7.1.1. STM hypotheses of the limited capacity
To begin with, some previous studies have suggested that “short-term memory limitations do not have a rational explanation” (Anderson, 1990, pp. 91/92) or larger capacities are biologically expensive or impossible. For instance, it has been postulated that greater STM size may have required additional tissue, which increases body mass and energetic expenditure, and therefore it is impossible with the biological characteristics of humans (e.g., Dukas, 1999). Other researchers rejected both of these assumptions (Todd et al., 2005). Moreover, the second assumption (i.e., assuming larger capacities as biologically expensive/impossible options) does not seem reasonable considering the diversity of extraordinary physiological and behavioral characteristics of different animal species. Also, if any of these suggestions is correct, we should perhaps be able to find various capacities of STM in different animals, which the present study does not indicate it.
One of the studies concerning the capacity of STM has been conducted by MacGregor (1987). Using a mathematical model, he highlighted the importance of efficient retrieval for STM. According to him, the limited capacity of STM could be the consequence of an efficiency of design. He argued that chunking facilitates retrieval when there are seven or five items in an unorganized memory. In a memory system evolved for efficiency, there is an upper effective limit to STM and a capacity beyond this limit would not be required.
In another study, Saaty and Ozdemir (2003) argued that in making preference judgments on pairs of elements in a group, the number of elements in the group should be no more than seven. The mind is sufficiently sensitive to improve large inconsistencies but not small ones and the most inconsistent judgment is easily determined. When the number of elements is seven or less, the inconsistency measurement is relatively large with respect to the number of elements involved. As the number of elements being compared is increased, the measure of inconsistency decreases slowly. Therefore, in order to serve both consistency and redundancy, it is best to keep the number of elements seven or less. When the number of elements increases past seven, the resulting increase in inconsistency is too small for the mind to single out the element that causes the greatest inconsistency to scrutinize and correct its relation to the other elements.
In a series of studies, Kareev has proposed that capacity limitation maximizes the chances for the early detection of strong and useful relations (Kareev, 1995; 2000; Kareev et al., 1997; for a controversial discussion of this hypothesis see Anderson et al., 2005; Juslin and Olsson, 2005; Kareev, 2005). From his standpoint, a STM capacity of size seven, which characterizes human adults, is of particular value in detecting imperfect correlations between features in the environment. The limited capacity may serve as an amplifier, strengthening signals which may otherwise be too weak to be noticed. He argued that, because correlations underlie all learning, their early detection is of great importance for the functioning and well-being of organisms. Therefore, the cognitive system might have evolved so as to increase the chances for early detection of strong correlations. In addition to the theoretical contribution, Kareev and colleagues in an experimental study found that people with smaller STMs are more likely to perceive a correlation than people with larger STMs (Kareev et al., 1997).
Some of the suggestions for the reason behind the limited capacity can be found in the studies of decision-making cognition. Here, it has been shown that people tend to rely on relatively small samples from payoff distributions (Hertwig and Pleskac, 2010). The size of these samples is often considered related to the capacity of STM (Hahn, 2014; Hertwig et al., 2004; Hertwig and Pleskac, 2010). In this context, a capacity-limited STM has been proposed as a possible cause (Hahn, 2014; Hertwig et al., 2004; Hertwig and Pleskac, 2010; Todd et al., 2005) or a requirement (Plonsky et al., 2015) for relying on small samples. More relevant to the present discussion, Todd et al. (2005) suggested that the benefits of using small samples or the costs of using too much information resulted in selective pressures that have produced particular patterns of forgetting in LTM and limits of capacity in STM (see also Hahn, 2014). So, what are these costs and benefits? Limited information use can lead simple heuristics to make more robust generalizations in new environments (Todd et al., 2005). Small samples amplify the difference between the expected earnings associated with the payoff distributions, thus making the options more distinct and choice easier (Hertwig and Pleskac, 2010). Relying on small samples has also been suggested to result in saving time and energy (Plonsky et al., 2015; Todd et al., 2005). Even if we assume that there is no cost (energy or time) for gathering information, by considering too much information, we are likely to add noise to our decision process, and consequently make worse decisions (Martignon and Hoffrage, 2002; Todd et al., 2005). Among these, the one which is perhaps associated with strong selective forces is saving time. There are different occasions that timely decisions play a vital role in the life of animals. But perhaps of most importance is the case of hunting situations. The encounters between prey and predators were an integral part of the daily life of our ancestors through deep evolutionary time. It is also clear that the penalties for any kind of inefficiency in such encounters are immediate and fatal, which thus results in intense selection for particular cognitive abilities and predation avoidance mechanisms (see Mathis and Unger, 2012; Rosier and Langkilde, 2011; Whitford et al., 2019). For instance, any prey that is attacked by several predators and cannot quickly decide which one to avoid at first or which way and which method to choose for escaping or perhaps defending will be eliminated at once. A similar discussion can be developed for predators (see Lemasson et al., 2009).
Another line of studies has stressed the importance of the limited capacity for foraging activities (e.g., Bélisle and Cresswell, 1997; Real, 1991; 1992; Thuijsman et al., 1995). According to it, the limited capacity may result in an overall optimization of food search behaviors. Similarly, Murray et al. (2017) have contended that the memory systems of anthropoids have been primarily evolved to reduce foraging errors. Foraging activities, however, do not appear to be the underlying reason for the capacity-limited STM. This is because, if foraging were the fundamental reason, then there would be remarkable sex differences in memory span, similar to that observed, for instance, in spatial abilities (Ecuyer-Dab and Robert, 2007; Voyer, Postma, Brake and Imperato-McGinley, 2007). According to the division of labor in ancestral hunter-gatherer societies, men were predominantly hunters and women were gatherers (Ecuyer-Dab and Robert, 2007; Marlowe, 2007), and it is likely that each one of these activities demands a different memory span. Namely, because a hunter has to focus on prey and ignore distracting information, while a successful gatherer can, or should, simultaneously consider many stationary targets (e.g., seeds, fruits, etc.). Contrary to this, many studies of sex differences in memory span show no significant difference (GrÉGoire and Van Der Linden, 1997; Monaco et al., 2013; Orsini et al., 1986; Peña-Casanova et al., 2009). Foraging activities, if they were the underlying reason, could also result in remarkable differences among different species. The present study, however, does not indicate such differences. Therefore, although the limited capacity may have provided benefits for foraging activities, it seems reasonable to propose that foraging, after all, is not the main and direct reason for the limited memory space.
Among the hypotheses reviewed here, Kareev's suggestion (i.e., early detection of useful relations) is among the ones that have received relatively more attention. Also, his assumption seems reasonable in a comparative context and appears consistent with the findings of the present review. But of more importance is the fact that a memory system that has the ability of early detection of useful relations is likely to cause higher performance in associative learning and also saving time in decision making. In the case of learning, Kareev himself noted that: “Because correlations underlie all learning, their early detection and, subsequently, accurate assessment are of great importance for the functioning and well-being of organisms” (Kareev, 2000, p. 398). Leaning, certainly, is one of the first and main challenges of any cognitive system. Besides, there are broad similarities in basic forms of learning in different species (Dugatkin, 2013). It is also certain that through deep evolutionary time there has been intense selection for individuals with higher performance in learning. In this regard, Dugatkin (2013) stated that: “The ability to learn should be under strong selection pressure, such that individuals that learn appropriate cues that are useful in their particular environment should be strongly favored by natural selection” (p. 141). In summary, these considerations motivate the idea that associative learning and saving time in decision making are most likely the underlying reasons for the emergence and maintenance of limited capacity.
7.1.2. WM hypotheses of the limited capacity
There are, on the other side, some other studies of the limited capacity that based their analyses on a capacity about three to four chunks or the focus of attention (i.e., WM). Some of them will be briefly reviewed here. Sweller (2003), for instance, proposed that no more than two or three elements can be handled in WM, because any more elements would result in more potential combinations than could be tested realistically. According to him, as the number of elements in WM increases, the number of permutations rapidly becomes very large (e.g., 5! = 120). With random choice, the greater the number of alternatives from which to choose while problem solving, the less likelihood that an appropriate choice will be made.
Many other possibilities have been discussed by Cowan (Cowan, 2001, 2005, 2010). For instance, based on the notion that it is biologically impossible for the brain to have a larger capacity, he declared that the representation of a larger number of items could fail because together they take too long to be activated in turn (Cowan, 2010). Another discussion by Cowan is that the WM capacity limit is the necessary price of avoiding too much interference (Cowan, 2005). According to him, activation of the memory system would go out of control if WM capacity was not limited to about four items at once. A relatively small central WM may allow all concurrently active concepts to become associated with one another without causing confusion or distraction (Cowan, 2010). Oberauer and Kliegl (2006) similarly stated that:
The capacity of working memory is limited by mutual interference between the items held available simultaneously. Interference arises from interactions between features of item representations, which lead to partially degraded memory traces. The degradation of representations in turn leads to slower processing and to retrieval errors. In addition, other items in working memory compete with the target item for recall, and that competition becomes larger as more items are held in working memory and as they are more similar to each other. (p. 624).