We used computational models to simulate how a range of possible neural mechanisms of repetition suppression would manifest themselves in brain response patterns measured with functional magnetic resonance imaging (fMRI). Subsequently, we determined which of these models best explained observed effects of stimulus repetition on brain responses. This research showed that recent visual experience with the same stimulus leads to a down-scaling of neuronal tuning curves, and that this effect is most profound in neurons selectively tuned to the repeated stimulus. The conceptual breakthrough realized by this study was communicated to the field via a publication in the high impact journal Nature Communications.
As a first step towards revealing the neural mechanisms of deep encoding of images, we have developed a method for measuring how similar images of objects are in terms of their meaning. This we have realized by determining how often words describing the depicted objects co-occur inside large text corpora (e.g. all text on Wikipedia). This enables us to assess the extent to which brain response patterns encode the meaning of the images we are looking at and to determine if this measure of deep encoding is enhanced for subsequently remembered images.
In addition, we have developed a behavioral paradigm that can efficiently measure how important thousands of low-level image features are for recognizing an image (e.g. recognizing images as depicting a cat or a dog, biorxiv, Alink & Charest, 2018). Intriguingly, the data recorded with this method revealed that individuals with a greater number of autistic traits rely more on fine-grained image details, which provides evidence for the idea that autism is related to an enhanced ‘eye for detail’.
