Seeing Ɔ, remembering C: Illusions in short-term memory
Bibliographic record
Abstract
Perception can be shaped by our expectations, which can lead to perceptual illusions. Similarly, long-term memories can be shaped to fit our expectations, which can generate false memories. However, it is generally assumed that short-term memory for percepts formed just 1 or 2 seconds ago accurately represents the percepts as they were at the time of perception. Here 4 experiments consistently show that within this timeframe, participants go from reliably reporting what was there (perceptual inference accurately reflecting the bottom-up input), to erroneously but with high confidence reporting what they expected to be there (memory report strongly influenced by top-down expectations). Together, these experiments show that expectations can reshape perceptual representations over short time scales, leading to what we refer to as short-term memory (STM) illusions. These illusions appeared when participants saw a memory display which contained real and pseudo-letters (i.e. mirrored letters). Within seconds after the memory display disappeared, high confidence memory errors increased substantially. This increase in errors over time indicates that the high confidence errors do not (purely) result from incorrect perceptual encoding of the memory display. Moreover, high confidence errors occurred mainly for pseudo-to-real letter memories, and much less often for real-to-pseudo-letter memories, indicating that visual similarity is not the primary cause of this memory-bias. Instead 'world knowledge' (e.g., which orientation letters usually have) appear to drive these STM illusions. Our findings support a predictive processing view of the formation and maintenance of memory in which all memory stages, including STM, involve integration of bottom-up memory input with top-down predictions, such that prior expectations can shape memory traces.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".