Project Overview
Learning task-relevant visual features by linking brain activity to large language models and deep convolutional neural networks
Department(s)
Neuroscience
Psychological and Brain Sciences
Computer Science
Abstract
Building artificial intelligence (AI) systems that approach human cognitive flexibility requires a better understanding of how the brain uses visual and linguistic information to achieve specific behavioral goals. While previous research in cognitive neuroscience and AI has focused on visual classification tasks, such as identifying objects or labeling scenes, real-world behavior is more nuanced and often depends on selecting task-relevant information, guided by the observer’s goals. Critically, this process draws not only on the visual features within the visual environment, but on conceptual and linguistic knowledge as well. This project examines how the human brain flexibly extracts and uses visual information in context and how this information is represented in computational models. The primary aim is to advance theories of human cognition through the lens of systems neuroscience, with a secondary aim to development more adaptive, human-aligned AI systems.
Student Qualifications
Must have successfully completed 1) Introduction to Neuroscience (NEUR 170) or Biological Psychology (PSYC 275), and 2) at least one course in computer programming (e.g., COSC 101 or NEUR 374). Successful candidates will possess a strong passion and curiosity in computational neuroscience.
Project Length
10 weeks
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