Objective and bias-free measures of candidate motivation during job applications

Society suffers from biases and discrimination, a longstanding dilemma that stems from ungrounded, subjective judgments. Especially unequal opportunities in labor remain a persistent challenge, despite the recent inauguration of top-down diplomatic measures. Here we propose a solution by using an objective approach to the measurement of nonverbal behaviors of job candidates that trained for a job assessment. First, we implemented and developed artificial intelligence, computer vision, and unbiased machine learning software to automatically detect facial muscle activity and emotional expressions to predict the candidates’ self-reported motivation levels. The motivation judgments by our model outperformed recruiters’ unreliable, invalid, and sometimes biased judgments. These findings mark the necessity and usefulness of novel, bias-free, and scientific approaches to candidate and employee screening and selection procedures in recruitment and human resources.

Countering Biased Judgments of Individuals Who Display Autism-Characteristic Behavior in Forensic Settings

According to expectancy violations theory, displays of behavior considered “unusual” during an interaction will trigger scrutiny of an individual. Such scrutiny may be detrimental in forensic contexts, where deception detection is emphasized. Autistic individuals, in particular, may be scrutinized unfavorably given unusual nonverbal behavior associated with the condition. Across two experiments using between-subjects’ designs, participants (overall N = 3,342) watched a scripted police-suspect interrogation, randomized to view the suspect display autism-related behaviors or none of those behaviors. Autistic behavior biased evaluations of deception and guilt as a function of violating individual behavioral expectations, regardless of whether decisive or ambiguous evidence framed the suspect as guilty or innocent. Promisingly, however, providing an autism information card attenuated such evaluations. Our research extends expectancy violations theory, advances understanding of determinants of forensic judgments, highlights important applied implications for nonverbal behavior displays in the justice system and recommends methods to protect against bias.

On the role of natural multidecadal oscillations on global warming and its hiatus

In this study, we investigate the role of the multidecadal oscillation patterns in the global temperature in the global warming hiatus. We analyze the global instrumental temperature records and multiple tree-ring temperature reconstruction records using wavelet transforms and register the presence of a multidecadal cycle of approximately 55-75 years. The hiatus and post-hiatus rise in temperature arises from the declining phase of the multidecadal oscillation which temporally compensates the rising phase. The unusual rise in the temperature after the hiatus is possibly explained by the positive uprising phase of this natural cycle. The origin of the global warming debate has been partly ascribed to faulty calculations or biased judgments. However, in these studies, little emphasis has been given to the possible presence of multidecadal oscillation patterns in the global temperature, which may lead to such an effect. Our result demonstrates that, phase of this cycle has accidentally played an important role in fueling the global warming debate. Therefore, while assessing any future climate changes, such possibilities should be accounted.

The tendency to stop collecting information is linked to illusions of causality

Previous research proposed that cognitive biases contribute to produce and maintain the symptoms exhibited by deluded patients. Specifically, the tendency to jump to conclusions (i.e., to stop collecting evidence soon before making a decision) has been claimed to contribute to delusion formation. Additionally, deluded patients show an abnormal understanding of cause-effect relationships, often leading to causal illusions (i.e., the belief that two events are causally connected, when they are not). Both types of bias appear in psychotic disorders, but also in healthy individuals. In two studies, we test the hypothesis that the two biases (jumping to conclusions and causal illusions) appear in the general population and correlate with each other. The rationale is based on current theories of associative learning that explain causal illusions as the result of a learning bias that tends to wear off as additional information is incorporated. We propose that participants with higher tendency to jump to conclusions will stop collecting information sooner in a causal learning study than those participants with lower tendency to jump to conclusions, which means that the former will not reach the learning asymptote, leading to biased judgments. The studies provide evidence in favour that the two biases are correlated but suggest that the proposed mechanism is not responsible for this association.

Bias in confidence: A critical test for discrete-state models of change detection

Ongoing discussions on the nature of storage in visual working memory have mostlyfocused on two theoretical accounts: On one hand we have a discrete-state accountpostulating that information in working memory is supported with high fidelity for alimited number of discrete items by a given number of “slots”, with no informationbeing retained beyond these. In contrast with this all-or-nothing view, we have acontinuous account arguing that information can be degraded in a continuous manner, reflecting the amount of resources dedicated to each item. It turns out that the core tenets of this discrete-state account constrain the way individuals can express confidence in their judgments, excluding the possibility of biased confidence judgments. Importantly, these biased judgments are expected when assuming a continuous degradation of information. We report two studies showing that biased confidence judgments can be reliably observed, a finding that rejects a large number of discrete-state models, dismissing the idea that change-detection judgments consist of a mixture of guesses and high-fidelity memory representations.

The log in my eye : awareness, acceptance, and correction of partisan political bias

People are often unaware of or under-estimate their own cognitive biases, suggesting that political bias -- measured by the influence of irrelevant political information on otherwise non-political judgments -- may be difficult to change. However, when it comes to political bias, peoples' expectation of bias (i.e., lay theories), have rarely been directly measured, despite their purported importance for judgment debiasing (Wegener and Petty, 1995). Furthermore, extant research has never directly asked people how acceptable they believe it is to make a judgment influenced by irrelevant political information. Study 1 asked participants to make a series of judgments, some more subjective (evaluating quotes) and others more objective (assessing guilt of defendant on trial). Some participants were presented with noninfluential irrelevant political information (control), while others were presented with influential irrelevant political information (e.g., Trump or Obama) for the judgments they were making. Some participants who were exposed to influential irrelevant political information were explicitly asked to not allow their judgments to be influenced by the information they were exposed to. As expected by the Flexible Correction Model (FCM), participant's lay theories (but not judgments of acceptability) guided their (mostly) successful attempt to correct for the influence of irrelevant political information, though only when evaluating quotes. When judging the guilt of a defendant on trial, participants asked to correct their judgments did so, with only an indirect influence from lay theories and judgments of acceptability. Secondary measures indicated correction instructions did not temper the desire to seek out irrelevant political information on subsequent judgments. People possess self- awareness into their own capacity to make biased judgments and often choose not to correct their judgments, despite having the ability to do so. Studies 2a and 2b replicated primary findings using online samples.

Tax Law and Redistribution

This chapter examines the implications of behavioral insights for tax design, taxpayers’ decision-making, and tax compliance. With regard to tax design, the chapter discusses policymakers’ own heuristics and biases, and their catering to (or exploitation of) the biased judgments of the public at large. Regarding economic decision-making, the chapter explores the dark and bright sides of tax saliency. With regard to compliance, it explains why people pay taxes, and how this compliance might be further enhanced. Finally, the chapter explains how cognitive factors affect taxpayers’ inclination to challenge tax liability. Additionally, the chapter describes the behavioral contribution to positive and normative analyses of redistribution, by shedding new light on how people form judgments about tax progressivity; the cognitive ramifications of poverty; wealth and subjective well-being; and the choice between methods and objects of redistribution. The chapter also comments on the use of taxes as a means of modifying human behavior.