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Data quality is one of the major concerns of using crowdsourcing websites such as Amazon Mechanical Turk (MTurk) to recruit participants for online behavioral studies. We compared two methods for ensuring data quality on MTurk: attention check questions (ACQs) and restricting participation to MTurk workers with high reputation (above 95% approval ratings). In Experiment 1 , we found that high-reputation workers rarely failed ACQs and provided higher-quality data than did low-reputation workers; ACQs improved data quality only for low-reputation workers, and only in some cases. Experiment 2 corroborated these findings and also showed that more productive high-reputation workers produce the highest-quality data. We concluded that sampling high-reputation workers can ensure high-quality data without having to resort to using ACQs, which may lead to selection bias if participants who fail ACQs are excluded post-hoc.

Although researchers often assume their participants are naive to experimental materials, this is not always the case. We investigated how prior exposure to a task affects subsequent experimental results. Participants in this study completed the same set of 12 experimental tasks at two points in time, first as a part of the Many Labs replication project and again a few days, a week, or a month later. Effect sizes were markedly lower in the second wave than in the first. The reduction was most pronounced when participants were assigned to a different condition in the second wave. We discuss the methodological implications of these findings.

According to the time-saving bias, drivers underestimate the time saved when increasing from a low speed and overestimate the time saved when increasing from a relatively high speed. Previous research used a specific type of task — drivers were asked to estimate time saved when increasing speed and to give a numeric response — to show this. The present research conducted two studies with multiple questions to show that the time-saving bias occurs in other tasks. Study 1 found that drivers committed the time-saving bias when asked to estimate (a) the time saved when increasing speed or (b) the distance that can be completed at a given time when increasing speed or (c) the speed required to complete a given distance in decreasing times. Study 2 showed no major differences in estimations of time saved compared to estimations of the remaining journey time and also between responses given on a numeric scale versus a visual analog scale. Study 3 tested two possible explanations for the time-saving bias: a Proportion heuristic and a Differences heuristic. Some evidence was found for use of the latter.

Privacy decision making has been examined in the literature from alternative perspectives. A dominant “normative” perspective has focused on rational processes by which consumers with stable preferences for privacy weigh the expected benefits of privacy choices against their potential costs. More recently, a behavioral perspective has leveraged theories from decision research to construe privacy decision making as a process in which cognitive heuristics and biases predictably occur. In a series of experiments, we compare the predictive power of these two perspectives by evaluating the impact of changes in the objective risk of disclosure and the impact of changes in the relative perceptions of risk of disclosure on both hypothetical and actual consumer privacy choices. We find that both relative and objective risks can, in fact, influence consumer privacy decisions. However, and surprisingly, the impact of objective changes in risk diminishes between hypothetical and actual choice settings. Vice versa, the impact of relative risk becomes more pronounced going from hypothetical to actual choice settings. Our results suggest a way to integrate diverse streams of the information systems literature on privacy decision making: in hypothetical choice contexts, relative to actual choice contexts, consumers may both overestimate their response to normative factors and underestimate their response to behavioral factors.

Health care resource allocation is a central moral issue in health policy, and opinions about it have been studied extensively. Allocation situations have typically been described and presented in a positive manner (i.e., who should receive medical aid). On the other hand, the negative valence allocation situation (i.e., who should not receive medical aid) has been relatively neglected. This paper demonstrates how positive versus negative framing of the exact same health care resource allocation situation can affect the perceived fairness of allocation principles. Participants usually perceived non-egalitarian principles (i.e., need, equity and tenure) to be fairer in positively framed situations (i.e., to deliver health care resources to certain patients) than negatively framed situation (i.e., not to deliver health care resources to other patients). However, framing did not affect the perceived fairness of the equality principle (i.e., a random draw). The paper offers a theoretical explanation for the effect of framing on the perceived fairness of health care resource allocation and discusses implications for both researchers and policy makers.

The time-saving bias describes people’s tendency to misestimate the time they can save by increasing the speed in which they perform an activity such as driving or completing a task. People typically underestimate time saved when increasing from a low speed and overestimate time saved when increasing from an already high speed. We suggest that this bias is the result of people’s failure to recognize the curvilinear relationship between increasing speed and reducing activity time: As initial speed rises, the same speed increases will yield smaller reductions in time. We explore a new technique to de-bias these faulty estimations: converting measurements of speed to a pace measure (e.g., minutes per fixed distance). Utilizing common driving scenarios, we show that participants who received pace data made more accurate estimations of journey duration at various speeds, time-savings at various speed increases and the required speed to complete a journey.

The MPG illusion and the time-saving bias both show that people misjudge the gains from increases in efficiency or speed, because people falsely believe that efficiency and speed are linearly related to consumption (e.g., gallons of fuel or journey time). This efficiency-consumption gap (ECG) has been demonstrated consistently in various situations. In parallel, people have also been found to show a diminished sensitivity to increases in magnitudes when judged under separate vs. joint evaluation modes (SE vs. JE). We show that these “two wrongs can make a right”: when people judge efficiency upgrades under SE mode, their subjective judgments follow a concave curve that closely resembles the curvilinear pattern of efficiency upgrades, making their preferences (artificially) less biased than they are under JE. In two studies we show that when asked for their willingness-to-pay (WTP) for upgrading products or services in two (a smaller vs. a larger) upgrade options, WTPs are less different in SE vs. JE modes. This means that people are exhibiting lower sensitivity to the upgrade size under SE which leads to a de-biasing effect. We show that because JE follow a linear trend, it yields biased preferences for efficiency measures, but not for consumption measures. In contrast, SE yield biased preferences for consumption, but not for efficiency measures.

People make systematic and predictable mistakes regarding estimations of average speed and journey time. In addition, people have been shown to commit a time-saving bias by underestimating the time that can be saved when increasing from a low speed and overestimating the time that can be saved when increasing from a relatively high speed. These misestimations have been shown to relate to biases in judgments of the speed required to arrive at a specific time and to choosing unduly high speed. Professional drivers, such as taxi drivers, might be less susceptible to these biases due to their increased driving experience. In the current study, we interviewed taxi drivers about a journey they were currently making and examined their estimations of journey time, average speed and time savings. Compared to a group of non-professional car drivers, taxi drivers showed the same considerable misestimations of driving speed, journey time and time savings as non-professionals. However, overestimations of time savings among taxi drivers were smaller than those made by car drivers. We discuss the practical significance of these findings.

Attitudes of public groups towards behavioral policy interventions (or nudges) can be important for both the policy makers who design and deploy nudges, and to researchers who try to understand when and why some nudges are supported while others are not. Until now, research on public attitudes towards nudges has focused on either state- or country-level comparisons, or on correlations with individual-level traits, and has neglected to study how different social groups (such as minorities) might view nudges. Using a large and representative sample, we tested the attitudes of two distinct minority groups in Israel (Israeli Arabs and Ultra-Orthodox Jews), and discovered that nudges that operated against a minority group’s held social norms, promoting a more general societal goal not aligned with the group’s norms, were often less supported by minorities. Contrary to expectations, these differences could not be explained by differences in trust in the government applying these nudges. We discuss implications for public policy and for the research and applications of behavioral interventions.

Gene expression can be regulated post-transcriptionally through dynamic and reversible RNA modifications. A recent noteworthy example is N(6)-methyladenosine (m(6)A), which affects messenger RNA (mRNA) localization, stability, translation and splicing. Here we report on a new mRNA modification, N(1)-methyladenosine (m(1)A), that occurs on thousands of different gene transcripts in eukaryotic cells, from yeast to mammals, at an estimated average transcript stoichiometry of 20% in humans. Employing newly developed sequencing approaches, we show that m(1)A is enriched around the start codon upstream of the first splice site: it preferentially decorates more structured regions around canonical and alternative translation initiation sites, is dynamic in response to physiological conditions, and correlates positively with protein production. These unique features are highly conserved in mouse and human cells, strongly indicating a functional role for m(1)A in promoting translation of methylated mRNA.