How to reason without words: inference as categorization

The idea that reasoning is a singular accomplishment of the human species has an ancient pedigree. Yet this idea remains as controversial as it is ancient. Those who would deny reasoning to nonhuman animals typically hold a language-based conception of inference which places it beyond the reach of languageless creatures. Others reject such an anthropocentric conception of reasoning on the basis of similar performance by humans and animals in some reasoning tasks, such as transitive inference. Here, building on the modal similarity theory of Vigo [J Exp Theor Artif Intell, 2008 (in press)], we offer an account in which reasoning depends on a core suite of subsymbolic processes for similarity assessment, discrimination, and categorization. We argue that premise-based inference operates through these subsymbolic processes, even in humans. Given the robust discrimination and categorization abilities of some species of nonhuman animals, we believe that they should also be regarded as capable of simple forms of inference. Finally, we explain how this account of reasoning applies to the kinds of transitive inferences that many nonhuman animals display.     

The Port-Royal Logic's Theory of Argument

This is a critical examination of Antoine Arnauld's Logic or the Art of Thinking (1662), commonly known as the Port-Royal Logic. Rather than reading this work from the viewpoint of post-Fregean formal logic or the viewpoint of seventeenth-century intellectual history, I approach it with the aim of exploring its relationship to that contemporary field which may be labeled informal logic and/or argumentation theory. It turns out that the Port-Royal Logic is a precursor of this current field, or conversely, that this field may be said to be in the same tradition.     

A model of plausibility

Plausibility has been implicated as playing a critical role in many cognitive phenomena from comprehension to problem solving. Yet, across cognitive science, plausibility is usually treated as an operationalized variable or metric rather than being explained or studied in itself. This article describes a new cognitive model of plausibility, the Plausibility Analysis Model (PAM), which is aimed at modeling human plausibility judgment. This model uses commonsense knowledge of concept-coherence to determine the degree of plausibility of a target scenario. In essence, a highly plausible scenario is one that fits prior knowledge well: with many different sources of corroboration, without complexity of explanation, and with minimal conjecture. A detailed simulation of empirical plausibility findings is reported, which shows a close correspondence between the model and human judgments. In addition, a sensitivity analysis demonstrates that PAM is robust in its operations.     

Do We “do”?

A normative framework for modeling causal and counterfactual reasoning has been proposed by Spirtes, Glymour, and Scheines (1993; cf. Pearl, 2000). The framework takes as fundamental that reasoning from observation and intervention differ. Intervention includes actual manipulation as well as counterfactual manipulation of a model via thought. To represent intervention, Pearl employed the do operator that simplifies the structure of a causal model by disconnecting an intervened-on variable from its normal causes. Construing the do operator as a psychological function affords predictions about how people reason when asked counterfactual questions about causal relations that we refer to as undoing, a family of effects that derive from the claim that intervened-on variables become independent of their normal causes. Six studies support the prediction for causal (A causes B) arguments but not consistently for parallel conditional (if A then B) ones. Two of the studies show that effects are treated as diagnostic when their values are observed but nondiagnostic when they are intervened on. These results cannot be explained by theories that do not distinguish interventions from other sorts of events.     

Situated robotics: from learning to teaching by imitation

This paper presents an approach to imitation learning in robotics focusing on low level behaviours, so that they do not need to be encoded into sets and rules, but learnt in an intuitive way. Its main novelty is that, rather than trying to analyse natural human actions and adapting them to robot kinematics, humans adapt themselves to the robot via a proper interface to make it perform the desired action. As an example, we present a successful experiment to learn a purely reactive navigation behaviour using robotic platforms. Using Case Based Reasoning, the platform learns from a human driver how to behave in the presence of obstacles, so that no kinematics studies or explicit rules are required.     

Formal reasoning in late adulthood: The role of semantic content and metacognitive strategy

Two experiments explored the availability of deductive or formal reasoning in late adulthood. In Experiment 1, fifty young (M=19.0 years) and 50 elderly adults (M=81.0 years) were assessed using adaptations of Wason's selection task and rated task content for familiarity, affect, and agreement. In Experiment 2, 100 young (M=21.0 years) and 100 elderly adults (M=81.0 years) were similarly assessed, with half of the subjects in each age group receiving a metacognitive strategy to facilitate reasoning. Results from Experiment 1 indicated equivalent reasoning among the groups on problems employed in earlier developmental research. In contrast, problems constructed to entail affect resulted in poorer performance by older adults. In Experiment 2, both young and older adults who used the metacognitive strategy reasoned equally acrossall problems. In both experiments, familiarity and agreement did not play a role in deductive reasoning performance, but affect seemed to be an interfering factor. Results are discussed in terms of competence-procedure and stability-decrement models of adult cognitive development.     

Children's evaluation of the certainty of another person's inductive inferences and guesses

In three studies, 5–10-year-old children and an adult comparison group judged another's certainty in making inductive inferences and guesses. Participants observed a puppet make strong inductions, weak inductions, and guesses. Participants either had no information about the correctness of the puppet's conclusion, knew that the puppet was correct, or knew that the puppet was incorrect. Children of all ages (but not adults) rated the puppet as more certain about statements the child knew to be correct than statements the child knew to be incorrect. When assessing another's certainty, children have difficulty inhibiting their own knowledge and focusing on the other's perspective. Children were more likely to differentiate between inductions and guesses when the puppet made an Incorrect Statement, but even the oldest children did not differentiate consistently. The distinction between induction and guessing appears to be only acquired gradually but is important as a contributor to more advanced forms of reasoning and epistemological understanding.     

The initial representation in reasoning towards an interpretation of conditional sentences

All accounts of human reasoning (whether presented at the symbolic or subsymbolic level) have to reckon with the temporal organization of the human processing systems and the ephemeral nature of the representations it uses. We present three new empirical tests for the hypothesis that people commence the interpretational process by constructing a minimal initial representation. In the case of if A then C the initial representation captures the occurrence of the consequent, C, within the context of the antecedent, A. Conditional inference problems are created by a categorical premise that affirms or denies A or C. The initial representation allows an inference when the explicitly represented information matches (e.g., the categorical premise A affirms the antecedent “A”) but not when it mismatches (e.g., “not-A” denies A). Experiments 1 and 2 confirmed that people tend to accept the conclusion that “nothing follows” for the denial problems, as indeed they do not have a determinate initial-model conclusion. Experiment 3 demonstrated the other way round that the effect of problem type (affirmation versus denial) is reduced when we impede the possibility of inferring a determinate conclusion on the basis of the initial representation of both the affirmation and the denial problems.     

The need to explain

How do reasoners deal with inconsistencies? James (1907) believed that the rational solution is to revise your beliefs and to do so in a minimal way. We propose an alternative: You explain the origins of an inconsistency, which has the side effect of a revision to your beliefs. This hypothesis predicts that individuals should spontaneously create explanations of inconsistencies rather than refute one of the assertions and that they should rate explanations as more probable than refutations. A pilot study showed that participants spontaneously explain inconsistencies when they are asked what follows from inconsistent premises. In three subsequent experiments, participants were asked to compare explanations of inconsistencies against minimal refutations of the inconsistent premises. In Experiment 1, participants chose which conclusion was most probable; in Experiment 2 they rank ordered the conclusions based on their probability; and in Experiment 3 they estimated the mean probability of the conclusions' occurrence. In all three studies, participants rated explanations as more probable than refutations. The results imply that individuals create explanations to resolve an inconsistency and that these explanations lead to changes in belief. Changes in belief are therefore of secondary importance to the primary goal of explanation.     

The Causal Structure of Utility Conditionals

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.