CogBattery: Human-Referenced Evaluation of Cognitive Task Behavior and Structure in Language Models

Dengzhe Hou, Lingyu Jiang, Fangzhou Lin, Kazunori D Yamada

CogBattery: Human-Referenced Evaluation of Cognitive Task Behavior and Structure in Language Models: 4 upvotes on Hugging Face Daily Papers, #10 of 21 papers on 2026-11-12. Day-by-day upvote history.

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It had 4 upvotes by its first day, 4 upvotes by the next day, 4 upvotes by day 3, 4 upvotes by day 7, and has 4 now. It ranked #83 of 133 papers in its week and #244 of 340 in its month. Its code repository has 1 GitHub stars.

Human cognitive tasks let us compare language models with humans under controlled conditions. Yet a model can reproduce the direction of a human effect while making different errors. CogBattery evaluates 54 open-weight language models on text adaptations of 13 cognitive paradigms, comparing each with human data from its source study. Like humans, models respond more to n-back lures than to controls, yet they make far more false alarms on ordinary non-targets. Most models' reported confidence separates correct from incorrect answers less well than humans' does, and most always choose the same wager, including models that classify accurately. These patterns span many model families. Across tasks, model scores share two dimensions. The first is closely tied to scale, yet larger models are not consistently closer to humans. Predefined cognitive groupings show no reliable predictive advantage over random groupings of the same size. These results show that matching human effect directions does not establish human-like behavior. CogBattery provides condition-level human references for testing errors and confidence reports in new models.

Paper page on Hugging Face · arXiv

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