A positive patterning advantage with complex but not simple patterning: A cue constellation approach

Determinants of a positive patterning advantage (that is, an advantage for positive patterning over negative patterning) in human causal reasoning were examined in an experiment that compared simple patterning discriminations (A, B vs. AB) to complex patterning discriminations (AB, BC, AC vs. ABC). As predicted by a cue constellation analysis of complex discriminations, a positive patterning advantage was found with complex patterning but not with simple patterning discriminations. This result may explain why some recent studies have found a positive patterning advantage where earlier studies had failed to find one. The interaction of patterning complexity with the positive patterning advantage appears to pose problems for rule-based accounts of patterning. The results support the view that associative theories of human causal reasoning are more easily distinguished from rule-based approaches when applied to conditions that make simple rules difficult to identify or implement.     

Heuristic thinking and human intelligence: a commentary on Marewski,Gaissmaier and Gigerenzer

Marewski, Gaissmaier and Gigerenzer (2009) present a review of research on fast and frugal heuristics, arguing that complex problems are best solved by simple heuristics, rather than the application of knowledge and logical reasoning. We argue that the case for such heuristics is overrated. First, we point out that heuristics can often lead to biases as well as effective responding. Second, we show that the application of logical reasoning can be both necessary and relatively simple. Finally, we argue that the evidence for a logical reasoning system that co-exists with simpler heuristic forms of thinking is overwhelming. Not only is it implausible a priori that we would have evolved such a system that is of no use to us, but extensive evidence from the literature on dual processing in reasoning and judgement shows that many problems can only be solved when this form of reasoning is used to inhibit and override heuristic thinking.     

Human Nonmonotonic Reasoning: the Importance of Seeing the Logical Strength of Arguments

Three studies of human nonmonotonic reasoning are described. The results show that people find such reasoning quite difficult, although being given problems with known subclass-superclass relationships is helpful. The results also show that recognizing differences in the logical strengths of arguments is important for the nonmonotonic problems studied. For some of these problems, specificity – which is traditionally considered paramount in drawing appropriate conclusions – was irrelevant and so should have lead to a “can’t tell” response; however, people could give rational conclusions based on differences in the logical consequences of arguments. The same strategy also works for problems where specificity is relevant, suggesting that in fact specificity is not paramount. Finally, results showed that subjects’ success at responding appropriately to nonmonotonic problems involving conflict relies heavily on the ability to appreciate differences in the logical strength of simple, non-conflicting, statements.     

Mental models and logical reasoning problems in the GRE

The Graduate Record Examination (GRE) contains a class of complex reasoning tests known as logical reasoning problems. These problems are demanding for human reasoners and beyond the competence of any existing computer program. This article applies the mental model theory of reasoning to the analysis of these problems. It predicts 3 main causes of difficulty, which were corroborated by the results of 4 experiments: the nature of the logical task (Experiment 1), the set of foils (Experiment 2), and the nature of the conclusions (Experiments 3 and 4). This article shows how these factors can be applied to the design of new problems.     

From simple desires to ordinary beliefs: the early development of everyday psychology

We provide evidence for the claim that before young children construe human action in terms of beliefs and desires they understand action only in terms of simple desires. This type of naive psychology--a simple desire psychology--constitutes a coherent understanding of human action, but it differs from the belief--desire psychology of slightly older children and adults. In this paper we characterize what we mean by a simple desire psychology and report two experiments. In Experiment 1 we demonstrate that 2-year-old can predict actions and reactions related to simple desires. In Experiment 2 we demonstrate that many 2-year-old pass desire reasoning tasks while at the same time failing belief reasoning tasks that are passed by slightly older children, and that are as comparable as possible to the desire tasks they pass with ease.     

An anthropomorphic method for number sequence problems

Number sequence problems appear frequently in IQ tests, where the task is to extrapolate finite sequences of integers. This paper presents a computational method for solving number sequence problems appearing in IQ tests. The assumption that these problems are solvable by humans is actively exploited to keep the computational complexity manageable. The method combines elements of artificial intelligence and cognitive psychology and is referred to as anthropomorphic because it makes use of a model of human reasoning. This model features a set of cognitive resources, a repertoire of patterns that encode integer sequences, and a notion of bounded computation for decoding patterns. The model facilitates the search for patterns matching a given integer sequence by quickly discarding many patterns on the grounds that they are too demanding to decode. The computational method was implemented as a computer program called Asolver and then tested against the programs Mathematica and Maple. On the number sequence problems of the IQ test PJP, Asolver scored above IQ 140, whereas the other programs scored below IQ 100.     

Automaton models of performance

This work illustrates the use of a stochastic automaton model for describing the temporal steps taken by a student in solving simple arithmetic prolems. The method provides a hierarchial classification of problems by difficulty expressed in terms of the structural features of the problems. The model parameters represent the error rates due to these features.     

Domain-specific reasoning: social contracts, cheating, and perspective change.

What counts as human rationality: reasoning processes that embody content-independent formal theories, such as propositional logic, or reasoning processes that are well designed for solving important adaptive problems? Most theories of human reasoning have been based on content-independent formal rationality, whereas adaptive reasoning, ecological or evolutionary, has been little explored. We elaborate and test an evolutionary approach. Cosmides' (1989) social contract theory, using the Wason selection task. In the first part, we disentangle the theoretical concept of a "social contract" from that of a "cheater-detection algorithm". We demonstrate that the fact that a rule is perceived as a social contract--or a conditional permission or obligation, as Cheng and Holyoak (1985) proposed--is not sufficient to elicit Cosmides' striking results, which we replicated. The crucial issue is not semantic (the meaning of the rule), but pragmatic: whether a person is cued into the perspective of a party who can be cheated. In the second part, we distinguish between social contracts with bilateral and unilateral cheating options. Perspective change in contracts with bilateral cheating options turns P & not-Q responses into not-P & Q responses. The results strongly support social contract theory, contradict availability theory, and cannot be accounted for by pragmatic reasoning schema theory, which lacks the pragmatic concepts of perspectives and cheating detection.     

推理心理研究中的逻辑加工与非逻辑加工评析

本文对西方心理学研究中有关推理心理学争论的两个热点问题之一：逻辑推理与非逻辑推理之争的发展线索和相互关系等问题进行了评述,指出了这两种理论争论中未能注意到的三个问题,并对试图解决这三个问题的“知识和试题双重结构模型”作了简要介绍。     

如何利用开源工具分析fMRI数据：基于多体素模式的有监督机器学习算法介绍

摘要：目前,多体素模式分析（MVPA）日渐普遍地应用于脑影像研究。近些年,机器学习的模式分类等算法在MVPA方法中被广泛应用,因其具有能够抽取高维数据模式,提高数据利用率的优点。其中一种典型的应用是利用解码的思想来解决神经表征问题,本文主要介绍了利用基于Python语言的工具库中有监督学习算法分析数据的过程。除介绍Nilearn结合Scikit-learn分析数据的步骤外,还比较不同算法的效率,为算法的选择及参数设备提供具体参考。     

Generalizing Deontic Action Logic

We introduce a multimodal framework of deontic action logic which encodes the interaction between two fundamental procedures in normative reasoning: conceptual classification and deontic classification. The expressive power of the framework is noteworthy, since it combines insights from agency logic and dynamic logic, allowing for a representation of many kinds of normative conflicts. We provide a semantic characterization for three axiomatic systems of increasing strength, showing how our approach can be modularly extended in order to get different levels of analysis of normative reasoning. Finally, we discuss ways in which the framework can be used to capture other formalisms proposed in the literature, as well as to model searching problems in Artificial Intelligence.

     

Modeling Mental Spatial Reasoning About Cardinal Directions

This article presents research into human mental spatial reasoning with orientation knowledge. In particular, we look at reasoning problems about cardinal directions that possess multiple valid solutions (i.e., are spatially underdetermined), at human preferences for some of these solutions, and at representational and procedural factors that lead to such preferences. The article presents, first, a discussion of existing, related conceptual and computational approaches; second, results of empirical research into the solution preferences that human reasoners actually have; and, third, a novel computational model that relies on a parsimonious and flexible spatio‐analogical knowledge representation structure to robustly reproduce the behavior observed with human reasoners.     

Reflective Argumentation: A Cognitive Function of Arguing

Why do we formulate arguments? Usually, things such as persuading opponents, finding consensus, and justifying knowledge are listed as functions of arguments. But arguments can also be used to stimulate reflection on one’s own reasoning. Since this cognitive function of arguments should be important to improve the quality of people’s arguments and reasoning, for learning processes, for coping with “wicked problems,” and for the resolution of conflicts, it deserves to be studied in its own right. This contribution develops first steps towards a theory of reflective argumentation. It provides a definition of reflective argumentation, justifies its importance, delineates it from other cognitive functions of argumentation in a new classification of argument functions, and it discusses how reflection on one’s own reasoning can be stimulated by arguments.     

Supposition and representation in human reasoning

We report the results of three experiments designed to assess the role of suppositions in human reasoning. Theories of reasoning based on formal rules propose that the ability to make suppositions is central to deductive reasoning. Our first experiment compared two types of problem that could be solved by a suppositional strategy. Our results showed no difference in difficulty between problems requiring affirmative or negative suppositions and very low logical solution rates throughout. Further analysis of the error data showed a pattern of responses, which suggested that participants reason from a superficial representation of the premises in these arguments and this drives their choice of conclusion. Our second experiment employed a different set of suppositional problems but with extremely similar proofs in terms of the rules applied and number of inferential steps required. As predicted by our interpretation of reasoning strategies employed in Experiment 1, logical performance was very much higher on these problems. Our third experiment showed that problems that could be solved by constructing an initial representation of the premises were easier than problems in which this representation was not sufficient. This effect was independent of the suppositional structure of the problems. We discuss the implications of this research for theories of reasoning based on mental models and inference rules.     

图形型归纳推理的神经机制:一项fMRI研究

利用fMRI脑成像技术探讨图形型归纳推理的神经机制。设计了一种由图形形状和条纹方向描述的简单几何图形组成的归纳推理任务,这种图形型任务与以往研究中常用的语句型归纳推理任务是同质的。根据两个图形共享特征数量的不同设计了两种实验任务:共享两个特征(2T)和共享一个特征(1T),以休息基线(Rest)作为控制任务。2T和1T任务均为归纳推理任务,但2T任务包含知觉特征整合成分,而1T任务不包括。结果发现:与控制任务相比,归纳推理任务在前额区(BA6、9、11、46、47)、尾状核、壳核和丘脑等脑区有显著激活,反映了"前额皮层-纹状体-丘脑"通路在图形型归纳推理中的重要作用;图形型归纳推理中的知觉信息整合与右侧额下回(BA47)、双侧尾状核头部、壳核等脑区有关。     

A novel evaluation of two related and two independent algorithms for eye movement classification during reading

Nystr?m and Holmqvist have published a method for the classification of eye movements during reading (ONH) (Nyström & Holmqvist, 2010). When we applied this algorithm to our data, the results were not satisfactory, so we modified the algorithm (now the MNH) to better classify our data. The changes included: (1) reducing the amount of signal filtering, (2) excluding a new type of noise, (3) removing several adaptive thresholds and replacing them with fixed thresholds, (4) changing the way that the start and end of each saccade was determined, (5) employing a new algorithm for detecting PSOs, and (6) allowing a fixation period to either begin or end with noise. A new method for the evaluation of classification algorithms is presented. It was designed to provide comprehensive feedback to an algorithm developer, in a time-efficient manner, about the types and numbers of classification errors that an algorithm produces. This evaluation was conducted by three expert raters independently, across 20 randomly chosen recordings, each classified by both algorithms. The MNH made many fewer errors in determining when saccades start and end, and it also detected some fixations and saccades that the ONH did not. The MNH fails to detect very small saccades. We also evaluated two additional algorithms: the EyeLink Parser and a more current, machine-learning-based algorithm. The EyeLink Parser tended to find more saccades that ended too early than did the other methods, and we found numerous problems with the output of the machine-learning-based algorithm.     

逻辑－信念冲突与推理难度对逻辑和信念判断的影响

双加工理论框架下的平行竞争模型和默认干涉模型对推理过程中启发式和分析式加工的启动次序和相互作用的性质提出了不同假设。本研究通过考察被试在信念判断和逻辑判断两种指导语下解决不同难度和不同类型推理问题的正确率和反应时,对两个模型的预测进行了检验。实验结果表明,解决简单问题时,问题类型对信念判断有显著影响,对逻辑判断无显著影响;解决复杂问题时,问题类型对逻辑判断的影响显著大于对信念判断的影响。实验结果支持平行竞争模型的假设,即两类加工在推理过程中同时启动,共同竞争最终的推理反应。     

The logic of social exchange: has natural selection shaped how humans reason? Studies with the Wason selection task

In order to successfully engage in social exchange--cooperation between two or more individuals for mutual benefit--humans must be able to solve a number of complex computational problems, and do so with special efficiency. Following Marr (1982), Cosmides (1985) and Cosmides and Tooby (1989) used evolutionary principles to develop a computational theory of these adaptive problems. Specific hypotheses concerning the structure of the algorithms that govern how humans reason about social exchange were derived from this computational theory. This article presents a series of experiments designed to test these hypotheses, using the Wason selection task, a test of logical reasoning. Part I reports experiments testing social exchange theory against the availability theories of reasoning; Part II reports experiments testing it against Cheng and Holyoak's (1985) permission schema theory. The experimental design included eight critical tests designed to choose between social exchange theory and these other two families of theories; the results of all eight tests support social exchange theory. The hypothesis that the human mind includes cognitive processes specialized for reasoning about social exchange predicts the content effects found in these experiments, and parsimoniously explains those that have already been reported in the literature. The implications of this line of research for a modular view of human reasoning are discussed, as well as the utility of evolutionary biology in the development of computational theories.     

Combined reasoning by automated cooperation

Different reasoning systems have different strengths and weaknesses, and often it is useful to combine these systems to gain as much as possible from their strengths and retain as little as possible from their weaknesses. Of particular interest is the integration of first-order and higher-order techniques. First-order reasoning systems, on the one hand, have reached considerable strength in some niches, but in many areas of mathematics they still cannot reliably solve relatively simple problems, for example, when reasoning about sets, relations, or functions. Higher-order reasoning systems, on the other hand, can solve problems of this kind automatically. But the complexity inherent in their calculi prevents them from solving a whole range of problems. However, while many problems cannot be solved by any one system alone, they can be solved by a combination of these systems.We present a general agent-based methodology for integrating different reasoning systems. It provides a generic integration framework which facilitates the cooperation between diverse reasoners, but can also be refined to enable more efficient, specialist integrations. We empirically evaluate its usefulness, effectiveness and efficiency by case studies involving the integration of first-order and higher-order automated theorem provers, computer algebra systems, and model generators.