Human-like dissociations between confidence and accuracy in convolutional neural networks
Fig 5
Dissociations between meta-d’ and d’.
We tested the three networks (4-layer CNN, VGG-19 and ResNet-50) on the task paradigm from Maniscalco et al. (2016) [18] which demonstrated a dissociation between d’ and meta-d’ when the contrast of one stimulus category (S1) remains fixed while the contrast of the other stimulus is increased in discrete steps (S2) For this design, meta-d’ increases with d’ as expected for trials in which the observer responds "S2", but meta-d’ decreases with d’ for trials where the observer responds "S1". Maniscalco et al. (2016) [18] showed that this behavioral effect can be explained by a model incorporating the positive evidence bias. Here, we simulated this task paradigm for the stimuli in Experiments 1–3. The responses generated by our networks show that they can indeed generate the meta-d’/d’ dissociations observed in humans for at least two out of three experiments. While the 4-layer network fails to reproduce this behavior for Experiment 2, VGG-19 and ResNet-50 fail to produce this behavior for Experiment 3, suggesting that these dissociations may depend on specific interactions between the stimuli and the networks.