In defeasible reasoning, initially drawn conclusions can be withdrawn in light of new information. In this paper, we examine how the conclusions drawn from conditionals describing positive or negative situations can be defeated by subsequent negative or positive information, respectively. Participants were confronted with conditionals of the form “If [situation], then I am happy/sad” which were either followed by no additional information or by additional information describing situations of the same or the opposite valence. The participant's task was to decide (...) on a question asking for a possible conclusion (“Am I happy?” vs. “Am I sad?”). We found a negativity bias in defeasible reasoning: negative information defeated positively charged conclusions more strongly than positive information defeated negatively charged conclusions. We discuss our results in relation to the new psychology of reasoning. (shrink)

Probabilistic theories of reasoning assume that people use their prior knowledge to estimate the conditional probability of q given p and that this probability predicts the acceptance of modus ponens inferences. But how do people reason with unfamiliar conditionals for which they do not have prior knowledge? Reasoning without prior knowledge has been extensively investigated in experiments in which participants were instructed to reason deductively. But it is still not clear how people reason with unfamiliar conditionals when they are instructed (...) to reason as in daily life. Can probabilities also predict reasoning with unfamiliar content? In two experiments we instructed participants to reason as in everyday life and to evaluate conclusions from familiar and unfamiliar conditionals. Results showed that reasoning with familiar conditionals can be predicted by the conditional probability. For unfamiliar conditionals, however, this was not the case. Conclusions from unfamiliar conditionals were accepted more strongly than what could be expected according to their conditional probability. Our findings challenge probabilistic theories of reasoning and suggest that other approaches, such as a dual-strategy model, describe our results more adequately. (shrink)

Argumentation is a very fertile area of research in Artificial Intelligence, and various semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. When these semantics make conflicting predictions, theoretical arbitration typically relies on ad hoc examples and normative intuition about what prediction ought to be the correct one. We advocate a complementary, descriptive-experimental method, based on the collection of behavioral data about the way human reasoners handle these (...) critical cases. We report two studies applying this method to the case of reinstatement (both in its simple and floating forms). Results speak for the cognitive plausibility of reinstatement and yet show that it does not yield the full expected recovery of the attacked argument. Furthermore, results show that floating reinstatement yields comparable effects to that of simple reinstatement, thus arguing in favor of preferred argumentation semantics, rather than grounded argumentation semantics. Besides their theoretical value for validating and inspiring argumentation semantics, these results have applied value for developing artificial agents meant to argue with human users. (shrink)

Although argumentation plays an essential role in our lives, there is no integrated area of research on the psychology of argumentation. Instead research on argumentation is conducted in a number of separate research communities that are spread across disciplines and have only limited interaction. With a view to bridging these different strands, we first distinguish between three meanings of the word ?argument?: argument as a reason, argument as a structured sequence of reasons and claims, and argument as a social exchange. (...) All three meanings are integral to a complete understanding of human reasoning and cognition. Cognitive psychological research on argumentation has focused mostly on the first and second of these meanings, so we present perspectives on argumentation from outside of cognitive psychology, which focus on the second and third. Specifically, we give anoverview of the methods, goals, and disciplinary backgrounds of research on the production, the analysis, and the evaluation of arguments. Finally, inintroducing the experimental studies included in this special issue, which were conducted by researchers from a range of theoretical backgrounds, weunderline the breadth of argumentation research as well as stress opportunities for mutual awareness and integration. (shrink)

Argument quality has consistently been shown to have strong and lasting persuasive effects. The question is what criteria people use to distinguish strong from weak arguments and how these criteria relate to the ones proposed in normative argumentation theory. In an experiment 235 participants without training in argumentation theory rated the acceptance of 30 claims about the desirability of a consequence that were supported by either an argument from analogy, from authority, or from consequences. The supporting arguments were systematically manipulated (...) to violate argument type specific criteria. Participants proved sensitive to the violation of most, but not all, argument type specific criteria. From a normative perspective these findings suggest that people act in a fairly adequate way. These findings also enable a more precise description of what people may do when critically appraising arguments, which has important implications for the use of argument quality as a methodological tool in persuasion research. (shrink)

High-quality arguments have strong and lasting persuasive effects, suggesting that people can distinguish high- from low-quality arguments. However, we know little of what norms people employ to make that distinction. Some studies have shown that, in evaluating arguments from consequences, people are more sensitive to differences with respect to the desirability of these consequences than to differences in the likelihood that these consequences will occur. This raises the question of whether people lack the criteria to distinguish high-quality from low-quality arguments (...) in support of such claims. In an experiment, 196 participants without any training in argument theory rated their acceptance of 30 claims about the likelihood that a certain outcome would result from a certain action. These claims were supported by an argument from authority, or from cause to effect, or from example. Arguments were systematically manipulated to violate nine specific criteria. For seven out of the nine criteria violation decreased acceptance of the claim supported. These findings show that people can use argument type specific criteria to distinguish high- from low-quality arguments. (shrink)

Normative theories can be useful in developing descriptive theories, as when normative subjective expected utility theory is used to develop descriptive rational choice theory and behavioral game theory. “Ought” questions are also the essence of theories of moral reasoning, a domain of higher mental processing that could not survive without normative considerations.

In common with a number of other authors I believe that there has been a paradigm shift in the psychology of reasoning, specifically the area traditionally labelled as the study of deduction. The deduction paradigm was founded in a philosophical tradition that assumed logicality as the basis for rational thought, and provided binary propositional logic as the agreed normative framework. By contrast, many contemporary authors assume that people have degrees of uncertainty in both premises and conclusions, and reject binary logic (...) as a workable normative system. I discuss a number of questions and challenges for this new psychology of reasoning, including the following: (a) Do we need an alternative normative system, such as Bayesianism, for the new paradigm? (b) Is there any longer a clear distinction between the study of deductive and inductive reasoning, the latter having its own tradition and literature? (c) Precisely how is the integrated study of reasoning and decision making facilitated by the new paradigm? (d) What difficulties with dual-processing approaches need to be resolved, if they are to take us forward? (shrink)

In recent years, the psychology of reasoning has been undergoing a paradigm shift, with general Bayesian, probabilistic approaches replacing the older, much more restricted binary logic paradigm. At the same time, dual processing theories have been gaining influence. We argue that these developments should be integrated and moreover that such integration is already underway. The new reasoning paradigm should be grounded in dual processing for its algorithmic level of analysis just as it uses Bayesian theory for its computational level of (...) analysis. Moreover, we propose that, within the new paradigm, these levels of analysis reflect on each other. Bayesianism suggests a specific theoretical understanding of dual processing. Just as importantly, the duality in processing carries over to duality in function; although both types of processes compute degrees of belief, they generate different functions. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

Research on reasoning about consequential arguments has been an active but piecemeal enterprise. Previous research considered in depth some subclasses ofconsequential arguments, but further understanding of consequential arguments requires that we address their greater variety, avoiding the risk of over-generalisation from specific examples. Ideally we ought to be able to systematically generate the set of consequential arguments, and then engage in random sampling of stimuli within that set. The current article aims at making steps in that direction, using the theory (...) of utility conditionals as a way to generate a large set of consequential arguments, and offering one study illustrating how the theory can be used for the random sampling of stimuli. Itis expected that further use of this method will bring more diversity to experimental research on consequential arguments, and more robustness to models of argumentation from consequences. (shrink)

People can reason about the preferences of other agents, and predict their behavior based on these preferences. Surprisingly, the psychology of reasoning has long neglected this fact, and focused instead on disinterested inferences, of which preferences are neither an input nor an output. This exclusive focus is untenable, though, as there is mounting evidence that reasoners take into account the preferences of others, at the expense of logic when logic and preferences point to different conclusions. This article summarizes the most (...) recent account of how reasoners predict the behavior and attitude of other agents based on conditional rules describing actions and their consequences, and reports new experimental data about which assumptions reasoners retract when their predictions based on preferences turn out to be false. (shrink)

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ‘‘if p then q’’ statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This (...) article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. (shrink)

In polite contexts, people find it difficult to perceive whether they can derive scalar inferences from what others say . Because this uncertainty can lead to costly misunderstandings, it is important to identify the cues people can rely on to solve their interpretative problem. In this article, we consider two such cues: Making a long Pause before the statement, and prefacing the statement with Well. Data from eight experiments show that Pauses are more effective than Wells as cues to scalar (...) inferences in polite contexts—because they appear to give a specific signal to switch expectations in the direction of bad news, whereas Well appears to give a generic signal to make extra processing effort. We consider the applied value of these findings for human–human and human–machine interaction, as well as their implications for the study of reasoning and discourse. (shrink)

Reasoning research has traditionally focused on the derivation of beliefs from beliefs, but it is increasingly turning to reasoning about decisions. In the absence of a single, entrenched normative model, the drive toward normativism is weaker in this new field than in its parent fields. The current balance between normativism and descriptivism is illustrated by three approaches to reasoning about decisions.

(2013). Formal Models of Reasoning in Cognitive Psychology. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 1-3. doi: 10.1080/19462166.2013.767559.

The new Bayesian paradigm in the psychology of reasoning aims to integrate the study of human reasoning, decision making, and rationality. It is supported by two findings. One, most people judge the probability of the indicative conditional, P(if A then B), to be the conditional probability, P(B|A), as implied by the Ramsey test. Two, they judge if A then B to be void when A is false. Their three-valued response table used to be called ‘defective’, but should be termed the (...) de Finetti table. We show how to study general de Finetti truth tables for negations, conjunctions, disjunctions, and conditionals. (shrink)

A study is reported testing two hypotheses about a close parallel relation between indicative conditionals, if A then B , and conditional bets, I bet you that if A then B . The first is that both the indicative conditional and the conditional bet are related to the conditional probability, P(B|A). The second is that de Finetti's three-valued truth table has psychological reality for both types of conditional— true , false , or void for indicative conditionals and win , lose (...) , or void for conditional bets. The participants were presented with an array of chips in two different colours and two different shapes, and an indicative conditional or a conditional bet about a random chip. They had to make judgements in two conditions: either about the chances of making the indicative conditional true or false or about the chances of winning or losing the conditional bet. The observed distributions of responses in the two conditions were generally related to the conditional probability, supporting the first hypothesis. In addition, a majority of participants in further conditions chose the third option, “void”, when the antecedent of the conditional was false, supporting the second hypothesis. (shrink)

Recientemente, se ha propuesto que la lógica no es el único factor que incide en el razonamiento condicional y que el valor y la utilidad son otros dos elementos importantes en este sentido. Al respecto, Bonnefon y Sloman añadieron en 2013 que la estructura de los condicionales es, del mismo modo, relevante en la actividad inferencial con condicionales de utilidad y trataron de demostrarlo por medio de varios experimentos. Sus resultados parecieron mostrar que, efectivamente, sus tesis eran correctas. No obstante, (...) plantearon igualmente que su propuesta y, por tanto, sus resultados eran compatibles con una teoría contemporánea de amplio alcance, la teoría de los modelos mentales, y en este artículo se trata de desarrollar esta idea con el propósito de comprobar si es cierta. (shrink)