Constructing Spatial Representations from Narratives and Non-Narrative Descriptions: Evidence from 7-year-olds
Bibliographic record
Abstract
Although narratives often contain detailed descriptions of space and setting and readers frequently report vividly imagining these story worlds, evidence for the construction of spatial representations during narrative processing is currently mixed. In the present study, we investigated 7 year old children's ability to construct spatial representations of narrative spaces and compared this to the ability to construct representations from non-narrative descriptions. We hypothesized that performance would be better in the narrative condition, where children have the opportunity to construct a multi-dimensional situation model built around the character's motivations and actions. Children listened to either a narrative that included a character traveling between 5 locations in her neighbourhood or a description of the same 5-location neighbourhood. Those in the narrative condition significantly outperformed those in the description condition in constructing the layout of the neighbourhood locations. Moreover, regression analyses revealed that whereas performance on the narrative version was predicted by narrative comprehension ability, performance on the description version was predicted by working memory ability. These results suggest the possibility that building spatial representations from narratives and non-narratives may engage different cognitive processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".