A Novel Method for Analyzing Sequential Eye Movements Reveals Strategic Influence on Raven's Advanced Progressive Matrices

Abstract:

Eye-movements are an important data source in vision science. However, the vast majority of eye-movement studies ignore sequential information in the data and utilize only first-order statistics. Here we present a novel application of a temporal-difference learning algorithm to construct a scanpath successor representation (SR; Dayan, 1993) that captures statistical regularities in temporally-extended eye-movement sequences. We demonstrate the effectiveness of the scanpath SR on eye movement data from participants solving items from Raven's Advanced Progressive Matrices Test. Analysis of the SRs revealed individual differences in scanning patterns captured by two principal components that predicted individual Raven scores much better than existing methods. These scanpath SR components were highly interpretable and provided new insight into the role of strategic processing on the Raven test. The success of the scanpath SR in terms of prediction and interpretability suggests that this method could prove useful in a much broader context.

Keywords: Eye movements, temporal-difference learning, problem-solving strategies, individual differences

Reprint (pdf) Matlab reports (html) Companion article

Matlab Reports

In the process of analyzing the data, developing the models, and fitting them to the data, we wrote various Matlab scripts. These scripts generated HTML reports via Matlab's "publish" feature. The most important reports are made available here. Use the Back button in your browser to return to this page.

• LRaven1_comp_optim.html -- The script that generated Figure 2 in the paper.
• LR1_SVD_study.html -- Learning the ropes of singular value decomposition (SVD).
• cvR2opt-bruteforce-35x28-vega-20110712.txt -- The script used to calculate the cross-validated R2 reported in Figure 4b.
• LRaven1_traditional_comparison.html -- Material for Vigneau, Caissie, & Bors (2006) section of Table 1.
• Transition-matrix-cvR2-20110710.pdf -- Material for the transition-probability section of Table 1.