In defense of epicycles: Embracing complexity in psychological explanations

As simpler scientific theories are widely considered preferable to more convoluted ones, it is plausible (and widely assumed, especially in recent Bayesian models of cognition) that biological learners are also guided by simplicity when acquiring mental representations, and that formal measures of complexity might indicate which learning problems are harder and which ones are easier. However, the history of science suggests that simpler scientific theories are not necessarily more useful if more convoluted ones make calculations easier. Here, I suggest that a similar conclusion applies to mental representations. Using case studies from perception, associative learning, word and rule learning as well as causal inference, I show that formal measures of complexity critically depend on assumptions about the underlying representational and processing primitives, and are generally unrelated to what is actually easy to learn and process in humans. When simpler hypotheses are preferred, they are preferred because they are also easier to process. Empirically viable notions of complexity thus need to build on the representational and processing primitives that are available to actual learners even if this leads to formally complex explanations.