Attitudes and Beliefs Toward Computerized Cognitive Training in the General Population
Bibliographic record
Abstract
Introduction: In recent years, computerized cognitive training programs have been developed commercially for widespread public consumption. Despite early enthusiasm, whether these programs enhance cognitive abilities in healthy adults is a contentious area of investigation. Given the mixed findings in the literature, researchers are beginning to investigate how beliefs and attitudes towards cognitive training impact motivation, expectations, and gains after cognitive training. Method: We collected survey data from 497 North American participants from Amazon’s Mechanical Turk (MTurk). This survey asked novel questions regarding respondents’ beliefs about the effectiveness of cognitive training for improving different domains of cognition, mood, and daily life; beliefs about whether computerized cognitive training programs are supported by research; and whether impressions of cognitive training have improved or worsened over time. Results: Almost half of the surveyed participants had used computerized cognitive training, and respondents with a self-reported psychological or neurological disorder were more likely to have used cognitive training platforms than participants without such conditions. Motivations for using cognitive training included curiosity; to improve or maintain cognition; to prevent cognitive decline; and/or for enjoyment or fun. Participants believed that computerized cognitive training is somewhat effective for improving mood and cognition across a variety of domains. Greater age and fewer years of education predicted perceived effectiveness of computerized cognitive training. Finally, participants largely reported unchanged opinions of cognitive training platforms over time. Conclusions: Our study suggests the need for future research regarding the general population’s beliefs and attitudes towards computerized cognitive training, along with knowledge translation for relevant stakeholders.
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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".