归纳推理和演绎推理的关系理论及其模型

推理是人类高级认知过程的一种,相关的心理学研究一般将其分为归纳推理和演绎推理两个方面。归纳推理是从特殊到一般的推理过程,与之相对的演绎推理则是从一般到特殊的过程。归纳推理和演绎推理的关系问题是当前心理推理研究领域的一个重点问题。这一问题主要有两种理论解释：一种是单过程理论,该理论认为归纳推理和演绎推理本质上是同一个认知过程,以单过程理论为基础构建的推理模型称为单维模型;另一种是双过程理论,认为归纳推理和演绎推理是两个不同的认知过程,并不同程度地受到启发和分析过程的影响。未来研究可多关注推理的时间进程,以及采用不同的研究方法对各自理论提供数据支持。     

前提顺序和性质对三段论推理的影响

三段论的推理研究关注的是以量词为特征的范畴命题驱动推理机制的认知过程。以气氛效应、匹配理论、换位假说和概率启发式模型为代表的表层特征理论从不同角度解释了三段论的前提顺序和性质对推理的影响。本文梳理了表层特征理论的理论假设,并采用实证数据对比了这些理论模型对三段论推理结论的预测准确性。研究发现,前提顺序影响结论端项的顺序,对结论的性质影响不大。前提性质对结论性质有着不同程度的影响。模型对比的结果表明,概率启发式模型的预测更加准确。将“无有效结论”的作答和结论端项顺序的预测纳入模型中是提高模型预测准确性的重要因素。     

现代测量理论下四大认知诊断模型述评

该文介绍并比较了现代测量理论下四大认知诊断模型的思想方法、模型结构及各自的特点性能等。LLTM是一个较早的认知诊断模型,它实现了认知与测量的结合;规则空间模型实现了对认知结构的诊断,并创造性地提出了Q矩阵理论;统一模型与融合模型是同一类模型：两者均沿用了规则空间模型的Q矩阵方法,但克服了规则空间模型中的一些不足;融合模型被认为是二十一世纪初创立的一个很成功的认知诊断模型。     

表象计算心理模型研究的新进展

对大脑工作机理的研究和解释一直是认知心理学研究的基本问题。知识表征图式的研究源于对基于模型推理的认知和计算。在人工智能中,基于模型的推理主要包括在语义领域用计算提供真命题,基于规则的推理系统则是一个规则建构过程。现代认知心理学的研究不断的表明．许多人类的空间推理是基于模型的,而不是基于规则的;心理模型是通过知觉输入或自然语言的描述而建构的,它们可以看作是从一表征形式向另一表征形式的转换。     

基于模型推理的科学认知论题

本文把近20年来在科学哲学中发展起来的、关于科学推理本质的一种新观点叫做“基于模型推理的科学认知论题”(the Scientific Cognition as Model-Based Reasoning Thesis)。按照这个论题,科学推理实质上是语义过程。对于逻辑实证主义科学哲学的科学理论陈述观和科学推理句法观来说,这种观点是一种有别于历史学派和其他后现代科学哲学思潮的挑战和修正,它也可以纠正库恩科学革命理论(特别是早期基于格式塔转换的间断性革命变化学说)的极端性。然而,现有相关研究主要限于用科学发现和概念变化的课题来阐述基于模型推理的概念架构(简称为“…     

贝叶斯推理的改进方法--以频率格式代替概率格式进行信息表征

贝叶斯推理规则是人工智能领域和认知心理学领域的一个重要理论,数学家、逻辑学家与心理学家都对其做了全面细致的研究。关于人的思维方式是否遵循贝叶斯推理规则,目前存在着两种截然相反的观点。一方面,古典的启迪概率主义者认为,概率理论是受过教育的人的基本常识,是思想的规律。Edwards(1968)最先用实验的方法测试人类的推理是否遵循贝叶斯定理,并认为,即使是“保守的”推理也在一定程度上与贝叶斯定理计算得出的结论相吻合。然而另一方面。Kahneman和Tversky(1972)却认为,人们明显地不是保守贝叶斯主义,甚至可以说根本就没遵循贝叶斯定理。古生物学者Gould(1992)持有更极端的看法,认为人们的大脑生来就不是遵循概率规则的。     

认知诊断模型发展及其应用方法述评

认知心理学和心理测量学结合派生出的认知诊断理论, 利用现代统计方法和计算机技术作为工具, 诊断被试的认知结构和认知过程。认知诊断有多种模型, 不同的模型有不同的特点及应用条件。模型的选择和认知诊断方法的应用对认知诊断的结果有重要的影响, 因此在选择模型之时需要了解各种认知诊断模型的发展过程及优缺点。     

从计算的观点看

20世纪的计算革命不仅为我们提供了崭新的科学研究的方法 ,而且为我们提供了全新的看问题的视角。计算机的理论和实践使我们逐步形成了一种新的世界观 ,即计算主义的世界观 :不仅认知和生命可以被看作是计算过程 ,而且整个宇宙都可以被看作是一个巨大的计算系统。一、认知和计算最早从计算的视角审视问题的是关心人的认识本质的哲学家。笛卡尔认为 ,人的理解就是形成和操作恰当的表述方式。洛克认为 ,我们对世界的认识都要经过观念这个中介 ,思维事实上不过是人类大脑对这些观念进行组合或分解的过程。霍布斯更是明确提出 ,推理的本质就是计…     

推理中心理模型理论的研究与发展

心理模型理论是推理研究中最有影响的理论之一,理论体系完整地解释了推理的进行,认为个体推理要经历理解、描述、有效检验三个阶段。心理模型理论对推理做了大量预测,假设单模型问题比多模型问题容易、系统错误与第一个前提模型有关、知识影响推理过程,并检验了预测的合理性。近十几年来,心理模型理论在推理各个领域中均得到发展,包括对推理在大脑中编码的研究,对元推理的研究及可能性推理等方面。     

假言推理中的概率效应

近来Oaksford等人的研究表明,概率信息对假言推理的认知过程有着显著的影响,在一定程度上可以用它来解释和预测个体在推理中的行为反应。该文针对这种新的观点,就假言推理中所涉及的前后件概率信息、条件概率信息、连接概率信息、因果概率信息以及额外前提条件等对假言推理的影响分别做了简要的阐述,并介绍了一些相关的实验研究,最后作了总结和展望。     

儿童生命认知和生命体验的发展特点

本研究在生命教育的社会背景下,以2、4、6、8、11年级的中小学生为研究对象,使用造句和补充句子的方式考察儿童对生命和死亡的认知及体验。结果表明:1)儿童对生命和死亡的认知发展是随着年级的升高从模糊不清、具体形象到认知生命本质直至表达态度、感悟哲理的一个渐次深入的过程;2)儿童对生命过程的体验总体上是积极的,且随着年级的升高体验的内涵越来越丰富;3)儿童对死亡的心理体验以恐惧和悲伤为主;但随着年级的升高,儿童对死亡体验哲理性和迷茫的逐渐增多。     

对话心智与身体：具身认知的内感受研究转向

具身认知观念试图强调心智是根植于身体的,身体经验会对认知过程产生重要影响。但对于心智与身体的对话是如何进行的？身体通过何种渠道如何影响心智？等问题困扰着具身认知的支持者。传统的具身认知研究遭遇可重复性危机,身体状态改变影响高级认知的结论受到质疑。近年来,越来越多的证据表明内感受与身体表征、具身情绪以及社会认知等存在着密切的联系,因而内感受很有可能在身体加工和高级认知过程中起着重要的调节作用。     

基于概念外延的用户认知表达-——以智能冰箱的用户认知研究为例

--用户认知具有主观性、模糊性、进化性及多维性等不确定性特点,难以编码与度量。提出以概念外延表达的集合论方法与模糊数学把研究用户认知转换成研究代表它的因素集合;提出用数据驱动的互联网文本挖掘获取因素集,采用TF-IDF与模糊统计求解因素的隶属度,完成用户认知的数学表达,为进一步研究提供数学基础;最后以海信智能冰箱项目进行论证。由于用户认知属于典型的不确定性复杂对象,论文所用方法适用于所有复杂对象的建模与求解。     

Bootstrapping the lexicon: a computational model of infant speech segmentation

Prelinguistic infants must find a way to isolate meaningful chunks from the continuous streams of speech that they hear. BootLex, a new model which uses distributional cues to build a lexicon, demonstrates how much can be accomplished using this single source of information. This conceptually simple probabilistic algorithm achieves significant segmentation results on various kinds of language corpora - English, Japanese, and Spanish; child- and adult-directed speech, and written texts; and several variations in coding structure - and reveals which statistical characteristics of the input have an influence on segmentation performance. BootLex is then compared, quantitatively and qualitatively, with three other groups of computational models of the same infant segmentation process, paying particular attention to functional characteristics of the models and their similarity to human cognition. Commonalities and contrasts among the models are discussed, as well as their implications both for theories of the cognitive problem of segmentation itself, and for the general enterprise of computational cognitive modeling.     

Exemplar models as a mechanism for performing Bayesian inference

Probabilistic models have recently received much attention as accounts of human cognition. However, most research in which probabilistic models have been used has been focused on formulating the abstract problems behind cognitive tasks and their optimal solutions, rather than on mechanisms that could implement these solutions. Exemplar models are a successful class of psychological process models in which an inventory of stored examples is used to solve problems such as identification, categorization, and function learning. We show that exemplar models can be used to perform a sophisticated form of Monte Carlo approximation known as importance sampling and thus provide a way to perform approximate Bayesian inference. Simulations of Bayesian inference in speech perception, generalization along a single dimension, making predictions about everyday events, concept learning, and reconstruction from memory show that exemplar models can often account for human performance with only a few exemplars, for both simple and relatively complex prior distributions. These results suggest that exemplar models provide a possible mechanism for implementing at least some forms of Bayesian inference.     

Optimal predictions in everyday cognition

Human perception and memory are often explained as optimal statistical inferences that are informed by accurate prior probabilities. In contrast, cognitive judgments are usually viewed as following error-prone heuristics that are insensitive to priors. We examined the optimality of human cognition in a more realistic context than typical laboratory studies, asking people to make predictions about the duration or extent of everyday phenomena such as human life spans and the box-office take of movies. Our results suggest that everyday cognitive judgments follow the same optimal statistical principles as perception and memory, and reveal a close correspondence between people's implicit probabilistic models and the statistics of the world.     

认知固执导致持续应激的认知与神经机制

认知固执(Perseverative Cognition, PC)是一种重复或长期的消极认知表征,既能够使用量表测量,也可以通过实验诱发。认知固执会导致持续应激反应,进而影响个体的身心健康。其导致持续应激反应的认知机制体现在个体难以识别安全信号,高认知固执个体会表现出低心率变异性的显著特征;在脑机制方面主要表现为前额叶的抑制控制功能失调、杏仁核与前扣带回的病理性改变以及前额叶-杏仁核的连接减弱。未来研究应深入探讨认知固执的影响因素以及干预措施,同时需要进一步分析内隐认知固执的测量方式及其对个体的影响。     

Everyday conditional reasoning: A working memory—dependent tradeoff between counterexample and likelihood use

Considerable evidence has revealed that working memory capacity is an important determinant of conditional reasoning performance. There are two accounts describing the conditional inference process, the probabilistic and the mental models accounts. According to the mental models account, reasoners retrieve and integrate counterexample information to attain a conclusion. According to the probabilistic account, reasoners base their judgments on probabilistic information. It can be assumed that reasoning according to the mental models process would require more working memory resources than would solving the inference on the basis of probabilistic information. By means of a verbal report study, we showed that participants with low working memory capacity more often use probabilistic information, whereas participants with higher working memory capacity are more likely to use counterexample information. Working memory capacity thus not only relates to reasoning performance, it also determines which process reasoners will engage in.     

Information‐Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory

A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners’ memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non‐linguistic auditory sequences. Participants repeatedly heard tone sequences varying systematically in their information‐theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information‐theoretic measures of sequential predictability significantly influenced listeners’ expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information‐theoretic structure of the sequences throughout the experimental session.     

Reasoning as simulation

The theory that human cognition proceeds through mental simulations, if true, would provide a parsimonious explanation of how the mechanisms of reasoning and problem solving integrate with and develop from mechanisms underlying forms of cognition that occur earlier in evolution and development. However, questions remain about whether simulation mechanisms are powerful enough to exhibit human-level reasoning and inference. In order to investigate this issue, we show that it is possible to characterize some of the most powerful modern artificial intelligence algorithms for logical and probabilistic inference as methods of simulating alternate states of the world. We show that a set of specific human perceptual mechanisms, even if not implemented using mechanisms described in artificial intelligence, can nevertheless perform the same operations as those algorithms. Although this result does not demonstrate that simulation theory is true, it does show that whatever mechanisms underlie perception have at least as much power to explain non-perceptual human reasoning and problem solving as some of the most powerful known algorithms.