TEAM Cognitive Training
Thesis Proposal
I pledge on my honor that I have not given or received any unauthorized assistance on this assignment/examination.
Timothy Briner
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Jacob Buchanan
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Sydnee Chavis
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Sy-Yu Chen
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Gregory Iannuzzi
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Vadim Kashtelyan
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Mentor: Dr. Michael Dougherty
Librarian: Glenn Moreton
Abstract
Cognitive ability determines how well people function successfully in everyday activities. This is especially true in the area of education, where individual differences in cognitive ability have been shown to predict performance in a number of core competency areas, including reading comprehension (REF) and quantitative reasoning (Ashcraft & Krause, 2007). While cognitive ability has long been believed to be a stable individual difference variable – perhaps genetically determined (Friedman et al., 2006) – recent work in cognitive neuroscience suggests that cognitive ability can be improved through extensive training (Ball et al., 2002; Buschkuehl et al. (2008); Erickson et al., 2007; Merzenich et al., 1996). We seek to train and improve peoples' working memory capacity and thus also improve their overall cognitive ability. Based on prior studies, we believe that visuo-spatial working memory training will lead to improvement in other cognitive abilities.
Introduction
American students are chronically underperforming in mathematics in comparison to other developed nations. For example, in the recent Trends in International Mathematics and Science Study, America's fourth grade students scored lower in mathematics than eight other countries, located in Asia or Europe, and eighth grade students scored lower than five countries, all located in Asia (Mullis et al., 2008). According to the National Assessment of Educational Progress (NAEP) report (2005), American students lack a basic understanding of mathematics. This has been cited as contributing to a growing achievement gap as the students progress through the education system (Mervis, 2007). In addition to international performance gaps, America faces its own internal performance gaps between certain demographics. The 2005 NAEP study demonstrated that 70% of African-American students and 60% of Hispanic students fell below the standard of basic understanding of high school mathematics, compared to 30% of whites and 27% of Asian-Americans who fell below this same standard.
While these achievement gaps are well established, much less progress has been made in identifying their cause. One explanation for the achievement gaps is the presence of cognitive deficits which ultimately determine quantitative reasoning ability. A deficit in a mental construct vital to quantitative reasoning would be detrimental to math performance. One construct which has been demonstrated to be correlated with quantitative reasoning ability is working memory (Bull & Scerif, 2001). Working memory is the ability to maintain and manipulate information when completing a task (Colom, Rubio, Shih, & Santacreu, 2006; Engle, 2002; Unsworth & Engle, 2008).
One hypothesis for the underperformance in mathematics and deficits in quantitative reasoning is a handicap on working memory. For example, math anxiety has been shown to negatively affect quantitative reasoning ability by functioning as a second task for working memory (Ashcraft & Krause, 2007).Math anxiety is a performance-based anxiety disorder, separate from general anxiety, seriously affecting at least 17% of the American population. It frequently causes a pattern of math avoidance, leading those affected to perform poorly on math assessments and avoid math-based classes and careers. Although only 17% of the population is considered "highly math anxious", even medium-math-anxious individuals show significant performance differences from low-math-anxious individuals. Thus, there is a necessary demand for research regarding the improvement of math learning and performance (Ashcraft & Krause, 2007; Ashcraft et al., 2007).
The impact of working memory drains, such as math anxiety, on quantitative reasoning ability may be greatly reduced if the capacity of working memory as a whole is increased. The purpose of our study is to show that because general cognitive ability and visuo-spatial working memory are predictive of quantitative reasoning and general cognitive ability and visuo-spatial working memory can be improved through extensive cognitive training, training on a visuo-spatial working memory task will lead to improvements in quantitative reasoning.
Literature Review
An individual draws upon their crystallized and general fluid intelligence when engaged in cognitively demanding tasks such as reading comprehension questions and math problems. These two components define a person's overall cognitive ability. Crystallized intelligence, the summation of an individual’s knowledge and experience, is applied to a problem via general fluid intelligence. General fluid intelligence is an individual's ability to identify relationships and draw correlations, and is comprised of short term memory and working memory (Engle, Laughlin, Tuholski, & Conway, 1999; Kane & Engle, 2003). Working memory is the individual's ability to maintain and manipulate information when completing a task. It is composed of a visuo-spatial sketchpad, a phonological loop, and a central executive (Swanson, Jerman, &Zheng, 2008). The visuo-spatial sketchpad is responsible for mental visualization and further mental manipulation of images. Similarly, the phonological loop is responsible for mental manipulation of sounds. The central executive oversees the manipulations in the visuo-spatial and phonological constructs, and directs attention towards solving a goal task while ignoring competing tasks. The process of ignoring competing tasks is called response inhibition (Unsworth, Schrock, & Engle, 2004). The speed at which the central executive places information into working memory determines an individual's perceptual speed. The maximum capacity and speed at which each construct functions places an upper limit on the individual's ability to solve problems related to quantitative reasoning ability at a given time.
Cognitive Ability is important for quantitative reasoning:
Ashcraft and Krause (2007) demonstrated the importance of working memory in quantitative reasoning by studying working memory capacity and math performance in high-math-anxious individuals as compared to low-math-anxious individuals. The subjects were tested using two different verbal span assessments, and no significant differences in working memory capacity were found. Both groups of subjects (high-math-anxious and low-math-anxious) were given a dual-task setting: they were prompted to hold an escalating number of letters (2, 4, or 6) while performing subtraction problems, and then asked to recall the letters in serial order. When given this computational task, high-math-anxious individuals exhibited significantly lower working memory performance. This is due to the effects of math anxiety on working memory: the anxiety functions as an additional task for working memory which draws cognitive resources from the goal task, inhibiting performance. Thus, high-math-anxious individuals were most severely affected by an increase in working memory load, demonstrating the critical importance of working memory to math performance.
Working memory has also been shown to be strongly correlated with problem solving abilities (Swanson et al., 2008). Swanson et al. performed a study on 353 children (167 male, 186 female) from grades 1, 2, and 3 from a Southern California public and private school district. All children were tested for risk of serious math problem solving difficulties (SMD) in the first year of the study (Wave 1). Children at risk for SMD were defined as having a Raven Colored Progressive Matrices test score greater than 85, but with a mean math performance below the 25th percentile in norm-referenced measures such as solving orally presented word problems and performing digit naming exercises. The Raven Colored Progressive Matrices task is a multiple choice measure of fluid intelligence, requiring participants to identify a missing segment to complete a sequence of colored matrices. The children were tested across three testing waves in a three year span in order to measure working memory capacity, general fluid intelligence, and risk for SMD.Children identified as at risk for SMD in Wave 1 showed a lower growth rate in work and lower levels of performance in measures of cognitive ability than those identified as not at risk. In addition, measures of fluid intelligence and two components of working memory (central executive, visuo-spatial sketchpad) in Wave 1 predicted Wave 3 problem solving accuracy. However, growth in problem solving accuracy was strongly correlated with growth in the central executive and phonological storage components of working memory. The strong correlation between working memory capacity and problem solving ability implies a relationship between working memory and quantitative reasoning.
High school students with high math ability were shown to have superior spatial abilities to average math students. In a study by O'Boyle et al. (2005), students who scored in the 99th percentile and students who scored in the 50th percentile on the Australian SAT were tested for spatial ability using a mental rotation task. Students in the 99th percentile scored significantly higher than students in the 50th percentile on this task. fMRI was used to monitor brain activity during this task. Students in the 99th percentile activated a unique brain network and showed activity in more regions of the brain when compared to students in the 50th percentile during the task. The study demonstrates the positive correlation between visuo-spatial working memory and quantitative reasoning.
Cognitive ability can be trained:
An individual’s capacity to form and developnew skills and habits is referred to as plasticity. Neural plasticity is the brain’s physical modification of neural circuits due to changes in neural activity. One significant form of changing neural activity is the acquisition of cognitive skills, defined as “abilities that an organism can improve through practice or observational learning and that involve judgments or processing… The capacity to acquire cognitive skills can be described as cognitive plasticity” (Mercado, 2008). Thus, by challenging an individual’s cognitive abilities through demanding tasks, the plastic nature of the brain allows an individual to improve cognitive abilities through the creation of new neural pathways (Mercado, 2008; Rosenzweig & Bennetta, 1996).
Based on the plastic nature of the brain, stressors can be tailored to improve particular cognitive domains in the form of training. Dr. Michael Merzenich showed that training programs designed to restore children’s language learning impairments can improve both their comprehensive skills and auditory perception. In 1998, Merzenich et al. demonstrated the ability to remedy the deficits inherent to language-learning impaired (LLI) children who have major temporal processing and fast-speech-element recognition deficits. LLI children trained 8-16 hours over a 20 day period with a computer program designed to improve their ability to recognize stimuli similar to what one must recognize in speech. After the training period, the LLI children demonstrated an increased ability to recognize speech and nonspeech sequences, substantially remediating the deficits in nearly all of the LLI children tested. This strongly indicates that training can overcome temporal processing deficits (Merzenich et al., 1996).
Similarly, in elderly individuals experiencing cognitive decline, working memory training has been shown to improve memory performance (Buschkuehl et al., 2008). In this study, 80 year old adults received working memory training for three months. A second group received physical training for an identical training duration. At the end of the training, adults who completed the working memory training demonstrated increased memory performance over the active group. Also, the experimental group improved in tasks not directly trained, demonstrating transfer effects of working memory training. Buschkuehl confirmed the notion that transfer will occur if the training task and the transfer task utilize overlapping regions of the brain (Dahlin et al., 2008). The transfer benefits were limited in scope and were not observed to extend to tasks beyond the domain of the trained cognitive region, but the demonstrated improvements from were still present three months after post testing (Li, 2008). Although there was a decline from post-test to follow up scores, there was still substantial improvement in score and processing time. With the proper maintenance, the cognitive training gains can be maintained and likely further improved upon following these studies.
Since the brain is neurally plastic as well as cognitively plastic, training programs result in changes to the physical neural networks of the brain in addition to improving performance on trained tasks (Erickson et al., 2007). Ericksonet al.studied how brain activity changes for people completing dual-switching tasks after training. They used a combination of two tasks: color discrimination, located in the upper half of the screen, and letter discrimination, located in the lower half of the screen. They kept the total number of visual stimuli on the screen equal for all trials. The trials and training was split into three different combinations of tasks. Single pure (SP) trials consisted of color combination and letter discrimination tasks given in separate blocks of time. Single-task single mixed (SM) trials consisted of a mix of color discrimination or letter discrimination tasks in the same blocks of time. Dual-task dual-mixed (DM) trials consisted of both color and letter discrimination tasks being given simultaneously. All of the participants were given an initial fMRI (functional Magnetic Resonance Imaging) test with SM and DM tasks, then the control group had a 2 or 3 week break before the final fMRI testing, whereas the training group had five 1-hour training sessions during the 2 or 3 weeks before being given the final fMRI test. The training group was split into three sections, each training either SP, SM, or DM tasks and all receiving continuous and immediate feedback. After the training, there was a greater change in response times for the training group than the control group. Performance accuracy reliably increased for the SM and DM training conditions but not for the SP condition. There was a larger reduction in brain activity in the focused regions for the training groups compared to the control group. This suggests that improvements due to training are related to reduced activation in those brain regions. In addition, two areas of the brain did show increased activity with training that correlated with better performance. The DM condition improved the participants’ performance the most, supporting their hypothesis that training for more demanding tasks will improve performance more. Thus, cognitive training can improve the executive control process as well as the physical processes in the brain.
Quantitative reasoning can be improved by cognitive training:
In a recent study by Jaeggi, Buschkuehl, Jonides, and Perrig(2008), fluid intelligence was improved by training working memory. In the study, participants’ fluid intelligence was evaluated using the Raven Progressive Matrices task before and after training working memory. Working memory was trained with the n-back task. The n-back test presents the participant with a visual cue and an audible letter. The participant is then prompted with one of the previously viewed visual cues and is asked to input the letter heard when that cue was seen. N is the numbered term in reverse sequence that the participant is asked to recall. Training with this task demonstrated improvements in fluid intelligence as measured by the Raven Progressive Matrices task (Jaeggi et al., 2008). These results demonstrate the existence of transfer effects (e.g. improvement in fluid intelligence) due to training on a working memory task.