Three Ways to Be Wrong About the Past Tense

This episode challenges the old idea of a single language box in the brain, using aphasia studies and hospital control groups to show how language processing is more distributed than once thought. It also explores how different languages rely on word order or verb marking in different ways, and why those differences matter when the brain is under stress.

Thirty years ago, the authors of Rethinking Innateness argued that grammar could come out of a learner with no grammar built in. Nobody could check it. Now there's an instrument. A reading of the Inside the Black Box essay on what Elman, Bates, and Karmiloff-Smith would make of transformer language models — attention as a lookup, structure nobody installed, and the part that isn't there.

Can a model learn past tense the way children seem to, if you remove the tokenizer from the equation? This final installment tests raw letters and raw sound, probes whether a U-shaped pattern can be forced, and shows why the answer points to distributed, frequency-driven learning rather than a discrete rule.

Part 3 of 4. After two episodes arguing there is no discrete past-tense rule inside a language model, this one turns the strongest possible search on the question: gradient descent, hunting for the single internal direction that best forces goed over went.

This episode explores a modern twist on Jean Berko’s famous wug test, comparing how large language models handle made-up verbs versus familiar ones. It digs into tokenization, internal feature probes, and why neural networks seem to approximate grammar through probabilistic patterns rather than clean symbolic rules.