On the Reliability of Laterality Effects in a Dichotic Emotion Recognition Task
Bibliographic record
Abstract
The present study examined the reliability of a dichotic emotion recognition task under three different conditions presumed to provide different levels of control of attention deployment. Sixty right-handed undergraduate students were randomly assigned to one of the three conditions. The task involved dichotic presentation of words pronounced in an angry, happy, sad, or neutral, emotional tone. The free recall condition applied no attention control. It required participants to report the emotion heard in each ear. Both the monitoring and ABX conditions presumably forced participants to divide their attention equally between the ears. In monitoring, participants were required to indicate when a target emotion was presented to either ear. Finally, the ABX condition required participants to indicate whether the emotional tone of a binaural stimulus matched either of the dichotic stimuli on the same trial. Results showed the expected left ear advantage (LEA). In addition, the monitoring and ABX procedures were found to be somewhat more reliable than the free recall procedure. The present study suggests that control of attention deployment strategies is critical in the reliable assessment of laterality. Issues related to task difficulty and its effect on the reliability and magnitude of laterality effects are also discussed.
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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 | 0.006 | 0.044 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".