Spatial and attentional influences on nonverbal magnitude discrimination in depressed and non-depressed individuals / by Stewart A. Madon.
Bibliographic record
Abstract
Numbers have been called the universal language. Yet the psychophysical properties of numerosity remain elusive, particularly as they relate to clinical disorders such as depression. It is known that the estimation and discrimination of magnitude remain a culturally unbiased phenomenon that could be used to negate the error associated with the use of linguistic or \npictorial stimuli during assessments. Also, recent imaging studies have proposed that different processing streams are associated with the spatial perception of foreground and background image characteristics, with contextual binding of these two fields occurring in the right hippocampus. Using a novel three-dimensionally shadowed circularly concentric centersurround stimulus in which dot arrays were placed in either or both of the shadowed foreground and background fields, we hoped to ascertain whether depression can affect the performance on a magnitude estimation task. Changing dot arrays in either of the two fields across two time epochs, participants were required to accurately indicate which of the intervals had the greater number. In some blocks, a ?red? coloured field was used to cue \nwhich of the two fields contained the changing dots. The inclusion of cue conditions allowed for the measurement of potential hippocampal and attentional dysfunctions in depressed individuals. Six depressed and 34 control (nondepressed) participants were recruited from psychology classes at Lakehead University. Dependent variables used to assess performance \nwere Reaction Time (RT) and difference threshold (magnitude estimation accuracy). Our results showed that the depressed performed significantly worse than controls in overall accuracy, but that no RT differences were observed between the groups. Further, we noted some interesting increases in response latency relating to atypical vs. typical foreground/background arrangements. Finally, we found support for the theory that the dorsal visual stream can also process task-specific visual information typically associated with ventral visual pathway. \n \nStudy participants : Lakehead University Psychology students (Thunder Bay, Northwestern Ontario).
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".