Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search

Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma–Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information‐theoretic formalism.     

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability

Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process—such as cognitive processing speed, response caution, and motor execution speed—in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision‐making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies—that the heritability of cognitive ability is based on cognitive processing speed—may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement.     

The Relation Between Factual and Counterfactual Conditionals

What is the relation between factual conditionals: If A happened then B happened, and counterfactual conditionals: If A had happened then B would have happened? Some theorists propose quite different semantics for the two. In contrast, the theory of mental models and its computer implementation interrelates them. It postulates that both can have a priori truth values, and that the semantic bases of both are possibilities: states that are possible for factual conditionals, and that were once possible but that did not happen for counterfactual conditionals. Two experiments supported these relations. Experiment 1 showed that, like factual conditionals, certain counterfactuals are true a priori, and others are false a priori. Experiment 2 replicated this result and showed that participants selected appropriate paraphrases, referring, respectively, to real and to counterfactual possibilities, for the two sorts of conditional. These results are contrary to alternative accounts of conditionals.     

Do Smarter People Employ Better Decision Strategies? The Influence of Intelligence on Adaptive Use of the Recognition Heuristic

Within the adaptive toolbox approach, it has repeatedly been shown that, on average, people tend to adapt their decision strategies to the decision context. Building upon these results, we investigated whether individuals systematically differ in their ability to successfully adapt to the situation when applying the fast‐and‐frugal recognition heuristic (RH). In decisions between recognized and unrecognized choice objects, individuals can base their choices solely on recognition, as predicted by the RH, or they can use further knowledge retrieved from memory. Since intelligence has been conceived as the ability to successfully adapt to different situations, we expected intelligence to influence the degree of adaptive use of the RH. To test this hypothesis, we first re‐analyzed data that referred to a decision domain for which RH‐use is known to perform well. As expected, individual RH‐use increased with general intelligence. Next, we designed an experiment addressing individual RH‐use in two new decision domains, one domain for which RH‐use was less effective than knowledge‐use and another domain for which both strategies were about equally effective. In addition, we tested whether fluid or crystallized intelligence best predicts adaptive use of the RH. RH‐use was found to decrease with fluid but not crystallized intelligence when RH‐use was less effective than use of further knowledge. In contrast, there was no significant association between either type of intelligence and RH‐use when none of the two strategies was optimal. We conclude that adaptive use versus non‐use of the RH is moderated by fluid intelligence. Copyright © 2017 John Wiley & Sons, Ltd.     

Do Self-Incentives and Self-Rewards Change Behavior? A Systematic Review and Meta-Analysis

Encouraging people to self-incentivize (i.e., to reward themselves in the future if they are successful in changing their behavior) or self-reward (i.e., prompt people to reward themselves once they have successfully changed their behavior) are techniques that are frequently embedded within complex behavior change interventions. However, it is not clear whether self-incentives or self-rewards per se are effective at bringing about behavior change. Nine databases were searched alongside manual searching of systematic reviews and online research registers. One thousand four hundred papers were retrieved, spanning a range of behaviors, though the majority of included papers were in the domain of “health psychology”. Ten studies matched the inclusion criteria for self-incentive but no studies were retrieved for self-reward. The present systematic review and meta-analysis is therefore the first to evaluate the unique effect of self-incentives on behavior change. Effect sizes were retrieved from 7 of the 10 studies. Analysis of the 7 studies produced a very small pooled effect size for self-incentives (k = 7, N = 1,161), which was statistically significant, d₊ = 0.17, CI [0.06, 0.29]. The weak effect size and dearth of studies raises the question of why self-incentivizing is such a widely employed component of behavior change interventions. The present research opens up a new field of inquiry to establish: (a) whether or not self-incentivizing and self-rewarding are effective behavior change techniques, (b) whether self-incentives and self-rewards need to be deployed alongside other behavior change techniques, and, (c) when and for whom self-incentives and self-rewards could support effective behavior change.     

Assessing Psychological Inflexibility Pertaining to Self in Patients With Posttraumatic Stress Disorder Using an Indirect Measure of (Nonassociative) Propositions

Relational frame theory (RFT) is a modern behavioral account of human language and cognition, which focuses on relations or propositions, rather than associations, as core explanatory constructs. In an attempt to measure such propositions, RFT researchers have developed the implicit relational assessment procedure (IRAP). It has been argued that the size of an IRAP effect may provide a metric for psychological inflexibility. The current study aimed to determine whether psychological inflexibility, as measured by the self-focused Natural Language-IRAP (NL-IRAP), would be higher in a clinical sample of individuals with a diagnosis of PTSD (N = 29) when compared to a nonclinical sample. Subsequently, the study investigated whether the self-focused NL-IRAP could be used to predict the presence of a clinical diagnosis, using a ROC analysis. As predicted, higher levels of psychological inflexibility were observed for the clinical group. The self-focused NL-IRAP also correctly classified the presence of PTSD (AUC = 76%) with a sensitivity level of 79.3% and a specificity level of 59.2%. Overall, the use of the IRAP as a nonassociative clinical measure appears promising.     

Modelling perceived risk and trust in driving automation reacting to merging and braking vehicles

Perceived risk and trust are crucial for user acceptance of driving automation. In this study, we identify important predictors of perceived risk and trust in a driving simulator experiment and develop models through stepwise regression to predict event-based changes in perceived risk and trust. 25 participants were tasked to monitor SAE Level 2 driving automation (ACC + LC) while experiencing merging and hard braking events with varying criticality on a motorway. Perceived risk and trust were rated verbally after each event, and continuous perceived risk, pupil diameter and ECG signals were explored as possible indictors for perceived risk and trust.The regression models show that relative motion with neighbouring road users accounts for most perceived risk and trust variations, and no difference was found between hard braking with merging and hard braking without merging. Drivers trust the automation more in the second exposure to events. Our models show modest effects of personal characteristics: experienced drivers are less sensitive to risk and trust the automation more, while female participants perceive more risk than males. Perceived risk and trust highly correlate and have similar determinants. Continuous perceived risk accurately reflects participants’ verbal post-event rating of perceived risk; the use of brakes is an effective indicator of high perceived risk and low trust, and pupil diameter correlates to perceived risk in the most critical events. The events increased heart rate, but we found no correlation with event criticality. The prediction models and the findings on physiological measures shed light on the event-based dynamics of perceived risk and trust and can guide human-centred automation design to reduce perceived risk and enhance trust.     

The role of psychological factors on the choice of different driving controls: On manual,partial, and highly automated controls

The present study investigates the role of psychological factors on the choice of three controls (modes) in driving a vehicle, namely highly automated, partially automated, and manual control. Traditional driving habits, resistance to change, and behavioural beliefs were all assessed along with individual and socioeconomic variables. Using survey data (n = 595) of car users, a model was developed to predict the share of different driving controls and determine the effects of psychological variables. Results indicate that up to 55% of people prefer driving with highly automated control, and 30% prefer partially automated control. Behavioural beliefs (e.g., attitudes toward highly automated control) are not as critical to driving control as habits. People with stronger driving habits are less likely to use highly automated controls. A one-unit increase in worry could reduce driving in highly automated control by 5.5% and increase manual control by 4.5%, and those who welcome the new technologies are more likely to prefer highly automated control. Some practical policy solutions are also provided.     

眼睛区域构型信息与特征信息的跨维共变增益效应及其加工特异性

面孔知觉可能在区域尺度上发生多维信息整合, 但迄今无特异性实验证据。本研究在两个实验中操纵面孔眼睛区域或嘴巴区域的单维构型或特征信息, 测量人们觉察单维变化或跨维共变的敏感度, 以此检测面孔区域尺度上的多维信息整合有何现象与规律, 进而揭示面孔知觉的多维信息整合机制。实验获得3个发现：(1)正立面孔眼睛区域的信息变化觉察呈现出“跨维共变增益效应”, 跨维信息共变觉察的敏感度显著高于任意一种单维信息变化觉察的敏感度; (2)“跨维共变增益效应”只在正立面孔的眼睛区域出现, 在倒置面孔的眼睛区域、正立面孔的嘴巴区域或倒置面孔的嘴巴区域都没有出现, 因此具有面孔区域特异性和面孔朝向特异性; (3)就单维信息变化觉察而言, 眼睛区域的敏感度不会受到面孔倒置的损伤, 但是嘴巴区域的敏感度会受到面孔倒置的显著损伤。综合可知, 面孔知觉确实会发生区域尺度上的信息整合, 它不是普遍性的信息量效应, 而是特异性的眼睛效应(只发生在正立面孔的眼睛区域)。这是面孔整体加工(face holistic processing)在单维信息分辨和多维信息整合之间建立联系的必经环节。这提示我们对全脸多维信息知觉整合的理解需要从传统的面孔整体加工假设升级到以眼睛为中心的层级化多维信息整合算法(a hierarchical algorithm for multi-dimensional information integration)。     

中文词类信息在副中央凹中的加工

本研究采用边界范式, 通过操纵预视词和目标词的词类一致性探讨中文阅读中副中央凹中能否加工词类信息。实验采用单因素3水平设计, 三种预视条件: 一致预视、词类不违背预视、词类违背预视。对实验数据进行线性混合模型及贝叶斯分析发现: 词类违背条件和词类不违背条件下对目标词的注视时间和注视概率没有显著差异, 即无词类信息的预视效应。该结果倾向于支持序列注意转移模型, 眼动控制模型的未来发展应更加关注灵活性和普适性。