Katharina Dobs

@kathadobs.bsky.social

563 Followers 391 Following

Professor @JLU | cognitive computational neuroscientist | mom of 3 | she/her www.vccnlab.org

Graphic showing the VCCN Lab’s contributions to CCN 2026. The design features the VCCN Lab logo on the left, a stylized neural network icon in the center, and “CCN 2026” on the right. Five posters are listed, each accompanied by a headshot of the presenting researcher and the scheduled poster session. Jakob Winkler: Rethinking the inversion effect as a graded phenomenon across object categories (Poster Session A, Tuesday, August 4, 9:30–11:15 am). Sule Tasliyurt-Celebi: Memorability varies with how visual information is sampled by humans and models (Poster Session C, Wednesday, August 5, 9:30–11:15 am). Alban Flachot: More shape bias does not mean more human-like visual representations (Poster Session C, Wednesday, August 5, 9:30–11:15 am). Lenny van Dyck: Different features shape category selectivity in human visual cortex and DNNs (Poster Session F, Thursday, August 6, 1:45–3:30 pm). Zhengqing Miao: Functionally specialized units reflect readout pathways rather than distinct feature encoding in artificial neural networks (Poster Session F, Thursday, August 6, 1:45–3:30 pm).
Left plot: Visualization of the critical features in sample pareidolia stimuli that the Dual-task CNN uses to classify these stimuli as either ‘face’ or ‘object’. Green areas indicate positive class attribution, essential for classification, while red areas signify negative class attribution, detrimental for classification. We calculated the mean pixel ratio within each mask (ranging from 0, indicating the lowest, to 1, the highest) for classifying the stimulus as ‘face’ (red bars) or as ‘object’ (yellow bars). Left plot: This mask-based ratio is calculated for all the CNNs. The analysis revealed that the Dual-task CNN primarily relies on facial features, especially the eye and mouth regions, to classify the pareidolia stimulus as a face.
Panels 1-3 display the results for each CNN: 1: Dual-task CNN (trained on face identification and object categorization), 2: Face-identification-and-Object-detection CNN, 3: Object-categorization-and-Face-detection CNN. In CNNs that included object categorization training, the representational similarity between pareidolia faces and real faces (‘Pareidolia ~ Faces’) increased across layers, indicating that these networks progressively represent pareidolia faces more like real faces in higher layers. Conversely, in CNNs lacking object categorization training, this similarity was weak, only reaching significance at early layers, and decreased across layers. The Dual-task CNN showed a unique representational pattern where pareidolia faces were represented closer to matched objects than to real faces (‘Pareidolia ~ Objects’), while still being more similar to real faces than objects (‘Pareidolia ~ Faces’) from mid-layers (layer 5) onward, matching pareidolia representations in MEG.
Plot showing correlations between all time points of the neural MEG responses and the penultimate layer of each task-optimized CNN. CNNs including optimization for object categorization (i.e., Dual-task CNN, Object-categorization CNN, and Object-categorization-and-Face-detection CNN) showed a higher correlation than CNNs lacking optimization for object categorization (i.e., Face-identification CNN and Face-identification-and-Object-detection CNN).
Top plots: Idealized model RDMs reflecting two hypotheses on the representations of face pareidolia: i) ‘Pareidolia ~ Faces’ hypothesis posits that pareidolia faces are represented similarly to real faces but differ from matched objects (left), and ii) ‘Pareidolia ~ Objects’ hypothesis suggests that pareidolia faces are represented similarly to objects but differ from faces (right). Bottom plot B: Partial correlations between all time points of the neural MEG data and each idealized model RDM, controlling for the other model RDM. These correlations reveal that the ‘Pareidolia ~ Faces’ model uniquely explains variance in the neural data at early time points, indicating that pareidolia faces are initially represented similarly to real faces (peak at 165 ms after stimulus onset). However, the ‘Pareidolia ~ Objects’ model uniquely explains increasing variance at later time points, suggesting that pareidolia faces progressively resemble matched objects and become segregated from real faces (peak at 255 ms after stimulus onset). Throughout the MEG time course, the partial correlation values for the ‘Pareidolia ~ Objects’ model (orange line) are generally higher than those for the ‘Pareidolia ~ Faces’ model (blue line), indicating that pareidolia faces are represented more similarly to objects than to real faces overall.
A top view of a coffee cup filled with coffee. The surface of the coffee features foam that resembles a smiling face, complete with two eyes and a wide mouth.