activity
20212026
most citedSEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction

4 citations · 12 across the 15 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism

Kushin Mukherjee, Na Yeon Kim, Maren Wehrheim +2

Understanding perceptual differences between autistic and neurotypical adults requires behavioral assays that are sensitive, reliable, and mechanistically informative. Facial emoti…

cs.AI2026

auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation

Ben Prystawski, Kushin Mukherjee, Daniel Wurgaft +4

AI-based scientific automation is increasingly possible by using agents to generate hypotheses, design experiments, and analyze data. Data collection is a major bottleneck in this…

cs.AI2025

Uncovering the Computational Ingredients of Human-Like Representations in LLMs

Zach Studdiford, Timothy T. Rogers, Kushin Mukherjee +1

The human ability to translate diverse perceptual and linguistic inputs into structured behavior has been thought to rest on learning robust representations of concepts. The rapid…

cs.AI2025

Evaluating Steering Techniques using Human Similarity Judgments

Zach Studdiford, Timothy T. Rogers, Siddharth Suresh +1

Current evaluations of Large Language Model (LLM) steering techniques focus on task-specific performance, overlooking how well steered representations align with human cognition. U…

cs.AI2023★ 1 cited

Conceptual structure coheres in human cognition but not in large language models

Siddharth Suresh, Kushin Mukherjee, Xizheng Yu +3

Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved e…