Challenging Assumptions and Exploring New Applications of Social, Cognitive, and Evolutionary Theories of Voice Perception
Bibliographic record
Abstract
Voice perception is an integral component to social connection and communication. Using the sound of a voice we infer information about a speaker’s physical, psychological, and emotional characteristics. These impressions that are formed have the potential to influence behavioural responses to others. This thesis examines some of the fundamental assumptions of voice perception by replicating and extending their findings. In chapter 2, the assumption that exposure to voices alters how attractive voices are was tested. We did not find evidence that increased exposure to high- or low- pitched voices affected attractiveness judgements. Given that exposure to voices did not alter their perceived attractiveness, we were curious to explore if attractiveness judgements were part of first impressions people formed from voices. In chapter 3, we explored what people consciously thought about when listening to voices. We then used machine learning to organize and analyse free form descriptions of participant impressions of voices. A diverse set of topics were used when talking about voices including gender, accent, and social traits. We also confirmed that valence, dominance, and attractiveness were all important social dimensions even when participants were not prompted by researchers to evaluate traits on those domains. We followed these results by testing if the same model of dominance, trust, attractiveness, and competence applied in a practical setting. We had participants judge the voices of doctors and nurses. Low-pitched female voices were perceived as more competent sounding than male voices when they were labelled as belonging to doctors. Low-pitched voices were judged as more dominant regardless of voice sex and profession and high-pitched female voices and low-pitched male voices were judged as most attractive regardless of profession. We replicated previous findings for attractiveness and dominance perceptions and extended the work by applying it to a novel context. Our findings challenge and expand on existing assumptions of voice perception.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".