stephen_m_kosslyn's picture
Founding Dean, Minerva Schools at the Keck Graduate Institute

We humans are more alike than different, but our differences are nonetheless pervasive and substantial. Think of differences in height and weight, of shoe size and thumb diameter. In this context, it's not surprising that our brains also differ. And in fact, ample research has documented individual differences not only in the sizes of specific brain regions, but also in how strongly activated the same parts of the brain are when people perform a given task. And more than this, both sorts of neural differences – structural and functional – have been shown to predict specific types of behavior. For example, my collaborators and I have shown that the strength of activation in one part of visual cortex predicts the ease of visualizing shapes.

Nevertheless, our society and institutionalized procedures rarely acknowledge such individual differences, and instead operate on a philosophy (usually implicit) of "one size fits all." It is impossible to estimate how much leverage we could gain if we took advantage of individuals' strengths, and avoided falling prey to their weaknesses. The technology now exists to do this in multiple domains.

How can we gain leverage from exploiting individual differences? One approach is first to characterize each person with a mental profile. This profile would rely on something like a "periodic table of the mind," which would characterize three aspects of mental function, pertaining to: (1) information processing (i.e., the ease of representing and processing information in specific ways), (2) motivation (what one is interested in, as well as his or her goals and values), and (3) the contents of one's knowledge (in particular, what knowledge base one has in specific areas, which then can be built upon). A value would be assigned to each cell of the table for a given person, creating an individual profile.

Being able to produce such profiles would open up new vistas for personalizing a wide range of activities. For example:

Learning. Researchers have argued that some people learn more effectively by verbal means, others by visual means if shapes are used, others by visual means if spatial relations are used, and so on. People no doubt vary in a wide range of ways in their preferred and most effective learning styles. Knowing the appropriate dimensions of the relevant individual differences will put us in a position to design teaching regimens that fit a given person's information processing proclivities, motivation, and current level of knowledge.

Communicating. A picture may be worth 1,000 words for many people, but probably not for everyone. The best way to reach people is to ensure that they are not overloaded with too much information or bored with too little, to appeal to what interests them, and to make contact with what they already know. Thus, both the form and content of a communication would profit from being tailored to the individual.

Psychotherapy. Knowing what motivates someone obviously is a key to effective psychotherapy, but so is knowing a client's or patient's strengths and weaknesses in information processing (especially if a cognitive therapy is used).

Jobs. Any sort of work task could be analyzed in terms of the same dimensions that are used to characterize individual differences (such as, for example, the importance of having a large working memory capacity or being interested in finding disparities in patterns). Following this, we could match a person's strengths with the necessary requirements of a task. In fact, by appropriate matching, a person could be offered jobs that are challenging enough to remain interesting, but not so challenging as to be exhausting and not so easy as to be stultifying.

Teams. Richard Hackman, Anita Wooley, Chris Chabris, and our colleagues used knowledge of individual differences to compose teams. We showed that teams are more effective if the individuals are selected to have complementary cognitive strengths that are necessary to perform the task. Our initial demonstrations are just the beginning; a full characterization of individual differences will promote much more effective composition of teams.

However, these worthy goals are not quite as simple to attain as they might appear. A key problem is that the periodic table approach suggests that each facet of information processing, motivation, and content is independent. That is, the approach suggests that each of these facets can be combined as if they were "mental atoms" – and, like atoms, that each function retains its identity in all combinations.

But in fact the various mental functions are not entirely independent. This fact has been appreciated almost since the inception of scientific psychology, when researchers identified what they called "the fallacy of pure insertion": A given mental process does not operate the same way in different contexts. For instance, one could estimate the time people require to divide a number by 10; this value could then be subtracted from the time people require to find the mean of 10 numbers, with the idea being that the residual should indicate the time to add up the numbers. But it does not. 

In short, a simple "periodic table of the mind," where a given mental function is assumed to operate the same way when inserted in the context of other functions, does not work. Depending on what other functions are in play, we are more or less effective at a given one – and there will no doubt be individual differences in the degree to which context modulates processing.

To begin to use individual differences in the ways summarized above, we need to pursue two strategies in parallel, one for the short term and one for the long term. First, a short-term strategy is simply to work backwards from a specific application: Do we want to teach someone calculus? For that person, we would to assess the relevant information processes within the context of their motivation and prior knowledge. Given current computer technology, this can easily be done. The teaching method (or psychotherapy technique, etc.) would then be tailored for the content for that person in that particular context.

Second, a long-term strategy is to identify higher-order regularities that not only characterize information processing, motivation, and content but also characterize the ways in which these factors interact. Such regularities may be almost entirely statistical, and may end up having the same status as some equations in physics (sophisticated algorithms already exist to perform such analyses); other regularities may be easier to interpret. For example, some people may discount future rewards more than others – but especially rewards that do not bear heavily on their key values, which in turn reduces the effort they will put into attaining such rewards. Once we characterize such regularities in how mental functions interact, we can then apply them to individuals and specify individual differences at this more abstract level.

Much will be gained by leveraging individual differences, instead of ignoring them as is commonly done today. We will not only make human endeavors more effective, but also make them more satisfying for the individual.