Article

Individual differences, aging, and IQ in two-choice tasks.

Department of Psychology, The Ohio State University, Columbus, OH 43210, United States.
Cognitive Psychology (Impact Factor: 4.05). 12/2009; 60(3):127-57. DOI: 10.1016/j.cogpsych.2009.09.001
Source: PubMed

ABSTRACT The effects of aging and IQ on performance were examined in three two-choice tasks: numerosity discrimination, recognition memory, and lexical decision. The experimental data, accuracy, correct and error response times, and response time distributions, were well explained by Ratcliff's (1978) diffusion model. The components of processing identified by the model were compared across levels of IQ (ranging from 83 to 146) and age (college students, 60-74, and 75-90 year olds). Declines in performance with age were not significantly different for low compared to high IQ subjects. IQ but not age had large effects on the quality of the evidence that was obtained from a stimulus or memory, that is, the evidence upon which decisions were based. Applying the model to individual subjects, the components of processing identified by the model for individuals correlated across tasks. In addition, the model's predictions and the data were examined for the "worst performance rule", the finding that age and IQ have larger effects on slower responses than faster responses.

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    ABSTRACT: Dyscalculia vs. severe math difficulties: Basic numerical capacities in elementary school Background: The current international classification systems ICD-10 and DSM-IV show a large conceptual overlap in their criteria for defining dyscalculia: Firstly, a child’s score in a standardized mathematics assessment should fall below a pre-specified percentile rank cutoff (usually 10%). Secondly, intelligence is required to be in the normal range (i.e., IQ ≥ 70 or 80). And thirdly, a substantial discrepancy between the mathematics and intelligence standard scores should be observed. In this paper, we will define children that conform to all three criteria as having developmental dyscalculia (DD), whereas children who fulfill only the first two criteria are defined to show severe math difficulties (MD). This discrepancy definition of DD, along with similar definitions for other learning disorders, has long been criticized in the scientific literature on methodological, conceptual, and ethical grounds. Further, at least for dyslexia, a substantial body of research has been published, showing that no reliable differences between high-IQ and low-IQ poor readers can be found on relevant core markers (e.g., word recognition) and that no different treatments are required for high-IQ and low-IQ readers (Stanovich, 2005). However, there are still only few studies comparing DD and MD children. One of the few studies (Gonzalez & Espinel, 2002) reported no substantial differences between these groups with respect to solving algebra word problems and using solution strategies. Ehlert et al. (2012), using a criterion-based test, did not find any differences in mathematical concept comprehension between DD and MD children. Aims: The main goal of this study was to compare DD and MD children with each other and with an unimpaired control group on a battery of tasks tapping basic numerical capacities. The tasks were chosen such that they broadly covered key aspects of numerical cognition, i.e., dot enumeration (subitizing and counting), number and magnitude comparison, transcoding, number sets and number line. A second goal pertained to a comparison of MD/DD classification strategies. The first strategy (A) used a well-established psychometric test that broadly covered basic numerical capacities and, to a lesser degree, arithmetic ability to identify DD/MD. As a second strategy (B), we used arithmetic ability exclusively to identify children with MD/DD. Methods: Overall, N = 68 children participated in this study. The following criteria for DD were used: IQ ≥ 80 (based on the WISC-IV perceptual reasoning index and vocabulary; Petermann & Petermann, 2011), standard reading score ≥ 80 (SLS 1-4; Mayringer & Wimmer, 2003), standard math score ≤ 80 (strategy A: ZAREKI-R; von Aster, Weinhold Zulauf & Horn, 2006; strategy B: mean of five arithmetic subtests, two from ZAREKI-R [mental calculation, word problems]; two from HRT 1-4 [addition, subtraction], Haffner, Baro, Parzer, & Resch, 2005; and the arithmetic subtest from WISC-IC), discrepancy between IQ and standardized math score > 1.5 standard deviations. For MD, the only difference was that the discrepancy between IQ and math assessment was required to not exceed 1.5 standard deviations, while all other criteria applied. Children in the control group conformed to the following criteria: IQ ≥ 80, standard math score ≥ 90, standard reading score ≥ 80. For strategy A (B), these criteria resulted in a selection of N = 27 (11) children with DD, N = 21 (18) children with MD, and N = 20 (39) children in the control group. All basic numerical capacities were administered on a computer with headphones. The first task was dot enumeration, where children had subitize one to three dots (6 items) or count four to nine dots (12 items) as fast as possible. The second task assessed symbolic magnitude comparison, where children had to indicate the larger of two single-digit numbers (24 items). Next, a mixed magnitude comparison task was administered in which the larger magnitude (single digit or number of dots) had to be selected (24 items). A subsequent transcoding task required children to type the numbers they had just heard (8 items). A discrete trial variant of the number set task (Geary et al., 2009) was presented next, in which children were supposed to quickly judge whether a target number on top of the screen corresponded to a number set shown below (140 items, speed test). Then, a number line task (Booth & Siegler, 2006) ranging from 0-100 was administered in which children had to indicate where a number shown on top of the screen was located on the number line (23 items). Two additional tasks were administered, a binary-choice reaction time task (20 items) to assess processing speed and a visual matrix span task (up to 16 items) to assess visuo-spatial working memory. 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