Reasoning Overview - Concepedia

Principles

Definition

Reasoning is the academic concept and research area concerned with the cognitive processes and logical structures by which individuals derive conclusions, make inferences, or evaluate arguments from premises or evidence. This field investigates the principles governing valid inference, inductive generalization, and abductive explanation, serving as a fundamental basis for understanding human cognition, knowledge acquisition, and artificial intelligence.

Ontological type

Core Processes

Theoretical Frameworks

Biases and Errors

Key developments

Key Figures

Contextual Knowledge Representations era

Lotfi A. Zadeh [1], associated with the University of California, Berkeley [3], stands as a central figure in the Contextual Knowledge Representations era. His papers The concept of a linguistic variable and its application to approximate reasoning—I [6] and The concept of a linguistic variable and its application to approximate reasoning—II [7] introduced linguistic variables and approximate reasoning, enabling structured handling of defaults and uncertainty in incomplete information. Herbert A. Simon [2], aligned with the University of Washington [4] and the University of California, Santa Barbara [5], helped shape the era's emphasis on diagrammatic and semantically rich reasoning. His papers Why a Diagram is (Sometimes) Worth Ten Thousand Words [8] and Problem Solving in Semantically Rich Domains: An Example from Engineering Thermodynamics* [9] illustrate diagrammatic and semantic representations as robust tools for solving problems under uncertainty and across domains.

Dual-Process Causal Integration era

Judea Pearl [1] is a foundational figure in causal reasoning in this era, with affiliations at Harvard University [3] and the University of California, Los Angeles [4]. His key contributions include articulating formal causal frameworks that connect probabilistic reasoning to interventions and counterfactuals in Causality: Models, Reasoning and Inference [7], which laid groundwork for identifying causal effects and guiding normative reasoning in computational models. Christopher Hitchcock [2] is another central figure, with affiliations at Rice University [5] and the California Institute of Technology [6]. In this shared program, Hitchcock [2] and other contributors advanced Causality: Models, Reasoning and Inference [7], clarifying the role of causal graphs and interventionist reasoning in the integration of dual-process perspectives with normative models.

Probabilistic Explainable Reasoning era

David G. Rand [1], affiliated with Harvard University [3] and the Institute of Cognitive and Brain Sciences [4], exemplifies the probabilistic-explainable reasoning focus of this era. His 2018 paper Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning [7] highlights how gaps in reasoning—not mere motivation—shape susceptibility, underscoring the need for probabilistic-mechanistic explanations in explainable reasoning. Jonathan St. B. T. Evans [2], affiliated with University of Liverpool [5] and the University of Saskatchewan [6], has been central to shaping the new psychology of reasoning in this period. His papers—Questions and challenges for the new psychology of reasoning [8], Do smart people have better intuitions? [9], and When logic and belief collide: Individual differences in reasoning times support a selective processing model [10]—collectively illuminate how reasoning, intuitions, and processing times interact with logic and belief, informing probabilistic explanations and transparent diagnostics in explainable reasoning.