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Much recent research and theorizing in the field of reasoning has been concerned with intuitive sensitivity to logical validity, such as the logic-brightness effect, in which logically valid arguments are judged to have a "brighter" typeface than invalid arguments. We propose and test a novel signal competition account of this phenomenon. Our account makes two assumptions: (a) as per the demands of the logic-brightness task, people attempt to find a perceptual signal to guide brightness judgments, but (b) when the perceptual signal is hard to discern, they instead attend to cues such as argument validity. Experiment 1 tested this account by manipulating the difficulty of the perceptual contrast. When contrast discrimination was relatively difficult, we replicated the logic-brightness effect. When the discrimination was easy, the effect was eliminated. Experiment 2 manipulated the ambiguity of the perceptual task, comparing discrimination performance when the perceptual contrast was labeled in terms of rating "brightness" or "darkness". When the less ambiguous darkness labeling was used, there was no evidence of a logic-brightness effect. In both experiments, individual sensitivity to the perceptual discrimination was negatively correlated with sensitivity to argument validity. Hierarchical latent mixture modeling revealed distinct individual strategies: responses based on perceptual cues, responses based on validity or guessing. Consistent with the signal competition account, the proportion of those responding to validity increased with perceptual discrimination difficulty or task ambiguity. The results challenge explanations of the logic-brightness effect based on parallel dual-process models of reasoning.

Measurement literacy is required for strong scientific reasoning, effective experimental design, conceptual and empirical validation of measurement quantities, and the intelligible interpretation of error in theory construction. This discourse examines how issues in measurement are posed and resolved and addresses potential misunderstandings. Examples drawn from across the sciences are used to show that measurement literacy promotes the goals of scientific discourse and provides the necessary foundation for carving out perspectives and carrying out interventions in science. [ABSTRACT FROM AUTHOR]

Four experiments examined the claims that people can intuitively assess the logical validity of arguments, and that qualitatively different reasoning processes drive intuitive and explicit validity assessments. In each study participants evaluated arguments varying in validity and believability using either deductive criteria (logic task) or via an intuitive, affective response (liking task). Experiment 1 found that people are sensitive to argument validity on both tasks, with valid arguments receiving higher liking as well as higher deductive ratings than invalid arguments. However, the claim that this effect is driven by logical intuitions was challenged by the finding that sensitivity to validity in both liking and logic tasks was affected in similar ways by manipulations of concurrent memory load (Experiments 1 and 2) and variations in individual working memory capacity (Experiments 3 and 4). In both tasks better discrimination between valid and invalid arguments was found when more working memory resources were available. Formal signal detection models of reasoning were tested against the experimental data using signed difference analysis (Stephens, Dunn, & Hayes, 2018b). A single-process reasoning model which assumes that argument evaluation in both logic and liking tasks involves a single latent dimension for assessing argument strength but different response criteria for each task, was found to be consistent with the data from each experiment (as were some dual-process models). The experimental and modeling results confirm that people are sensitive to argument validity in both explicit logic and affect rating tasks, but that these results can be explained by a single underlying reasoning process.

When asked to assess the deductive validity of an argument,people are influenced by their prior knowledge of the content.Recently, two competing explanations for this belief biaseffect have been proposed, each based on signal detectiontheory. Under a response bias explanation, people set morelenient decision criteria for believable than for unbelievablearguments. Alternatively, believable and unbelievablearguments may differ in subjective argument strength for bothvalid and invalid items. Two experiments tested theseaccounts by asking participants to assess the validity ofcategorical syllogisms and rate their confidence. Conclusion-believability was manipulated either within- or between-groups. A two-step signal detection model was applied toexamine the effects on the relative location of the decisionthreshold and the distributions of argument strength.Equivalent belief bias effects were found when believabilitywas manipulated within- and between-groups, supporting theview that the belief bias effect is due to response bias.

Laboratory-based mock crime studies have often been interpreted to mean that (i) eyewitness confidence in an identification made from a lineup is a weak indicator of accuracy and (ii) sequential lineups are diagnostically superior to traditional simultaneous lineups. Largely as a result, juries are increasingly encouraged to disregard eyewitness confidence, and up to 30% of law enforcement agencies in the United States have adopted the sequential procedure. We conducted a field study of actual eyewitnesses who were assigned to simultaneous or sequential photo lineups in the Houston Police Department over a 1-y period. Identifications were made using a three-point confidence scale, and a signal detection model was used to analyze and interpret the results. Our findings suggest that (i) confidence in an eyewitness identification from a fair lineup is a highly reliable indicator of accuracy and (ii) if there is any difference in diagnostic accuracy between the two lineup formats, it likely favors the simultaneous procedure.

When asked to determine whether a syllogistic argument is deductively valid, people are influenced by their prior beliefs about the believability of the conclusion. Recently, two competing explanations for this belief bias effect have been proposed, each based on signal detection theory (SDT). Under a response bias explanation, people set more lenient decision criteria for believable than for unbelievable arguments. Under the alternative argument strength explanation, believability affects the reasoning stage of processing an argument, with believable and unbelievable arguments differing in subjective strength for both valid and invalid items. Two experiments tested these accounts by asking participants to make validity judgments for categorical syllogisms and to rate their confidence. Conclusion-believability was manipulated both within group (Experiment 1) and between groups (Experiment 2). A novel two-step version of the signal detection model was fit to receiver operating characteristic (ROC) curves for believable and unbelievable arguments. Model fits confirmed that in both experiments there was a shift in decision criterion but not argument discriminability as a function of argument believability. Crucially, when believability is manipulated between groups, this shift is expected under the response bias account but not under the argument strength account. Therefore, the results support the view that belief bias primarily reflects changes in response bias: people require less evidence to endorse a syllogism as valid when it has a believable conclusion. This has important implications for theories of deductive reasoning.

Three-experiments examined the number of qualitatively different processing dimensions needed to account for inductive and deductive reasoning. In each study, participants were presented with arguments that varied in logical validity and consistency with background knowledge (believability), and evaluated them according to deductive criteria (whether the conclusion was necessarily true given the premises) or inductive criteria (whether the conclusion was plausible given the premises). We examined factors including working memory load (Experiments 1 and 2), individual working memory capacity (Experiments 1 and 2), and decision time (Experiment 3), which according to dual-processing theories, modulate the contribution of heuristic and analytic processes to reasoning. A number of empirical dissociations were found. Argument validity affected deduction more than induction. Argument believability affected induction more than deduction. Lower working memory capacity reduced sensitivity to argument validity and increased sensitivity to argument believability, especially under induction instructions. Reduced decision time led to decreased sensitivity to argument validity. State-trace analyses of each experiment, however, found that only a single underlying dimension was required to explain patterns of inductive and deductive judgments. These results show that the dissociations, which have traditionally been seen as supporting dual-processing models of reasoning, are consistent with a single-process model that assumes a common evidentiary scale for induction and deduction.