Rips的“证明心理学理论”述评

该文对Rips提出的“证明心理学理论”做了综合述评。这一理论主要包含三方面的内容:对推理过程与人类记忆相互关系的解释;(2)根据逻辑学中的“自然推理规则”进行修正后用于解释人类推理过程的推理规则;(3)关于如何控制推理过程的论述。Rips认为他于1983年设计并实施的以“自然推理系统”所含各推理规则为实验材料的实验结果支持该理论的基本观点。     

5岁儿童对生物特征的限制性推理的实验研究

本研究用推理法范式研究5岁儿童在不同条件下,对生长、生病、进食、呼吸和排泄等生物特征的限制于生物类别的限制性推理.结果表明,以人为例引入特征及对特征给予生机论的解释,都能提高儿童的限制性类别推理.儿童对生物特征的认识中,存在拟人化的推理模式和生机论的因果关系解释系统.儿童对动物和植物的认识发展是不均衡的,对动物的认识优先发展;对动植物共有的特征已有所认识,但对各个特征的认识发展是不均衡的.     

Sticking to the Evidence? A Behavioral and Computational Case Study of Micro‐Theory Change in the Domain of Magnetism

Constructing an intuitive theory from data confronts learners with a “chicken‐and‐egg” problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken‐and‐egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We present 4‐ and 5‐year‐olds with two different simplified magnet‐learning tasks. Children appropriately constrain their beliefs to two hypotheses following ambiguous but informative evidence. Following a critical intervention, they learn the correct theory. In the second study, children infer the correct number of categories given no information about the possible causal laws. Children's hypotheses in these tasks are explained as rational inferences within a Bayesian computational framework.     

Where do hypotheses come from?

Why are human inferences sometimes remarkably close to the Bayesian ideal and other times systematically biased? In particular, why do humans make near-rational inferences in some natural domains where the candidate hypotheses are explicitly available, whereas tasks in similar domains requiring the self-generation of hypotheses produce systematic deviations from rational inference. We propose that these deviations arise from algorithmic processes approximating Bayes’ rule. Specifically in our account, hypotheses are generated stochastically from a sampling process, such that the sampled hypotheses form a Monte Carlo approximation of the posterior. While this approximation will converge to the true posterior in the limit of infinite samples, we take a small number of samples as we expect that the number of samples humans take is limited. We show that this model recreates several well-documented experimental findings such as anchoring and adjustment, subadditivity, superadditivity, the crowd within as well as the self-generation effect, the weak evidence, and the dud alternative effects. We confirm the model’s prediction that superadditivity and subadditivity can be induced within the same paradigm by manipulating the unpacking and typicality of hypotheses. We also partially confirm our model’s prediction about the effect of time pressure and cognitive load on these effects.     

Inferring causal networks from observations and interventions

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.     

FNDSB: A fuzzy-neuro decision support system for back pain diagnosis

Back pain is a common pain felt in the back. In the world, it is the fifth common reason for physician visits and in the U.S. about 90% of human adults have back pain at some time in their life. In this paper, we proposed a decision support system for intelligent diagnosis of back pain using a fuzzy-neuro technique to manage the fuzzy concepts. It is also provide an intelligent decision support platform that can assist physicians to diagnose and produce accurate medical advices. The proposed system consists of a user interface that receive symptoms and produce accurate diagnosis with its severity, a fuzzy inference system which contains the strategies of reasoning process based on fuzzification and defuzzification techniques, a fuzzy-neuro system that composed of neural network with fuzzy logic concepts, and a knowledge base which collects of linguistic fuzzy rules. The FNDSB describe knowledge acquisition and representation methods, a way of production linguistic fuzzy rules organization, fuzzification and defuzzification of clinical parameters as input and output values using a Triangular Membership Function (TMF) and Centroid of Area (CoA) techniques respectively. The FNDSB was evaluated using a case study of 10 patients from Al-diwaniyah teaching hospital. According to the evaluation results the system performance around 83.6% efficiency in producing accurate back pain diagnosis.