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Theory of Mind enables us to represent and reason about other people's mental states like beliefs and knowledge. By considering what other people know, this allows us to strategically construct believable lies. Previous work has shown that people construct lies to be consistent with others' beliefs even when those beliefs differ from their own. However, in most real world cases, we don't know everything that the other person knows. We propose that to produce believable lies, the sender considers what private information the receiver may have. Here, we develop our theory into a computational model and test it in a novel paradigm that allows us to distinguish between knowledge shared between the lie sender and receiver and knowledge private to the receiver. Our main model successfully captures how people lie in this paradigm over alternative models. Overall, our work furthers our understanding of human social cognition in adversarial situations. 

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 people overwhelmingly reported solving the puzzles themselves, we found no evidence for participants' thinking speeds influencing their judgments about the other person's thinking, suggesting that people can correct for first-person biases. 

Abstract Why have core knowledge? Standard answers typically emphasize the difficulty of learning core knowledge from experience, or the benefits it confers for learning about the world. Here, we suggest a complementary reason: Core knowledge is critical for learning not just about the external world, but about the mind itself. 

How veridical is perception? Rather than representing objects as they actually exist in the world, might perception instead represent objects only in terms of the utility they offer to an observer? Previous work employed evolutionary modeling to show that under certain assumptions, natural selection favors such “strict‐interface” perceptual systems. This view has fueled considerable debate, but we think that discussions so far have failed to consider the implications of two critical aspects of perception. First, while existing models have explored single utility functions, perception will often serve multiple largely independent goals. (Sometimes when looking at a stick you want to know how appropriate it would be as kindling for a campfire, and other times you want to know how appropriate it would be as a weapon for self‐defense.) Second, perception often operates in an inflexible, automatic manner—proving “impenetrable” to shifting higher‐level goals. (When your goal shifts from “burning” to “fighting,” your visual experience does not dramatically transform.) These two points have important implications for the veridicality of perception. In particular, as the need for flexible goals increases, inflexible perceptual systems must become more veridical. We support this position by providing evidence from evolutionary simulations that as the number of independent utility functions increases, the distinction between “interface” and “veridical” perceptual systems dissolves. Although natural selection evaluates perceptual systems only on their fitness, the most fit perceptual systems may nevertheless represent the world as it is.