Graphs can help people arrive at data-supported conclusions. However, graphs might also induce bias by shifting the amount of evidence needed to make a decision, such as deciding whether a treatment had some kind of effect. In 2 experiments, we manipulated the early base rates of treatment effects in graphs. Early base rates had a large effect on a signal detection measure of bias in future graphs even though all future graphs had a 50% chance of showing a treatment effect, regardless of earlier base rates. In contrast, the autocorrelation of data points within each graph had a larger effect on discriminability. Exploratory analyses showed that a simple cue could be used to correctly categorize most graphs, and we examine participants’ use of this cue among others in lens models. When exposed to multiple graphs on the same topic, human judges can draw conclusions about the data, but once those conclusions are made, they can affect subsequent graph judgment.

Economic models typically allow for “free disposal” or “reversibility” of information, which implies non-negative value. Building on previous research on the “curse of knowledge” we explore situations where this might not be so. In three experiments, we document situations in which participants place positive value on information in attempting to predict the performance of uninformed others, even when acquiring that information diminishes their earnings. In the first experiment, a majority of participants choose to hire informed—rather than uninformed—agents, leading to lower earnings. In the second experiment, a significant number of participants pay for information—the solution to a puzzle—that hurts their ability to predict how many others will solve the puzzle. In the third experiment, we find that the effect is reduced with experience and feedback on the actual performance to be predicted. We discuss implications of our results for the role of information and informed decision making in economic situations.

To the known causes of overconfidence in decisions and judgments, we reveal another source that derives from a bias during the act of decision making. While this bias, the predecisional distortion of information, is well studied, its impact on overconfidence is not. We demonstrate how the distortion of information creates overconfidence in those professionals often regarded as singularly overconfident, entrepreneurs. When these professionals use a sequence of relevant information to make an accept-reject decision about a business opportunity, a cycle of confidence-distortion-confidence builds unjustified confidence in the chosen action – and does so whether that action is to accept or reject the venture. Overconfidence is a well-recognized cause of flawed decision making. Our work demonstrates the paradoxical converse of this claim, that flawed decision making can be a cause of overconfidence.

This scoping review of conceptualizations of fundamentalism scrutinizes the concept's domain of application, defining characteristics, and liability to bias. We find fundamentalism in four domains of application: Christianity, other Abrahamic religions, non-Abrahamic religions, and non-religious phenomena. The defining characteristics which we identify are organized into five categories: belief, behavior, emotion, goal, and structure. We find that different kinds of fundamentalisms are defined by different characteristics, with violent and oppressive behaviors, and political beliefs and goals being emphasized for non-Christian fundamentalisms. Additionally, we find that the locus of fundamentalism studies is the Global North. Based on these findings, we conclude that the concept is prone to bias. When conceptualizing fundamentalism, three considerations deserve attention: the mutual dependency between the domain of application and the specification of defining characteristics; the question of usefulness of scientific concepts; and the connection between conceptual ambiguity and the risk of bias in the study of fundamentalism.

There is a large literature evaluating the dual process model of cognition, including the biases and heuristics it implies. However, our understanding of what causes effortful thinking remains incomplete. To advance this literature, we focus on what triggers decision-makers to switch from the intuitive process (System 1) to the more deliberative process (System 2). We examine how the framing of incentives (gains versus losses) influences decision processing. To evaluate this, we design experiments based on a task developed to distinguish between intuitive and deliberative thinking. Replicating previous research, we find that losses elicit more cognitive effort. Most importantly, we also find that losses differentially reduce the incidence of intuitive answers, consistent with triggering a shift between these modes of cognition. We find substantial heterogeneity in these effects, with young men being much more responsive to the loss framing. To complement these findings, we provide robustness tests of our results using aggregated data, the imposition of a constraint to hinder the activation of System 2, and an analysis of incorrect, but unintuitive, answers to inform hybrid models of choice.

This paper provides results on a form of adaptive testing that is used frequently in intelligence testing. In these tests, items are presented in order of increasing difficulty. The presentation of items is adaptive in the sense that a session is discontinued once a test taker produces a certain number of incorrect responses in sequence, with subsequent (not observed) responses commonly scored as wrong. The Stanford-Binet Intelligence Scales (SB5; Riverside Publishing Company, 2003) and the Kaufman Assessment Battery for Children (KABC-II; Kaufman and Kaufman, 2004), the Kaufman Adolescent and Adult Intelligence Test (Kaufman and Kaufman 2014) and the Universal Nonverbal Intelligence Test (2nd ed.) (Bracken and McCallum 2015) are some of the many examples using this rule. He and Wolfe (Educ Psychol Meas 72(5):808–826, 2012. https://doi.org/10.1177/0013164412441937) compared different ability estimation methods in a simulation study for this discontinue rule adaptation of test length. However, there has been no study, to our knowledge, of the underlying distributional properties based on analytic arguments drawing on probability theory, of what these authors call stochastic censoring of responses. The study results obtained by He and Wolfe (Educ Psychol Meas 72(5):808–826, 2012. https://doi.org/10.1177/0013164412441937) agree with results presented by DeAyala et al. (J Educ Meas 38:213–234, 2001) as well as Rose et al. (Modeling non-ignorable missing data with item response theory (IRT; ETS RR-10-11), Educational Testing Service, Princeton, 2010) and Rose et al. (Psychometrika 82:795–819, 2017. https://doi.org/10.1007/s11336-016-9544-7) in that ability estimates are biased most when scoring the not observed responses as wrong. This scoring is used operationally, so more research is needed in order to improve practice in this field. The paper extends existing research on adaptivity by discontinue rules in intelligence tests in multiple ways: First, an analytical study of the distributional properties of discontinue rule scored items is presented. Second, a simulation is presented that includes additional scoring rules and uses ability estimators that may be suitable to reduce bias for discontinue rule scored intelligence tests.

Lord developed an approximation for the bias function for the maximum likelihood estimate in the context of the three-parameter logistic model. Using Taylor's expansion of the likelihood equation, he obtained an equation that includes the conditional expectation, given true ability, of the discrepancy between the maximum likelihood estimate and true ability. All terms of orders higher than n−1 are ignored where n indicates the number of items. Lord assumed that all item and individual parameters are bounded, all item parameters are known or well-estimated, and the number of items is reasonably large. In the present paper, an approximation for the bias function of the maximum likelihood estimate of the latent trait, or ability, will be developed using the same assumptions for the more general case where item responses are discrete. This will include the dichotomous response level, for which the three-parameter logistic model has been discussed, the graded response level and the nominal response level. Some observations will be made for both dichotomous and graded response levels.

Rationale and the actual procedures of two nonparametric approaches, called Bivariate P.D.F. Approach and Conditional P.D.F. Approach, for estimating the operating characteristic of a discrete item response, or the conditional probability, given latent trait, that the examinee's response be that specific response, are introduced and discussed. These methods are featured by the facts that: (a) estimation is made without assuming any mathematical forms, and (b) it is based upon a relatively small sample of several hundred to a few thousand examinees.

Some examples of the results obtained by the Simple Sum Procedure and the Differential Weight Procedure of the Conditional P.D.F. Approach are given, using simulated data. The usefulness of these nonparametric methods is also discussed.

Samejima has recently given an approximation for the bias function for the maximum likelihood estimate of the latent trait in the general case where item responses are discrete, generalizing Lord's bias function in the three-parameter logistic model for the dichotomous response level. In the present paper, observations are made about the behavior of this bias function for the dichotomous response level in general, and also with respect to several widely used mathematical models. Some empirical examples are given.