In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

The cultural origins of human cognition

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

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Building machines that learn and think like people.

饒斯對功效主義（Utilitarianism）的批評

The World Atlas of Language Structures Online

Social cognition depends on our capacity for ‘mentalizing’, or explaining an agent’s behaviour in terms of their mental states. The development and neural substrates of mentalizing are well-studied, but its computational basis is only beginning to be probed. Here we present a model of core mentalizing computations: inferring jointly an actor’s beliefs, desires and percepts from how they move in the local spatial environment. Our Bayesian theory of mind (BToM) model is based on probabilistically inverting artificial-intelligence approaches to rational planning and state estimation, which extend classical expected-utility agent models to sequential actions in complex, partially observable domains. The model accurately captures the quantitative mental-state judgements of human participants in two experiments, each varying multiple stimulus dimensions across a large number of stimuli. Comparative model fits with both simpler ‘lesioned’ BToM models and a family of simpler non-mentalistic motion features reveal the value contributed by each component of our model. A Bayesian theory of mind model is shown to infer and quantify the mental state and judgements of humans in decision-making scenarios. The model is a key step towards enabling machines to ‘intuit’ human thoughts and desires.

Rational quantitative attribution of beliefs, desires and percepts in human mentalizing

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

What determines how languages categorize colors? We analyzed results of the World Color Survey (WCS) of 110 languages to show that despite gross differences across languages, communication of chromatic chips is always better for warm colors (yellows/reds) than cool colors (blues/greens). We present an analysis of color statistics in a large databank of natural images curated by human observers for salient objects and show that objects tend to have warm rather than cool colors. These results suggest that the cross-linguistic similarity in color-naming efficiency reflects colors of universal usefulness and provide an account of a principle (color use) that governs how color categories come about. We show that potential methodological issues with the WCS do not corrupt information-theoretic analyses, by collecting original data using two extreme versions of the color-naming task, in three groups: the Tsimane', a remote Amazonian hunter-gatherer isolate; Bolivian-Spanish speakers; and English speakers. These data also enabled us to test another prediction of the color-usefulness hypothesis: that differences in color categorization between languages are caused by differences in overall usefulness of color to a culture. In support, we found that color naming among Tsimane' had relatively low communicative efficiency, and the Tsimane' were less likely to use color terms when describing familiar objects. Color-naming among Tsimane' was boosted when naming artificially colored objects compared with natural objects, suggesting that industrialization promotes color usefulness.

https://www.pnas.org/content/pnas/114/40/10785.full.pdf

Color naming across languages reflects color use

Children's understanding of the costs and rewards underlying rational action.

We review the idea that Theory of Mind—our ability to reason about other people's mental states—can be formalized as inverse reinforcement learning. Under this framework, expectations about how mental states produce behavior are captured in a reinforcement learning (RL) model. Predicting other people’s actions is achieved by simulating a RL model with the hypothesized beliefs and desires, while mental-state inference is achieved by inverting this model. Although many advances in inverse reinforcement learning (IRL) did not have human Theory of Mind in mind, here we focus on what they reveal when conceptualized as cognitive theories. We discuss landmark successes of IRL, and key challenges in building human-like Theory of Mind.

Julian Jara-Ettinger

Papers

Rational quantitative attribution of beliefs, desires and percepts in human mentalizing

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

Color naming across languages reflects color use

Children's understanding of the costs and rewards underlying rational action.

Theory of mind as inverse reinforcement learning