When we look around us, some things look “alive,” others do not. What is it to “look alive”—to perceive animacy? Empirical work supports the view that animacy is genuinely perceptual. We should construe perception of animacy as perception of agents and behavior. This proposal explains how static and dynamic animacy cues relate, and explains how animacy perception relates to social cognition more broadly. Animacy perception draws attention to objects that are apt to be well‐understood folk psychologically, enabling us to marshal (...) our folk psychological resources efficiently. (shrink)

What’s the difference between those psychological posits that are ‘me” and those that are not? Distinguishing between these psychological kinds is important in many domains, but an account of what the distinction consists in is challenging. I argue for Psychological Constructionism: those psychological posits that correspond to the kinds within folk psychology are personal, and those that don’t, aren’t. I suggest that only constructionism can answer a fundamental challenge in characterizing the personal level – the plurality problem. The things that (...) plausibly qualify as personal are motley. Other attempts at accounting for the personal level either cannot accommodate this plurality, or cannot explain what unifies the personal. Given arguments others have given for a pluralistic conception of folk psychology, constructionism explains and predicts this plurality in a systematic and unified way, thereby solving the plurality problem. (shrink)

How do we know about other minds on the basis of perception? The two most common answers to this question are that we literally perceive others’ mental states, or that we infer their mental states on the basis of perceiving something else. In this paper, I argue for a different answer. On my view, we don’t perceive mental states, and yet perceptual experiences often immediately justify mental state attributions. In a slogan: other minds are neither seen nor inferred. I argue (...) that this view offers the best explanation of our deeply equivocal intuitions about perception-based mental state attributions, and also holds substantial interest for the epistemology of perception more generally. (shrink)

Quilty-Dunn et al. argue that deep convolutional neural networks (DCNNs) optimized for image classification exemplify structural disanalogies to human vision. A different kind of artificial vision – found in reinforcement-learning agents navigating artificial three-dimensional environments – can be expected to be more human-like. Recent work suggests that language-like representations substantially improves these agents’ performance, lending some indirect support to the language-of-thought hypothesis (LoTH).