Do Votes Speak Louder than Motives? Moral Judgments and Tolerance in the 2016 Presidential Election

When judging a voter’s decision, does that voter’s reason for casting their vote influence moral and interpersonal judgments about them? In the context of the 2016 U.S. Presidential Election, past research suggests two competing predictions. First, people regularly account for an actor’s intentions when forming judgments of the actor, indicating that judgments may vary according to a voter’s motives. However, people are unlikely to see nuance among outgroups, especially amid divisive political partisanship, suggesting that judgments would ignore information about voters’ motives. In Study 1, results supported the first prediction, showing that both Hillary Clinton and Donald Trump supporters distinguished between different voting motives when making moral and interpersonal judgments of outgroup voters. In Studies 2 and 3, when some voters’ motives became more extreme, Clinton and Trump supporters again distinguished between voting motives for outgroup and ingroup voters, respectively, albeit in a different pattern of results.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147150/1/asap12153.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147150/2/asap12153_am.pd

Why Are People's Decisions Sometimes Worse with Computer Support?

In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action (e.g. training or procedures that do not counteract all the causes of the apparently "over-reliant" behaviour). We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature

Design and Evaluation of Path Planning Decision Support for Planetary Surface Exploration

Human intent is an integral part of real-time path planning and re-planning, thus any decision aiding system must support human-automation interaction. The appropriate balance between humans and automation for this task has previously not been adequately studied. In order to better understand task allocation and collaboration between humans and automation for geospatial path problem solving, a prototype path planning aid was developed and tested. The focus was human planetary surface exploration, a high risk, time-critical domain, but the scenario is representative of any domain where humans path plan across uncertain terrain. Three visualizations, including elevation contour maps, a novel visualization called levels of equal costs, and a combination of the two were tested along with two levels of automation. When participants received the lower level of automation assistance, their path costs errors were less than 35% of the optimal, and they integrated manual sensitivity analysis strategies. When participants used the higher level of automation assistance, path costs errors were reduced to a few percentages, and they saved on average 1.5 minutes in the task. However, this increased performance came at the price of decreased situation awareness and automation bias.We would like to acknowledge the NASA Harriett G. Jenkins Predoctoral Fellowship and the Office of Naval Research for sponsoring this research

A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores

The increased use of algorithmic predictions in sensitive domains has been accompanied by both enthusiasm and concern. To understand the opportunities and risks of these technologies, it is key to study how experts alter their decisions when using such tools. In this paper, we study the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. We focus on the question: Are humans capable of identifying cases in which the machine is wrong, and of overriding those recommendations? We first show that humans do alter their behavior when the tool is deployed. Then, we show that humans are less likely to adhere to the machine's recommendation when the score displayed is an incorrect estimate of risk, even when overriding the recommendation requires supervisory approval. These results highlight the risks of full automation and the importance of designing decision pipelines that provide humans with autonomy.Comment: Accepted at ACM Conference on Human Factors in Computing Systems (ACM CHI), 202

Using social and behavioural science to support COVID-19 pandemic response

The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behavior with the recommendations of epidemiologists and public health experts. Here we review experimental and correlational data from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic, and also highlight important gaps researchers should move quickly to fill in the coming weeks and months

Dopamine Beta Hydroxylase Genotype Identifies Individuals Less Susceptible to Bias in Computer-Assisted Decision Making

Computerized aiding systems can assist human decision makers in complex tasks but can impair performance when they provide incorrect advice that humans erroneously follow, a phenomenon known as “automation bias.” The extent to which people exhibit automation bias varies significantly and may reflect inter-individual variation in the capacity of working memory and the efficiency of executive function, both of which are highly heritable and under dopaminergic and noradrenergic control in prefrontal cortex. The dopamine beta hydroxylase (DBH) gene is thought to regulate the differential availability of dopamine and norepinephrine in prefrontal cortex. We therefore examined decision-making performance under imperfect computer aiding in 100 participants performing a simulated command and control task. Based on two single nucleotide polymorphism (SNPs) of the DBH gene, −1041 C/T (rs1611115) and 444 G/A (rs1108580), participants were divided into groups of low and high DBH enzyme activity, where low enzyme activity is associated with greater dopamine relative to norepinephrine levels in cortex. Compared to those in the high DBH enzyme activity group, individuals in the low DBH enzyme activity group were more accurate and speedier in their decisions when incorrect advice was given and verified automation recommendations more frequently. These results indicate that a gene that regulates relative prefrontal cortex dopamine availability, DBH, can identify those individuals who are less susceptible to bias in using computerized decision-aiding systems

Visual Working Memory Capacity and Proactive Interference

Background: Visual working memory capacity is extremely limited and appears to be relatively immune to practice effects or the use of explicit strategies. The recent discovery that visual working memory tasks, like verbal working memory tasks, are subject to proactive interference, coupled with the fact that typical visual working memory tasks are particularly conducive to proactive interference, suggests that visual working memory capacity may be systematically under-estimated. Methodology/Principal Findings: Working memory capacity was probed behaviorally in adult humans both in laboratory settings and via the Internet. Several experiments show that although the effect of proactive interference on visual working memory is significant and can last over several trials, it only changes the capacity estimate by about 15%. Conclusions/Significance: This study further confirms the sharp limitations on visual working memory capacity, both in absolute terms and relative to verbal working memory. It is suggested that future research take these limitations into account in understanding differences across a variety of tasks between human adults, prelinguistic infants and nonlinguistic animals