Automatic and controlled components of judgment and decision making

The categorization of inductive reasoning into largely automatic processes (heuristic reasoning) and controlled analytical processes (rule-based reasoning) put forward by dual-process approaches of judgment under uncertainty (e.g., K. E. Stanovich & R. F. West, 2000) has been primarily a matter of assumption with a scarcity of direct empirical findings supporting it. The present authors use the process dissociation procedure (L. L. Jacoby, 1991) to provide convergent evidence validating a dual-process perspective to judgment under uncertainty based on the independent contributions of heuristic and rule-based reasoning. Process dissociations based on experimental manipulation of variables were derived from the most relevant theoretical properties typically used to contrast the two forms of reasoning. These include processing goals (Experiment 1), cognitive resources (Experiment 2), priming (Experiment 3), and formal training (Experiment 4); the results consistently support the author's perspective. They conclude that judgment under uncertainty is neither an automatic nor a controlled process but that it reflects both processes, with each making independent contributions.     

Judging the Probability of Hypotheses Versus the Impact of Evidence: Which Form of Inductive Inference Is More Accurate and Time‐Consistent?

Inductive reasoning requires exploiting links between evidence and hypotheses. This can be done focusing either on the posterior probability of the hypothesis when updated on the new evidence or on the impact of the new evidence on the credibility of the hypothesis. But are these two cognitive representations equally reliable? This study investigates this question by comparing probability and impact judgments on the same experimental materials. The results indicate that impact judgments are more consistent in time and more accurate than probability judgments. Impact judgments also predict the direction of errors in probability judgments. These findings suggest that human inductive reasoning relies more on estimating evidential impact than on posterior probability.     

归纳推理和演绎推理的关系理论及其模型

推理是人类高级认知过程的一种,相关的心理学研究一般将其分为归纳推理和演绎推理两个方面。归纳推理是从特殊到一般的推理过程,与之相对的演绎推理则是从一般到特殊的过程。归纳推理和演绎推理的关系问题是当前心理推理研究领域的一个重点问题。这一问题主要有两种理论解释：一种是单过程理论,该理论认为归纳推理和演绎推理本质上是同一个认知过程,以单过程理论为基础构建的推理模型称为单维模型;另一种是双过程理论,认为归纳推理和演绎推理是两个不同的认知过程,并不同程度地受到启发和分析过程的影响。未来研究可多关注推理的时间进程,以及采用不同的研究方法对各自理论提供数据支持。     

归纳推理中的属性中心性效应

通过三个实验研究了属性的中心性程度对归纳推理的影响,结果表明,在归纳推理中存在属性中心性效应,中心属性比其他属性（次中心属性、再次中心属性）更能影响归纳推理。但属性中心性效应的出现受到了前提对象和结论对象相似性程度的制约,当前提对象和结论对象的相似程度高时,中心属性比其他属性更能影响归纳推理;随着相似性水平的降低,中心属性的归纳推理力度越来越弱,而次中心属性、再次中心属性的归纳推理力度则会依次表现出越来越强的趋势,到最后当前提对象和结论对象的属性完全不相似时,最次中心属性的归纳推理力度达到最高水平,而中心属性的归纳推理力度则降低到最低水平。由于再次中心属性的归纳推理力度的最高水平没有显著高于随机水平,而中心属性的归纳推理力度的最低水平却显著低于随机水平,因此,研究者认为,在前提对象和结论对象完全不相似的情况下,被试将确信不能采用中心属性来进行归纳推理的力度判断,而采用猜测的方式选择了肯定可以排除的项目之外的其他项目,这体现了具有不确定的归纳推理活动的过程的典型特征     

Feature-based versus category-based induction with uncertain categories

Previous research has suggested that when feature inferences have to be made about an instance whose category membership is uncertain, feature-based inductive reasoning is used to the exclusion of category-based induction. These results contrast with the observation that people can and do use category-based induction when category membership is known. The present experiments examined the conditions that drive feature-based and category-based strategies in induction under category uncertainty. Specifically, 2 experiments investigated whether reliance on feature-based inductive strategies is a product of the lack of coherence in the categories used in previous research or is due to the use of a decision-only induction procedure. Experiment 1 found that feature-based reasoning remained the preferred strategy even when categories with relatively high internal coherence were used. Experiment 2 found a shift toward category-based reasoning when participants were trained to classify category members prior to feature induction. Together, these results suggest that an appropriate conceptual representation must be formed through experience with a category before it is likely to be used as a basis for feature induction.     

Developmental Changes in Strategies for Gathering Evidence About Biological Kinds

How do people gather samples of evidence to learn about the world? Adults often prefer to sample evidence from diverse sources—for example, choosing to test a robin and a turkey to find out if something is true of birds in general. Children below age 9, however, often do not consider sample diversity, instead treating non-diverse samples (e.g., two robins) and diverse samples as equivalently informative. The current study (N = 247) found that this discontinuity stems from developmental changes in standards for evaluating evidence—younger children chose to learn from samples that best approximate idealized views of what category members are supposed to be like (e.g., the fastest cheetahs), with a gradual shift across age toward samples that cover more within-category variation (e.g., cheetahs of varying speeds). These findings have implications for the relation between conceptual structure and inductive reasoning, and for the mechanisms underlying inductive reasoning more generally.     

归纳推理的认知神经机制

归纳推理是从特殊推导至一般的高级认知过程, 其认知神经机制是当前研究的新方向。对已有研究进行分析发现：(1)归纳推理的核心过程与前额叶和晚期ERP成分密切相关; (2) 归纳推理可能存在双系统; (3)归纳推理过程中大脑偏侧化的现象存在争议。以上三个问题需要后续归纳推理的认知神经机制研究进行验证。     

Mathematical Reasoning: Adults' Ability to Make the Inductive-Deductive Distinction

In this study, I examined adults' ability to distinguish necessary deductive and indeterminate inductive forms of argument in mathematics. Only 30% of a sample of college students distinguished deductive and inductive forms of argument and experienced deductively derived conclusions as necessary and inductively derived conclusions as uncertain. Forty percent failed to distinguish deductive and inductive forms and experienced inductively derived and deductively derived conclusions as necessary. Thirty percent distinguished deductive and inductive arguments but experienced deductively derived and inductively derived conclusions as uncertain. As observed in other reasoning domains, the introduction of personal beliefs or knowledge about the argument content appeared to affect adult reasoners' application of knowledge about forms of argument and judgments of necessity. The results suggest the following conclusions. Adults' experience of the conclusions from mathematical inductive and deductive arguments as provisional conclusions or necessary conclusions depends on a complex coordination involving ability to attend to abstract premise-conclusion relations and beliefs about the nature of mathematical objects and regularities. Thus, two major achievements are involved, explaining the low numbers able to judge necessity in mathematics.     

Relations between premise similarity and inductive strength

According to the diversity principle, diverse evidence is strong evidence. There has been considerable evidence that people respect this principle in inductive reasoning. However, exceptions may be particularly informative. Medin, Coley, Storms, and Hayes (2003) introduced a relevance theory of inductive reasoning and used this theory to predict exceptions, including the nondiversity-by-propertyreinforcement effect. A new experiment in which this phenomenon was investigated is reported here. Subjects made inductive strength judgments and similarity judgments for stimuli from Medin et al. (2003). The inductive strength judgments showed the same pattern as that in Medin et al. (2003); however, the similarity judgments suggested that the pattern should be interpreted as a diversity effect, rather than as a nondiversity effect. It is concluded that the evidence regarding the predicted nondiversity-byproperty-reinforcement effect does not give distinctive support for relevance theory, although this theory does address other results.     

图形型归纳推理的神经机制:一项fMRI研究

利用fMRI脑成像技术探讨图形型归纳推理的神经机制。设计了一种由图形形状和条纹方向描述的简单几何图形组成的归纳推理任务,这种图形型任务与以往研究中常用的语句型归纳推理任务是同质的。根据两个图形共享特征数量的不同设计了两种实验任务:共享两个特征(2T)和共享一个特征(1T),以休息基线(Rest)作为控制任务。2T和1T任务均为归纳推理任务,但2T任务包含知觉特征整合成分,而1T任务不包括。结果发现:与控制任务相比,归纳推理任务在前额区(BA6、9、11、46、47)、尾状核、壳核和丘脑等脑区有显著激活,反映了"前额皮层-纹状体-丘脑"通路在图形型归纳推理中的重要作用;图形型归纳推理中的知觉信息整合与右侧额下回(BA47)、双侧尾状核头部、壳核等脑区有关。     

A General Structure for Legal Arguments About Evidence Using Bayesian Networks

A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where BNs have been used in the legal context, they are presented as completed pieces of work, with no insights into the reasoning and working that must have gone into their construction. This means the process of building BNs for legal arguments is ad hoc, with little possibility for learning and process improvement. This article directly addresses this problem by describing a method for building useful legal arguments in a consistent and repeatable way. The method complements and extends recent work by Hepler, Dawid, and Leucari (2007) on object‐oriented BNs for complex legal arguments and is based on the recognition that such arguments can be built up from a small number of basic causal structures (referred to as idioms). We present a number of examples that demonstrate the practicality and usefulness of the method.     

Intolerance of uncertainty and insula activation during uncertain reward

Individuals with high intolerance of uncertainty (IU) have been shown to exhibit abnormal threat responding, which may be mediated by hyperactive anterior insula (aINS) response to uncertainty. Research has indicated that individuals with high IU also exhibit abnormal positive valence responding, suggesting that IU may impact responding to uncertainty regardless of the valence of the potential outcome. To date, no study has investigated the neural processes associated with IU and response to uncertain positive stimuli, such as rewards. Therefore, this study was designed to examine the association between individual differences in IU and neural activation during uncertain reward using functional magnetic resonance imaging (fMRI). Thirty-seven adults completed a self-report measure of IU and a reward task during fMRI. Consistent with the threat literature, greater IU was associated with increased aINS activation during uncertain reward. This association was more robust for the prospective IU subscale, a dimension characterized by worry about future events. Together with prior studies, these findings provide evidence that IU is related to abnormal threat and reward responding, and that these deficits may be similarly linked to hyperactive aINS response to uncertainty.     

The Influence of Task Dynamics on Inductive Generalizations: How Sequential and Simultaneous Presentation of Evidence Impacts the Strength and Scope of Property Projections

Young children are remarkably flexible reasoners insofar as they modify their inferences to accommodate the conceptual information or perceptual relations represented in an inductive problem. Children’s inductive reasoning is highly sensitive to what evidence is presented to them. Four experiments with 115 preschoolers (M_age = 4;8) and 119 adults (M_age = 21;9) examined whether induction is influenced by how evidence is presented. Specifically, these studies explored the extent to which presenting evidence exemplars at the same time (i.e., simultaneous presentation) or one by one (i.e., sequential presentation) would influence property projections to a range of targets. Experiment 1 revealed that simultaneous presentation yielded a higher rate and a broader scope of projections than did sequential presentation. Experiment 2 confirmed that these effects were not due to how items were labeled. Experiments 3 and 4 explored the interplay between evidence presentation and specific task features that impact how participants compare evidence and target exemplars. In Experiment 3, there were no differences between the 2 presentation formats when evidence exemplars were removed prior to the projection phase, thereby eliminating the opportunity to compare evidence exemplars and targets. Finally, Experiment 4 showed that sequential presentation yielded a high rate of projections when participants were not afforded the opportunity to compare exemplars within the evidence sample. These results have implications for understanding the mechanisms that guide children’s inductive decisions.     

Children's understanding of posterior probability

Do young children have a basic intuition of posterior probability? Do they update their decisions and judgments in the light of new evidence? We hypothesized that they can do so extensionally, by considering and counting the various ways in which an event may or may not occur. The results reported in this paper showed that from the age of five, children's decisions under uncertainty (Study 1) and judgments about random outcomes (Study 2) are correctly affected by posterior information. From the same age, children correctly revise their decisions in situations in which they face a single, uncertain event, produced by an intentional agent (Study 3). The finding that young children have some understanding of posterior probability supports the theory of naive extensional reasoning, and contravenes some pessimistic views of probabilistic reasoning, in particular the evolutionary claim that the human mind cannot deal with single-case probability.     

Induction,Normality and Reasoning with Arbitrary Objects

This paper concerns the apparent fact — discussed by Sinan Dogramaci (2010) and Brian Weatherson (2012) — that inductive reasoning often interacts in disastrous ways with patterns of reasoning that seem perfectly fine in the deductive case. In contrast to Dogramaci's and Weatherson's own suggestions, I argue that these cases show that we cannot reason inductively about arbitrary objects. Moreover, as I argue, this prohibition is neatly explained by a certain hypothesis about the rational basis of inductive reasoning — namely, the hypothesis that inductive reasoning is fundamentally reasoning about what normally happens (in a non‐statistical sense).     

Probabilistic argumentation systems: A new way to combine logic with probability

Probability is usually closely related to Boolean structures, i.e., Boolean algebras or propositional logic. Here we show, how probability can be combined with non-Boolean structures, and in particular non-Boolean logics. The basic idea is to describe uncertainty by (Boolean) assumptions, which may or may not be valid. The uncertain information depends then on these uncertain assumptions, scenarios or interpretations. We propose to describe information in information systems, as introduced by Scott into domain theory. This captures a wide range of systems of practical importance such as many propositional logics, first order logic, systems of linear equations, inequalities, etc. It covers thus both symbolic as well as numerical systems. Assumption-based reasoning allows then to deduce supporting arguments for hypotheses. A probability structure imposed on the assumptions permits to quantify the reliability of these supporting arguments and thus to introduce degrees of support for hypotheses. Information systems and related information algebras are formally introduced and studied in this paper as the basic structures for assumption-based reasoning. The probability structure is then formally represented by random variables with values in information algebras. Since these are in general non-Boolean structures some care must be exercised in order to introduce these random variables. It is shown that this theory leads to an extension of Dempster–Shafer theory of evidence and that information algebras provide in fact a natural frame for this theory.     

"What if…": The Use of Conceptual Simulations in Scientific Reasoning

The term conceptual simulation refers to a type of everyday reasoning strategy commonly called "what if" reasoning. It has been suggested in a number of contexts that this type of reasoning plays an important role in scientific discovery; however, little direct evidence exists to support this claim. This article proposes that conceptual simulation is likely to be used in situations of informational uncertainty, and may be used to help scientists resolve that uncertainty. We conducted two studies to investigate the relationship between conceptual simulation and informational uncertainty. Study 1 was an in vivo study of expert scientists; the results suggest that scientists do use conceptual simulation in situations of informational uncertainty, and that they use conceptual simulation to make inferences from their data using the analogical reasoning process of alignment by similarity detection. Study 2 experimentally manipulated experts' level of uncertainty and provides further support for the hypothesis that conceptual simulation is more likely to be used in situations of informational uncertainty. Finally, we discuss the relationship between conceptual simulation and other types of reasoning using qualitative mental models.     

Attention and anticipation in response to varying levels of uncertain threat: An ERP study

Individual differences in responding to uncertainty have been proposed as a key mechanism of how anxiety disorders develop and are maintained. However, most empirical work has compared responding to uncertain versus certain threat dichotomously. This is a significant limitation because uncertainty in daily life occurs along a continuum of probability, ranging from very low to high chances of negative outcomes. The current study investigated (1) how varying levels of uncertainty impact attention and anticipatory emotion, and (2) how these effects are moderated by individual differences in risk factors for anxiety disorders, particularly intolerance of uncertainty (IU) and worry. Participants (n = 65) completed a card task in which the probability of shock varied across trials. Two event-related potential components were examined: the P2, an index of attention, and the stimulus-preceding negativity (SPN), an index of anticipation. The P2 tracked the level of uncertainty and was smaller for more uncertain outcomes. Participants higher in IU exhibited greater differences in the P2 across levels of uncertainty. The SPN did not track specific levels of uncertainty but was largest for uncertain threat compared with certain threat and safety. Greater worry was associated with blunting of the SPN in anticipation of all outcomes. Thus, attention appears to be sensitive to variations in uncertainty, whereas anticipation seems sensitive to uncertainty globally. The two processes appear to be distinctly related to anxiety risk factors. These results highlight the value of examining multiple aspects of anticipatory responding to varying levels of uncertainty for understanding risk for anxiety disorders.     

工作记忆容量和加工速度在归纳推理能力老化中的作用

利用结构方程模型考察了工作记忆容量和加工速度在归纳推理老化中的作用。样本为217名社区老人。结果发现：(1)工作记忆容量在归纳推理老化中具有重要作用．语音环路和视空问初步加工系统在推理过程巾参与程度相当。(2)在年龄和归纳推理能力之问存在年龄——加工速度——工作记忆容量——归纳推理这样一条层级关系。年龄、加工速度和工作记忆容量共同解释归纳推理中57％的变异。     

Uncertainty is associated with biased expectancies and heightened responses to aversion

Uncertainty is an omnipresent force in peoples' lives that has been shown to amplify the negative impact of aversive events. This amplified aversiveness, together with the negative attitudes that individuals can have toward uncertainty, suggests that a cue indicating uncertainty about future events might be associated with biased expectancies of negative outcomes or biased contingency estimates, similar to biases that have been observed for traditional fear-relevant cues, such as snakes or spiders. Participants in this study saw three different cues: one that indicated with certainty that an aversive picture would follow, one that indicated with certainty that a neutral picture would follow, and one that indicated uncertainty about whether an aversive or neutral picture would follow. Online self-report data revealed negatively biased expectancies of aversion after uncertain cues. The degree of this online expectancy bias predicted participants' estimates, at the conclusion of the experiment, of the relationship between uncertain cues and aversive pictures. Aversive pictures after the uncertain cue (relative to those after the certain cue) were accompanied by increased skin conductance responses and self-reported negative mood. These findings that uncertainty is accompanied by biased expectancies of aversion and heightened responses to aversion warrant extensions of this research in anxiety disorders, given evidence for intolerance of uncertainty and anticipatory dysfunction in the pathology of such disorders.