 The neurobiological paradigm for informational semantics is the feature detector: one or more neurons that are (i) maximally responsive to a particular type of stimulus, and (ii) have the function of indicating the presence of that stimulus type. Examples of such stimulus-types for visual feature detectors include high-contrast edges, motion direction, and colors. A favorite feature detector among philosophers is the alleged fly detector in the frog. Lettvin et al. (1959) identified cells in the frog retina that responded maximally to small shapes moving across the visual field. The idea that these cells’ activity functioned to detect flies rested upon knowledge of the frogs’ diet. (Bechtel 1998 provides a useful discussion.) Using experimental techniques ranging from single-cell recording to sophisticated functional imaging, neuroscientists discovered a host of neurons that are maximally responsive to a variety of complex stimuli. However, establishing condition (ii) on a feature detector is much more difficult. Even some paradigm examples have been called into question. David Hubel and Torsten Wiesel’s (1962) Nobel Prize-winning work establishing the receptive fields of neurons in striate (visual) cortex is often interpreted as revealing cells whose function is edge detection. However, Lehky and Sejnowski (1988) challenged this interpretation. They trained an artificial neural network to distinguish the three-dimensional shape and orientation of an object from its two-dimensional shading pattern. Their network incorporates many features of visual neurophysiology. Nodes in the trained network turned out to be maximally responsive to edge contrasts, but did not appear to have the function of edge detection. (See Churchland and Sejnowski 1992 for a review.)