Other scientists like the general concept of divisive normalization but suggest it can be refined to account for more complex aspects of human decision-making. Yu, for example, says it works well for simple decisions but may falter under more sophisticated conditions. “The divisive normalization model does make sense, but the experimental setting in which they were probing decision-making is very simplistic,” Yu said. “To account for the broader array of phenomena in human decision-making, we need to augment the model and look at more complex decision-making scenarios.”
The divisive normalization framework emerged from work in the visual system. Yu suggests that applying it to decision-making is more complex. Scientists know a lot about the information that the visual system is trying to encode: a two-dimensional scene painted in color, light and shadow. Natural scenes conform to a set of general, easy-to-calculate properties that the brain can use to filter out redundant information. In simple terms, if one pixel is green, its neighboring pixels are more likely to be green than red.
But the decision-making system operates under more complex constraints and has to consider many different types of information. For example, a person might choose which house to buy depending on its location, size or style. But the relative importance of each of these factors, as well as their optimal value—city or suburbs, Victorian or modern—is fundamentally subjective. It varies from person to person and may even change for an individual depending on their stage of life. “There is not one simple, easy-to-measure mathematical quantity like redundancy that decision scientists universally agree on as being a key factor in the comparison of competing alternatives,” Yu said.
She suggests that uncertainty in how we value different options is behind some of our poor decisions. “If you’ve bought a lot of houses, you’ll evaluate houses differently than if you were a first-time homebuyer,” Yu said. “Or if your parents bought a house during the housing crisis, it may later affect how you buy a house.”
Moreover, Yu argues, the visual and decision-making systems have different end-goals. “Vision is a sensory system whose job is to recover as much information as possible from the world,” she said. “Decision-making is about trying to make a decision you’ll enjoy. I think the computational goal is not just information, it’s something more behaviorally relevant like total enjoyment.”
For many of us, the main concern over decision-making is practical—how can we make better decisions? Glimcher said that his research has helped him develop specific strategies. “Rather than pick what I hope is the best, instead I now always start by eliminating the worst element from a choice set,” he said, reducing the number of options to something manageable, like three. “I find that this really works, and it derives from our study of the math. Sometimes you learn something simple from the most complex stuff, and it really can improve your decision-making.”
This post appears courtesy of Quanta Magazine.
* This article originally suggested that Paul Glimcher created divisive normalization. We regret the error.