The distribution of word orders across languages is highly nonuniform, with subject-verb-object (SVO) and subject-object-verb (SOV) orders being prevalent. Recent work suggests that the SOV order may be the default in human language. Why, then, is SVO order so common? We hypothesize that SOV/SVO variation can be explained by language users' sensitivity to the possibility of noise corrupting the linguistic signal. In particular, the noisy-channel hypothesis predicts a shift from the default SOV order to SVO order for semantically reversible events, for which potential ambiguity arises in SOV order because two plausible agents appear on the same side of the verb. We found support for this prediction in three languages (English, Japanese, and Korean) by using a gesture-production task, which reflects word-order preferences largely independent of native language. Other patterns of crosslinguistic variation (e.g., the prevalence of case marking in SOV languages and its relative absence in SVO languages) also straightforwardly follow from the noisy-channel hypothesis.

The uncanny valley posits that very human-like robots are unsettling, a phenomenon amply demonstrated in adults but unexplored in children. Two hundred forty 3- to 18-year-olds viewed one of two robots (machine-like or very human-like) and rated their feelings toward (e.g., "Does the robot make you feel weird or happy?") and perceptions of the robot's capacities (e.g., "Does the robot think for itself?"). Like adults, children older than 9 judged the human-like robot as creepier than the machine-like robot-but younger children did not. Children's perceptions of robots' mental capacities predicted uncanny feelings: children judge robots to be creepy depending on whether they have human-like minds. The uncanny valley is therefore acquired over development and relates to changing conceptions about robot minds.

Preverbal infants engage in statistical and probabilistic inference to learn about their linguistic and physical worlds. Do they also employ probabilistic information to understand their social world? Do they infer underlying causal mechanisms from statistical data? Here, we show, with looking‐time methods, that 10‐month‐olds attend to statistical information to understand their social–psychological world and plausibly infer underlying causal mechanisms from violations of physical probabilities.

Children acquire extensive knowledge from others. Today, children receive information from not only people but also technological devices, like social robots. Two studies assessed whether young children appropriately trust technological informants. One hundred and four 3-year-olds learned the names of novel objects from either a pair of social robots or inanimate machines, where 1 informant was previously shown to be accurate and the other inaccurate. Children trusted information from an accurate social robot over an inaccurate one, as they have been shown to do for human informants, and even more so when they perceived the robots as having psychological agency. However, children did not learn selectively from inanimate, but accurate, machines. Children can learn from technological devices (e.g., social robots) but trust their information more when the device appears to have mindful agency.