Main content
Using artificial neural networks to relate external sensory features to internal decisional evidence
Date created: | Last Updated:
: DOI | ARK
Creating DOI. Please wait...
Category: Project
Description: All theories of perceptual decision-making postulate that external sensory information is transformed into the internal evidence that is used to guide behavior. However, the nature of this external-to-internal transformation is generally unknown. In two experiments, we examined how a particular stimulus feature – orientation – is transformed into internal evidence. Subjects judged whether Gabor patches were tilted clockwise or counterclockwise. The results of Experiment 1 demonstrated that increasing orientation offset in fine-scale increments resulted in a linear increase in sensitivity (d´), suggesting a linear external-to-internal transformation. However, the results of Experiment 2 demonstrated that increasing orientation offset in coarse-scale increments had little effect on sensitivity, suggesting a highly non-linear transformation. These behavioral results imply that a given sensory feature may not have a one-to-one mapping with the internal representation of evidence across different tasks. Further, we evaluated whether an artificial neural network (ANN) trained on orientation categorization can reproduce the observed external-to-internal transformations. The ANN mirrored the empirical results – fine-scale increments in orientation offset were linearly transformed into internal evidence, but coarse-scale increments in orientation offset had little influence on internal evidence. These results begin to reveal how external sensory information is transformed into internal decisional evidence and suggest that ANNs could serve as a hypothesis-generation platform for this critical transformation.