May 24, 1994 - May 24, 2027

  • Date:16ThursdayMay 2024

    Vision and AI

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    12:15 - 13:15
    Large-scale study of human memory for meaningful narratives
    Jacob Ziskind Building
    Lecture Hall - Room 1
    Misha Tsodyks
    Department of Computer Science and Applied Mathematics
    AbstractShow full text abstract about The statistical study of human memory requires large-scale e...»
    The statistical study of human memory requires large-scale experiments, involving many stimuli conditions and test subjects. While this approach has proven to be quite fruitful for meaningless material such as random lists of words, naturalistic stimuli, like narratives, have until now resisted such a large-scale study, due to the quantity of manual labor required to design and analyze such experiments.
    Large language models (LLMs) have provided the necessary technological breakthrough for this purpose, given their ability to generate human-like text and carry out novel tasks after being prompted by instructions in natural language, without additional training. In this work, we develop a pipeline that uses large language models (LLMs) both to design naturalistic narrative stimuli for large-scale recall and recognition memory experiments, as well as to analyze the results. We performed online memory experiments with a large number of participants and collected recognition and recall data for narratives of different sizes. We found that both recall and recognition performance scale linearly with narrative length