Using neural networks to study concept formation in a sonar discrimination task

Behavioral data show that echolocating bats can discriminate between different speeds of a moving object. A neural network system presented with echoes of simulated bat sounds recorded from targets moving at different speeds exhibits similar performance. In both cases, discriminations were successfully made between targets moving at 30 Hz and a slower variable speed (5, 10, and 20 Hz). However, in both cases, it is not clear what underlying concept was learned and used to perform the task. In other words, did the bat and the neural network perform the tasks based on a relative concept oí faster (and thus learned to recognize "this is the faster speed") or did they perform the task based on an absolute concept of 30 Hz speed (and thus learned to recognize "this is the 30 Hz speed")? Both concepts would produce the observed performance of the bat and the neural networks. In this paper, we developed an approach for using neural networks to explore which concept was used to perform the task, and what factors may influence which type of concept is developed. First, we observed the behavior of the neural network when presented with a novel speed of 40 Hz. Its behavior on the novel 40 Hz speed suggested what underlying concept was implemented in the system (if it classified the novel 40 Hz as "30 Hz," then it used a relative concept of faster; if it classified the novel 40 Hz speed as "not-30 Hz," then it used an absolute concept, specifically quantifying the 30 Hz speed). Second, we examined what factors influenced the formation of the concept. Our computer simulations of neural networks that discriminated between 30 Hz and the slower variable speeds of 5, 10, and 20 Hz showed that the formation of a concept depends on the magnitude of the difference between the speeds used in training, i.e., the difficulty of the task. Specifically, when the difference between the speeds in the training set was large, the system formulated a relative concept of fast and slow. However, when the difference was small, the system formulated an absolute concept. Our neural network simulation of a system that learns to discriminate between a variety of speeds (i.e., 5,10, and 20 Hz vs. 30 Hz speeds) demonstrated that the network developed the simplest concept that allowed it to perform the task correctly. Taken together, our results show that the development of concepts is influenced by the underlying computational demands of the task, and that neural networks can be used to explore the processes involved in concept formation.