We propose a new way to describe affordances for action. Previous characterizations of affordances treat action possibilities as binary categories-either possible or impossible-separated by a critical point. Here, we show that affordances are probabilistic functions, thus accounting for variability in motor performance. By measuring an affordance function, researchers can describe the likelihood of success for every unit of the environment. We demonstrate how to fit an affordance function to performance data using established psychophysical procedures and illustrate how the threshold and variability parameters describe different possibilities for action. Finally, we discuss the implications of probabilistic affordances for development, perception, and decision making.
ASJC Scopus subject areas
- Social Psychology
- Computer Science(all)
- Ecology, Evolution, Behavior and Systematics
- Experimental and Cognitive Psychology