A feedforward mechanism for human-like contour integration
Fig 5
Model and human behavioral correspondence for contour integration.
(A) Contour stimuli containing global curvatures (β) spanning a broad range. (B) Scatter-plot depicting the correlation between the broadly-tuned model’s contour signal strength and human percent correct across trials, showing weak correspondence (Pearson’s r = 0.1907). (C) Line plot illustrating the broadly-tuned model’s performance against human performance for different global curvature levels, highlighting the model’s insensitivity to increasing curvature. The broadly-tuned model’s performance is shown in red and human performance is shown in grey. (D) Line plot illustrating the correlation of models, that were trained on curvatures within a specific narrow range (resulting in narrowly-tuned models), with humans, peaking at β = 20° and approaching noise ceiling (r = 0.785). (E) Scatter-plot depicting the correlation between the narrowly-tuned (at 20°) model’s contour signal strength and human percent correct across trials, showing strong correspondence (Pearson’s r = 0.768). (F) Line plot illustrating the narrowly-tuned model’s (at 20°) performance against human performance for different global curvature levels, highlighting the human-like sensitivity to curvature. The narrowly-tuned model’s performance is shown in green and human performance is shown in grey.