Pragmatic versus syntactic approaches to training deductive reasoning

Two views have dominated theories of deductive reasoning. One is the view that people reason using syntactic, domain-independent rules of logic, and the other is the view that people use domain-specific knowledge. In contrast with both of these views, we present evidence that people often reason using a type of knowledge structure termed pragmatic reasoning schemas. In two experiments, syntactically equivalent forms of conditional rules produced different patterns of performance in Wason's selection task, depending on the type of pragmatic schema evoked. The differences could not be explained by either dominant view. We further tested the syntactic view by manipulating the type of logic training subjects received. If people typically do not use abstract rules analogous to those of standard logic, then training on abstract principles of standard logic alone would have little effect on selection performance, because the subjects would not know how to map such rules onto concrete instances. Training results obtained in both a laboratory and a classroom setting confirmed our hypothesis: Training was effective only when abstract principles were coupled with examples of selection problems, which served to elucidate the mapping between abstract principles and concrete instances. In contrast, a third experiment demonstrated that brief abstract training on a pragmatic reasoning schema had a substantial impact on subjects' reasoning about problems that were interpretable in terms of the schema. The dominance of pragmatic schemas over purely syntactic rules was discussed with respect to the relative utility of both types of rules for solving real-world problems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26121/1/0000197.pd

From concrete to abstract rules : A computational sketch

International audienceA multi-dimensional stimulus can elicit a range of responses depending on which dimension or combination of dimensions is considered. Such selection can be implicit, providing a fast and automatic selection, or explicit, providing a slower but contextualized selection. Both forms are important but do not derive from the same processes. Implicit selection results generally from a slow and progressive learning that leads to a simple response (concrete / first-order) while explicit selection derives from a deliberative process that allows to have more complex and structured response (abstract / second-order). The prefrontal cortex (PFC) is believed to provide the ability to contextualize concrete rules that leads to the acquisition of abstract rules even though the exact mechanisms are still largely unknown. The question we address in this paper is precisely about the acquisition, the representation and the selection of such abstract rules. Using two models from the literature (PBWM and HER), we explain that they both provide a partial but differentiated answer such that their unification offers a complete picture

Penerapan Asas Hukum Dalam Pembentukan Peraturan Perundang-Undangan

Legal norms (legal norms, rechtnormen) actually regulate internal personal life (internal life) in a civilized and humanistic manner and also regulate interpersonal relationships in social processes. Legal principles can be in the form of a legal norm that is high in location and many things depend on it and the principle can just a norm. This study aims to determine the application of legal principles in statutory regulations; and to find out the application of other principles in the field of laws and regulations. The research method uses a qualitative normative juridical research method with data collection sourced from library research. Based on the nature of this research, it is an explanatory research, namely research that explains and strengthens a theory on the results of existing research. The results of the study show that legal principles are not concrete legal rules, but are the background of concrete and general or abstract regulations. In general, legal principles are not stated in the form of concrete regulations or in the form of articles, but the law cannot be understood without these principles and the application of other principles in accordance with the legal field of the relevant legislation, including: in criminal law, for example the principle of legality, the principle of presumption of innocence and in civil law, for example in contract law, among others: the principle of agreement, freedom of contract, and good faith

Graph Query by Example

Abstract. Model-driven tools use model queries for many purposes, including validation of well-formedness rules, specication of derived features, and directing rule-based model transformation. Query languages such as graph patterns may facilitate capturing complex structural relationships between model elements. Specifying such queries, however, may prove dicult for engineers familiar with the concrete syntax only, not with the underlying abstract representation of the modeling language. The current paper presents an extension to the EMF-IncQuery model query tool that lets users point out, using familiar concrete syntax, an example of what the query results should look like, and automatically derive a graph query that nds other similar results. Keywords: by example, model query, graph pattern, EMF-IncQuery 1 Introduction Model-driven Engineering (MDE) approaches treat models as primary artifacts of the engineering process, relying on automated model processing steps. Models are usually thought of as typed, attributed graphs. This underlying structure is dened by the metamodel of the modeling language and is called the abstract syntax. On the other hand, the preferred way the model is presented to (and edited by) humans is in the form of visual diagrams, textual notations, tree structures, etc., called the concrete syntax. The two representations can have substantial dierences, e.g., an edge in concrete syntax may correspond to a node in abstract syntax, or to a structure of several elements (see Model queries are important components in model-driven tool chains: they are widely used for specifying derived features, well-formedness constraints, reports, and guard conditions for behavioural models, design space rules or model transformations. Although model queries can be implemented using a generalpurpose programming language (Java), specialized query languages may be mor

Nonlinear modelling of the in-plane-out-of-plane interaction in the seismic analysis of masonry infills in r.c. framed buildings

Abstract A five-element macro-model, with four diagonal out-of-plane (OP) nonlinear beams and one horizontal in-plane (IP) nonlinear truss, takes into account the OP and IP failure modes occurring, in the event of seismic loading, for masonry infills (MIs) inserted in reinforced concrete (r.c.) framed buildings. Pivot hysteretic models predict the nonlinear IP and OP force-displacement laws of the infill panel, based on geometrical rules defining loading and unloading branches. Firstly, a calibration of the proposed IP-OP interaction model of MIs is carried out considering full-scale experimental results of traditional masonry typologies. To evaluate the interaction, the numerical results of simultaneous IP and OP cyclic tests on MIs at the top, intermediate and lowest levels of an existing six-storey r.c. framed building are presented, assuming different displacement histories: i) OP loading faster than IP, at the sixth storey; ii) equal IP and OP loading, at the third storey; iii) IP loading faster than OP, at the first storey

Modelling Identity Rules with Neural Networks

In this paper, we show that standard feed-forward and recurrent neural networks fail to learn abstract patterns based on identity rules. We propose Repetition Based Pattern (RBP) extensions to neural network structures that solve this problem and answer, as well as raise, questions about integrating structures for inductive bias into neural networks. Examples of abstract patterns are the sequence patterns ABA and ABB where A or B can be any object. These were introduced by Marcus et al (1999) who also found that 7 month old infants recognise these patterns in sequences that use an unfamiliar vocabulary while simple recurrent neural networks do not. This result has been contested in the literature but it is confirmed by our experiments. We also show that the inability to generalise extends to different, previously untested, settings. We propose a new approach to modify standard neural network architectures, called Repetition Based Patterns (RBP) with different variants for classification and prediction. Our experiments show that neural networks with the appropriate RBP structure achieve perfect classification and prediction performance on synthetic data, including mixed concrete and abstract patterns. RBP also improves neural network performance in experiments with real-world sequence prediction tasks. We discuss these finding in terms of challenges for neural network models and identify consequences from this result in terms of developing inductive biases for neural network learning

Informal proof, formal proof, formalism

Increases in the use of automated theorem-provers have renewed focus on the relationship between the informal proofs normally found in mathematical research and fully formalised derivations. Whereas some claim that any correct proof will be underwritten by a fully formal proof, sceptics demur. In this paper I look at the relevance of these issues for formalism, construed as an anti-platonistic metaphysical doctrine. I argue that there are strong reasons to doubt that all proofs are fully formalisable, if formal proofs are required to be finitary, but that, on a proper view of the way in which formal proofs idealise actual practice, this restriction is unjustified and formalism is not threatened

Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers