Here are the latest articles published on Toms Hardware. See the latest news, reviews and roundups and access our tech archives. Complex span andn back measures of working memory A meta analysis. The meta analysis results can be summarized as follows. Difference Between Serial And Random Access Memory Full' title='Difference Between Serial And Random Access Memory Full' />Use a password manager. Until you do this, no matter how hard you try all the rules above, you will keep picking bad passwords. Heres how Your random string. FM24W256 256Kbit 32 K 8 Serial I2C FRAM Cypress Semiconductor Corporation 198 Champion Court San Jose, CA 951341709 4089432600. First, the correlations of the complex span measures with n back tasks were significantly greater than zero, but still weaker than would be expected for measures of the same underlying WM construct. In this respect, the meta analysis results confirm those of Kane et al. Jaeggi, Buschkuehl et al., 2. Of course, the meta analytic results extend the findings of Kane et al. Jaeggi, Buschkuehl, et al. As we mentioned in the introduction, some studies have reported rather large zero order correlations among complex span and n back tasks, indicating more overlap in the processes involved in the successful completion of both tasks. The meta analysis results indicated significant heterogeneity among the complex span and n back correlations in the literature. Moderator analyses determined that the verbal or visuospatial nature of the to be remembered items influenced the magnitude of the complex span and n back correlation. One prediction was that higher correlations would be obtained when the different WM tasks used stimuli from the same domain, instead of from across domains. For example, Redick et al. The moderator analyses both partially support and partially undermine this prediction. The highest correlation was obtained when both the complex span and n back tasks used visuospatial content. However, the lowest correlation was obtained when both the complex span and n back tasks used verbal to be remembered information. Thus, as can be seen in Table 3, a more accurate description of the complex span and n back results would be that the correlation between the types of tasks tended to be higher if one or both included visuospatial content as to be remembered memory items. For the simple span results, the simple span and n back correlation did not differ from the complex span and n back correlation. Interestingly, when examining digit span specifically, the digit span backward correlation with n back was greater than the digit span forward correlation with n back. In addition, the digit span backward correlation with n back r. CI. 2. 4 to. 3. CI. Stag Slab Serif Font Bold. A speculative interpretation is that the digit span backward task requires subjects to temporally reorder information after the digits have been encoded, thereby forcing them to update the relative serial position of the items in memory e. Similarly, across trials in the n back task, subjects must change the serial position of items that have been previously encoded as new items are continuously presented e. H goes from being item n, to item n 1, to item n 2, etc. The similarity of these reordering processes may account for the higher n back correlation with digit span backward than with either digit span forward or verbal complex span tasks see Oberauer, 2. Szmalec, Verbruggen, Vandierendonck, Kemps, 2. Sample composition as a moderator variable. An often overlooked aspect of individual differences studies is whether the sample included a wide variation in cognitive abilities. Redick et al. 2. The pattern of results in Redick et al. Spearmans 1. 92. IQ. Although IQ estimates are not available for all subjects in the present meta analysis, it is apparent that, across studies, the samples represented different points along the mental ability continuum. For example, Roberts and Gibson 2. Massachusetts Institute of Technology and obtained an average complex span and n back correlation of r. In two separate samples of University of Georgia students, Unsworth 2. Unsworth et al. 2. In Study 1 of Jaeggi, Buschkuehl, et al. In contrast, Burgess et al. Washington University St. Louis students and community volunteers and obtained an average complex span and n back correlation of r. Greenstein and Kassel 2. Chicago area community members and obtained an average complex span and n back correlation of r. Greenstein and Kassels results are particularly interesting, given that they used the same two and three back task as Kane et al. In fact, Greenstein and Kassel argued that their sample was probably more diverse in general cognitive ability p. Kane et al. 2. 00. Making some assumptions about the IQ range of the subjects in these example studies, the pattern of correlations is consistent with the law of diminishing returns. In order to further investigate the role of sample composition in complex span and n back correlations, we analyzed previously unpublished data. One hundred fifty five subjects from 1. Importantly, we also had access to the self reported college status of these subjects. Seventy five of the subjects were enrolled as students at Georgia Tech GT or Georgia State University GSU, whereas 8. This division of the sample led to roughly equal sample sizes that were also large enough to afford meaningful conclusions. The correlations are presented in Table 4. In the overall sample, the zero order correlations were moderate and larger than the complex span and n back correlation obtained in the meta analysis, but similar in magnitude to those from studies using similar sampling methods e. Burgess et al., 2. When examining the subsamples, the patterns of correlations differed, despite the similar sample sizes. In the GT GSU subsample, the complex span and n back correlations were not significant, but in the None Other subsample, the complex span and n back correlations were significant. For symmetry span, the subsample results indicated 1. None Other subsample than in the GT GSU subsample. Table 4. Complex span and n back correlations as a function of college status. One possible reason that the samples with high ability subjects have tended to produce smaller correlations is that there is insufficient variability on the tasks, as compared to more diverse samples. Restriction of range would certainly be a problem that could limit the correlation magnitude. Although we cannot speak for the studies in the meta analysis, restriction of range did not appear to be a problem in the GT GSU subsample here. As can be seen in Table S3, although the GT GSU subsample had a higher mean than the None Other subsample on operation span, symmetry span, and three back accuracy all ps lt. Levenes tests for variance differences were only significant for operation span p. Implications for WM research. The most important finding from the meta analysis is that complex span and n back tasks are weakly correlated, which is important, considering that the tasks are thought to both be measures of the same WM system. For comparison, in Daneman and Merikles 1. The correlations in Daneman and Merikle are not assumed to be measures of the same underlying WM construct, yet they are higher than the meta analysis correlation reported here between complex span and n back. Because of positive manifold, in which reliable tests produce positive correlations with each other, regardless of the exact underlying construct that the measure is designed to assess, it is striking how small the correlation is between complex span and n back tasks. The meta analysis results validate what Kane et al. Namely, WM researchers should be specific in interpreting the results of studies that use complex span versus n back tasks as WM measures. The results of WM research using the different categories of tasks cannot simply be used interchangeably. Therefore, as Kane et al. WM, but it may shed little light on the nature of individual differences in WM as measured by complex span tasks. Likewise, for WM training studies using n back tasks, attempts to find near transfer to complex span tasks are somewhat misguided. In light of the small amount of shared variance between the types of WM tasks, practice on the n back task need not affect performance on complex span measures of WM at all, and vice versa.