Humans can adapt their knowledge acquired for one problem to solve other problems of different problem domains. To seek for evidence of knowledge transfer, we investigated the human navigation behavior on the network of Wikipedia. We showed that the performance of human players is between that of the best navigator with full knowledge on the network structure of Wikipedia and of the poorest navigator with no knowledge on it. This suggests that human players transferred their knowledge on the nominal concepts onto the network structure of Wikipedia. Further, we conducted an analysis of the degree of confidence in decision making based on reinforcement learning. We found that human players might be very certain even about their first choice, from which we suspect that human players can solve the new problem by transferring their everyday knowledge successfully from the beginning and this can be a true power of knowledge transfer across problem domains.

To help others, we need to infer one’s goal and intention and make an action which complements one’s action yet to meet the underlying goal. In this study, we consider the computational mechanism how a person can infer the other’s intention and goal from his or her action, which is not completed or fails to meet the goal. As a minimal motor control task toward a goal, we analyzed single-link pendulum control tasks and its variation. By analyzing two types of pendulum control tasks, we show that a sort of fractal dimension of movements is characteristic of the difference in the underlying motor controllers. Further, using the fractal dimension as a criterion of similarity between movements, we show that the simulated pendulum controller can make an action toward the goal, toward which other’s incomplete action was made, but was not observable in behavior due to its failure.

Analogy is considered a core cognitive capacity that enables relational reasoning. It has been commonly believed that relational reasoning can be tested by the four-term analogy task of type “what is to c as b is to a?” or “a:b::c:?” in short. In this study, we challenge this common belief by investigating an alternative hypothesis that this classic task is solvable only by attributional reasoning on attributional similarities, rather than relational reasoning on the relation of the two relations. A word vector model was employed to demonstrate how these two types of reasoning were performed in the classic three-stem analogy task “a:b::c:?” and the two-stem analogy task “a:b::?:?”. The results showed that the two-stem analogy task was solvable only using relational reasoning, while the three-stem analogy task was solvable using both types of reasoning. It suggests that the two-stem analogy task is a better test for relational reasoning.

Past theoretical studies on word learning have offeredsimple sampling models as a means of explaining realword learning, with a particular goal of addressing thespeed of word learning: people learn tens of thousandsof words within their first 18 years. The present studyrevisits past theoretical claims by considering a more re-alistic word frequency distribution in which a large num-ber of words are sampled with extremely small probabil-ities (e.g., according to Zipf’s law). Our new mathemati-cal analysis of a recently-proposed simple learning modelsuggests that the model is unable to account for wordlearning in feasible time when the distribution of wordfrequency is Zipfian (i.e., power-law distributed). Toameliorate the difficulty of learning real-world word fre-quency distributions, we consider a type of active, self-directed learning in which the learner can influence theconstruction of contexts from which they learn words.We show that active learners who choose optimal learn-ing situations can learn words hundreds of times fasterthan passive learners faced with randomly-sampled situ-ations. Thus, in agreement with past empirical studies,we find theoretical support for the idea that statisticalstructure in real-world situations–potentially structuredfor learning by both a self-directed learner, and by abeneficent teacher–is a potential remedy for the patho-logical case of learning words with Zipf-distributed fre-quency.

Past mechanistic accounts of children’s word learningclaim that a simple type of cross-situational learning ispowerful enough to match observed rates of learning,even in quite ambiguous situations. However, a limita-tion in some of these analyses is their reliance on an un-realistic assumption that the learner only hears a word insituations containing the intended referent. This studyanalyzed a more general type of cross-situational learn-ing based on the relative frequency of word-object pairs,and found it to be slower than the simple mechanismanalyzed in prior work. We then analytically exploredwhether relative-frequency learning can be improved byincorporating the mutual exclusivity (ME) principle–an assumption that words map to objects 1-to-1. Ouranalyses show that with a certain type of correlation inword-to-word relationship, ME makes relative frequencylearning as efficient as fast-mapping, which can learn aword in one exposure.