The purpose of Ohio University’s SCOPE Lab is to conduct empirical and theoretical research on the

nature of concept learning, perception, and inference in humans. A key aspect of our research involves

modeling empirical results structurally. Structural models characterize behavior in terms of the structural properties

of the stimulus: namely, continuity, invariance, well-formedness, cardinality, and complexity, among others. Often,

the mathematical methods and theories necessary to construct the most effective and parsimonious structural

models are not known. Thus, we are also committed to the development of mathematical modeling frameworks that

better capture the particular structure in question. To this effect, we have proposed a variety of formal frameworks

for modeling Boolean concept learning. These have been based on complexity and invariance principles

(Vigo, 2006, 2009).

Our empirical work is broad in scope. Recently we have been exploring human concept learning and categorical

decision-making behavior using eye tracking technology and fMRI. More specifically, we use eye tracking

techniques to explore correlations between saccades and the concept learning behavior predicted by a variety

of mathematical models, including the concept invariance model (Vigo, 2006, 2009); similarly, in collaboration

with researchers at Indiana University at Bloomington, we use fMRI to explore possible neural correlates to the

Boolean categorization behavior predicted by the same models. The ultimate goal is to develop cognitively

plausible and multilayered-robust mathematical models of categorical learning behavior and concept formation for

which there may be clear neural correlates: that is, for which the behavior, the associated saccades, and the BOLD

signal activation levels in regions of interest are all predicted by the models.

Other research activities in the SCOPE Lab include, but are not limited to, the development of mathematical and

computational models that predict decision making behavior as a function of similarity assessment, dissimilarity

assessment, and categorization. Also, we are interested in researching how humans judge similarity and dissimilarity

between structural or configural stimuli such as human faces. In related work, we have proposed a mathematical

model of similarity that predicts the empirical similarity ordering of a key class of configural stimuli associated with

deductive inference (Vigo, 2009). Last, but not least, the SCOPE Lab conducts empirical and theoretical research

on problem solving behavior in mathematical domains such as geometry, algebra, and physics, and on the nature

of aesthetic judgments. For more information on our research, visit the SCOPE Lab, contact us here.