Keep talking: (Mis)understanding the hedonic trajectory of conversation.
Bibliographic record
Abstract
= 1,093 participants, including 966 spoken conversations) address these gaps. We find that people misunderstand the hedonic trajectory of conversation: After enjoying the initial minutes of conversation with a new acquaintance, participants expected their enjoyment to decline as their conversations continued, but experienced stable or increasing enjoyment in reality. This miscalibration arose at least partly because participants underestimated how much they would have to discuss. Thus, instructing participants to mentally simulate the conversation in detail drew their attention to the conversation material they could discuss and helped to calibrate their enjoyment predictions. When left uncorrected, misunderstanding the hedonic trajectory of conversation can undermine well-being. In one study, participants preferred to spend less time in conversation and more time alone than was optimal for their enjoyment-a finding that emerged even among participants who reported wanting to enjoy themselves. Throughout our experiments we assessed various conversational contexts (including whether participants had one long conversation with a single partner or several short conversations with different partners), and features of conversation (including participants' perceived and actual interest in talking to each other, fatigue, and the intimacy of conversation), thus shining novel light on conversational dynamics more broadly. People hold incorrect assumptions about how social interaction changes over time and, consequently, may avoid longer-lasting conversations that would forge closer connections. (PsycInfo Database Record (c) 2022 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".