A Computational Logic Approach to Human Syllogistic Reasoning

2021

The presented empirical research is based on a new model for the categorical syllogisms, described and partially tested elsewhere. This model assumes that deriving a conclusion involves a pattern completion process, similar to the completion of perceptual patterns. Specifically, inferring a conclusion would require the generation of the missing part of an abstract pattern of logical semantic features. This pattern is named schema, because it is organized around a particular argumentative goal. Such a schema emerges through the frequent contact with a corresponding class of argumentative experiences. As, presumably, the usual pragmatic argumentative contexts imply predominantly valid syllogisms, pragmatic syllogistic schemas would emerge mainly for them. These pragmatic schemas for the valid syllogisms are supposed to be based on a particular mixed semantics of their syllogistic judgments, including both intensional (class-property) and extensional (subclass-class) relationships. The...

Memory & Cognition, 1999

2001

In this paper we propose a mental models theory of syllogistic reasoning which does not incorporate a falsification procedure and clearly specifies which conclusions will be generated and in what order of preference. It is assumed the models constructed vary in terms of the number of uncertain representations of end terms, and the directness of the representation of the subjects of valid conclusions. These key factors determine which quantified conclusion will be generated, as well as the varying tendency to respond that "nothing follows". The theory is shown to provide a close fit to meta-analysis data derived from past experiments.

Current Psychology, 1995

2021

Judging if a conclusion follows logically from a given set of premises can depend much more on the believability than on the logical validity of the conclusion. This so-called belief bias effect has been replicated repeatedly for many decades now. An interesting observation is, however, that process models for deductive reasoning and models for the belief bias have not much of an overlap - they have largely been developed independently. Models for the belief bias often just implement first order logic for the reasoning part, thereby neglecting a whole research field. This paper aims to change that by presenting a first attempt at substituting the first order logic components of two models for belief, selective scrutiny and misinterpreted necessity, with two state of the art approaches for modeling human syllogistic reasoning, mReasoner and PHM. In addition, we propose an approach for extending the traditionally dichotomous predictions to numerical rating scales thereby enabling more...

Acta Psychologica, 2014

A number of heuristic-based hypotheses have been proposed to explain how people solve syllogisms with automatic processes. In particular, the matching heuristic employs the congruency of the quantifiers in a syllogismby matching the quantifier of the conclusion with those of the two premises. When the heuristic leads to an invalid conclusion, successful solving of these conflict problems requires the inhibition of automatic heuristic processing. Accordingly, if the automatic processing were based on processing the set of quantifiers, no semantic contents would be inhibited. The mental model theory, however, suggests that people reason using mental models, which always involves semantic processing. Therefore, whatever inhibition occurs in the processing implies the inhibition of the semantic contents. We manipulated the validity of the syllogism and the congruency of the quantifier of its conclusion with those of the two premises according to the matching heuristic. A subsequent lexical decision task (LDT) with related words in the conclusion was used to test any inhibition of the semantic contents after each syllogistic evaluation trial. In the LDT, the facilitation effect of semantic priming diminished after correctly solved conflict syllogisms (match-invalid or mismatch-valid), but was intact after no-conflict syllogisms. The results suggest the involvement of an inhibitory mechanism of semantic contents in syllogistic reasoning when there is a conflict between the output of the syntactic heuristic and actual validity. Our results do not support a uniquely syntactic process of syllogistic reasoning but fit with the predictions based on mental model theory.

Journal of Logic, Language and Information, 2013

The explanatory power of logic is vast and therefore it has proved a valuable tool for many disciplines, including the building-blocks of cognitive science, such as philosophy, computer science, mathematics, artificial intelligence, and linguistics. Logic has a great track record in providing interesting insights by means of formalization, and as such it is very useful in disambiguating psychological theories. Logically formalized cognitive theories are not only the source of unequivocal experimental hypotheses, but they also lend themselves naturally to computational modeling. Most importantly, modern logic has at its service a rich variety of tools to assess and compare such psychological theories. This toolbox can be utilized in evaluating cognitive models along the following dimensions:

2020

The prevailing focus on aggregated data and the lacking groupto-individual generalizability it entails have recently been identified as a major cause for the low performance of cognitive models in the field of syllogistic reasoning research. This article attempts to add to the discussion about the performance of current syllogistic reasoning models by considering the parameterization capabilities some cognitive models offer. To this end, we propose a model evaluation setting targeted specifically toward analyzing the capabilities of a model to fine-tune its inferential mechanisms to individual human reasoning data. This allows us to (1) quantify the degree to which models are able to capture individual human reasoning behavior, (2) analyze the efficiency of the parameters used by models, and (3) examine the functional differences between the prediction capabilities of competing models on a more detailed level. We apply this method to two state-of-the-art models for syllogistic reaso...

Frontiers in Education, 2020

We report on a study on syllogistic reasoning conceived with the idea that subjects' performance in experiments is highly dependent on the communicative situations in which the particular task is framed. From this perspective, we describe the results of Experiment 1 comparing the performance of undergraduate students in 5 different tasks. This between-subjects comparison inspires a within-subject intervention design (Experiment 2). The variations introduced on traditional experimental tasks and settings include two main dimensions. The first one focuses on reshaping the context (the pragmatics of the communication situations faced) along the dimension of cooperative vs. adversarial attitudes. The second one consists of rendering explicit the construction/representation of counterexamples, a crucial aspect in the definition of deduction (in the classical semantic sense). We obtain evidence on the possibility of a significant switch in students' performance and the strategies they follow. Syllogistic reasoning is seen here as a controlled microcosm informative enough to provide insights and we suggest strategies for wider contexts of reasoning, argumentation and proof.

Cognition, 1989

This paper is about syllogistic reasoning, i.e., reasoning from such pairs of premises as, All the chefs are musicians; some of the musicians are painters. We present a computer model that implements the latest account of syllogisms, which is based on the theory of mental models. We also report four experiments that were designed to test this account. Experiments 1 and 2 examined the strategies revealed by the participants' use of paper and pencil as aids to reasoning. Experiment 3 used a new technique to externalize thinking. The participants had to refute, if possible, putative conclusions by constructing external models that were examples of the premises but counterexamples of the conclusions. Experiment 4 used the same techniques to examine the participants' strategies as they drew their own conclusions from syllogistic premises. The results of the experiments showed that individuals not trained in logic can construct counterexamples, that they use similar operations to those implemented in the computer model, but that they rely on a much greater variety of interpretations of premises and of search strategies than the computer model does. We re-evaluates current theories of syllogistic reasoning in the light of these results.