Automated scoring of the autobiographical interview with natural language processing.

The autobiographical interview has been used in more than 200 studies to assess the content of autobiographical memories. In a typical experiment, participants recall memories, which are then scored manually for internal details (episodic details from the central event) and external details (largely non-episodic details). Scoring these narratives requires a significant amount of time. As a result, large studies with this procedure are often impractical, and even conducting small studies is time-consuming. To reduce scoring burden and enable larger studies, we developed an approach to automatically score responses with natural language processing. We fine-tuned an existing language model (distilBERT) to identify the amount of internal and external content in each sentence. These predictions were aggregated to obtain internal and external content estimates for each narrative. We evaluated our model by comparing manual scores with automated scores in five datasets. We found that our model performed well across datasets. In four datasets, we found a strong correlation between internal detail counts and the amount of predicted internal content. In these datasets, manual and automated external scores were also strongly correlated, and we found minimal misclassification of content. In a fifth dataset, our model performed well after additional preprocessing. To make automated scoring available to other researchers, we provide a Colab notebook that is intended to be used without additional coding.

Does Episodic Retrieval Contribute to Creative Writing? An Exploratory Study.

Previous research indicates that episodic retrieval contributes to divergent creative thinking. However, this research has relied on standard laboratory tests of divergent creative thinking, such as generating creative uses for objects; it is unknown whether episodic retrieval also contributes to domain-specific forms of creativity. Here we start to explore whether episodic retrieval contributes to content generation on one such domain-specific task: creative writing. In two experiments, we use an episodic specificity induction (ESI) that selectively impacts tasks that draw on episodic retrieval. If episodic retrieval contributes to content generation during creative writing, then ESI should selectively increase the number of episodic details that people subsequently generate on a creative writing task. In our first experiment, we found evidence that ESI increased the number of episodic details participants generated. We observed a similar, though non-significant, trend in the second experiment. These findings constitute a starting point for examining the contribution of episodic retrieval to creative writing, but additional studies will be needed to more definitively characterize the nature and extent of these contributions.

Schema-related eye movements support episodic simulation.

Recent work indicates that eye movements support the retrieval of episodic memories by reactivating the spatiotemporal context in which they were encoded. Although similar mechanisms have been thought to support simulation of future episodes, there is currently no evidence favoring this proposal. In the present study, we investigated the role of eye movements in episodic simulation by comparing the gaze patterns of individual participants imagining future scene and event scenarios to across-participant gaze templates for those same scenarios, reflecting their shared features (i.e., schemas). Our results provide novel evidence that eye movements during episodic simulation in the face of distracting visual noise are (1) schema-specific and (2) predictive of simulation success. Together, these findings suggest that eye movements support episodic simulation via reinstatement of scene and event schemas, and more broadly, that interactions between the memory and oculomotor effector systems may underlie critical cognitive processes including constructive episodic simulation.