Advancing Automated Spatio-Semantic Analysis in Picture Description Using Language Models
This provides a more efficient tool for clinicians and researchers to assess cognitive impairment by automating labor-intensive manual or dictionary-based methods, though it is incremental as it builds on existing BERT-based approaches.
The study tackled the problem of automating the extraction and ordering of content information units (CIUs) from picture descriptions for cognitive-linguistic impairment assessment, achieving 93% median precision and 96% median recall in CIU detection with a 24% sequence error rate.
Current methods for automated assessment of cognitive-linguistic impairment via picture description often neglect the visual narrative path - the sequence and locations of elements a speaker described in the picture. Analyses of spatio-semantic features capture this path using content information units (CIUs), but manual tagging or dictionary-based mapping is labor-intensive. This study proposes a BERT-based pipeline, fine tuned with binary cross-entropy and pairwise ranking loss, for automated CIU extraction and ordering from the Cookie Theft picture description. Evaluated by 5-fold cross-validation, it achieves 93% median precision, 96% median recall in CIU detection, and 24% sequence error rates. The proposed method extracts features that exhibit strong Pearson correlations with ground truth, surpassing the dictionary-based baseline in external validation. These features also perform comparably to those derived from manual annotations in evaluating group differences via ANCOVA. The pipeline is shown to effectively characterize visual narrative paths for cognitive impairment assessment, with the implementation and models open-sourced to public.