Summary of Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks, by Aaron Traylor et al.
Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks
by Aaron Traylor, Jack Merullo, Michael J. Frank, Ellie Pavlick
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Transformer-based models have achieved success on various tasks requiring “cognitive branching,” maintaining multiple goals while accomplishing others. This is unlike cognitive neuroscience’s sophisticated frontostriatal mechanisms, which enable selective gating, role-addressable updating, and readout of information between distinct memory addresses. Despite lacking these mechanisms, Transformer models still solve such tasks. We analyze the mechanisms that emerge within a vanilla attention-only Transformer trained on a sequence modeling task inspired by working memory gating in cognitive neuroscience. Our findings show that the self-attention mechanism specializes in mirroring input and output gating mechanisms present in biologically-inspired architectures. This suggests opportunities for future research exploring computational similarities between modern AI and human brain models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how a special type of computer model, called Transformer, is able to solve complex tasks that involve multiple goals. In the human brain, these types of tasks are thought to rely on a specific set of mechanisms that allow us to focus on one thing while also remembering other things. The Transformers don’t have these same mechanisms built-in, so it’s not clear how they’re able to accomplish similar tasks. Researchers trained a simple Transformer model on a task designed to study how the brain works and found that the model developed its own way of “gating” information, which is similar to what happens in the human brain. This could lead to new ways of understanding how our brains work and even help us develop more powerful computer models. |
Keywords
» Artificial intelligence » Attention » Self attention » Transformer
