The Bridge: Connecting Science and Practice:The Case for Using Working Memory in Practice
Bryan D. Edwards, Ana M. Franco Watkins, Samuel T. McAbee, and Luis Faura with Craig Wallace, Lynda Zugec, and Mark L. Poteet
“The Bridge: Connecting Science and Practice” is a TIP column that seeks to help facilitate additional learning and knowledge transfer in order to encourage sound, evidence-based practice. It can provide academics with an opportunity to discuss the potential and/or realized practical implications of their research, as well as learn about cutting edge practice issues or questions that could inform new research programs or studies. For practitioners, it provides opportunities to learn about the latest research findings that could prompt new techniques, solutions, or services that would benefit the external client community. It also provides practitioners with an opportunity to highlight key practice issues, challenges, trends, etc., that may benefit from additional research. In this issue, we review and consider the case for using working memory in practice!
The Case for Using Working Memory in Practice
By Bryan D. Edwards, Ana M. Franco Watkins, Samuel T. McAbee, and Luis Faura
The reliance on cognitive ability (or general mental ability) for selection, placement, and training underlies one of the oldest and deepest research practices in the history of I-O psychology. It is generally accepted among I-O psychologists that cognitive ability is the strongest predictor of learning and task performance. However, many of our cognitive psychologist colleagues make a strong case that working memory capacity is the strongest predictor of learning and performance. Indeed, working memory training programs such as lumocity, cogmed, and jungle memory are quite popular. As such, the purpose of the present article is to span a bridge between both cognitive and I-O psychology as well as between science and practice by encouraging the use of working memory as a predictor for training and job performance.
The Case for Working Memory
In nearly 40 years of research, cognitive psychology has established that working memory is a strong predictor of human learning and task performance (Baddeley & Hitch, 1974; Daneman & Carpenter, 1980; Engle, 2002). Despite the link between working memory, learning, and performance in the cognitive psychology literature, working memory has been ignored in I-O psychology research and practice. However, there are several reasons to expect that individual differences in working memory capacity are relevant to learning and task performance in work settings.
Working memory allows individuals to attend, manipulate, and store information while simultaneously ignoring irrelevant or distracting information. Working memory capacity is typically measured using complex span tasks, in which test takers juggle two competing tasks: processing information while keeping other information actively in mind to be recalled or used later (Conway et al., 2005; Engle, 2002). Working memory is essential for acquiring and integrating information. Individual differences in working memory capacity predict performance across tasks at various levels of complexity, from simple tasks such as following directions and note taking, to more complex tasks that involve reasoning and problem solving (Engle, Kane, & Tuholski, 1999), multitasking (Hambrick, Oswald, Darowski, Rench, & Brou, 2010), learning (Lewandowsky, 2011), and decision making (Franco-Watkins, Davis, & Johnson, 2016). Critically, the mechanisms underlying learning and performance in the above areas are essential for successful training and performance at work. With such strong evidence, why would working memory not predict training and job performance? Bosco, Allen, and Singh (2015) revealed that working memory was at least as strong a predictor of performance as general mental ability and, in some cases, was a stronger predictor.
There are three primary reasons why working memory should be at least as strong a predictor of learning and task performance as cognitive ability. First, working memory is important for learning and task performance because it is responsible for rehearsal and storage of new information into long-term memory (e.g., declarative and procedural knowledge). Indeed, many contemporary cognitive researchers view working memory as the key explanatory mechanism for individual differences in cognitive ability through the number of items held in memory (e.g., Halford, Cowan, & Andrews, 2007) and controlled attention (e.g., Cowan et al., 2005; Engle, 2002). Second, working memory coordinates different elements from long-term memory and the environment to be used for the task at hand (Baddeley, 2000). Many reasoning, problem-solving, or decision-making tasks require the integration of new or preexisting knowledge to perform a given task. Third, individual differences in working memory reflect controlled attention that allows people to maintain information in an active state in the face of ongoing processing and/or distraction (Engle, 2002). This ability to keep goal or task-relevant information active while ignoring task-irrelevant information is important for the development of action plans, goal setting, goal management, and monitoring, and for maintaining multiple concurrent and sometimes competing goals in active attention. For example, in a complex task situation where the goal is to develop a financial plan that requires numerous calculations, the person must maintain the information relevant to the primary task (e.g., budgeting, planning) while ignoring the other distractions (e.g., notifications from smart phone apps, ideas relevant to other projects, coworkers' conversations). Thus, working memory is adaptive because it allows individuals to keep task-relevant information active and accessible while completing complex cognitive tasks (Conway et al., 2005).
One final potential advantage of working memory relative to general mental ability is that measures of working memory capacity might also display less adverse impact than cognitive ability assessments (Bosco et al., 2015). Indeed, Bosco et al. (2015) found adverse impact ratios for Black–White differences in working memory that were approximately half of the ratios for cognitive ability.
Case Study: Working Memory in a Distribution Setting
The authors are currently involved in the use of generating short, applied versions of working memory (e.g., Oswald, McAbee, Redick, & Hambrick, 2015) and collecting data for selection. In one example we administered a measure of personality, cognitive ability, and working memory to a sample of employees at a distribution center and matched the scores to archival measures of performance. Working memory was positively related to performance (r = .23) as was cognitive ability (r = .32), and both predicted unique variance in job performance. This particular distribution center had a relatively large Hispanic population, which allowed us to compare subgroup differences on the Spanish and English versions of the working memory operation span and ability measures. We did not record ethnicity in our data collection but participants were allowed to select the Spanish or English versions of the working memory and ability assessments. Although scores on the English version were higher than the Spanish version on both measures, the standardized subgroup differences on ability were more than double the subgroup differences on the working memory measure. Furthermore, the correlation between working memory and performance was r = .20 for the sample of Spanish-speaking participants and r = .11 for the English-speaking participants.
Suggestions for Implementation
For practitioners wanting to use working memory for selection or training, it is important to recognize that working memory requires remembering information in the face of distractions. Although measures that require respondents only to memorize letters, numbers, or objects do assess (short term) memory, the ability to process information in the presence of a distractor task (but free from outside distractions) is core to assessing working memory capacity. Figure 1 demonstrates a typical working memory assessment (operation span). In Step 1, the letter “G” is presented and must be retained in working memory. The distractor task is the math problem in Step 2. Respondents must get the math correct to verify that the distraction is operating. A second letter is presented in Step 3. Then, both the “G” and the “H” must be recalled in Step 4 by selecting the letters from among 12 letters in the order in which they were presented earlier.
To translate research-based working memory measures for use in selection, a few issues need to be considered. First, the stimuli in laboratory tasks are typically words, letters, or positions on a grid (see Conway et al., 2005, for a review), however, alternate forms for more general use have begun to sprout up (see Hicks, Foster, & Engle, 2016). Thus, the stimuli used in typical tasks can be revised to be more work-related and more face valid. Likewise, the distraction task does not need to be math problems. Any distraction task that has a processing component and a way to verify that the respondent did pay attention to the distractor would work. Second, working memory measures used in research are often overly lengthy and time consuming. Thus, there is a need to create shorter assessments for use in applied settings (see Oswald et al., 2015) that adequately capture working memory. Third, in the absence of proctoring, some working memory measures are susceptible to cheating. For instance, test takers can simply write down the order of the letters presented in the operation span task without having to recall them from memory. Although these practical concerns must be addressed, we are optimistic that working memory will become a more common approach to measuring 21st century skills.
Calling Potential Contributors to “The Bridge: Connecting Science and Practice”
As outlined in Poteet, Zugec, and Wallace (2016), the TIP Editorial Board and Professional Practice Committee continue to have oversight and review responsibility for this column. We invite interested potential contributors to contact us directly with ideas for columns. If you are interested in contributing, please contact either Lynda (firstname.lastname@example.org) or Craig at (email@example.com).
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Poteet, M. L., Zugec, L. & Wallace, J. C. (2016). The bridge: Connecting science and practice. The Industrial-Organizational Psychologist, 53(4), 17-23. Retrieved from http://www.siop.org/tip/april16/pdfs/bridge.pdf