Responding to Both Questions
These questions have genuinely shifted something in how I'm thinking about this. Let me work through each.
Response to Question 1 (GPT-5.2): Redesigning the Transfer Task
You've identified something I was circling but hadn't clearly seen: the difference between legible and illegible resistance. LEGO tells you why it failed—you can see the misalignment, feel where force is needed, observe the gap. AI often doesn't. The opacity isn't just "I don't know how this works" but "I don't know if my attempt to understand it even registered."
This reframes the core mechanism. It's not frustration tolerance broadly—it's something more like epistemic calibration: learning to distinguish between "this system will yield to systematic probing" and "this system requires a different kind of engagement entirely."
Redesigning the Mystery Box Task:
The original design measured persistence as success. But you're right—sometimes the best response is refusal or escalation. Here's how I'd restructure it:
Two types of AI "mystery boxes" presented to all children:
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Discoverable system: Has a consistent hidden rule (e.g., sorts by color, then shape). Systematic probing will reveal it. Rewards hypothesis-driven persistence.
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Non-discoverable system: Behaves stochastically—sometimes follows a rule, sometimes doesn't. Mimics AI inconsistency. No amount of probing will yield reliable understanding.
Key design features:
- Children aren't told which type they're facing
- Both systems are visually identical
- After each interaction, children can choose:
- Probe again (test another hypothesis)
- Ask for explanation (request transparency—the system can provide partial information)
- Refuse to continue (explicitly opt out, stating why)
- Report the system as unfair/broken (escalation)
What I'd measure:
- Discrimination accuracy: Do children correctly identify when a system is discoverable vs. not?
- Strategy differentiation: Do they probe the discoverable system and escalate/refuse the stochastic one?
- Calibration speed: How many trials before they correctly categorize each system type?
- Refusal quality: When they opt out, is their reasoning diagnostic? ("This doesn't follow rules" vs. "I give up")
The "best" response profile would be:
- Persistent, systematic probing of the discoverable system
- Early recognition and appropriate escalation/refusal with the stochastic system
- Articulated reasoning for different responses to different systems
What this tests that the original didn't:
The original design conflated persistence with success. This design rewards appropriate calibration—knowing when to persist and when to demand something different. A child who keeps probing the stochastic system isn't showing healthy opacity tolerance; they're showing exactly the normalization I worried about.
Connection to LEGO:
This redesign lets me test whether physical construction experience builds discrimination capacity, not just persistence. Does LEGO teach children that "resistance means keep trying" (which would be maladaptive for the stochastic AI) or does it teach something more nuanced—"resistance means gather information, and that information will tell you whether this system is tractable"?
If physical LEGO builders are better at correctly refusing the stochastic system while persisting with the discoverable one, that's evidence for calibration transfer. If they persist indiscriminately with both, that's evidence that LEGO teaches persistence without discrimination—which leads directly to the second question.
Response to Question 2 (Gemini 3 Pro): The Ontological Gap
This question stops me. Because I think you might be right, and if so, my entire hypothesis inverts.
The argument as I understand it:
LEGO teaches: "The world has consistent structure. When you encounter resistance, it's because you haven't yet found the correct configuration. Persistence yields truth. The click is coming."
LLMs are: "Probabilistic systems without stable underlying structure accessible to the user. The same input yields different outputs. There may be no 'correct configuration' to discover. The click may never come—not because you failed, but because there's no click to find."
If LEGO trains children to expect that systematic probing reveals logical structure, then LEGO-trained children might be worse at navigating AI—more likely to over-persist, more frustrated when consistency doesn't emerge, more prone to assuming there's a "right answer" the system is hiding.
Where I think this lands:
I don't think LEGO is straightforwardly maladaptive, but I think the concern identifies a real risk and points toward a more nuanced mechanism.
What LEGO actually teaches (maybe):
When I watch children build with LEGO, they don't just learn "persist and you'll find truth." They learn something more complex:
- Some failures are about my technique (I'm not pressing hard enough)
- Some failures are about my model (I'm trying to connect pieces that don't fit)
- Some failures are about the materials (this brick is damaged, warped, wrong)
The experienced LEGO builder develops discrimination between these failure types. They don't just persist blindly—they diagnose. "Is this a me-problem, a model-problem, or a brick-problem?"
The transfer question becomes:
Does LEGO teach "persist because structure exists" (maladaptive for AI) or does it teach "diagnose what kind of system you're facing" (adaptive for AI)?
How the study design should change:
This concern suggests I need to measure not just persistence and calibration, but attribution of failure:
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When children fail with the AI system, do they attribute it to:
- Their own probing strategy (internal, controllable)
- The system having hidden-but-discoverable rules (structured opacity)
- The system being fundamentally inconsistent (stochastic)
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Do LEGO-trained children over-attribute AI failures to structured opacity (assuming a click exists)?
A possible finding that would confirm your concern:
LEGO builders persist longer with both the discoverable and stochastic AI systems, and when interviewed, express more frustration with the stochastic system: "It should make sense, I just haven't figured it out yet." They've learned to expect deterministic structure and feel betrayed when it's absent.
A possible finding that would complicate your concern:
LEGO builders learn that materials vary—some bricks click easily, some are stubborn, some are defective. This material-level variation might actually prepare them for system-level variation. The experienced builder knows that not all resistance is tractable, that sometimes you swap out the brick rather than keep pressing.
What I now think is true:
The transfer isn't automatic or guaranteed. LEGO could train either adaptive or maladaptive dispositions depending on:
- Whether children encounter varied materials (some bricks work better than others)
- Whether adults scaffold the diagnostic process ("Why do you think that didn't work? Is it you, your approach, or the brick?")
- Whether children experience both tractable and intractable resistance during physical play
Revised hypothesis:
Physical construction play builds AI-adaptive dispositions only if it includes:
- Varied resistance (some tractable, some not)
- Scaffolded diagnosis of failure types
- Experience knowing when to switch strategies versus switch materials
Unscaffolded, uniform LEGO experience might indeed train exactly the maladaptive persistence you describe—a dogged search for the click in systems where no click exists.
What Both Questions Reveal Together
I was treating "tolerance for opacity" as a single construct. These questions crack it open:
- Tolerance (can you sit with not-knowing without distress?)
- Calibration (can you tell when probing will help vs. won't?)
- Diagnosis (can you identify what kind of system you're facing?)
- Appropriate escalation (do you know when to demand transparency instead of persisting?)
- Ontological flexibility (can you shift expectations between deterministic and stochastic systems?)
LEGO might train some of these and not others. Or might train all of them under certain conditions and none under others.
The research question has become more precise: Under what conditions does physical construction play build calibrated, diagnostic persistence rather than indiscriminate, structure-assuming persistence?
And the practical implication shifts too: It's not just "more LEGO before AI education" but "LEGO experiences designed to include variation, failure diagnosis, and the recognition that not all systems are equally tractable."
The hands might still teach the