Learning Relevance
I’ve been casually reading Scott Atran and Douglas Medin’s The Native Mind and the Cultural Construction of Nature since I came back from the U.S. in January. I picked the book up for a few reasons. One, I was familiar with Scott Atran’s work after running across it while I was studying at the University of Michigan. Atran is an anthropologist who has been working to integrate psychology and anthropology in pursuit of a better perspective on how the natural environment and the social landscape interacts to affect belief, behavior, and practice. Two, I am interested in how cognition facilitates learning and behavior, especially in a shared resources or public infrastructure context. Some of Atran’s more recent work deals with negotiations and intercultural understanding for problems ranging from terrorism, common resources, and Iran’s nuclear policy. Third, the discussions and research in the book can be helpful for artists, designers, teachers, and evolutionary biologists who want to gain better control or understanding of how, effectively, epistemology develops.
I found one particular passage to be quite helpful for a project I am working on at the moment. It deals with relevance drawing from Sperber and Wilson’s book on communication and cognition. Relevance is a pretty subjective measure of how much something matters to someone. The articulation of relevance in these pages shows ghosts of Bateson’s difference that makes a difference, but here there is an efforts to start to describe exactly what aspects of cognition make something relevant–that is, how does the environment and one’s interactions in it affect meaning? pay attention teachers…this is where it gets relevant to learning.
Here’s some notes:
Relevance: if processing an input at a certain time yields cognitive effects.
Cognitive Effects =
- revision of previous beliefs
- derivation of contextual conclusions following from input taken together with previously available information
So:
greater cognitive effect = greater relevance
While:
greater effort = lower relevance
Thus:
Salient information has greater relevance given the lower effort it requires. Atran and Medin make this point be describing their research with different groups’ interpretations (interpretations = mappings from objects, situations, problems, and events to words. In an interpretation, one word can mean many objects) of ecological relationships and taxonomy. They also studied school children who had a more nuanced view of ecology and compared them to urban children to try to help understand why they had different experiences in the classroom. The conclusions supported the idea that textbooks and instruction was not relevant enough to support the expansion of learning among those with more nuanced perspectives (perspectives = mappings from reality to an internal language such that each distinct object, situation, problem, or event gets mapped to a unique word).
Learning, then, is guided by what is already known. What is learned first often becomes a category ideal. It’s like when your idea of what tastes good, what a certain kind of flower is, or how to do a task is based on what you first learn. It’s also affects things like what we think of when we think of a bear. My image of a bear may be based on North American species like the black bear or grizzly. In India, an image of a bear may be based on their Himalayan relatives.
This seems to resonate somewhat with patterns of cognitive bias studied across different organisms in evolutionary biology in an attempt to get a better understanding of sexual selection. Cognitive or sensory bias, as studied in evolutionary biology, refers to an organism’s set of preferences. It’s similar to judgment biases studied by psychologists and micro economists (e.g. Tversky and Kahneman). However, in biological terms, sensory bias often has a genetic/sensory basis and can significantly affect mating and reproduction. Some well-studied examples include how Tungara frogs (Ryan lab at UTexas) or even crickets (Zuk lab at UC Riverside) influence mate choice with different call structures or signals (e.g. deep, red, loud, frequent, etc).
So in an experimental, teaching, or design setting, good examples of categories are ones that are familiar, have a high word frequency (use = familiarity + context), or that represent ideals. So as we design interfaces, software, interactions, and signs for access, it makes sense to consider categories that are culturally relevant and that have legacies of use in context. Additional learning uses these categories as supports (scaffolds?) to build on.
This is why representation of goals and categories is so important. The implicit organization of knowledge around goals creates category ideals, subsequently driving category based inference–that is, the creation of new knowledge from what already exists.
So in terms of deriving an experimental practice from these ideas, a student at CEMA, Aliya, has been trying to look at how naming objects as concepts (decategorization?) rather than the names they have been given. Thus a “chair” becomes a “people holder” or a “step ladder” depending on new contexts of use. It leads to the question, “How do we take objects from everyday life & create a stimulus that provides an opportunity for reflection & engagement on the use, interaction, and consumption that the object supports—all while waiting for whatever that object does?”
