The past is “fake”: Facilitated processing of wishes compared with counterfactual conditionals in 4- and 5-year-olds
Bibliographic record
Abstract
Understanding counterfactual utterances, such as "If dinosaurs were still alive, we could see them in the zoo," requires entertaining alternatives to reality. Children's relatively late comprehension of counterfactual language is often attributed to its cognitive complexity. However, counterfactuals also present linguistic challenges, such as the misleading "fake" past tense that signals counterfactuality rather than referencing a past event. In our study, we investigated whether linguistic complexity influences children's counterfactual comprehension. We compared two constructions that differ in their dedication to expressing counterfactual meaning and examined whether the "fake" past tense leads children to misinterpret counterfactuals as referring to real past events. The results of a referent selection task with 23 American English-speaking 4- and 5-year-olds and 30 adults show that the performance of children and some adults was facilitated in the linguistically more transparent counterfactual wish-constructions (e.g., "I wish he had a banana milkshake") compared with more complex counterfactual conditionals ("If he had a banana milkshake, he would give me a banana coin"). This suggests that difficulties in comprehending counterfactual conditionals may stem more from linguistic challenges than from an inability to reason counterfactually. We argue that the counterfactual's misleading morphological information-the "fake" past-sometimes leads to misinterpretation, by children and even some adults, as referring to a "real" past. Together, these results highlight how the clarity of a construction's linguistic form affects both the age at which it is acquired and how easily it is processed, challenging the view that counterfactual comprehension difficulties are purely conceptual.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".