Up to the magical number seven (maximum number of items in short term memory): Physical limitations? Evolutionary optimal number?

Up to the magical number seven: An evolutionary perspective on the capacity of short term memory. Majid Manoochehri. Heliyon, Volume 7, Issue 5, May 2021, e06955. https://doi.org/10.1016/j.heliyon.2021.e06955

Abstract: Working memory and its components are among the most determinant factors in human cognition. However, in spite of their critical importance, many aspects of their evolution remain underinvestigated. The present study is devoted to reviewing the literature of memory studies from an evolutionary, comparative perspective, focusing particularly on short term memory capacity. The findings suggest the limited capacity to be the common attribute of different species of birds and mammals. Moreover, the results imply an increasing trend of capacity from our non-human ancestors to modern humans. The present evidence shows that non-human mammals and birds, regardless of their limitations, are capable of performing memory strategies, although there seem to be some differences between their ability and that of humans in terms of flexibility and efficiency. These findings have several implications relevant to the psychology of memory and cognition, and are likely to explain differences between higher cognitive abilities of humans and non-humans. The adaptive benefits of the limited capacity and the reasons for the growing trend found in the present study are broadly discussed.

Keywords: Working memoryShort term memoryEvolution of memoryEvolution of cognitive system

7. Hypotheses concerning the capacity

Why memory span has a limited capacity or why there is an increasing trend of capacity towards humans? In the first place, I will argue the potential reasons for the limited capacity. In order to provide a more explicit discussion, the relevant studies are divided into two groups: those that based their discussion on a capacity about seven items or a temporary, passive storage (i.e., STM) and those that based their discussion on a capacity about three to four items or the focus of attention (i.e., WM).

7.1. Hypotheses of the limited capacity

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).

7.2. The increasing trend of capacity

Archaeological evidence of an enhancement in the WM system has been presented by Coolidge and Wynn (2005) (see also Coolidge et al., 2013; Wynn and Coolidge, 2006). The core idea of their hypothesis is a genetic mutation that affected neural networks approximately 60,000 to 130,000 years ago and increased the capacity of general WM and phonological storage. In the case of phonological storage, which is of more interest in the present review, they stipulated that: “A relatively simple mutation that increased the length of phonological storage would ultimately affect general working-memory capacity and language” (Coolidge and Wynn, 2005, p. 14). They proposed that the enhancement of WM capacities was the final piece in the evolution of human executive reasoning ability, language, and culture. From their point of view, the larger capacity is a necessary precondition for symbolic thought, which selective pressures contributed to the growth of it. They noted that an increase in WM capacities of pre-modern H. sapiens would have allowed greater articulatory rehearsal, consequently allowing for automatic long-term storage, and the beginnings of introspection, self-reflection, and consciousness. In line with Coolidge and colleagues’ hypothesis, Aboitiz et al. (2010) proposed that during the course of human evolution, a development in the phonological loop occurred. They maintained that this development produced a significant increase in STM capacity and subsequently resulted in the evolution of language.

Many researchers, at least in the field of archeology, tend to agree with the idea of enhanced WM (e.g., Aboitiz et al., 2010; Haidle, 2010; Lombard and Wadley, 2016; Nowell, 2010; Putt, 2016, for a review of criticisms, see Welshon, 2010), though there seems to be a disagreement on its time. Almost all, however, suggest a time in the Pleistocene about or after the appearance of the genus Homo (Aboitiz et al., 2010; Coolidge and Wynn, 2005; Haidle, 2010; Putt, 2016). Some also suggest a gradual development (Haidle, 2010).

Once we accept the idea of the enhanced WM, important questions arise as to the cause and the process of this phenomenon. The enhancement of WM has been argued as a prerequisite for the evolution of some complex cognitive abilities of humans, such as language (Aboitiz et al., 2010; Coolidge and Wynn, 2005) and tool use (Haidle, 2010; Lombard and Wadley, 2016). For instance, Aboitiz et al. (2010) pointed out the existence of selective benefits for individuals with larger phonological capacities, which, in their view, were linguistically more apt. From their standpoint:

The development of the phonological loop produced a significant increase in short-term memory capacity for voluntary vocalizations, which facilitated learning of complex utterances that allowed the establishment of stronger social bonds and facilitated the communication of increasingly complex messages, eventually entailing external meaning and generating a syntactically ordered language. (p. 55).

In the case of tool use, Haidle (2010) argued that a basic trait of all object behaviors is the increased distance between problems and solutions. Given this, more complex object behaviors possess longer distances. According to her, during the process of tool use the immediate desire (e.g., getting the kernel of the nut) must be set aside and replaced by one or several intermediate objectives, such as finding or producing an appropriate tool. Thus, thinking must depart from the immediate problem and shift to abstract conceptualizations of potential solutions, which results in sequences of physical actions with objects appropriate to achieve a solution in the near future (see also Lombard and Wadley, 2016). Given her discussion, it is clear how individuals or populations with an enhanced WM system, which provides the possibility of maintaining and manipulating more information, could take advantage of their superiority to excel others in tool-use performance and, consequently, to win competitions.

Arguably, if we assume the enhancement of WM as a gradual process, which has started long before our common ancestor with chimpanzees (as it was found by the present study), neither tool use nor language can be considered as the primary reason for it. But complex problem solving, because of its commonness, can be nominated as the primary cause, which has then been supported by tool use and language (see also Putt, 2016). This assumption can be aided by the evidence indicating the critical role of an elaborate WM system in problem solving tasks (Logie et al., 1994; Zheng et al., 2011).

After all, there are many obscure aspects regarding the evolution of WM. Needless to say, deep disagreements in the related fields and issues, such as the process and timeline of language evolution (Progovac, 2019), make the puzzle more difficult to solve. It goes beyond the limits of the present article to pursue this further, but perhaps a possible way to settle this problem is looking for advantages and disadvantages of high and low STM/WM capacities (Engle, 2010). Such findings from experimental psychology in conjunction with archaeological and comparative evidence can shed light on the evolution of the WM system.

8. Conclusion

The first and obvious implication of the present findings is that the limited capacity is the common attribute of different species of birds and mammals. The present results also indicate an increasing trend of capacity from our non-human ancestors to modern humans. Among the potential explanations of the limited capacity, associative learning and saving time in decision making, particularly because of the strong selective forces that associate with them and their vital importance for different species, seem to be more likely. On the other hand, the enhancement of the WM system appears to be a prerequisite for the evolution of some higher cognitive abilities of modern humans, such as language, tool use, and complex problem solving.

A question yet to be answered is whether the current size of STM/WM in humans is the end of the line or not. The current size has been considered by some to be the end point (e.g., MacGregor, 1987). As opposed to it, Cowan declared that it is possible to imagine that larger capacities would have been preferable or doable, but still did not happen. Therefore, our current capacity just reflects our place in the middle of an ongoing evolutionary process, not an end point. If this is the case, one might expect the present capacity to expand in the future, assuming that it offers a sufficient survival advantage (Cowan, 2005). However, the current review suggests that considering the resistance of memory span scores to the Flynn effect, it is difficult to expect substantial changes in small periods of time.

All in all, many of us, instead of the wild nature, are living in artificial and unnatural environments. Are these unnatural environments along with their overwhelming and escalating complex problem solving tasks imperceptibly pushing us towards a WM system with even a larger capacity and, if so, what is the price for that? Here, the most important point to stress is that evolution does not drive towards perfection. However, thanks to our elaborate information processing system and consciousness, we now have the ability to purposefully plan for the future of our own evolution.

The evidence reviewed in this paper shows that many species of birds and mammals are capable of performing memory strategies, although there seem to be some differences between humans and non-humans in terms of flexibility and efficiency. An enhancement in the capacity of the WM system might be the reason, or part of the reason, for the emergence of superior memory strategies in humans.

Striking similarities in the primacy and recency effect in conjunction with other evidence, such as similarities in the size of STM and performing memory strategies, suggest a similar memory structure in different species of birds and mammals. This is in accordance with Wright's inference that there is a qualitative similarity in memory processing across mammals and birds. The present findings have several implications relevant to the psychology of memory and cognition. For instance, the differences found in the ability to perform memory strategies and the size of STM, may provide an explanation for some of the differences between cognitive abilities of humans and non-humans.