Effects of self–other decision-making on time-based intertemporal choice

The effects of self–other decision-making on intertemporal choice have been revealed in many studies using a monetary outcome. However, the outcome of intertemporal choice is not restricted to money; time is also a scarce and nonrenewable resource outcome. Thus, we conducted a series of experiments to address the effects of self–other decision-making on time-based intertemporal choice, a type of intertemporal choice that uses time as an outcome. Over the course of three experiments, differences in self–other decision-making were evidenced. Participants who made decisions for others were more likely to prefer the smaller but sooner (SS) option over the larger but later (LL) option and considered the gain of the SS option to be significantly greater than that of the LL option. Participants who made decisions for themselves were likely to prefer the LL option over the SS option. However, they considered the gains of the LL and the SS option to be indifferent. Changing the role of decision-making could affect the ability of individuals to consider the future consequences of their decisions. The effects of self–other decision-making on time-based intertemporal choice could be explained by the accounts of economic reasoning and construal level theory. The findings indicated that the effects of self–other decision-making on time-based intertemporal choice, which could be generated simply by rewording questions, can help individuals make optimal long-term choices without the need for increased control.     

Singular Clues to Causality and Their Use in Human Causal Judgment

It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for causal judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to causal understanding, not multiple instance clues such as empirical association, contingency, and conditional probabilities.     

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data).     

Are there gender differences in cognitive reflection? Invariance and differences related to mathematics

ABSTRACT

Cognitive reflection is recognized as an important skill, which is necessary for making advantageous decisions. Even though gender differences in the Cognitive Reflection test (CRT) appear to be robust across multiple studies, little research has examined the source of the gender gap in performance. In Study 1, we tested the invariance of the scale across genders. In Study 2, we investigated the role of math anxiety, mathematical reasoning, and gender in CRT performance. The results attested the measurement equivalence of the Cognitive Reflection Test – Long (CRT- L), when administered to male and female students. Additionally, the results of the mediation analysis showed an indirect effect of gender on CRT-L performance through mathematical reasoning and math anxiety. The direct effect of gender was no longer statistically significant after accounting for the other variables. The current findings suggest that cognitive reflection is affected by numerical skills and related feelings.     

Fundamental failure to think logically about scientific questions: An illustration of tunnel vision with the application of Wason's Card Selection Test to criminal evidence

Logic and science are in some respects fundamentally different disciplines, in that for example, application of rules of logic can yield conclusions that are at odds with physical reality. Not surprisingly, people have ample difficulty with logical thinking. Nonetheless, in some instances, logical thinking can fuel empirical decision making. In the current research, it was established that applying rules of logic to a particular area of empirical decision making, that is, criminal fact finding, is indeed quite difficult (Study 1). Furthermore, the ability to think logically was found to be associated with superior evaluation of criminal evidence (Study 2). Implications of these findings are discussed.     

Salience Versus Proportional Reasoning: Rethinking the Mechanism Behind Graphical Display Effects

Two experiments examined predictions from two separate explanations for previously observed display effects for communicating low‐probability risks: foreground:background salience and proportional reasoning. According to foreground:background salience, people's risk perceptions are based on the relative salience of the foreground (number of people harmed) versus the background (number of people at risk), such that calling attention to the background makes the risk seem smaller. Conversely, the proportional reasoning explanation states that what matters is whether the respondent attends to the proportion, which conveys how small the risk is. In Experiment 1, we made the background more salient via color and bolding; in contrast to the foreground:background salience prediction, this manipulation did not influence participants' risk aversion. In Experiment 2, we separately manipulated whether the foreground and the background were displayed graphically or numerically. In keeping with the proportional reasoning hypothesis, there was an interaction whereby participants given formats that displayed the foreground and background in the same modality (graphs or numbers, thereby making the proportion easier to form) saw the probability as smaller and were less risk averse than participants given the information in different modalities. There was also a main effect of displaying the background graphically, providing some support for foreground:background salience. In total, this work suggests that the proportional reasoning account provides a good explanation of many display effects related to communicating low‐probability risks, although there is some role for foreground:background salience as well. Copyright © 2018 John Wiley & Sons, Ltd.     

Real Objects Can Impede Conditional Reasoning but Augmented Objects Do Not

In this study, Knauff and Johnson‐Laird's (2002) visual impedance hypothesis (i.e., mental representations with irrelevant visual detail can impede reasoning) is applied to the domain of external representations and diagrammatic reasoning. We show that the use of real objects and augmented real (AR) objects can control human interpretation and reasoning about conditionals. As participants made inferences (e.g., an invalid one from "if P then Q" to "P"), they also moved objects corresponding to premises. Participants who moved real objects made more invalid inferences than those who moved AR objects and those who did not manipulate objects (there was no significant difference between the last two groups). Our results showed that real objects impeded conditional reasoning, but AR objects did not. These findings are explained by the fact that real objects may over‐specify a single state that exists, while AR objects suggest multiple possibilities.     

Predicting Political Biases Against the Occupy Wall Street and Tea Party Movements

Accusations of hypocrisy have flown against both supporters and opponents of the Occupy Wall Street (OWS) and Tea Party movements. Integrating the ideologically objectionable premise model (IOPM), a newly devised model of political judgment, with political tolerance research, we find that how the political activities of OWS and Tea Party demonstrators are described determines whether or not biases against these groups emerge among people low and high in right‐wing authoritarianism (RWA). Specifically, people low in RWA were more biased against the Tea Party than OWS regardless of whether the groups engaged in normatively threatening or reassuring political behavior, whereas people high in RWA were more biased against OWS than the Tea Party when the groups engaged in normatively threatening (and therefore objectionable), but not normatively reassuring (and therefore acceptable) behavior. These findings further support the IOPM's contention that premise objectionableness, not right‐wing orientation, determines biases in political judgment.     

The Wished‐For Always Wins Until the Winner Was Inevitable All Along: Motivated Reasoning and Belief Bias Regulate Emotion During Elections

How do biases affect political information processing? A variant of the Wason selection task, which tests for confirmation bias, was used to characterize how the dynamics of the recent U.S. presidential election affected how people reasoned about political information. Participants were asked to evaluate pundit‐style conditional claims like “The incumbent always wins in a year when unemployment drops” either immediately before or immediately after the 2012 presidential election. A three‐way interaction between ideology, predicted winner (whether the proposition predicted that Obama or Romney would win), and the time of test indicated complex effects of bias on reasoning. Before the election, there was partial evidence of motivated reasoning—liberals performed especially well at looking for falsifying information when the pundit's claim predicted Romney would win. After the election, once the outcome was known, there was evidence of a belief bias—people sought to falsify claims that were inconsistent with the real‐world outcome rather than their ideology. These results suggest that people seek to implicitly regulate emotion when reasoning about political predictions. Before elections, people like to think their preferred candidate will win. After elections, people like to think the winner was inevitable all along.     

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5).