Minding the Gap: Understanding the Difference in Performance on Opacity and False Belief Tasks
Bibliographic record
Abstract
Two tasks have commonly been used to investigate the developing representations of children: false belief (FB) and opacity tasks (described in more detail below). While both tasks require tracking of another’s representations, children consistently find opacity tasks harder. One possible reason for this—that the forced-choice nature of FB tasks makes them easier than opacity tasks—is the focus of the current study. Our research derives from a broader body of interdisciplinary research that includes issues in cognitive development (e.g., representational development), language development (e.g., syntax of complement clauses), and philosophy of language (e.g., propositional attitudes). In false belief tasks participants must keep track of another’s representations in order to predict her actions. For example, if Anna places an object in location A (cupboard), and then is absent while the object is moved to location B (fridge), she will represent the object as being in location A. Children who can monitor Anna’s representations will correctly predict she will look for the object in location A. Opacity tasks, like false belief tasks, also require participants to track another’s representations, but in opacity tasks the same referent has more than one term. For example, a woman may be both Sue’s mom and also a police officer. Each is correct, but replacing one term with its co-referential term can affect the truth value of the resulting sentence, setting up what is known as an ‘opaque context’ (Quine, 1995). Even if it is true, for instance, that “Mark believes the police officer rescued the cat from the tree”, it may be false that “Mark believes Sue’s mom rescued the cat from the tree” because Mark may not represent the woman as both Sue’s mom and as a police officer. To succeed in an opacity task, participants must recognize that Mark’s representation of the woman may not take in all possible representations. As stated earlier, while both false belief and opacity tasks require attention to representation, children find opacity tasks harder. Kamawar and Homer (2000) claim this is due to the structure of the tasks. In false belief tasks, an object can be represented as being at location A or B, but not at “location AB”; i.e., the situation sets up a forced choice between the two possible representations. By contrast, in opacity tasks an object can be represented as both A and B—“The woman, who is both Sue’s mom and a police officer”—and there is no forced choice required. To evaluate the above claim, we explicitly set up forced-choice situations in opacity tasks and then compared participants’ performance on these tasks against their performance on standard false belief tasks. If the claim is valid, participants should perform equally well or better on the forced-choice version of the opacity task than on the standard FB tasks. The planned total sample size for the study is 25 children 3-5 years old. To date, 15 children have taken part by completing three of each of the following four tasks: regular FB, forced-choice FB (e.g., “Where will Anna look for the object, in the cupboard or the fridge?”), opacity, and forced-choice opacity (e.g., “Does Mark think the cat was rescued by a police officer or by Sue’s mom?”). Preliminary results support the claim that the forced-choice nature of false belief tasks helps to make them easier than opacity tasks. Children’s mean performance (out of 3) was as follows: regular opacity = .40; forced-choice opacity = 1.33; standard FB = 1.88; and forced-choice FB = 2.40. As expected, children found the standard opacity tasks far more difficult than the standard FB tasks [t (14) = 5.36, p < .001]. There was also a significant difference between forced-choice opacity tasks and standard opacity tasks [t (14) = 3.51, p < .01]. However, performance on forcedchoice opacity differed from standard FB only marginally [t (14) = 2.06, p = .059]. In conclusion, preliminary analyses suggest that Kamawar & Homer’s (2000) claim has some merit. Making the options explicit improves performance on opaque contexts, but does not necessarily make it equal to standard FB. While the structural difference between opacity and FB may account for some of the difference in difficulty, it does not account for all of it.
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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 | 0.007 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".