Visual word recognition: Attention, intention, context, and processing dynamics.
Bibliographic record
Abstract
The notion that some mental processes are "automatic" while others are "controlled" is a distinction that appears in virtually all cognition textbooks, as well as in thousands of papers and book chapters. Indeed, so entrenched is the automatic side of this distinction that various leading computational accounts make no mention of it, but instead assume it implicitly. These models, and the field more generally, assume that processing is stimulus triggered and does not need any form of attention or an intention as a preliminary. Further, the fundamental processing dynamics underlying such automatic processing is widely seen as consisting of interactive activation and autonomous in that it unfolds in the same way across contexts. I review a number of findings from my lab that lead me to a different conclusion. Visual word recognition requires a consideration and integrated understanding of automaticity, attention, intention, context, and cognitive processing. I present various findings that challenge the preeminent role ascribed to interactive activation as implemented in the dominant computational models. I conclude that, going forward, the time is due for computational models of visual word recognition (and researchers in the field more generally) to acknowledge that the findings reported here constitute benchmarks that constrain theory and present opportunities for making meaningful advances in our understanding of visual word recognition (and perhaps of cognition more generally). A few proposals for how we might think about some of these processes are offered. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".