Personality and reasoning ability during retirement age: Report from a Swedish population-based longitudinal study

We examined the association between personality and level and change in reasoning ability in a population-based sample of older adults (62–68 years) using a three-year annual follow-up longitudinal study design (HEARTS; N = 3851). Personality traits were measured using the Mini-IPIP scale and reasoning using a short form of Raven’s Matrices. Findings from a structural equation model, controlling for age, education, and sex, revealed that higher levels on extraversion, conscientiousness, and neuroticism were associated with lower reasoning ability (βs: −0.17 to −0.09). Higher levels of openness were associated with better reasoning (β: 0.16). We found no association with rate of change. This evidence replicates previous findings demonstrating that personality traits are associated with individual differences in cognition among older adults.     

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.     

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.     

Complexity of categorical syllogisms: An integration of two metrics

The complexity of categorical syllogisms was assessed using the relational complexity metric, which is based on the number of entities that are related in a single cognitive representation. This was compared with number of mental models in an experiment in which adult participants solved all 64 syllogisms. Both metrics accounted for similarly large proportions of the variance, showing that complexity depends on the number of categories that are related in a representation of the combined premises, whether represented in multiple mental models, or by a single model. This obviates the difficulty with mental models theory due to equivocal evidence for construction of more than one mental model. The “no valid conclusion” response was used for complex syllogisms that had valid conclusions. The results are interpreted as showing that the relational complexity metric can be applied to syllogistic reasoning, and can be integrated with mental models theory, which together account for a wide range of cognitive performances.     

Adding possibilities can reduce the Gambler's Fallacy: A naïve-probability paradox

This paper reports a novel paradox of intuitive probabilistic reasoning detected in naïve reasoners’ responses in two separate experiments where we manipulated the number of sets (or possibilities) of the problem keeping constant the probability of the critical set. Experiment 1 showed that the incidence of the Gambler's Fallacy (GF) was reduced when the number of sets was increased. In Experiment 2, a reduction of the GF also occurred but, more importantly, the percentage of correct responses of the participants increased when three sets of possibilities instead of two were used. Therefore, both Experiments 1 and 2 demonstrated that an increase in the extensional complexity of a problem can, under certain circumstances, lead to facilitation. These results support the importance of the extensional features in solving chance problems and are consistent with the model theory of reasoning.     

Belief bias is stronger when reasoning is more difficult

Three studies examine the influence of varying the difficulty of reasoning on the extent of belief bias, while minimising the possibility that the manipulation would influence the way participants approach the task. Specifically, reasoning difficulty was manipulated by making variations in problem content, while maintaining all other aspects of the problems constant. In Study 1, 191 participants were presented with consistent and conflict problems varying in two levels of difficulty. The results showed a significant influence of problem difficulty on the extent of the belief bias, such that the effect of belief was more pronounced for difficult problems. This effect was stronger in Study 2 (73 participants) where the difference in the difficulty of the problems was purposely accentuated. The results of both studies stress the importance of controlling for problem difficulty when studying belief bias. Study 3 examined one consequence of this, i.e., the classic belief vs. logic interaction could be eliminated by manipulating problem difficulty. Theoretical implications for dual-process accounts of belief bias are also discussed.     

Is reasoning from counterfactual antecedents evidence for counterfactual reasoning?

In most developmental studies the only error children could make on counterfactual tasks was to answer with the current state of affairs. It was concluded that children who did not show this error are able to reason counterfactually. However, children might have avoided this error by using basic conditional reasoning (Rafetseder, Cristi-Vargas, & Perner, 2010 Perner, J. and Rafetseder, E. 2010. “Counterfactual and other forms of conditional reasoning: Children lost in the nearest possible world”. In Understanding counterfactuals/Understanding causation, Edited by: Hoerl, C., McCormack, T. and Beck, S. R. New York: Oxford University Press.  [Google Scholar]). Basic conditional reasoning takes background assumptions represented as conditionals about how the world works. If an antecedent of one of these conditionals is provided by the task, then a likely conclusion can be inferred based only on background assumptions. A critical feature of counterfactual reasoning is that the selection of these additional assumptions is constrained by actual events to which the counterfactual is taken to be counterfactual. In contrast, in basic conditional reasoning one enriches the given antecedent with any plausible assumptions, unconstrained by actual events. In our tasks basic conditional reasoning leads to different answers from counterfactual reasoning. For instance, a doctor, sitting in the park with the intention of reading a paper, is called to an emergency at the swimming pool. The question, “If there had been no emergency, where would the doctor be?” should counterfactually be answered “in the park”. But by ignoring the doctor's intentions, and just reasoning from premises about the default location of a hospital doctor who has not been called out to an emergency, one might answer: “in the hospital”. Only by 6 years of age did children mostly give correct answers.