Several models of working memory have been suggested. This overview begins with the most commonly referred to, the Baddeley and Hitch (1974), and Baddeley, (1992) model of working memory (WM). This consists of four components. Two of these are short-term domain-specific memory systems: the visuospatial sketchpad (responsible for the maintenance of visual and spatial information such as colour, shape, placement and direction of movement) and the phonological loop (for the storage and maintenance of auditory and verbal information). The phonological loop was later divided into two further subsections: a phonological short-term store and an articulatory subvocal rehearsal process whereby an auditory input lasts for approximately two seconds before the trace fades, unless rehearsed. The core component of WM is the central executive. This is thought to govern, regulate, control and coordinate these two (slave) systems. A more recently included fourth component, the episodic buffer (Baddeley 2000; 2003), is thought to provide temporary storage of multimodal information which is then integrated from the various subsystems and with long-term memory (LTM) to form a unitary episodic representation (Lee et al., 2007).
A less compartmentalised concept has been suggested by Kane et al. (2007) whereby WM functions as a link between STM and attentional control reactivating and inhibiting memory traces as appropriate and relevant. This idea is similar to Cowan’s embedded processes model of memory, though he places more emphasis on WM as a global space between short-term memory (STM) and LTM with limited attentional duration (Cowan, 1998). Unsworth and Engle (2007) suggest STM and WM employ the same basic process but that they operate to different extents. Their controlled attention framework combines the components of active maintenance (primary; STM and WM) with controlled search and retrieval processes (secondary; WM only), which displaces items from the primary system. Alternatively Ericsson and Kintsch (1995) suggest that the function of WM is the ability to efficiently assess task-relevant information held within the LTM. The more acquired knowledge held in the LTM, the more able an individual is to overcome the limited capacity of their WM. Lee et al., (2007) consider this idea a connectionist approach that implies an interaction between biological factors and experience. Rather than separated systems, they assume increased processing capacity is acquired through learning. As they explain,
“Any architectural changes caused by these factors would have effects on both the processing capacity of the network and the nature of the representations embodied in the network.” (Lee et al., 2007, p. 337).
The extent to which each individual finds the tasks associated with various aspects of these models, either in general or with content/context-specificity to employ storage, attentional resources, and utilise for example rehearsal strategies, is a complex issue both theoretically and empirically. Cowan et al. (2005) showed that when a task requires executive-attentional resources, it could prevent the strategy of continual rehearsal and information grouping. For individuals with higher ability, when attentional resources are not necessarily required, measures of WM can be equated with measures of STM. With children, it has been demonstrated that there are important methodological differences with simple span tests. For example, backwards digit span tests place a much heavier demand in WM than forward digit span tests (see St. Clair-Thompson, 2010; St. Clair-Thompson & Allen, 2013). This will be discussed in more detail in chapter three.
In relation to general intelligence, or ‘g’, Kyllonen and Christal (1990) provided evidence for a correlation between WM capacity and reasoning ability (r = .8-.9), leading them to speculate that WM capacity is the ‘Factor X’ that underlies individual differences in g. The authors’ acknowledged the arbitrariness of the tests they utilised but argued that this reflected the lack of specificity regarding operational definitions of WM (and associated tests). The actual battery of tests devised measured observable (or manifested) variables in the hope of uncovering a latent variable of WM, which could serve as a specific predictor of fluid g.
Engle et al. (1999) were able to distinguish between tasks that require storage (immediate memory) and tasks that require storage plus some form of additional processing, as these had been noted as showing differential patterns behaviourally (in patient populations and predicting reading abilities), and also in neuroimaging studies. Süß et al. (2002) extended these studies to nonverbal tasks and also considered other
“signature functions” of WM such as coordination, integration, updating and switching
(Conway, Kane & Engle, 2003, p. 548). Since both Süß (2002) and colleagues and Engle and colleagues (1999) found a consistent correlation between WM capacity and g of a magnitude between r = .59 and r = .65, Conway and colleagues consequently suggested that WM capacity is “related to an executive attention ability, which supports the active
maintenance of goal-related information in the face of interference”. (Conway, Kane &
They cite further evidence from neuroimaging studies of the differences not only between ‘storage-only’ and ‘storage plus some form of processing tasks’ but also between verbal and nonverbal tasks. For example, studies indicate storage only tasks activate Broca’s area for verbal material but the right-hemisphere pre-motor cortex for spatial material (Smith & Jonides, 1999). However, in contrast more demanding storage plus processing tasks result in both content specific and domain general activation in the dorsolateral prefrontal cortex and anterior cingulate cortex. This evidence led Conway and colleagues to conclude that there is a relationship between WM capacity, g and executive attention.
Furthermore, latent variable analyses (or structural equation modeling) suggest WM capacity accounts for between one third and one half of the variance in g. If g is heritable and stable, a logical deduction is that the aspects of WM that overlap would be limited in capacity. Furthermore, attention and executive functioning are concepts that require self-regulatory skills and which have in common several related process such as inhibition (self-control), cognitive flexibility (situational behavioural adaptation) and planning (action selection). These cognitive skills (e.g. attention) are necessary in order to achieve strategically planned objectives whilst regulating action, especially when considered in the context of environmental feedback (Miyake et al., 2000; Pennington & Oronoff, 1996).
With regard to the potential contribution of reading musical notation, Meinz and Hambrick (2010) conducted a study on WM based on a sight-reading task of piano music. Although deliberate practice accounted for 45.1% of the variance, there was a significant incremental positive effect of WM capacity (7.4%) above and beyond that in which WM capacity had zero correlation with either deliberate practice per se and sight-reading practice specifically. Contrary to the assertion that WM capacity reflects acquired skills (Ericsson 2003; Ericsson & Kintsch, 1995), Meinz and Hambrick suggest there is no link. Instead they put forward the view that WM capacity is highly heritable and “although
necessary for acquiring expertise – will not always be sufficient to overcome limitations due to basic abilities” (Meinz & Hambrick, 2010, p. 5). In a second paper, Hambrick and
Meinz (2011) suggest a ‘vocabulary’ of skills associated to specific tasks (i.e. domain specific learning) and highlight that it is the interplay between these two factors which is important, specifically with regard to their circumvention-of-limits hypothesis (CoL H1) notated as Ability x Knowledge. They suggest in “theoretically neutral terms, working-
memory capacity can be thought of as the limits on the ability to simultaneously store and process information” (Hambrick & Meinz, 2011, p. 3) as measured by complex span
Moving on to issues surrounding the notion of transfer effects, Hambrick and Oswald (2005) had tested the CoL H1 by giving participants a series of movements to recall using an isomorphic task. This utilised knowledge of baseball (i.e. templates stored in the LTM, possibly via enculturation) by depicting spaceships flying around a solar system, (i.e. the analogue being baseball players running around the baseball diamond). Although they found a positive effect of baseball knowledge on memory performance in the baseball task, they did not find any transfer to the spaceship condition; that is no WM capacity x Domain Knowledge interaction. Their evidence suggests modal specificities are important (e.g. spatial, auditory), as is situational awareness and different phases within individual learning/performance trajectories. However, it is not clear how the authors tested what previously held knowledge of baseball the participants could recall in this study.
The interdisciplinary usage of the term transfer effects, and differences in methodological approaches to studying this phenomenon may have resulted in differing interpretations by fields of research such as in music education research and music psychology. Therefore, in order to address any contextual inconsistences, a history of the term is briefly recounted in the following section.