Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models
By Maarten Sap, Eric Horvitz, Yejin Choi, Noah A. Smith, James W. Pennebaker. 2020
44 citations recorded by the source corpus.
We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Large Language Models, Cognitive Science & Psychology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Diyi Yang
- Jiaao Chen
- Omar Shaikh
- Caleb Ziems
- Zhehao Zhang
- William B. Held
- Hinrich Schütze
- Timo Schick
In Experimental and Cognitive Psychology
In Large Language Models
Citations in the corpus
- LongForm: Effective Instruction Tuning with Reverse Instructions (2023, neutral): “They cover a diverse range of topics, including events, careers, biographies, and writing tasks such as reviews, summaries and essays – and this is just looking at the 11% of our LongForm-C dataset with reverse instructions (RI).”
- Can Large Language Models Transform Computational Social Science? (2023, methodology): “To model these explanations, Sap et al. (2020a) treat the task as a standard conditional generation problem.”