The Goal Circuit Model: A Hierarchical Multi‐Route Model of the Acquisition and Control of Routine Sequential Action in Humans

Human control of action in routine situations involves a flexible interplay between (a) task‐dependent serial ordering constraints; (b) top‐down, or intentional, control processes; and (c) bottom‐up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top‐down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal‐directed action that goes beyond existing models by operationalizing an interface between two putative systems—a routine and a non‐routine system—thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non‐routine goal‐based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system.     

Choice‐making,Expectations, and Treatment Positivity: How and When Choosing Shapes Aversive Experiences

Every day we use products and treatments with unknown but expected effects, such as using medication to manage pain. In many cases, we have a choice over which products or treatments to use; however, in other cases, people choose for us or choices are unavailable. Does choosing (versus not choosing) have implications for how a product or treatment is experienced? The current experiments examined the role of choice‐making in facilitating so‐called expectation assimilation effects—or situations in which a person's experiences (e.g., discomfort and pain) are evaluated in a manner consistent with their expectations. In Experiment 1, participants were initially exposed to a baseline set of aversive stimuli (i.e., sounds). Next, some participants were given expectations for two “treatments” (i.e., changes in screen display) that could ostensibly reduce discomfort. Critically, participants were either given a choice or not about which of the two treatments they preferred. Participants in a control condition were not provided with treatment expectations. Results revealed that discomfort experiences assimilated to expectations only when participants were provided with choice. Experiment 2 replicated this finding and provided evidence against the idea that demand characteristics and choice‐making unrelated to the core task (i.e., choices without associated expectations) could account for the results. Further, Experiment 2 showed that choosing reduced discomfort because of increased positivity about the treatment. Results are discussed in the context of extant research on choice‐making and expectation effects. Copyright © 2013 John Wiley & Sons, Ltd.     

From ABLE to TAPAS: A New Generation of Personality Tests to Support Military Selection and Classification Decisions

This article discusses 3 modern forced-choice personality tests developed for the U.S Armed Services to provide resistance to faking and other forms of response distortion: the Assessment of Individual Motivation, the Navy Computerized Adaptive Personality Scales, and the Tailored Adaptive Personality Assessment System. These tests represent the transition from Likert to forced-choice formats and from static to computerized adaptive item selection to meet the challenges of large-scale, high-stakes testing environments. For each test, we briefly describe the personality constructs that are assessed, the response format and scoring methods, and selected ongoing research and development efforts. We also highlight the potential of these tests for personnel selection, classification, and diagnostic screening purposes.     

Extreme Outcomes Sway Risky Decisions from Experience

Whether buying stocks or playing the slots, people making real‐world risky decisions often rely on their experiences with the risks and rewards. These decisions, however, do not occur in isolation but are embedded in a rich context of other decisions, outcomes, and experiences. In this paper, we systematically evaluate how the local context of other rewarding outcomes alters risk preferences. Through a series of four experiments on decisions from experience, we provide evidence for an extreme‐outcome rule, whereby people overweight the most extreme outcomes (highest and lowest) in a given context. As a result, people should be more risk seeking for gains than losses, even with equally likely outcomes. Across the experiments, the decision context was varied so that the same outcomes served as the high extreme, low extreme, or neither. As predicted, people were more risk seeking for relative gains, but only when the risky option potentially led to the high‐extreme outcome. Similarly, people were more risk averse for relative losses, but only when the risky option potentially led to the low‐extreme outcome. We conclude that in risky decisions from experience, the biggest wins and the biggest losses seem to matter more than they should. Copyright © 2013 John Wiley & Sons, Ltd.     

Driving speed choice: The role of conscious monitoring and control (reinvestment) when driving

This study aimed to examine the role of reinvestment - the propensity to consciously monitor and control actions (movement specific reinvestment) and to consciously monitor and evaluate decision making processes (Decision specific reinvestment) while driving in everyday risky scenarios. The study also aimed to evaluate the association between reinvestment and previously validated driver attitude measures. Fifty one participants completed a series of questionnaires (Driving Self-Efficacy Scale, Driver Attitude Questionnaire, Movement Specific Reinvestment Scale, Decision Specific Reinvestment Scale) after which they completed a test phase in a driving simulator. In the test phase, driving scenarios included roads with different markings (i.e., double yellow, wide centrelines, wire rope barriers, Audio Tactile Profiled markings) and alerting scenarios (i.e., police car present, high crash risk area sign, reduced speed zone). Results revealed that on risky roads (wide centrelines), participants with a high propensity for decision specific reinvestment drove slower than those with a low propensity. Driver experience, attitudes towards speeding and scores on the Decision Reinvestment subscale of the Decision Specific Reinvestment Scale significantly predicted speed choice. More experienced participants with higher scores on the Decision Reinvestment subscale were more likely to drive slower and participants with worse attitudes towards speeding were likely to drive faster. Participants with a low propensity for movement specific reinvestment (specifically, Movement Self-Consciousness) reduced their speed to a greater extent than those with a high propensity when driving in the police car scenario. There was some evidence to suggest that high decision specific and movement specific reinvesters were more likely to be involved in crashes and receive driving infringements. The current study is the first to demonstrate a significant relationship between reinvestment and driving. The implications of these findings for road safety are discussed.     

内隐多效性选择的ERP证据

采用事件相关电位技术,考察内隐多效性选择及其神经机制。被试的任务是认真观看配对的纯色图片刺激,并进行分类按键反应。实验结果发现,在P2上,积极启动刺激比对照刺激激发了更大的P2波幅; 对比对照刺激,积极启动刺激在左脑、大脑中部和后脑诱发了更大的P2波幅,其中右脑的效应最大。在N2上,积极启动刺激比对照刺激激发了更小的N2波幅; 对比对照刺激,积极启动刺激在左脑、大脑中部和后脑诱发了更小的N2波幅,其中右脑的效应最大。在P3上,积极启动刺激比对照刺激激发了更大的P3波幅; 对比对照刺激,积极启动刺激在左脑、大脑中部和后脑诱发了更大的P3波幅,其中右脑的效应最大。研究结果表明当外显目标与内隐目标同时被呈现时,被试趋向于做多效性选择,这可能表明无意识信息加工具有相当大的权重。且右后部脑区可能是加工多效性刺激的核心和关键区域,P3成分可能反应了内隐多效性选择的精细加工过程,能够作为内隐多效性选择的有效的ERPs指标。     

When Intertemporal Rewards Are Hedonic,Larger Units of Wait Time Boost Patience

In intertemporal choices between smaller‐sooner (SS) and larger‐later (LL) rewards, five studies reveal that patience for the LL option is influenced by an interactive effect of the measurement units used to express wait time (large vs. small) and the type of the reward (hedonic vs. utilitarian). Specifically, larger time units boost patience, but more so when rewards are hedonic rather than utilitarian. In line with the numerosity heuristic, the effect of time units on patience is mediated by larger time units shrinking wait time perception. This effect arises because hedonic (vs. utilitarian) rewards promote a reliance on heuristics rather than systematic calculations. Therefore, a more calculative mindset diminishes the effect of units even for hedonic rewards and eliminates the hedonic‐utilitarian asymmetry. These results contribute to research on numerosity, intertemporal choice, and hedonic‐utilitarian differences, and offer a simple tool for practitioners to influence patience.     

Heuristics versus direct calculation,and age-related differences in multiplication: an evidence accumulation account of plausibility decisions in arithmetic

We offer a new theoretical angle for cognitive arithmetic, which is that evidence accumulation may play a role in problem plausibility decisions. We build upon previous studies that have considered such a hypothesis, and here formally evaluate the paradigm. We develop the finding that performance differences, due to variations in strategy use and aging effects, can indeed be reasonably explained through these accumulation-to-bound cognitive models. Results suggest that these models may be effectively used to learn more about the underlying cognitive processes. In this study, we modelled young (18–24) and older (68–82) adults’ solution times in performing arithmetic verification (e.g. whether 8?×?5?=?41 is true/false). The domain-relevant factors in strategy use (problem-verification heuristics) and aging differences (older/younger adult groups) were analyzed by a response process model of the latency data, that is fit by participant and item. Lower thresholds accounted for the faster response times (RTs) for problems solved with heuristics (arithmetic rule-violation checking strategies), as opposed to problems solved by calculation approaches. A more rapid accumulation accounted for faster RTs on problems in which two arithmetic rules were violated (strategy combination) rather than one. Third, higher thresholds (i.e. preferring to have greater certainty before responding) accounted for older adults’ slower speed. These findings are in support of accumulation models being relevant for more complex cognitive tasks, as well as to account for the age-related differences therein.     

The synergistic effect of prosociality and physical attractiveness on mate desirability

Mate selection requires a prioritization and joint evaluation of different traits present or absent in potential mates. Herein, we focus on two such traits – physical attractiveness and prosociality – and examine how they jointly shape impressions of overall desirability. We report on two related experiments which make use of an innovative methodology combining large samples of raters and target persons (i.e., stimuli) and information on targets’ behaviour in economic games representing altruistic behaviour (Experiment 1) and trustworthiness (Experiment 2), two important facets of prosociality. In accordance with predictions derived from a cognitive perspective on mate choice and sexual strategies theory, the results show that the impact of being prosocial on an individual's overall desirability was increased further by them also being physically attractive, but only in long-term mating contexts. Furthermore, we show that men's mate preferences for certain prosocial traits (i.e., trustworthiness) were more context-dependent than women's due to differential evolutionary pressures for ancestral men and women.     

Discounting: A practical guide to multilevel analysis of choice data

Multilevel modeling provides the ability to simultaneously evaluate the discounting of individuals and groups by examining choices between smaller sooner and larger later rewards. A multilevel logistic regression approach is advocated in which sensitivity to relative reward magnitude and relative delay are considered as separate contributors to choice. Examples of how to fit choice data using multilevel logistic models are provided to help researchers in the adoption of these methods.