“直觉调整差误”偏向对贝叶斯推理成绩的影响

采用贝叶斯推理问题作为实验材料,探讨被试解决贝叶斯推理问题的启发式策略,从认知偏向的角度分析该问题的困难原因。结果发现:(1)贝叶斯推理问题解决过程中的可得性启发与后验概率估计过程中的直觉调整差误密切相关;(2)贝叶斯推理问题解决过程中文、理科被试都同样会产生直觉调整差误偏向;(3)贝叶斯推理问题解决过程中的后验概率估计值和对推理问题中包括虚报率信息的命题的可得性测量所得主观概率之间存在显著的负相关。     

Optimal designs for the generalized partial credit model

Analysing ordinal data is becoming increasingly important in psychology, especially in the context of item response theory. The generalized partial credit model (GPCM) is probably the most widely used ordinal model and has found application in many large-scale educational assessment studies such as PISA. In the present paper, optimal test designs are investigated for estimating persons’ abilities with the GPCM for calibrated tests when item parameters are known from previous studies. We find that local optimality may be achieved by assigning non-zero probability only to the first and last categories independently of a person's ability. That is, when using such a design, the GPCM reduces to the dichotomous two-parameter logistic (2PL) model. Since locally optimal designs require the true ability to be known, we consider alternative Bayesian design criteria using weight distributions over the ability parameter space. For symmetric weight distributions, we derive necessary conditions for the optimal one-point design of two response categories to be Bayes optimal. Furthermore, we discuss examples of common symmetric weight distributions and investigate under what circumstances the necessary conditions are also sufficient. Since the 2PL model is a special case of the GPCM, all of these results hold for the 2PL model as well.     

Priors in perception: Top-down modulation,Bayesian perceptual learning rate,and prediction error minimization

I discuss top-down modulation of perception in terms of a variable Bayesian learning rate, revealing a wide range of prior hierarchical expectations that can modulate perception. I then switch to the prediction error minimization framework and seek to conceive cognitive penetration specifically as prediction error minimization deviations from a variable Bayesian learning rate. This approach retains cognitive penetration as a category somewhat distinct from other top-down effects, and carves a reasonable route between penetrability and impenetrability. It prevents rampant, relativistic cognitive penetration of perception and yet is consistent with the continuity of cognition and perception.     

Beyond differences between the body schema and the body image: insights from body hallucinations

The distinction between the body schema and the body image has become the stock in trade of much recent work in cognitive neuroscience and philosophy. Yet little is known about the interactions between these two types of body representations. We need to account not only for their dissociations in rare cases, but also for their convergence most of the time. Indeed in our everyday life the body we perceive does not conflict with the body we act with. Are the body image and the body schema then somehow reshaping each other or are they relatively independent and do they only happen to be congruent? On the basis of the study of bodily hallucinations, we consider which model can best account for the body schema/body image interactions.     

贝叶斯推理中的“自击中率参照抑制”

本文旨在研究贝叶斯推理中基础比率、击中率起作用的机制。通过设置肝炎情境、中彩情境的贝叶斯推理问题,同时操作基础比率和击中率的水平,考察449名大学生被试的概率估计情况。结果表明,总体上,贝叶斯推理中的基础比率和击中率均起作用,表现为后验概率估计值随基础比率水平的提高而增加,随击中率水平的提高而增加。然而,在低基础比率的中彩情境中,后验概率估计值先随击中率的增加而增加;之后却随击中率水平的继续提高而显著降低,出现自击中率参照抑制。这似乎说明,击中率在贝叶斯推理中并非一致地起作用,而可能与推理者的知识经验对击中率加工的调节有关。     

A probabilistic model of cross-categorization

Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that cross-categorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models’ predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities.     

The mental representation of causal conditional reasoning: mental models or causal models

In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving pairs of conditionals—such as if P₁then Q and if P₂then Q—are considered. From a causal perspective, the causal direction of these conditionals is critical: are the P_icauses of Q; or symptoms caused byQ. The rich variety of inference patterns can naturally be modelled by Bayesian networks. A pair of causal conditionals where Q is an effect corresponds to a “collider” structure where the two causes (P_i) converge on a common effect. In contrast, a pair of causal conditionals where Q is a cause corresponds to a network where two effects (P_i) diverge from a common cause. Very different predictions are made by fully explicit or initial mental models interpretations. These predictions were tested in two experiments, each of which yielded data most consistent with causal model theory, rather than with mental models.     

What Can Artificial Intelligence Learn from Wittgenstein＇s On Certainty？.

Meta-philosophically speaking, the philosophy of artificial intelligence （AI） is intended not only to explore the theoretical possibility of building ＂thinking machines,＂ but also to reveal philosophical implications of specific AI approaches. Wittgenstein＇s comments on the analytic/empirical dichotomy may offer inspirations for AI in the second sense. According to his ＂river metaphor＂ in On Certainty, the analytic/empirical boundary should be delimited in a way sensitive to specific contexts of practical reasoning. His proposal seems to suggest that any cognitive modeling project needs to render the system context-sensitive by avoiding representing large amounts of truisms in its cognitive processes, otherwise neither representational compactness nor computational efficiency can be achieved. In this article, different AI approaches （like the Common Sense Law of Inertia approach, the Bayesian approach and the connectionist approach） will be critically evaluated under the afore-mentioned Wittgensteinian criteria, followed by the author＇s own constructive suggestion on what AI needs to try to do in the near future.