A key challenge in language acquisition is learning morphological transforms relating word roots to derived forms. Unsu-pervised learning algorithms can perform morphological segmentation by finding patterns in word strings (e.g. Goldsmith,2001), but struggle to distinguish valid segmentations from spurious ones because they look only at sequences of characters(or phonemes) and ignore meaning. For example, a system that correctly discovers ¡add -s¿ as a valid suffix from seeingdog, dogs, cat, cats, etc, might incorrectly infer that ¡add -et¿ is also a valid suffix from seeing bull, bullet, mall, mallet, etc.We propose that learners could avoid these errors with a simple semantic assumption: morphological transforms shouldapproximately preserve meaning. We extend an algorithm from Chan (2008) by integrating proximity in vector-spaceword embeddings as a criterion for valid transforms. On the Brown CHILDES corpus, we achieve both higher accuracyand broader coverage than the purely syntactic approach.

Theory of Mind enables us to attribute mental states to others. But we not only make inferences about mental states (like what someone believes or wants), but about mental processes (like if someone is distracted or whether they remember something). Here, we present a computational formalization of these kinds of inferences. We propose that inferences about mental processes are structured around a principle of rational mental effort: the expectation that other people allocate mental resources rationally so as to minimize thinking costs incurred while pursuing their goals. We develop this theory into a computational model in the context of the Rush Hour puzzle game. In two behavioral experiments testing different inferences about mental processing, we find that our model predicts participant judgments. This work advances our understanding of the richness of the human mind’s ability to think about other minds, and even about thinking itself.

We readily get intuitions about a problem's complexity, how much thinking it will require to solve, and how long it should take, both for ourselves and for others. These intuitions allow us to make inferences about other people's mental processing---like whether they are thinking hard, remembering, or merely mind-wandering. But where do these intuitions come from? Prior work suggests that people try solving problems themselves so as to draw inferences about another person's thinking. If we use our own thinking to build up expectations about other people, does this introduce biases into our judgments? We present a behavioral experiment testing for effects of first-person thinking speed on judgments about another person's thinking in the puzzle game Rush Hour. Although participants overwhelmingly reported solving the puzzles themselves, we found no evidence for participants' thinking speeds influencing their judgments about another person's thinking, suggesting that people can correct for first-person biases.