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They're claiming transfer to Gf *and* 8 month maintenance of the far
transfer gains? That's further than anyone has claimed, even Jaeggi
2011. I'm particularly curious as to what a verbal WM (criterion) task
is. But alas, I don't see any PDF. I'm going to exercise a little
discretion here and not include it in the FAQ (is it even using an
n-back task?) until there's more than an ambitious abstract.
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gwern
http://www.gwern.net
> Buschkuehl et al. (2008) proposed an adaptive visual WM training program to old-old adults: Their results showed substantial gains in the WM trained tasks. Short and long-term transfer effects were found only for tasks with the same stimuli content. Similarly, Li et al. (2008) found in young and older adults specific improvement in the task practiced—a spatial 2 n-back WM task–that involved two conditions: one standard, one more demanding. Transfer effects were found on a more demanding 3 n-back visual task as well as on numerical n-back tasks. Although near transfer effects to the same (visual) and also different (numerical) modality were shown, no far transfer effects to more complex WM tasks (operation and rotation span tests) were found.
> With regard to maintenance effects, Buschkuehl et al. (2008) failed to find any maintenance 1 year after completion of training, in comparison with pretest. In contrast, Li et al. (2008) showed a maintenance of practice gains and of near-transfer effects at 3-month follow-up; nonetheless, in contrast with young adults, older participants showed a performance decrement from postpractice to follow-up.
> Common measures used in cognitive aging research, and theoretically related to WM, were chosen: short-term memory, fluid intelligence, inhibition, and processing speed (Craik & Salthouse, 2000; Verhaeghen, Steitz, Sliwinski, & Cerella, 2003). For nearest-transfer effects, a visuospatial WM task (Dot Matrix task; adapted from Miyake, Friedman, Rettinger, Shah, & Hegarty, 2001) was included. This task involves processes (elaboration and processing phase) similar to the one practiced. However, the nature of the material and the secondary requirement are different from those of the trained task. The Forward and Backward Digit Span tests were used to assess near-transfer effects because they are part of the general memory factor, but the task requests were different from those of the WM tasks (see Bopp & Verhaeghen, 2005). Because these tasks measure the same narrow or same broad ability, we expect transfer effects onto them. To determine the presence of far transfer effects, we chose classic tasks: the Cattell task to measure nonverbal reasoning ability; the Stroop Color test to index inhibition-related mechanisms; and the Pattern Comparison test to assess processing speed. The transfer abilities were chosen with consideration of their relationship to WM processes. Working memory impairment in older adults is generally attributed to general mechanisms such as inhibition and processing speed (Borella et al., 2008). Furthermore, WM is frequently advanced as one of the mechanisms that also accounts for age-related differences in intelligence tasks (de Ribaupierre & Lecerf, 2006; Rabbitt & Lowe, 2000; Schaie & Hertzog, 1986), because WM and intelligence share a limited
> Experimental and control group participants did not differ ( p Ͼ .05) in age, years of formal education, or Wechsler Adult Intelligence Scale—Revised (WAIS–R) vocabulary score (Wechsler, 1981). Demographics for each group are listed in Table 1.
The task:
> The Categorization Working Memory Span task (CWMS; Borella et al. 2008; De Beni, Borella, Carretti, Marigo, & Nava, 2008) is similar to the classic WM tasks, such as the Listening Span test (Borella et al., 2008), the only difference being that it involves processing lists of words rather than sentences, limiting the role of semantic processing. The materials consisted of 10 sets of words, each set comprising 20 lists of words, which were organized in series of word lists of different lengths (from 2 to 6). Each list contained 5 words of high–medium frequency. Furthermore, the lists contained zero, one, or two animal nouns, present in any position, including last. An example list is house, mother, dog, word, night. Of the total number of words (200) in the task, 28% were animal words.
> Participants listened to the lists of words audiorecorded presented at a rate of 1 s per word and had to tap their hand on the table whenever they heard an animal noun (processing phase). The interval between series of word lists was 2 s (the presentation was thus paced by the experimenter). At the end of the series, participants recalled the last word of each string in serial order (maintenance phase). Two practice trials of 2-word length were given before the experiment started.
> Words recalled were written down by the experimenter on a prepared form. The total number of correctly recalled words was used as the measure of WM performance (maximum score 20). This score has been demonstrated to show large correlations with visuospatial (Jigsaw Puzzle test) and verbal (Listening Span test) WM tasks (Borella et al., 2008), and measures of fluid intelligence (Borella et al., 2006).
> Half (five) of the sets were used as a pretest task, the other five as posttest. The two sets were counterbalanced across testing sessions.
> Far-transfer effects: Fluid intelligence (Cattell test), inhibition-related processes (Stroop Color task), and processing speed (Pattern Comparison test).
> Culture Fair test, Scale 3 (Cattell & Cattell, 1963). Scale 3 of the Cattell test consists of two parallel forms (A and B), each containing four subtests to be completed in 2.5 to 4 min, depending on the subtest. In the first subtest, Series, participants saw an incomplete series of abstract shapes and figures and had to choose from six alternatives that best completed the series. In the second subtest, Classifications, participants saw 14 problems comprising abstract shapes and figures and had to choose which 2 of the 5 differed from the other 3. In the third subtest, Matrices, participants were presented with 13 incomplete matrices containing four to nine boxes of abstract figures and shapes plus an empty box and six choices: Their task was to select the answer that correctly completed each matrix. In the final subtest, Conditions, participants were presented with 10 sets of abstract figures, lines, and a single dot, along with five alternatives: Their task was to assess the relationship among the dot, figures, and lines, then choose the alternative in which a dot could be positioned in the same relationship.
> The dependent variable was the number of correctly solved items across the four subsets (maximum score of 50).
> One of the two parallel forms (A or B) was administered at pretest, the other at posttest in counterbalanced fashion across testing sessions.
> Far-transfer effect. For the Cattell test, results indicated that trained participants performed significantly better than did controls (Mdiff ϭ 3.22, p Ͻ .001). Posttest and follow-up performances were significantly better than on pretest (Mdiff ϭ 3.40, p Ͻ .001, and Mdiff ϭ 2.75, p Ͻ .001, respectively). No significant difference was found between posttest and follow-up. Post hoc comparisons revealed that only the trained group showed significant improvement in performance between pretest and both posttest ( p Ͻ .001) and follow-up ( p Ͻ .001), although posttest performance was not different from that of follow-up. By contrast, no significant difference was found for the control group. The trained group performed better at both posttest and follow-up than did the control group ( p Ͻ .001).
> First, the participants involved in our study were young-old (mean age of 69 years), whereas in Buschkuehl et al.’s (2008 study as well as that of Li et al. (2008), they were old-old adults (mean age of 80.1 and 74.5 years, respectively). In the context of episodic memory, the meta-analysis by Verhaeghen et al. (1992) has pointed out that the benefit of interventions is negatively related to participant age (see also Singer, Lindenberger, & Baltes, 2003). It has been shown that cognitive plasticity is reduced over the adult life span (Jones et al., 2006), with young-old exhibiting larger training-related gains than old-old (Singer et al., 2003). The importance of participant age is evident from considering the results of training focused on executive control tasks–for example, task-switching (Buchler, Hoyer, & Cerella, 2008; Karbach & Kray, 2009; Kramer, Hahn, & Gopher, 1999), dual tasks (Bherer et al., 2005, 2008), or general executive functions (Basak et al., 2008)—for which transfer effects emerged with a sample comprising young-old (age range between 60 and 75 years, mean age between 65 and 71 years; Basak et al., 2008; Bherer et al., 2005, 2008; Karbach & Kray, 2009; Kramer et al., 1995). The question of whether transfer effects of WM training can also be determined by participant age range is of interest and should be addressed in further research.
> Second, as is mentioned at the beginning of this section, the task and the procedure used to train participants can be considered an important source of difference. For example, Buschkuehl et al. (2008) reported that trained participants claimed to have generated task-specific strategies in one of the variants of the WM task in which they were trained, leading to greater training gains (62%) with respect to the other two variants (44% and 15%, respectively). The difficulty of transferring the gains obtained in a specific task to other tasks suggests that the WM training by Buschkuehl et al. did not foster an increase in flexibility, but simply the tendency to find a strategy to recall as many items as possible but in the context of each WM task. In the case of Li et al. (2008), the modest transfer effects to the WM task can be explained by reflecting on the nature of the trained task: n-back task, which involves the manipulation and maintenance of information as well as updating of temporal order and contextual information and binding processes between stimuli and certain representation (Oberauer, 2005). Although the n-back shares common processing mechanisms with complex span tasks, the underlying mechanisms of the n-back are not completely understood (Schmiedek, Hildebrandt, Lövden, Wilhelm, & Lindenberger, 2009). Moreover, the few studies that used it with other WM tasks— complex span tasks— have shown variable correlations (from very low or null-Kane, Conway, Miura, & Colflesh, 2007; Roberts & Gibson, 2002–to large–Schmiedek et al., 2009; Shamosh et al., 2008).
> Transfer effects were not maintained over time for Stroop Color task, but the same was also true—surprisingly—for visuospatial WM (nearest-transfer task) and short-term memory tasks (near transfer). Nonetheless, for most of the tasks, training effects dissipate in time, confirming findings suggesting that unless attempts are made to provide reinforcement to maintain the benefit of an intervention, training gains are lost (see Ball et al., 2002). Booster sessions, in this sense, seem to have positive effects on the maintenance of training benefit (Brehmer, Li, Muller, van Oertzen, & Lindenberger, 2007). Brehmer et al. (2007) showed maintenance of the training benefit through re-presenting mnemonic instructions to reactivate the learned strategies, or more generally the learned attitude. It is possible that repractice on the WM task presented during training before follow-up could have maintained immediate transfer effects.
> Overall, the findings do not actually disentangle whether the WM training used has led to “formation of a new skill, . . . or whether cognitive mechanisms and capacities of general applicability have been enhanced” (Li et al., 2008, pp. 731–732). Nonetheless, in general the patterns of results we have found inform about the potential for short-term modifiability of WM-related mechanisms and specific maintenance changes at the information-processing level, and certainly suggest some degree of plasticity in older adults.
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gwern
http://www.gwern.net