We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (shrink)

Participants compared the mental capacities of various human and nonhuman characters via online surveys. Factor analysis revealed two dimensions of mind perception, Experience and Agency. The dimensions predicted different moral judgments but were both related to valuing of mind.

The uncanny valley—the unnerving nature of humanlike robots—is an intriguing idea, but both its existence and its underlying cause are debated. We propose that humanlike robots are not only unnerving, but are so because their appearance prompts attributions of mind. In particular, we suggest that machines become unnerving when people ascribe to them experience, rather than agency. Experiment 1 examined whether a machine’s humanlike appearance prompts both ascriptions of experience and feelings of unease. Experiment 2 tested whether a machine capable (...) of experience remains unnerving, even without a humanlike appearance. Experiment 3 investigated whether the perceived lack of experience can also help explain the creepiness of unfeeling humans and philosophical zombies. These experiments demonstrate that feelings of uncanniness are tied to perceptions of experience, and also suggest that experience—but not agency—is seen as fundamental to humans, and fundamentally lacking in machines. (shrink)

This article proposes a new model of human concept learning that provides a rational analysis of learning feature‐based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space—a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well‐known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7‐feature concepts—a more natural setting in several (...) ways—and again finds that the model explains human performance. (shrink)

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form—where form could be a tree, ring, chain, grid, etc.. Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we (...) introduce a new computational model of how organizing structure can be discovered, utilizing a broad hypothesis space with a preference for sparse connectivity. Given that the inductive bias is more general, the model's initial knowledge shows little qualitative resemblance to some of the discoveries it supports. As a consequence, the model can also learn complex structures for domains that lack intuitive description, as well as predict human property induction judgments without explicit structural forms. By allowing form to emerge from sparsity, our approach clarifies how both the richness and flexibility of human conceptual organization can coexist. (shrink)