Observed Variability and Values Matter: Toward a Better Understanding of Information Search and Decisions from Experience

The search for different options before making a consequential choice is a central aspect of many important decisions, such as mate selection or purchasing a house. Despite its importance, surprisingly little is known about how search and choice are affected by the observed and objective properties of the decision problem. Here, we analyze the effects of two key properties in a binary choice task: the options' observed and objective values, and the variability of payoffs. First, in a large public data set of a binary choice task, we investigate how the observed value and variability relate to decision‐makers' efforts and preferences during search. Furthermore, we test how these properties influence the chance of correctly identifying the objectively maximizing option, and how they affect choice. Second, we designed a novel experiment to systematically analyze the role of the objective difference between the options. We find that a larger objective difference between options increases the chance for correctly identifying the maximizing option, but it does not affect behavior during search and choice. Copyright © 2013 John Wiley & Sons, Ltd.     

CAT选题策略分类概述

选题是计算机化自适应测验（CAT）测试过程的关键环节,选题策略的目标是要达到较高的测量精度,同时也实现试题曝光率控制及其他测验目标的实现.本文根据选题策略的基本原理和衍生发展,将众多CAT选题策略分为五大选题策略系列：Fisher函数系列、K-LI函数系列、α分层系列、贝叶斯系列、b匹配系列;并根据测验目标（测验精度、试题曝光率控制、内容平衡、多条件约束）对这些选题策略进行了细分,并对CAT选题策略的选择思路进行归纳.     

Disparate roads to certainty processing strategy choices under need for closure

This paper describes a programme of research addressing an intriguing inconsistency in research findings about cognitive processes under a high need for cognitive closure (NFC). While early studies demonstrated that individuals who seek closure opt for closed-minded cognitive strategies, a growing body of research has identified a number of circumstances in which individuals who are high in NFC engage in effortful, open-minded information processing to an even greater extent than their low NFC counterparts. This has posed the challenge of delineating the circumstances under which people motivated to reduce uncertainty (i.e., attain closure) engage in effortful and open-minded cognition from those situations in which they rely on simplistic, low-effort strategies. This also calls for theoretical advancement in NFC theory. We discuss our proposed solution to this puzzle and the implications of this model for real-world social phenomena.     

A psychometric comparison of anxiety-relevant attention measures

Background and Objectives: Preferential attention to threat, emotional response inhibition, and attentional control each purportedly play a key role in anxiety disorders. Divergent psychometric properties among attention measures may produce differential detection of anxiety-related associations and treatment-related changes. However, no studies have directly compared the psychometric properties of these attention measures in the same sample.

Design: Eighty-five young adults (M?=?19.41 years, SD?=?1.47, 48 Females) completed a cognitive task battery and a subset of 60 participants (M?=?19.42 years, SD?=?1.48, 33 Females) completed the task battery again approximately two weeks later.

Method: To assess preferential attention to threat, emotional response inhibition, and attentional control, the cognitive task battery included a dot-probe task, emotion and gender Stroop tasks, and a flanker task. Tasks varied in how attention was directed and if emotional stimuli were included. Internal consistency and test-retest reliability were compared across measures.

Results: Within the same sample, internal consistency and reliability differed across attention measures. Explicit attention measures (emotional Stroop and flanker) exhibited stronger internal consistency and greater test-retest reliability compared to implicit measures (dot-probe and gender Stroop).

Conclusions: These results inform clinical research using attention measures to assess anxiety-related differences and treatment response.     

Implementation Intentions,Information, and Voter Turnout: An Experimental Study

Are citizens more likely to vote when they are asked to make plans about how they will cast their ballots? Such planning—typically described as “implementation intentions”—has been shown to increase many types of desirable behaviors, including exercising and healthy eating, receiving vaccinations, physical rehabilitation, and recycling. Important earlier work in political science suggests voter turnout can also be influenced by implementation intention interventions, whereby electors are prompted to “make a plan” to vote (Nickerson & Rogers, 2010 ), though this finding has gone largely unreplicated. At the same time, elections management bodies (EMBs) in many contexts regularly conduct informational campaigns in the period leading up to elections, though little is known about the effects of such efforts upon turnout. Using data from an online experiment conducted at the time of the 2015 Canadian Federal Election, we demonstrate that implementation intention interventions can improve voter turnout but that this effect is conditional upon electors being exposed to informational materials about how to vote in the election. When survey respondents were provided with information on voting requirements and methods, and then prompted with questions forcing them to contemplate the act of casting their ballots, we observe a sizable increase in turnout rates.     

Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques

Autocorrelation and partial autocorrelation, which provide a mathematical tool to understand repeating patterns in time series data, are often used to facilitate the identification of model orders of time series models (e.g., moving average and autoregressive models). Asymptotic methods for testing autocorrelation and partial autocorrelation such as the 1/T approximation method and the Bartlett's formula method may fail in finite samples and are vulnerable to non-normality. Resampling techniques such as the moving block bootstrap and the surrogate data method are competitive alternatives. In this study, we use a Monte Carlo simulation study and a real data example to compare asymptotic methods with the aforementioned resampling techniques. For each resampling technique, we consider both the percentile method and the bias-corrected and accelerated method for interval construction. Simulation results show that the surrogate data method with percentile intervals yields better performance than the other methods. An R package pautocorr is used to carry out tests evaluated in this study.     

Toward a contemporary quantitative model of punishment

A direct‐suppression, or subtractive, model of punishment has been supported as the qualitatively and quantitatively superior matching law‐based punishment model (Critchfield, Paletz, MacAleese, & Newland, 2003; de Villiers, 1980; Farley, 1980). However, this conclusion was made without testing the model against its predecessors, including the original (Herrnstein, 1961) and generalized (Baum, 1974) matching laws, which have different numbers of parameters. To rectify this issue, we reanalyzed a set of data collected by Critchfield et al. (2003) using information theoretic model selection criteria. We found that the most advanced version of the direct‐suppression model (Critchfield et al., 2003) does not convincingly outperform the generalized matching law, an account that does not include punishment rates in its prediction of behavior allocation. We hypothesize that this failure to outperform the generalized matching law is due to significant theoretical shortcomings in model development. To address these shortcomings, we present a list of requirements that all punishment models should satisfy. The requirements include formal statements of flexibility, efficiency, and adherence to theory. We compare all past punishment models to the items on this list through algebraic arguments and model selection criteria. None of the models presented in the literature thus far meets all of the requirements.     

Time–Frequency Cross Mutual Information Analysis of the Brain Functional Networks Underlying Multiclass Motor Imagery

To study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time–frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain–computer interface (BCI) electroencephalography data. The authors explored the brain network revealed by these MI tasks using statistical analysis and the analysis of topologic characteristics, and observed significant differences in the reaction level, reaction time, and activated target during 4-class MI tasks. There was a great difference in the reaction level between the execution and resting states during different tasks: the reaction level of the left-hand MI task was the greatest, followed by that of the right-hand, feet, and tongue MI tasks. The reaction time required to perform the tasks also differed: during the left-hand and right-hand MI tasks, the brain networks of subjects reacted promptly and strongly, but there was a delay during the feet and tongue MI task. Statistical analysis and the analysis of network topology revealed the target regions of the brain network during different MI processes. In conclusion, our findings suggest a new way to explain the neural mechanism behind MI.