Neural network models as evidence for different types of visual representations

... shape representations, and receptive fields in neural network models on the grounds that first-order correlations between input and output unit activities con explain the results. We reply briefly ta Cook’s orguments here (ond in Kosslyn, Chabris, Morsolek, Jacobs, & Koenig, 1995) and discuss how new simulations can confirm the importance of receptive field size as a crucial variable in the encod-ing of categorical ond coordinate spatial relations and the corresponding shape representations; such simulations would testify to the computational distinction between the different types of representations