The Meandering Course from Psycholinguistics to Cognitive Science and Neuroimaging: The Organization of (Inter-) Disciplinary Research in the History of the Max Planck Institute for Psycholinguistics
Bibliographic record
Abstract
This article is a historical and philosophical exploration of the psycholinguistics work at the Max Planck Institute for Psycholinguistics (MPIPL) from its very beginnings until 2002. Historical archival and interview materials provide useful and important evidence on the meandering course of the research programs at the MPIPL that so tidily mirror the historical changes in the larger field. From the psycholinguistics trajectory of Jerome Bruner (1915–2016) to recent behavioural neuroscience work, pioneered for example by psychiatrist Detlev Ploog (1920–2005), what we can learn from these traditions of psychology is that they harbour potential still not fully harvested by neuroscience. Pim Levelt (b. 1938) and his colleagues at the MPIPL observed these important changes in their research programs over a period of three decades, exemplified by their work on the neural basis of vocalization. In fact, both Levelt and Ploog had for some time been interested in the comparative aspects and differences of corticopyramidal tract instantiations of speech-motor systems while also considering Bruner’s psychological work in regard to this endeavour. Yet, the “switch” to neurolinguistics and neurobehaviourism at the Nijmegen institute occurred only with the late 1990s and early 2000s, fomented especially by collaboration with the Donders Institute at Radboud University in Nijmegen.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".