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Workshop
All Things Attention: Bridging Different Perspectives on Attention
Abhijat Biswas · Reuben Aronson · Khimya Khetarpal · Akanksha Saran · Ruohan Zhang · Grace Lindsay · Scott Niekum

@ Physical
Event URL: https://attention-learning-workshop.github.io/ »

Attention is a widely popular topic studied in many fields such as neuroscience, psychology, and machine learning. A better understanding and conceptualization of attention in both humans and machines has led to significant progress across fields. At the same time, attention is far from a clear or unified concept, with many definitions within and across multiple fields.

Cognitive scientists study how the brain flexibly controls its limited computational resources to accomplish its objectives. Inspired by cognitive attention, machine learning researchers introduce attention as an inductive bias in their models to improve performance or interpretability. Human-computer interaction designers monitor people’s attention during interactions to implicitly detect aspects of their mental states.

While the aforementioned research areas all consider attention, each formalizes and operationalizes it in different ways. Bridging this gap will facilitate:
- (Cogsci for AI) More principled forms of attention in AI agents towards more human-like abilities such as robust generalization, quicker learning and faster planning.
- (AI for cogsci) Developing better computational models for modeling human behaviors that involve attention.
- (HCI) Modeling attention during interactions from implicit signals for fluent and efficient coordination
- (HCI/ML) Artificial models of algorithmic attention to enable intuitive interpretations of deep models?

Author Information

Abhijat Biswas (Carnegie Mellon University)
Reuben Aronson (Carnegie Mellon University)
Khimya Khetarpal (McGill University, Mila Montreal)
Akanksha Saran (Microsoft Research)
Ruohan Zhang (Stanford University)
Grace Lindsay (New York University)
Scott Niekum (UT Austin)

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