Ilia Sucholutsky
Biographic Data
| ID | 1751458 |
|---|---|
| NAME | Ilia Sucholutsky |
| GIVEN NAMES | Ilia |
| FAMILY NAME | Sucholutsky |
| SIGNATURE | SUCHOLUTSKY I |
| AFFILIATIONS | Princeton University |
| ORCID | 0000-0003-4121-7479 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 3 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Characterizing the Large‐Scale Structure of Multimodal Semantic Networks
Humans organize semantic knowledge into complex networks that encode relations between concepts. The structure of those networks has broad implications for human cognitive processes, and for theories of semantic development. Evidence from large lexical networks such as those derived from word associations suggest that semantic networks are characterized by high sparsity and clustering while maintaining short average paths between concepts, a phen…
Building machines that learn and think with people
Large language models surpass human experts in predicting neuroscience results
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…
Building machines that learn and think with people
Large language models surpass human experts in predicting neuroscience results
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…
Building machines that learn and think with people
Large language models surpass human experts in predicting neuroscience results
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…
Characterizing the Large‐Scale Structure of Multimodal Semantic Networks
Humans organize semantic knowledge into complex networks that encode relations between concepts. The structure of those networks has broad implications for human cognitive processes, and for theories of semantic development. Evidence from large lexical networks such as those derived from word associations suggest that semantic networks are characterized by high sparsity and clustering while maintaining short average paths between concepts, a phen…
Explainable Artificial Intelligence (XAI (2 works) · Psychology (2 works) · AI-based Problem Solving and Planning (1 works) · Artificial Intelligence (1 works) · Child and Animal Learning Development (1 works) · Cognitive science (1 works) · Computer Science (1 works) · Data science (1 works) · ENCODE (1 works) · Language and cultural evolution (1 works)