The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.
Semantic Network Array Processor as a massively parallel computing platform for high performance and large-scale natural language processing
