2 citations · 6 across the 17 of their papers we have counts for
17 papers
On the use of foundation models in cognitive science
Raj Sanjay Shah, Alex Warstadt, Michael Frank +1
A host of recent studies have evaluated the cognitive and developmental alignment of Foundation Models (FMs). These investigations include evaluations of their correspondence to ad…
Human-Like Anaphor Resolution in Large Language Models
Keane Zhang, Varshini Chinta, Raj Sanjay Shah +1
Anaphors are expressions that refer to other expressions, called antecedents. The process of connecting the two is called resolution. Cognitive science has identified multiple fact…
When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations
Harsh Nishant Lalai, Raj Sanjay Shah, Hanspeter Pfister +2
Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. While recent Vision Language Models…
The World According to LLMs: How Geographic Origin Influences LLMs' Entity Deduction Capabilities
Harsh Nishant Lalai, Raj Sanjay Shah, Jiaxin Pei +3
Large Language Models (LLMs) have been extensively tuned to mitigate explicit biases, yet they often exhibit subtle implicit biases rooted in their pre-training data. Rather than d…
A Neural Network Model of Complementary Learning Systems: Pattern Separation and Completion for Continual Learning
James P Jun, Vijay Marupudi, Raj Sanjay Shah +1
Learning new information without forgetting prior knowledge is central to human intelligence. In contrast, neural network models suffer from catastrophic forgetting: a significant…
Modeling Understanding of Story-Based Analogies Using Large Language Models
Kalit Inani, Keshav Kabra, Vijay Marupudi +1
Recent advancements in Large Language Models (LLMs) have brought them closer to matching human cognition across a variety of tasks. How well do these models align with human perfor…
The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories
Raj Sanjay Shah, Sashank Varma
Many studies have evaluated the cognitive alignment of Pre-trained Language Models (PLMs), i.e., their correspondence to adult performance across a range of cognitive domains. Rece…
Understanding Graphical Perception in Data Visualization through Zero-shot Prompting of Vision-Language Models
Grace Guo, Jenna Jiayi Kang, Raj Sanjay Shah +2
Vision Language Models (VLMs) have been successful at many chart comprehension tasks that require attending to both the images of charts and their accompanying textual descriptions…
Development of Cognitive Intelligence in Pre-trained Language Models
Raj Sanjay Shah, Khushi Bhardwaj, Sashank Varma
Recent studies show evidence for emergent cognitive abilities in Large Pre-trained Language Models (PLMs). The increasing cognitive alignment of these models has made them candidat…
How Well Do Deep Learning Models Capture Human Concepts? The Case of the Typicality Effect
Siddhartha K. Vemuri, Raj Sanjay Shah, Sashank Varma
How well do representations learned by ML models align with those of humans? Here, we consider concept representations learned by deep learning models and evaluate whether they sho…
Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention
Andrew Li, Xianle Feng, Siddhant Narang +4
When reading temporarily ambiguous garden-path sentences, misinterpretations sometimes linger past the point of disambiguation. This phenomenon has traditionally been studied in ps…
Towards a Path Dependent Account of Category Fluency
David Heineman, Reba Koenen, Sashank Varma
Category fluency is a widely studied cognitive phenomenon, yet two conflicting accounts have been proposed as the underlying retrieval mechanism -- an optimal foraging process deli…
Cobweb: An Incremental and Hierarchical Model of Human-Like Category Learning
Xin Lian, Sashank Varma, Christopher J. MacLellan
Cobweb, a human-like category learning system, differs from most cognitive science models in incrementally constructing hierarchically organized tree-like structures guided by the…
Natural Mitigation of Catastrophic Interference: Continual Learning in Power-Law Learning Environments
Atith Gandhi, Raj Sanjay Shah, Vijay Marupudi +1
Neural networks often suffer from catastrophic interference (CI): performance on previously learned tasks drops off significantly when learning a new task. This contrasts strongly…
Understanding the Countably Infinite: Neural Network Models of the Successor Function and its Acquisition
Vima Gupta, Sashank Varma
As children enter elementary school, their understanding of the ordinal structure of numbers transitions from a memorized count list of the first 50-100 numbers to knowing the succ…
Pre-training LLMs using human-like development data corpus
Khushi Bhardwaj, Raj Sanjay Shah, Sashank Varma
Pre-trained Large Language Models (LLMs) have shown success in a diverse set of language inference and understanding tasks. The pre-training stage of LLMs looks at a large corpus o…
Human Behavioral Benchmarking: Numeric Magnitude Comparison Effects in Large Language Models
Raj Sanjay Shah, Vijay Marupudi, Reba Koenen +2
Large Language Models (LLMs) do not differentially represent numbers, which are pervasive in text. In contrast, neuroscience research has identified distinct neural representations…