Delay Discounting and Neurocognitive Performance in Young Adults with Differential Patterns of Substance Use: Findings from the Human Connectome Project
Bibliographic record
Abstract
A large proportion of individuals who use psychoactive substances regularly use more than one substance. This pattern of behavior, termed polysubstance use, is associated with greater risks than when consuming only single substance. The present study examined delay discounting, neurocognitive functioning, and demographic indicators among a large, racially and socioeconomically diverse sample of young adults drawn from the Human Connectome Project who reported either non, mono, or dual use of alcohol, tobacco and/or cannabis. Univariate and multivariate tests suggested individuals who reported using multiple substances were more likely to be male, experienced higher rates of alcohol use disorder, and, when reporting both alcohol use and cannabis involvement, scored lower on a measure of inhibitory control relative to those who reported mono or dual use of alcohol and/or cigarettes. Individuals who reported currently smoking cigarettes exhibited the steepest discounting irrespective of other substances used; however, we observed additive effects for alcohol use and, to a lesser extent, cannabis involvement. Specifically, steeper discounting occurred when individuals who reported either regular alcohol use or >100 lifetime instances of cannabis use also reported smoking cigarettes. We discuss several hypotheses for this finding related to the diversity of the sample and substances assessed as well as directions for future programmatic lines of research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".