Expectation and IOR: Effects on eye movements and ESP
Bibliographic record
Abstract
There is renewed interest in the idea that expectation contributes to the inhibition of return (IOR) effect in spatial orienting. Spalek (2007) asked participants to report where they thought a target was programmed to appear (although a target never actually appeared) following presentation of an abrupt-onset peripheral cue. Participants reported "expecting" a target to appear opposite the cue with greater-than-chance likelihood, and to appear at the cued location with lower-than-chance likelihood. However, using a different task in which participants guessed in which of four locations a target was hidden, Danziger and Rafal (2009) reported the opposite result: greater-than-chance selection of the cued location. The present study aimed to determine why opposite patterns of results occurred in these two studies. In Experiment 1, a procedure similar to Spalek (2007) was used, and similar results were observed. In Experiment 2, participants shifted their eyes to the location where they expected the target to appear following an abrupt-onset cue, triggering the appearance of the target. In contrast, to Experiment 1, participants made "expectation" eye movements to the cued location with greater-than-chance likelihood. A potential key difference between Experiments 1 and 2 is that expectations could be confirmed or disconfirmed by target presentation in Experiment 2 but not in Experiment 1. The procedure in Experiment 3 replicated Experiment 1 but with targets presented following each response. Indeed, the results of Experiment 3 resembled those of Experiment 2. The results of these experiments demonstrate that there is unlikely to be one foolproof way to measure "expectation", and that behaviours that appear to measure expectation are sensitive to task-specific factors that can lead to either of two conclusions; that a pattern of measured expectation is consistent with the behavioural IOR effect, or that a pattern of measured expectation is completely counter the behavioural IOR effect. Meeting abstract presented at VSS 2014
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".