Using category structures to test iterated learning as a method for identifying inductive biases

Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses produced by a participant on one trial to generate the stimuli that either they or another participant will see on the next. A formal analysis of this "iterated learning" procedure, based on the assumption that the learners are Bayesian agents, predicts that it should reveal the inductive biases of these learners, as expressed in a prior probability distribution over hypotheses. This article presents a series of experiments using stimuli based on a well-studied set of category structures, demonstrating that iterated learning can be used to reveal the inductive biases of human learners.     

Action understanding as inverse planning

Humans are adept at inferring the mental states underlying other agents’ actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents’ behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent’s behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an “intentional stance” [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a “teleological stance” [Gergely, G., Nádasdy, Z., Csibra, G., & Biró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165-193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent.     

Statistical inference and sensitivity to sampling in 11-month-old infants

Research on initial conceptual knowledge and research on early statistical learning mechanisms have been, for the most part, two separate enterprises. We report a study with 11-month-old infants investigating whether they are sensitive to sampling conditions and whether they can integrate intentional information in a statistical inference task. Previous studies found that infants were able to make inferences from samples to populations, and vice versa [Xu, F., & Garcia, V. (2008). Intuitive statistics by 8-month-old infants. Proceedings of the National Academy of Sciences of the United States of America, 105, 5012-5015]. We found that when employing this statistical inference mechanism, infants are sensitive to whether a sample was randomly drawn from a population or not, and they take into account intentional information (e.g., explicitly expressed preference, visual access) when computing the relationship between samples and populations. Our results suggest that domain-specific knowledge is integrated with statistical inference mechanisms early in development.     

利/害条件下归纳推理的心理效应分离

研究设计了两类包含内容的归纳推理任务, 一类是对获得收益的可能性的推理; 另一类是避免伤害的推理。实验显示, 不同内容的归纳推理结果有显著差异。其中, 避免伤害的归纳推理表现出将伤害可能过度推延的特点; 而获得收益的归纳推理则表现出对收益可能推延不足。此外, 不同的自我卷入水平——自我卷入式推理或者非自我卷入式推理——也会对推理产生显著影响。在避害条件下, 自我卷入式推理比非自我卷入式推理表现出更强烈的过度推延的特点; 在获利条件下, 自我卷入式推理比非自我卷入式推理表现出更严重的推延不足。研究经过分析提出, 这种差异可能暗示了归纳推理的领域特殊性特征。     

Predictive coding: an account of the mirror neuron system

Is it possible to understand the intentions of other people by simply observing their actions? Many believe that this ability is made possible by the brain’s mirror neuron system through its direct link between action and observation. However, precisely how intentions can be inferred through action observation has provoked much debate. Here we suggest that the function of the mirror system can be understood within a predictive coding framework that appeals to the statistical approach known as empirical Bayes. Within this scheme the most likely cause of an observed action can be inferred by minimizing the prediction error at all levels of the cortical hierarchy that are engaged during action observation. This account identifies a precise role for the mirror system in our ability to infer intentions from actions and provides the outline of the underlying computational mechanisms.     

文本阅读中预期推理激活与编码的语境条件研究

本研究通过三个实验考察了语义联系和因果制约对文本阅读中预期推理激活和编码的贡献。结果表明,预期语境与预期事件间的语义联系不能使预期推理在文本阅读过程中得到激活和编码,语境与预期事件间的强因果关系是文本阅读过程中预期推理激活与编码的必要条件;提示阅读过程中的预期是因果预期而非联想预期。结果为建构主义推理观提供了实验依据。     

条件推理与概率判断

选取6个具体内容的条件命题和1个纯形式的条件命题作为实验材料,以大学生为被试,深入探讨了条件推理的认知加工机制。结果发现,大学生被试对四种推理形式的认可程度主要与范畴前提的概率值成正比,条件推理似乎是一种基于概率判断的直觉而高效的认知加工过程,进一步证实了Oaksford（2000）以及Evans（2003）等人的观点。     

Psychology is About Processes

Out of the eight points of methodological criticism against contemporary psychology formulated by Watson (Psychological Bulletin 31:755–776, 1934) and put forward by Toomela in this issue, the overemphasis on prediction, the neglect of individual differences, the habit of the differences between the mental states of subjects in objective experimental conditions are particularly important. Modern cognitive psychology has began to remedy those problems, in part by proposing broad, integrative theories. It is not useful to subdivide psychology into “schools of thought” defined by their methodological practices.