Communication Misperceptions: Mispredicting the Outcomes of Interpersonal Interactions
Bibliographic record
Abstract
We communicate with others constantly: in our work life, our family life, and beyond. However, emerging research suggests that these ubiquitous interpersonal communications are fraught with misperceptions. This symposium brings together novel research papers identifying communication misperceptions across various domains that are critical to interpersonal and organizational success such as interviews, conversations, contracts, and negotiations. We discuss the extent to which people rely on uninformative cues, and the misperceptions that arise when people attempt to reach agreements. In particular, the papers presented will show that people (1) overlook social forces such as responsiveness that promote connection in conversation, causing them to undervalue communication media that entail dialogue for connecting with others; (2) overestimate how helpful communication cues are for evaluating speakers’ abilities; (3) overestimate recipients’ subjective feeling of consent to undesirable agreements, which undermines their trust and organizational commitment; and (4) underestimate the normativity of negotiations and have exaggerated concerns about jeopardizing an agreement. Moreover, this set of papers shows the detrimental consequences of communication misperceptions, particularly for missed opportunities: We miss opportunities to form new and better connections, accurately evaluate others, as well as negotiate better terms and empower consent. Taken together, this symposium highlights the fraught nature of interpersonal communication, and points to avenues for improving communications. These papers underscore the importance of understanding and correcting these communication misperceptions in interpersonal and organizational contexts. People are insensitive to social forces that promote connection in conversation Presenter: Michael Kardas; Northwestern Kellogg School of Management Presenter: Nicholas Epley; U. Of Chicago How verbal, nonverbal, and prosodic cues mislead interpersonal inferences Presenter: Nicole Abi-Esber; Harvard Business School Presenter: Adam Mastroianni; Columbia Business School Presenter: Alison Wood Brooks; Harvard U. You knew what you were getting into, Honesty increases perceptions, but not feelings, of consent Presenter: Rachel Schlund; Cornell U. Presenter: Vanessa Bohns; Cornell U. I avoid because I care, Negotiation avoidance due to (inflated) concern about jeopardizing a deal Presenter: Einav Hart; George Mason U. Presenter: Julia Bear; Stony Brook U.-State U. of New York
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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 | 0.045 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".