Development of intuitive rules: Evaluating the application of the dual-system framework to understanding children’s intuitive reasoning

Theories of adult reasoning propose that reasoning consists of two functionally distinct systems that operate under entirely different mechanisms. This theoretical framework has been used to account for a wide range of phenomena, which now encompasses developmental research on reasoning and problem solving. We begin this review by contrasting three main dual-system theories of adult reasoning (Evans & Over, 1996; Sloman, 1996; Stanovich & West, 2000) with a well-established developmental account that also incorporates a dual-system framework (Brainerd & Reyna, 2001). We use developmental studies of the formation and application of intuitive rules in science and mathematics to evaluate the claims that these theories make. Overall, the evidence reviewed suggests that what is crucial to understanding how children reason is the saliency of the features that are presented within a task. By highlighting the importance of saliency as a way of understanding reasoning, we aim to provide clarity concerning the benefits and limitations of adopting a dual-system framework to account for evidence from developmental studies of intuitive reasoning.     

Contrast classes and matching bias as explanations of the effects of negation on conditional reasoning

In this paper the arguments for optimal data selection and the contrast class account of negations in the selection task and the conditional inference task are summarised, and contrasted with the matching bias approach. It is argued that the probabilistic contrast class account provides a unified, rational explanation for effects across these tasks. Moreover, there are results that are only explained by the contrast class account that are also discussed. The only major anomaly is the explicit negations effect in the selection task (Evans, Clibbens, & Rood, 1996), which it is argued may not be the result of normal interpretative processes. It is concluded that the effects of negation on human reasoning provide good evidence for the view that human reasoning processes may be rational according to a probabilistic standard.     

Deontic and epistemic reasoning in children revisited: Comment on Dack and Astington

Dack and Astington (Journal of Experimental Child Psychology 110 2011 94–114) attempted to replicate the deontic reasoning advantage among preschoolers reported by Cummins (Memory & Cognition 24 1996 823–829) and by Harris and Nuñez (Child Development. 67 1996 572–1591). Dack and Astington argued that the apparent deontic advantage reported by these studies was in fact an artifact due to a methodological confound, namely, inclusion of an authority in the deontic condition only. Removing this confound attenuated the effect in young children but had no effect on the reasoning of 7-year-olds and adults. Thus, removing reference to authority “explains away” young children’s apparent precocity at this type of reasoning. But this explanation rests on (a) a misunderstanding of norms as targets of deontic reasoning and (b) conclusions based on a sample size that was too small to detect the effect in young children.     

Dual-process theories of reasoning: The test of development

Dual-process theories have become increasingly influential in the psychology of reasoning. Though the distinction they introduced between intuitive and reflective thinking should have strong developmental implications, the developmental approach has rarely been used to refine or test these theories. In this article, I review several contemporary dual-process accounts of conditional reasoning that theorize the distinction between the two systems of reasoning as a contrast between heuristic and analytic processes, probabilistic and mental model reasoning, or emphasize the role of metacognitive processes in reflective reasoning. These theories are evaluated in the light of the main developmental findings. It is argued that a proper account of developmental phenomena requires the integration of the main strengths of these three approaches. I propose such an integrative theory of conditional understanding and argue that the modern dual-process framework could benefit from earlier contributions that made the same distinction between intuition and reflective thinking, such as Piaget’s theory.     

An encompassing framework for Paraconsistent Logic Programs

We propose a framework which extends Antitonic Logic Programs [Damásio and Pereira, in: Proc. 6th Int. Conf. on Logic Programming and Nonmonotonic Reasoning, Springer, 2001, p. 748] to an arbitrary complete bilattice of truth-values, where belief and doubt are explicitly represented. Inspired by Ginsberg and Fitting's bilattice approaches, this framework allows a precise definition of important operators found in logic programming, such as explicit and default negation. In particular, it leads to a natural semantical integration of explicit and default negation through the Coherence Principle [Pereira and Alferes, in: European Conference on Artificial Intelligence, 1992, p. 102], according to which explicit negation entails default negation. We then define Coherent Answer Sets, and the Paraconsistent Well-founded Model semantics, generalizing many paraconsistent semantics for logic programs. In particular, Paraconsistent Well-Founded Semantics with eXplicit negation (WFSX_p) [Alferes et al., J. Automated Reas. 14 (1) (1995) 93–147; Damásio, PhD thesis, 1996]. The framework is an extension of Antitonic Logic Programs for most cases, and is general enough to capture Probabilistic Deductive Databases, Possibilistic Logic Programming, Hybrid Probabilistic Logic Programs, and Fuzzy Logic Programming. Thus, we have a powerful mathematical formalism for dealing simultaneously with default, paraconsistency, and uncertainty reasoning. Results are provided about how our semantical framework deals with inconsistent information and with its propagation by the rules of the program.     

High-level reasoning and base-rate use: do we need cue-competition to explain the inverse base-rate effect?

Previous accounts of the inverse base-rate effect (D. L. Medin & S. M. Edelson, 1988) have revolved around the concept of cue-competition. In this article, the authors propose that high-level reasoning in the form of an eliminative inference mechanism may contribute to the effect. A quantitative implementation of this idea demonstrates that it has the power by itself to produce the pattern of base-rate effects in the Medin and Edelson (1988) design. Four predictions are derived that contradict the predictions by attention to distinctive input (ADIT; J. K. Kruschke, 1996), up to date the most successful account of the inverse base-rate effect. Results from 3 experiments disconfirm the predictions by ADIT and demonstrate the importance of high-level reasoning in designs of the Medin and Edelson kind. Implications for the interpretation of the inverse base-rate effect and the attention-shifting mechanisms presumed by ADIT are discussed.     

直觉还是推理,道德判断如何形成?

道德判断形成的机制一直存在争议,许多研究者通过实证研究来探索诸因素对道德判断的影响,这有助于理清不同因素的作用,但却难以构建道德判断形成的理论框架。文章系统性地回顾和总结了道德判断形成的五个经典理论模型及其相关理论,并结合实证研究的成果梳理了道德判断形成中理性、情绪、认知、直觉和推理之间的关系。基于此,构建了以直觉和推理为基本路径,基于规则的认知和情绪为调节因素的道德判断形成的理论框架。现有的理论模型和实证研究在该框架下均可以的得到较好的解释。同时,根据该框架,建议未来的实证研究中应当注重对影响因素的分类以及对实验情景的控制。     

Using a dialectical scientific brief in peer review

This paper presents a framework that editors, peer reviewers, and authors can use to identify and resolve efficiently disputes that arise during peer review in scientific journals. The framework is called a scientific dialectical brief. In this framework, differences among authors and reviewers are formatted into specific assertions and the support each party provides for its position. A literature review suggests that scientists use five main types of support; empirical data, reasoning, speculation, feelings, and status. It is suggested that the scientific dialectical brief format can streamline the review process by facilitating rapid differentiation between stronger and weaker support, so that valuable time can be focused on the better-substantiated claims. The paper concludes with some suggestions for implementation. The author researches, lectures and publishes in the area of physical and social aspects of environmental quality. He is also a practicing architect. This paper is based on a presentation at a workshop, “Advances in Peer Review Research”, American Association for the Advancement of Science Meeting, Baltimore, MD, February 9, 1996.     

儿童归纳推理中的标签作用

传统的归纳推理观主要包括特征相似性观和概念观。在基于这两种传统观点的研究过程中,研究者逐渐发现类别知识,特别是标签起到的独特作用,但目前关于不同类型标签作用的分析还缺乏系统性。该文从分析目前两大主要标签研究现状——语言标签和类别标签入手,试图明确标签作用和关系、解释混淆和误解;同时,作者就以标签为载体的研究同儿童语言发展间存在的密切联系提出了儿童归纳推理发展的语言认知模式。文章最后认为未来的研究方向应集中在对机制、技术和深层次内部关系三方面问题的探讨上。     

Reasoning with prioritized information by iterative aggregation of distance functions

We introduce a general framework for reasoning with prioritized propositional data by aggregation of distance functions. Our formalism is based on a possible world semantics, where conclusions are drawn according to the most ‘plausible’ worlds (interpretations), namely: the worlds that are as ‘close’ as possible to the set of premises, and, at the same time, are as ‘faithful’ as possible to the more reliable (or important) information in this set. This implies that the consequence relations that are induced by our framework are derived from a pre-defined metric on the space of interpretations, and inferences are determined by a ranking function applied to the premises. We study the basic properties of the entailment relations that are obtained by this framework, and relate our approach to other methods of maintaining incomplete and inconsistent information, most specifically in the contexts of (iterated) belief revision, consistent query answering in database systems, and integration of prioritized data sources.     

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.

     

AI &; Law,Logic and Argument Schemes

This paper reviews the history of AI & Law research from the perspective of argument schemes. It starts with the observation that logic, although very well applicable to legal reasoning when there is uncertainty, vagueness and disagreement, is too abstract to give a fully satisfactory classification of legal argument types. It therefore needs to be supplemented with an argument-scheme approach, which classifies arguments not according to their logical form but according to their content, in particular, according to the roles that the various elements of an argument can play. This approach is then applied to legal reasoning, to identify some of the main legal argument schemes. It is also argued that much AI & Law research in fact employs the argument-scheme approach, although it usually is not presented as such. Finally, it is argued that the argument-scheme approach and the way it has been employed in AI & Law respects some of the main lessons to be learnt from Toulmin’s The Uses of Argument.     

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.     

A Logic For Inductive Probabilistic Reasoning

Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from “70% of As are Bs” and “a is an A” infer that a is a B with probability 0.7. Direct inference is generalized by Jeffrey’s rule and the principle of cross-entropy minimization. To adequately formalize inductive probabilistic reasoning is an interesting topic for artificial intelligence, as an autonomous system acting in a complex environment may have to base its actions on a probabilistic model of its environment, and the probabilities needed to form this model can often be obtained by combining statistical background information with particular observations made, i.e., by inductive probabilistic reasoning. In this paper a formal framework for inductive probabilistic reasoning is developed: syntactically it consists of an extension of the language of first-order predicate logic that allows to express statements about both statistical and subjective probabilities. Semantics for this representation language are developed that give rise to two distinct entailment relations: a relation ⊨ that models strict, probabilistically valid, inferences, and a relation that models inductive probabilistic inferences. The inductive entailment relation is obtained by implementing cross-entropy minimization in a preferred model semantics. A main objective of our approach is to ensure that for both entailment relations complete proof systems exist. This is achieved by allowing probability distributions in our semantic models that use non-standard probability values. A number of results are presented that show that in several important aspects the resulting logic behaves just like a logic based on real-valued probabilities alone.     

Reasoning with Factors

The paper proposes an analysis and a formalisation of factor-based reasoning. After examining the relevance of factors in legal reasoning, binary and scalable factors (dimensions) are distinguished and the relations between them are discussed. An account of a fortiori reasoning with both types of factors is developed. This article reports ideas which are discussed and developed in Sartor (2005). We refer to the latter work for the theoretical framework in which our analysis of factors is embedded.     

Culture and the quest for universal principles in moral reasoning

The importance of including cultural perspectives in the study of human cognition has become apparent in recent decades, and the domain of moral reasoning is no exception. The present review focuses on moral cognition, beginning with Kohlberg's model of moral development which relies heavily on people's justifications for their judgments and then shifting to more recent theories that rely on rapid, intuitive judgments and see justifications as more or less irrelevant to moral cognition. Despite this dramatic shift, analyses of culture and moral decision-making have largely been framed as a quest for and test of universal principles of moral judgment. In this review, we discuss challenges that remain in trying to understand crosscultural variability in moral values and the processes that underlie moral cognition. We suggest that the universalist framework may lead to an underestimation of the role of culture in moral reasoning. Although the field has made great strides in incorporating more and more cultural perspectives in order to understand moral cognition, theories of moral reasoning still do not allow for substantial variation in how people might conceptualize the domain of the moral. The processes that underlie moral cognition may not be a human universal in any simple sense, because moral systems may play different roles in different cultures. We end our review with a discussion of work that remains to be done to understand cultural variation in the moral domain.     

Context,visual salience,and inductive reasoning

An important debate in the reasoning literature concerns the extent to which inference processes are domain-free or domain-specific. Typically, evidence in support of the domain-specific position comprises the facilitation observed when abstract reasoning tasks are set in realistic context. Three experiments are reported here in which the sources of facilitation were investigated for contextualised versions of Raven's Progressive Matrices (Richardson, 1991) and non-verbal analogies from the AH4 test (Richardson & Webster, 1996). Experiment 1 confirmed that the facilitation observed for the contextualised matrices was in part due to extraneous aspects of commentaries originally intended to activate domainspecific processes. Experiments 2 and 3 indicated that the remainder of the facilitation for the matrices, and all of the facilitation for the analogies, could be explained by visual salience: Converting the item elements into realistic objects had enabled them and their transitions to be identified more easily. Hence, performance at simplified abstract items was as good as, or better than, at contextualised items. It is concluded that facilitation effects cannot be interpreted as showing that domain-specific processes constitute a self-contained system separate from domain-free processes. In turn, this means that domain-free processes cannot be dismissed as being unimportant for reasoning.     

A Brief Proof of the Full Completeness of Shin’s Venn Diagram Proof System

In an article in the Journal of Philosophical Logic in 1996, “Towards a Model Theory of Venn Diagrams,” (Vol. 25, No. 5, pp. 463–482), Hammer and Danner proved the full completeness of Shin’s formal system for reasoning with Venn Diagrams. Their proof is eight pages long. This note gives a brief five line proof of this same result, using connections between diagrammatic and sentential representations.     

Introduction to the special section on the neural substrate of analogical reasoning and metaphor comprehension

The special section on the neural substrate of relational reasoning includes 4 articles that address the processes and brain regions involved in analogical reasoning (Green, Kraemer, Fugelsang, Gray, & Dunbar, 2011; Maguire, McClelland, Donovan, Tillman, & Krawczyk, 2011) and in metaphor comprehension (Chettih, Durgin, & Grodner, 2011; Prat, Mason, & Just, 2011). We see this work as an example of how neuroscience approaches to cognition can lead to increased understanding of cognitive processes. In this brief introduction, we first situate the 4 articles in the context of prior cognitive neuroscience work on relational reasoning. We then highlight the main issues explored in these articles: different sources of complexity and difficulty in relational processing, potential differences between the roles of the 2 hemispheres, and the impact of individual differences in various cognitive abilities. The 4 articles illustrate a range of methodologies, including functional magnetic resonance imaging (fMRI; Green et al., 2011; Prat et al., 2011), event-related potentials (ERPs; Maguire et al., 2011), and different types of semantic priming (Chettih et al., 2011; Prat et al., 2011). They highlight the connections between research on analogy and on metaphor comprehension and suggest, collectively, that a cognitive neuroscience approach to relational reasoning can lead to converging conclusions.     

Contrary-to-duty obligations

We investigate under what conditions contrary-to-duty (CTD) structures lacking temporal and action elements can be given a coherent reading. We argue, contrary to some recent proposals, that CTD is not an instance of defeasible reasoning, and that methods of nonmonotonic logics are inadequate since they are unable to distinguish between defeasibility and violation of primary obligations. We propose a semantic framework based on the idea that primary and CTD obligations are obligations of different kinds: a CTD obligation pertains to, or pre-supposes, a certain context in which a primary obligation is already violated. This framework is presented initially as an extension of Standard Deontic Logic (SDL), a normal modal logic of type KD, and is illustrated by application to a series of examples. The concluding section is concerned with some resemblances between CTD and defeasible reasoning. We show first that the SDL-based framework contains a flaw and must be adjusted. A discussion of possible adjustments, including an alternative treatment in terms of a preference-based semantics, reveals difficulties that are reminiscent of problems in defeasible reasoning and intensional accounts of defeasible conditionals.