Human memory is adaptive. Our capacity to remember and forget helps us solve problems, everything from remembering where the car is parked to recognizing the person who owes us money. Understanding the architecture of human memory, including its evolutionary and cultural origins, has obvious relevance to educational practices. Creating informed education curricula benefits from understanding the natural constraints that shape the way people learn and remember.
To understand those constraints, the first step is to acknowledge that memory evolved – having been shaped and sculpted by the processes of natural selection. Specific selection pressures conferred selection advantages to organisms capable of using the past in the service of the present. Nature’s main criterion, as embodied through the process of natural selection, is the enhancement of inclusive fitness. At some point in our ancestral past, memory developed because it helped solve problems related to survival and ultimately, reproduction. An organism with the capacity to remember the location of food, or categories of potential predators, was more likely to survive than an organism lacking this capacity.
For much of the past decade, our laboratory has been investigating whether human memory is biased or tuned to solve fitness-relevant adaptive problems. Such problems include remembering threats to survival, sources of nourishment, sources of contamination, potential mating partners, cheaters and free-riders, and so on. This idea that memory is problem-oriented – and specialized to retain certain kinds of information – is controversial and novel in the memory field. Most memory researchers assume that memory is controlled by a few domain-general processes, such as the “richness of encoding,” that apply to any kind of information content. We have argued instead that memory evolved to solve specific adaptive problems, such as remembering the locations of predators, and that general remembering is largely derivative of these specialized functions. Then to maximize retention, one needs to develop learning strategies that piggyback on these natural tendencies.
For example, our laboratory has shown that memory is strikingly good when information is processed with respect to its fitness consequences. We asked people to imagine being stranded in the grasslands of a foreign land, one in which they would need to find steady supplies of food and water and avoid predators, and then to rate the relevance of information to this imagined scenario (e.g., “How relevant is the word wagon to surviving in this context?”). Later surprise retention tests revealed very strong retention advantages for items processed with respect to this survival scenario – even better than the “best of the best” of known encoding strategies such as forming a visual image or relating information to the self. We have also found strong retention advantages for animate (living) versus inanimate (nonliving) things. Even attributing animacy to a non-word, such as imagining “plave” to be a living thing, boosts memory for that item compared to standard controls. We have also shown that it is easier to learn foreign language words when the definitions refer to animate as opposed to inanimate things.
Recognizing these inherent mnemonic tunings, such as a bias to remember information when it is processed for its fitness consequences, is a vital step in the development of effective learning strategies: ones that facilitate learning and lead to long-lasting retention. Application of these strategies to applied educational contexts is an ongoing concern of the lab.