Investigating the influence of local and personal common ground on memory for conversation using an online referential communication task.
Bibliographic record
Abstract
To maintain efficiency during conversation, interlocutors form and retrieve memory representations for the shared understanding or common ground that they have with their partner. Here, an online referential communication task (RCT) was used in two experiments to examine whether the strength and type of common ground between dyads influence their ability to form and recall referential labels for images. Results from both experiments show a significant association between the strength of common ground formed between dyads for images during the RCT and their verbatim-but not semantic-recall memory for image descriptions about a week later. Participants who generated the image descriptions during the RCT also showed superior verbatim and semantic recall memory performance. In Experiment 2, a group of friends with pre-existing personal common ground were significantly more efficient in their use of words to describe images during the RCT than a group of strangers without personal common ground. However, personal common ground did not lead to enhanced recall memory performance. Together, these findings provide evidence that individuals can remember some verbatim words and phrases from conversations, and partially support the theoretical notion that common ground and memory are intricately linked conversational processes. The null findings with regard to semantic recall memory suggest that the structured nature of the RCT may have constrained the types of memory representations that individuals formed during the interaction. Findings are discussed in relation to the multidimensional nature of common ground and the importance of developing more natural conversational tasks for future work. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".