This paper reviews how the "usefulness" of representations is conceptualized across philosophy, neuroscience, and computer science. It proposes a three-level framework—Representations as Information, Usable, and Used—to organize diverse perspectives on neural representations.

This paper argues that the widespread adoption of frictionless Generative AI risks "cognitive agency surrender" by exploiting human cognitive miserliness and inducing automation bias. It proposes "Scaffolded Cognitive Friction," using Multi-Agent Systems as computational Devil's Advocates to introduce beneficial epistemic tension, alongside multimodal computational phenotyping to measure its effect.

This paper introduces SymSeqBench, a unified framework comprising SymSeq and SeqBench, for generating, manipulating, and analyzing structured symbolic sequences. It offers a comprehensive benchmark suite for evaluating artificial learning systems across various cognitively relevant domains, grounded in Formal Language Theory.