Summary of Towards a Path Dependent Account Of Category Fluency, by David Heineman et al.
Towards a Path Dependent Account of Category Fluency
by David Heineman, Reba Koenen, Sashank Varma
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores the mechanisms underlying category fluency, a cognitive phenomenon where people recall words from different categories. Two prevailing theories have been proposed: optimal foraging processes and random walk sampling from a semantic network. Existing models predict paradoxically identical results when predicting human patch switches, leaving room for debate. To resolve this discrepancy, the authors reformulate models as sequence generators and propose a metric based on n-gram overlap to evaluate generated sequences against human-written ones. The study finds that category switch predictors do not necessarily produce human-like sequences, requiring additional biases and global cues to replicate human behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how people remember words from different categories. There are two ways people might be doing this: either they’re searching for the right word or they’re randomly picking one. Right now, we don’t know which way it actually works because all the models that try to predict what people will do say the same thing, even though they’re using different methods. The authors of this paper want to figure out which method is correct by creating new models and testing them against real human behavior. |
Keywords
» Artificial intelligence » N gram » Recall
