It is currently not known what are the best working memory training strategies to offset the age-related declines in fluid cognitive abilities. In this randomized clinical double-blind trial, older adults were randomly assigned to one of two types of working memory training – one group was trained on a predictable memory updating task (PT) and another group was trained on a novel, unpredictable memory updating task (UT). Unpredictable memory updating, compared to predictable, requires greater demands on cognitive control (Basak and Verhaeghen, 2011a). Therefore, the current study allowed us to evaluate the role of cognitive control in working memory training. All participants were assessed on a set of near and far transfer tasks at three different testing sessions – before training, immediately after the training, and 1.5 months after completing the training. Additionally, individual learning rates for a comparison working memory task (performed by both groups) and the trained task were computed. Training on unpredictable memory updating, compared to predictable, significantly enhanced performance on a measure of episodic memory, immediately after the training. Moreover, individuals with faster learning rates showed greater gains in this episodic memory task and another new working memory task; this effect was specific to UT. We propose that the unpredictable memory updating training, compared to predictable memory updating training, may a better strategy to improve selective cognitive abilities in older adults, and future studies could further investigate the role of cognitive control in working memory training.
In order to maintain quality of life until late adulthood and decrease the health burden of a rapidly aging society, it is important that we develop an understanding of the principles of cognitive optimization, because gains in longevity have not been matched by maintenance of cognitive function into very old age. In particular, fluid cognition declines rapidly with age, particularly after 60 years, and includes abilities such as episodic memory, reasoning, and multi-tasking (Park and Bischof, 2010; Stine-Morrow and Basak, 2011). A plausible reason for impairments in these cognitive abilities with age is the disruption of the fronto-parietal brain networks that underlie working memory and cognitive control (Park and Reuter-Lorenz, 2009; Raz et al., 2010). One proposed principle of cognitive optimization is the enhancement of cognitive control in working memory, particularly in older adults (Basak and Zelinski, 2013).
Both cognitive control and working memory have been argued to be the underlying “core” components of fluid cognition (Stine-Morrow and Basak, 2011). Working memory, the ability to concurrently store and actively transform information (e.g., Mayr et al., 1996), is related to many complex cognitive skills, e.g., reasoning (Kyllonen and Christal, 1990). It also underlies many age-related deficits in fluid cognition, including episodic memory (Verhaeghen and Salthouse, 1997; Verhaeghen et al., 2005; Lewis and Zelinski, 2010). Moreover, significant and early declines of verbal episodic memory have long been considered to be the best cognitive marker of the earliest stages of Alzheimer’s disease (Dubois et al., 2007). Therefore, training cognitively healthy older adults in these “core” cognitive components may not merely improve their fluid cognitive abilities, but can also potentially delay the onset of memory-related disorders, such as Alzheimer’s disease.
The primary aim of the current study was to evaluate two different strategies to optimize cognition in older adults over a short period time by using theory-driven, simple cognitive training protocols. An understanding of the role of cognitive control in working memory, including the variations in the retrieval-related temporal dynamics and its adaptability with extensive practice, can inform us about the best strategies to use to enhance cognitive vitality into late adulthood. Such informed principles of cognitive optimization may potentially delay the onset of pathological memory-related disorders in the healthy aging population, and in turn, decrease the medical-care burden of a rapidly aging society.
Predictability of Focus Switching and Aging in Working Memory
Cowan’s hierarchical model of working memory (Cowan, 1988, 2001) posited a two-tier hierarchy based on the accessibility of information via a zone of immediate access, labeled the focus of attention (FoA), and a larger activated portion of long-term memory (LTM), where the items are stored in a readily available but not in an immediately accessible state. One of the most intriguing findings in cognitive psychology has been the limited capacity of the FoA. For tasks requiring serial attention processes, e.g., the continuous memory updating paradigms (McElree, 2001; Oberauer, 2002, 2006; Verhaeghen et al., 2004; Verhaeghen and Basak, 2005;Vaughan et al., 2008; Basak and Verhaeghen, 2011a,b), the capacity of FoA has been limited to just one item. If more than one information unit was to be processed, the other information units were temporarily stored in the outer store, while the current information in the FoA was updated. To process an item stored in the outer store, a retrieval operation was required that shifted the item from the outer store into the FoA (focus switch). This focus switch process increased the retrieval latency of that information (Verhaeghen and Basak, 2005). Therefore, measurement of the capacity of the FoA has typically involved the assessment of the focus switch costs, which is considered to be a measure of cognitive control (Garavan, 1998; Verhaeghen and Basak, 2005). Retrieval dynamics of the zone outside the FoA have been disputed between two prominent theories. One theory has proposed that these retrieval dynamics, viz., focus switch costs, are constant (McElree, 2001), whereas the other theory has argued that they increase as a function of the number of items in the outer store (Oberauer, 2002). Due to this disagreement between the two theories, we shall here refer to the zone outside the FoA as the “outer store” (Verhaeghen et al., 2004; Verhaeghen and Basak, 2005).
The current study was guided by a previously published hierarchical theory of working memory (Verhaeghen et al., 2004; Verhaeghen and Basak, 2005; Basak, 2006; Vaughan et al., 2008; Basak and Verhaeghen, 2011a,b;Basak and Zelinski, 2013), henceforth referred to as the Theory of Working Memory Adaptability (ToWMA; see Figure 1). This theory is both significant and novel in integrating three different families of results regarding the hierarchies of working memory (Cowan, 1988; McElree, 2001; Oberauer, 2002, 2006; Verhaeghen et al., 2004;Verhaeghen and Basak, 2005; Basak, 2006; Vaughan et al., 2008; Basak and Verhaeghen, 2011a,b) by accounting for the probe-cue expectancy that is missing from the previous hierarchical models. Importantly, this theory makes specific predictions regarding the retrieval-related temporal dynamics in the outer store, the change in these dynamics over time, and the best strategies to improve a variety of untrained fluid cognitive skills in both younger and older adults.
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