


Bob French LEADCNRS, U. of Burgundy JeanPierre Thibaut LEADCNRS, U. of Burgundy
We use eyetracking data, analyzed by a neural network and by Linear Discriminant Analysis (LDA), to study the temporal dynamics of children's analogy making. We determine how well the number of itemtoitem saccades while solving an analogy problem predicts whether or not a child will correctly answer the problem. For the A:B::C:D visual analogy problems, by the first third of the trial we can tell with 64% accuracy whether or not the problem will be answered correctly. Twothirds of way through the trial, we can predict with 82% accuracy the answer that will be given. By looking only at the final third of the trial, we can predict with up to 90% accuracy what the child will do. Average gaze times at the Target and Distractor items have the same predictive power as the itemtoitem saccade information.
Using eyetracking to predict children's success or failure on analogy tasks (164 KB)