news: Gender gap is closing; men still ahead by 1 SD (Cockcroft & Israel, 2011; Flynn & Rossi-Casé, 2011; Savage-McGlynn, 2012) • Vigneau and Bors (2008, p. 705) report r = 0.12 between male gender and RAPM score (n = 506; 326 women) • Bad news: The racial stereotype threat effect appears to remain prevalent (Brown & Day, 2006; McKay et al, 2002; Nguyen & Ryan, 2008). • Furnham and Moutafi (2012): Strong potential for age discrimination when used for employee selection (r = -0.56 between age and RAPM score; n = 383, mean age=38.14, SD=9.59) • Possibly because “Gf reflects the ability to use abstract relational reasoning…in which prior experience and learned knowledge are of little use” (Chuderski, 2013). the RaPM: A widely-used measure of gf

  6. Rapm performance: achievement age vs. chronological age Source: Brouwers, Van de Vijver, & Van Hemert, 2009 (p. 333); N = 244,316, sample data sets collected from 45 countries between 1944 and 2003

  7. Rapm item sample with areas of interest, vs. Wais matrix reasoning Surprisingly difficult to find information on how well-correlated these two tests are; however, Mackintosh (1998) reports that r .4-.75 between Raven’s and the first two incarnations of the WAIS (WAIS-III was first to include Matrix Reasoning)

  8. Alan Baddeley (1986, 1992, 2003, 2012) has constructed what has become perhaps the most accepted and influential, multicomponent model of working memory: Operationalizing constructs: working memory

  9. n = 160; Unsworth & Engle, 2005 • Although the OSpan task (originally developed by Turner & Engle, 1989) is a widely-used measure both in studies of WM alone and of the WM-Gf relationship, the inconsistent pattern of point-biserial correlations suggests that other tasks may yield superior – or at least unique – predictive value for understanding WMC’s connection to Raven’s performance (e.g., Kane, Conway, Miura, & Colflesh, 2007) • The complex span paradigm’s ancestry may be traced to the reading/sentence span task devised by Daneman and Carpenter (1980) mentioned in our text (358) RAPM and complex span wmc paradigm

  10. Variations on the complex span paradigm Hambrick et al (2009) found r = .37 for Symmetry Span and RAPM versus .30 for Operation Span (n = 131) Salthouse et al (2008), however, found a more dramatic difference with a larger sample (n = 791): r = .52 for SymmSpan, .24 for Ospan Conway et al. (2005):

  11. Little, Lewandowsky, and Craig (2013; n=130) Measures used: -Reading (sentence) span -Operation span -Spatial short-term memory task (similar to Corsi block tapping, displayed below) -Memory Updating task (shown on next slide) Bottom left panel (r = .53) displays correlation observed in study Point biserial correlations between wmc and rapm: insights from more recent data

  12. Memory updating task (salthouse, babcock, & Shaw, 1991)

  13. Rapm and the n-back wmc paradigm Gray, Chabris, & Braver, 2003 (p. 317); according to Chuderski and Neca (2012), lures tap into one’s propensity to commit false alarms, or “the (in)efficiency of control” (p. 1692).

  14. differential impact of wmc tasks on gf link Barbey, Colom, Paul, & Grafman(2013), using the WAIS-III MR as their measure of Gf, the N-back paradigm (bottom left) for WM monitoring, and a letter-number sequencing task (top left) for WM manipulation, found results suggesting that the LNS task was more closely related to Gf in terms of both physical similarity (e.g., overlapping brain regions) and scores than n-back HOWEVER, as one can see, their n-back task did NOT employ lure trials!

  15. The control group, which was subjected to more PI buildup, had Ospan scores more correlated (r = .49) w/ RAPM than the experimental (intralist variation), whose participants were released from PI starting on Item 4 (Bunting, 2006) Proactive interference and rapm, part 1 of 2

  16. As one can see, the lowest observed correlations with RAPM for the experimental (interlist variation) condition were for PI-release sets, for both word and digit stimuli Proactive interference and the rapm, pt 2

  17. Ebisch et al. (2013) developed an induction (top left) and a visualization (top right) task, both designed to tap Gf. Although the sample size was small (n = 22) and entirely comprised of females, the obtained results strongly implicate right hemisphere connectivity strength between anterior insular cortex (aIC) and medial frontal/dorsal anterior cingulate cortices (mFC/dACC) as being vital to successful performance. Identifying the rapm/wais-MR neural correlates

  18. Right aic, mfc, & dacc: key brain structures?

  19. Eye movement strategies on the rapm R2 = 0.56; “the most accurate prediction of Raven’s scores based on eye-tracking data reported to date” (Hayes, Petrov, & Sederberg, 2011, p. 4) Figure A illustrates the “systematic” strategy (r = 0.31; assoc. w/ high scores) Figure A shows the “toggling” strategy (r = -0.25; assoc. w/ low scores)

  20. Two main strategies (Bethel-Fox, Lohman, & Snow, 1984; Vigneau, Caissie, & Bors, 2006; Hayes, Petrov, & Sederberg, 2011; Jarosz & Wiley, 2012; Vakil et al, 2012): • Constructive matching (bottom left) involves forming a mental representation (idealized response) prior to looking at the response bank (characteristic of higher scorers; inductive) • Response elimination (bottom right), by contrast, is marked by an over-reliance on the available response bank options, instead of trying to derive a solution by reconciling the elements within the problem matrix (characteristic of lower scorers; deductive) Constructive matching vs. Response elimination: the two identified strategies

  21. Jarosz and Wiley (2012) created two alternative versions of the RAPM, one of which had response banks for each problem that included the most commonly-chosen incorrect answer (per the official Raven manual) -Referred to as the high salience condition (see bottom left illustration) Found that low-WMC participants had a higher rate of toggling, especially on high-salience items, and spent more time on response banks Eye-tracking evidence for rapm salience effect “highly salient” “low salience”

  22. More Results of the salience manipulation

  23. Vakil et al: replication with the colored and standard progressive matrices

  24. A word on pupillometry Van der Meer et al. (2010), using the above geometric analogies test that also taps Gf, collected data (n = 37) suggesting that high-Gf individuals experience increased pupillary dilation and have shorter RTs relative to individuals with average Gf

  25. Rules on the rapm: the carpenter et al taxonomy Although the above rule taxonomy, developed by Carpenter, Just, and Shell (1990; n=12) does a fairly thorough job describing the types of patterns present on the RAPM, later research has found that, while the number of rules and rule tokens (instances of a rule) needed to solve each problem is strongly correlated with RAPM performance (r= .75, replicated to r=.58 in Vigneau, Caissie, & Bors, 2006; n=55), factors related to eye movements, such as how many matrix cells were inspected (r=.49), rate of toggling (-.43), proportion of time spent on matrix during each problem(.48) , proportion of time on response bank (-.44), and number of toggles (-.27), all appear important, as well (see also Hayes et al, 2011; Jarosz & Wiley, 2012, Vakil et al, 2012)

  26. Wiley, Jarosz, Cushen, and Colflesh (2011, Study 1, below) , using Ospan as their WMC measure, found that the point-biserial correlations for WMC and Raven’s items were generally highest for problems on which a new (first-use) rule must be used. -Testament to the role of PI? In Study 2 (left), they created two alternative RAPMs (only 16 items each): one required a new rule combo be used every problem (novel condition), the other only required five rule combos (repeated condition) More on rule learning and the rapm

  27. Associative learning and the rapm Tamez, Myerson, and Hale (2008; n=60), using both verbal and non-verbal three-term paired associate learning tasks, found (somewhat surprisingly) that the verbal task actually predicted more variance (.489) than the nonverbal one (.369). Nevertheless, results from both tasks, combined with the findings of Carpenter et al. (1990), Vigneau et al. (2006) and Wiley et al. (2011) strongly support the role of learning on the RAPM

  28. HIGHLY SPEEDED GROUP: 20 MIN (n = 298) MODERATELY SPEEDED GROUP: 40 MIN (n = 289) “UNSPEEDED” GROUP: 60 MIN (n = 303) The significance of processing speed (Gs) Source: Chuderski, 2013 (p. 251); all p values<.001, brackets display 95% CIs for r values

  29. Broadway and Engle (2011; n=52) found that the temporal judgments (e.g., of how much time has passed) of low-WMC individuals “were consistently too long for the shortest duration [500 ms] and too short for the longest [2500 ms],” whereas those of high-WMC participants were accurate across durations • Better temporal resolution  better time management during testing ? • Troche and Rammsayer (2009; n=200) suggested that the relationship between temporal judgments and psychometric intelligence was mediated by WMC (participants had to compare a 50-ms standard interval to subsequent variable ones) • Partchev and De Boeck (2012) conclude that, “given the higher variance of fast intelligence [fast responses] compared to slow intelligence, the ability of fast respondents is measured in a more reliable way than the ability of slow respondents” (p. 30) • Need more research comparing performance under self-paced and timed conditions • Goldhammer and Klein Entink (2011; n=230) argue, based on their data, that “reasoning speed and ability are negatively correlated [r=-.27, see below] but clearly distinguishable constructs.” More on processing speed/timing

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