553 citations · 1.4k across the 41 of their papers we have counts for
45 papers
Improving social media for democratic discourse
Fan Cheng, Amirhossein Farzmahdi, Pinyuan Feng +8
Social media have expanded opportunities for communication and political participation, but today's dominant platforms are optimized primarily for engagement and advertising revenu…
Training Object Permanence in World Models
Haotian Zhang, Fengyuan Yu, Dezhi Luo +28
Object permanence and solidity are hallmarks of human cognitive priors. Recent studies show that video generation models, a paradigmatic class of current world models, have begun t…
VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
Junxiang Xu, Ruisi Wang, Fanyi Pu +49
Native visual reasoning treats visual generation as the medium of reasoning itself: visual states (i.e. images and videos) are not merely inputs to be understood or outputs to be r…
Dual-Primal Graph VAEs for Noisy Label Aggregation
Patrick Stinson, Nikolaus Kriegeskorte
Inferring the ground-truth from noisy crowdsourced labels is an important theoretical and practical problem. Neural network-based methods offer an alternative to classical Bayesian…
Comparing Linear Probes with Mahalanobis Cosine Similarity
Zhuofan Josh Ying, Peter Hase, Nikolaus Kriegeskorte
Linear probes are widely used in interpretability research and often compared by cosine similarity. The Mahalanobis cosine similarity (MCS) between two directions, which reweights…
Decomposing how prompting steers behavior
Fan L. Cheng, Nikolaus Kriegeskorte
Prompting steers large language models (LLMs) and vision-language models (VLMs) without weight updates, but it remains unclear how instruction changes reshape internal representati…
Growing a Neural Network in Breadth, Depth, and Time
Eivinas Butkus, Kedar Garzón Gupta, Nikolaus Kriegeskorte
Spatial and temporal resource constraints are critical for both biological and artificial intelligent systems. Here we define differentiable cost terms for breadth, depth, and time…
Perceived face similarity reflects inverse-generative and naturalistic discriminative objectives
Wenxuan Guo, Heiko H. Schütt, Kamila Maria Jozwik +3
The perceptual representations supporting our ability to recognize faces remain a computational mystery. Deep neural networks offer mechanistic hypotheses for human face perception…
Use and usability: concepts of representation in philosophy, neuroscience, cognitive science, and computer science
Ben Baker, Richard D. Lange, Andrew Richmond +4
Representations play a central role in the study of both biological and artificial intelligence, as well as philosophy of mind. Across neuroscience, computer science, and philosoph…
Do vision models perceive illusory motion in static images like humans?
Isabella Elaine Rosario, Fan L. Cheng, Zitang Sun +1
Understanding human motion processing is essential for building reliable, human-centered computer vision systems. Although deep neural networks (DNNs) achieve strong performance in…
Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
Mu Nan, Muquan Yu, Weijian Mai +12
Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computationa…
Human-like Object Grouping in Self-supervised Vision Transformers
Hossein Adeli, Seoyoung Ahn, Andrew Luo +3
Vision foundation models trained with self-supervised objectives achieve strong performance across diverse tasks and exhibit emergent object segmentation properties. However, their…
The Truthfulness Spectrum Hypothesis
Zhuofan Josh Ying, Shauli Ravfogel, Nikolaus Kriegeskorte +1
Large language models (LLMs) have been reported to linearly encode truthfulness, yet recent work questions this finding's generality. We reconcile these views with the truthfulness…
In Silico Mapping of Visual Categorical Selectivity Across the Whole Brain
Ethan Hwang, Hossein Adeli, Wenxuan Guo +2
A fine-grained account of functional selectivity in the cortex is essential for understanding how visual information is processed and represented in the brain. Classical studies us…
Towards Interpretable Visual Decoding with Attention to Brain Representations
Pinyuan Feng, Hossein Adeli, Wenxuan Guo +3
Recent work has demonstrated that complex visual stimuli can be decoded from human brain activity using deep generative models, offering new ways to probe how the brain represents…
Transformer brain encoders explain human high-level visual responses
Hossein Adeli, Sun Minni, Nikolaus Kriegeskorte
A major goal of neuroscience is to understand brain computations during visual processing in naturalistic settings. A dominant approach is to use image-computable deep neural netwo…
Collective inference of the truth of propositions from crowd probability judgments
Patrick Stinson, Jasper van den Bosch, Trenton Jerde +1
Every day, we judge the probability of propositions. When we communicate graded confidence (e.g. "I am 90% sure"), we enable others to gauge how much weight to attach to our judgme…
Source Invariance and Probabilistic Transfer: A Testable Theory of Probabilistic Neural Representations
Samuel Lippl, Raphael Gerraty, John Morrison +1
As animals interact with their environments, they must infer properties of their surroundings. Some animals, including humans, can represent uncertainty about those properties. But…
Generative Adversarial Collaborations: A practical guide for conference organizers and participating scientists
Gunnar Blohm, Benjamin Peters, Ralf Haefner +6
Generative adversarial collaborations (GACs) are a form of formal teamwork between groups of scientists with diverging views. The goal of GACs is to identify and ultimately resolve…
How does the primate brain combine generative and discriminative computations in vision?
Benjamin Peters, James J. DiCarlo, Todd Gureckis +10
Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as we…
How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?
Greta Tuckute, Dawn Finzi, Eshed Margalit +8
In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior. However, there is no cons…
The Topology and Geometry of Neural Representations
Baihan Lin, Nikolaus Kriegeskorte
A central question for neuroscience is how to characterize brain representations of perceptual and cognitive content. An ideal characterization should distinguish different functio…
Affinity-based Attention in Self-supervised Transformers Predicts Dynamics of Object Grouping in Humans
Hossein Adeli, Seoyoung Ahn, Nikolaus Kriegeskorte +1
The spreading of attention has been proposed as a mechanism for how humans group features to segment objects. However, such a mechanism has not yet been implemented and tested in n…
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments
Tal Golan, Wenxuan Guo, Heiko H. Schütt +1
Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitativel…