As simpler scientific theories are preferable to more convoluted ones, it is plausible to assume (and widely assumed, especially in recent Bayesian models of cognition) that biological learners are also guided by simplicity considerations 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 and rule learning, 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. An empirically viable notion of complexity thus need to take into consideration the representational and processing primitives that are available to actual learners even if this leads to formally complex explanations.
Effects of directional and neutral category labels in bidimensional rule-learning problems
