Correcting the record on Watson, Rayner, and Little Albert: Albert Barger as “Psychology’s lost boy”.
Bibliographic record
Abstract
In 1920, John B. Watson and Rosalie Rayner attempted to condition a phobia in a young infant named "Albert B." In 2009, Beck, Levinson, and Irons proposed that Little Albert, as he is now known, was actually an infant named Douglas Merritte. More recently, Fridlund, Beck, Goldie, and Irons (2012) claimed that Little Albert (Douglas) was neurologically impaired at the time of the experiment. They also alleged that Watson, in a severe breach of ethics, probably knew of Little Albert's condition when selecting him for the study and then fraudulently hid this fact in his published accounts of the case. In this article, we present the discovery of another individual, Albert Barger, who appears to match the characteristics of Little Albert better than Douglas Merritte does. We examine the evidence for Albert Barger as having been Little Albert and, where relevant, contrast it with the evidence for Douglas Merritte. As for the allegations of fraudulent activity by Watson, we offer comments at the end of this article. We also present evidence concerning whether Little Albert (Albert Barger) grew up with the fear of furry animals, as Watson and Rayner speculated he might.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.017 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".