Bibliographic record
Abstract
In recent years, computational neuroscientists have suggested that human behaviour, including perception, occurs in a manner consistent with Bayesian inference. According to the Bayesian ideal observer model, the observer combines cues from multiple sensory streams as a weighted average based on each cue’s reliability. Most cue-combination research has focused on integration of cues between sensory modalities or within the visual modality. Cue combination within the tactile modality has been relatively rarely studied, and it is still not known whether cues from individual digits combine optimally. In this thesis, we use the ideal observer model to determine whether cues from three different digits are combined optimally. We predicted that cues from multiple digits would be combined according to the optimal cue combination model. To test our hypothesis, we devised a two-interval forced choice (2IFC) task where participants had to discriminate the distal/proximal location of a 1-mm thick edge across the fingerpad(s) of the index (D2), middle (D3), and ring (D4) fingers. We used a Bayesian adaptive method, the ψ method, to compute participants’ psychometric functions for single-digit (D2, D3, and D4) and multiple-digit (D23, D24, D34, and D234) conditions. We determined the stimulus level ∆x, the distance (mm) between the distal and proximal stimuli locations, at 76% correct probability. This distance corresponds to a sensitivity index d'=1 and is the σ value of the participant’s stimulus measurement distribution. We then used the single-digit σ values to predict optimal cue combination for the multiple-digits combinations. We did not observer optimal cue-combination between the digits in this study. We outline potential implications the results of this experimental have on determining how the nervous system combines cues between digits, focusing on theoretical and experimental updates to the experiment that might result in the observation of optimal cue combination between digits.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".