I Don't Like the Tone of Your Voice: Infants Use Vocal Affect to Socially Evaluate Others
Bibliographic record
Abstract
Infants can make social judgments about characters by visually observing their interactions with others (e.g., Hamlin, Wynn & Bloom, Nature, 2007, 450, 557). Here, we ask whether infants can form similar judgments about potential social partners based solely on their tone of voice. In Experiment 1, we presented 10.5‐month‐olds with two visually neutral puppets. One puppet spoke in a positive affect and the other spoke in a negative affect. When the puppets were placed within reach of the infants, infants selected the formerly positive puppet. This preference disappeared when the voices were paired with nonsocial objects (Experiment 2). In Experiment 3, 10.5‐month‐olds were once again exposed to the same emotionally negative and positive voices. However, no visual characters were present. At test, infants’ visual orientation controlled how long they heard the neutral versions of each voice. Here, infants listened longer to the neutral voice of the formerly positive speaker. That is, just as in Experiment 1, infants’ preferences for the emotionally neutral test stimuli were shaped by their earlier exposure to emotionally charged recordings of that voice. Our findings provide convergent evidence to suggest that infants possess sophisticated social evaluation abilities, preferring to interact with prosocial over antisocial others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".