Human concept learning is typically active: learners choose which instances to query or test in order to reduce uncertainty about an underlying rule or category. Active concept learning must balance informativeness of queries against the stability of the learner that generates and scores hypotheses. We study this trade-off in a neuro-symbolic Bayesian learner whose hypotheses are executable programs proposed by a large language model (LLM) and reweighted by Bayesian updating. We compare a Rational Active Learner that selects queries to maximize approximate expected information gain (EIG) and the human-like Positive Test Strategy (PTS) that queries instances predicted to be positive under the current best hypothesis. Across concept-learning tasks in the classic Number Game, EIG is effective when falsification is necessary (e.g., compound or exception-laden rules), but underperforms on simple concepts. We trace this failure to a support mismatch between the EIG policy and the LLM proposal distribution: highly diagnostic boundary queries drive the posterior toward regions where the generator produces invalid or overly specific programs, yielding a support-mismatch trap in the particle approximation. PTS is information-suboptimal but tends to maintain proposal validity by selecting "safe" queries, leading to faster convergence on simple rules. Our results suggest that "confirmation bias" may not be a cognitive error, but rather a rational adaptation for maintaining tractable inference in the sparse, open-ended hypothesis spaces characteristic of human thought.

翻译：人类概念学习通常是主动的：学习者选择查询或测试哪些实例，以减少对底层规则或类别的不确定性。主动概念学习必须在查询的信息量与生成和评分假设的学习者稳定性之间取得平衡。我们研究了一个神经符号贝叶斯学习器中的这种权衡，其假设是由大型语言模型（LLM）提出的可执行程序，并通过贝叶斯更新进行重新加权。我们比较了理性主动学习器（选择查询以最大化近似期望信息增益，EIG）和类人正向测试策略（PTS，查询在当前最佳假设下预测为正的实例）。在经典数字游戏的概念学习任务中，EIG在证伪必要的情况下（例如复合规则或包含例外的规则）是有效的，但在简单概念上表现不佳。我们将此失败归因于EIG策略与LLM提议分布之间的支持度不匹配：高度诊断性的边界查询将后验推向生成器产生无效或过于具体程序的区域，导致粒子近似中的支持度不匹配陷阱。PTS在信息上并非最优，但倾向于通过选择"安全"查询来维持提议的有效性，从而在简单规则上实现更快的收敛。我们的结果表明，"确认偏误"可能并非认知错误，而是为在人类思维特有的稀疏、开放式假设空间中维持可处理的推理而做出的理性适应。