Owls and Wading Birds: Generalization Gradients in Expertise

Owls and Wading Birds: Generalization Gradients in Expertise Nam H. Nguyen Garrison W. Cottrell {nhnguyen, gary}@ucsd.edu UCSD Department of Computer Science and Engineering, 9500 Gilman Drive La Jolla, CA 92093-0114 USA Abstract Recently, Tanaka et al. (in press) have shown that subjects trained at the subordinate level (henceforth, “experts”) exhibit an advantage over subjects trained at the basic level on the same stimuli in performing discrimination tasks within their domain of expertise, showing that “mere exposure” to a category is not enough to induce discrimination behavior consistent with expertise. In addition, experts generalize their discrimination performance in a graded fashion to novel exemplars from known classes, as well as novel exemplars from novel, but related classes. We applied our two-component neurocomputational model of perceptual expertise to this domain (Sugimoto & Cottrell, 2001; Joyce & Cottrell, 2004). To our surprise, we found that we could not match the data with our original model. Ironically, we needed to add a new component that models “mere exposure” in order to account for the discrimination performance on basic level categories.  What is required for perceptual expertise? Is it simple exposure to lots of examples of a class, or is more required? Tanaka and colleagues have shown recently that frequent exposure to exemplars of a domain alone is not enough to reach expert levels of discrimination. Instead, they found, subordinate level labeling of stimuli is required, or at least, is sufficient. Tanaka et al. (in press) trained subjects on images of owls and wading birds. Half the subjects learned to label owls at the species level (e.g., “Great Grey Owl”), and the wading birds at the basic level (all were labeled “wading birds”). Half did the opposite. For simplicity, we will describe the results from the point of view of the subjects who were trained to discriminate owls at the species level. Subjects were tested before training on their ability to discriminate species in a same/different task. The discrimination test was repeated after training. The results were first, that the subordinate level training produced greater post-training gains in species discrimination relative to the basic level training. The advantage in discrimination also transferred to novel exemplars of trained owl species by a smaller amount, and finally, a small, but significant advantage for novel exemplars of novel species of owl was obtained. The trained images of wading birds only showed small gains over their pre-training baseline, and there was no advantage for untrained exemplars or novel species of wading birds. Transfer of discrimination thus only occurred in the category learned at the subordinate level, and was graded by similarity to previously learned items. In our previous studies, we have developed a neurocomputational model that can explain many face and emotion discrimination and perceptual expertise effects. Our neural network model has two modules that learn basic and subordinate level classification tasks simultaneously. In this paper, we apply our model to Tanaka et al.’s domain. We find it necessary to add an auto-encoder module (an unsupervised network that extracts compact representations of its environment) to our network to account for subjects’ discrimination performance on basic-level stimuli, since it helps to spread out the representation of basic stimuli as well as subordinate stimuli. The auto-encoder module can be considered as a model for mere exposure. As Harnad (1987) pointed out, there is a need for at least three capacities in order to categorize: discrimination, identification, and an invertible description. In our model, the first capacity falls out of the second two. It is the level of naming or identification that gives rise to differing levels of discrimination, but being able to describe the object well enough to reproduce it (as in the hidden layer of an autoencoder) is necessary to account for discrimination of basic level (but frequently encountered) objects. Tanaka’s Experimental  The stimuli consisted of digitized photographs of owls and wading birds . All participants had the same exposure to stimuli. Participants first completed a pre- assessment “same/different” discrimination task to get the baseline for later comparison. In this task, participants were shown two bird images sequentially in time, and asked to respond either “same” or “different”. For “same” trials, the birds were two different images of the same species (e.g., two different images of “screech owls”). For “different” trial, the birds were images depicting two species from the same family (e.g., “screech owl” and “burrowing owl”).  Tanaka’s stimuli. During training, participants were divided into two groups, one group learned to classify items as a wading bird (basic