The pseudocontingency framework provides a parsimonious strategy for inferring the contingency between two variables by assessing the base rates. Frequently occurring levels are associated, as are rarely occurring levels. However, this strategy can lead to different contingency inferences in different contexts, depending on how the base rates vary across contexts. Here, we examine how base-rate consistency influences base-rate learning and reliance by contrasting consistent with inconsistent base rates. We hypothesized that base-rate learning is facilitated, and that people rely more on base rates if base rates are consistent. In Experiment 1, the base rates across four contexts implied the same (consistent) or different (inconsistent) contingencies. Base rates were learned equally accurately, and participants inferred contingencies that followed the base rates but deviated from the genuine contingencies within contexts, regardless of consistency. In Experiment 2, we additionally manipulated whether the context was a plausible moderator of the contingency. While we replicated the first experiment's results when the context was a plausible moderator, base-rate inferences were stronger for consistent base rates when the context was an implausible moderator. Possibly, when a moderation-by-context was implausible, participants also relied on the base-rate correlation across contexts, which implied the same contingency when base rates were consistent but was zero when the base rates were inconsistent. Thus, our findings suggest that contingency inferences from base rates involve top-down processes in which people decide how to use base-rate information.

Collecting an adequate amount of information for a decision is an important skill. However, previous experiments on speed-accuracy trade-offs in sample-based decisions revealed marked oversampling that was impervious to various interventions (Fiedler, McCaughey, et al., 2021). When faced with the threat of being preempted by a rival in making decisions, participants seem to reduce information search substantially (Phillips et al., 2014). Such a decrease provides unique opportunities for metareasoning, which should advance people's understanding of the task and improve their performance. To test this possibility, in the present research (N = 101), participants had to compete with a fast (computer-simulated) rival and indeed substantially reduced self-determined sample size compared to a control condition. This speed increase also carried over to a subsequent decision block without rival, albeit participants regressed to a slower strategy. Mere exposure to a teammate using small samples either in an equivalent competitive version of the task or the standard solitary version led to similar reductions in sample size. This demonstrates that competition is not a necessary requirement for participants to make use of the metareasoning opportunity to improve task performance. Further research is needed to uncover the metacognitive underpinnings of improving performance and facilitate people taking full advantage of such opportunities for metareasoning.

To understand and explain sample-based impression formation, it is necessary to consider both the Brunswikian uncertainty caused by sampling from the stimulus environment and the Thurstonian uncertainty arising from the cognitive processing thereof. Impression judgments must be formed in view of both sources of uncertainty. Even when an ecological sample of a target’s traits is held constant, the resulting distribution of target information in the judge’s mind can vary substantively as a function of semantic and affective responses, top-down inferences, and contextual influences. In the research reviewed in the present chapter, we investigate the interplay of Brunswikian and Thurstonian sampling in a person-impression task based on self-truncated trait samples, in which judges can stop sampling at the very moment when their internal mindset optimally prepares them to form a distinct impression. This task setting produces distinct self-truncation effects; the resulting impression judgments are polarized, conflict-free, and typically driven by small samples (after early truncation). When exactly the same traits are presented in a yoked-control design to other judges, who cannot exploit self-truncation effects, their judgment patterns are similar but clearly less pronounced, reflecting the same Brunswikian trait samples detached from the Thurstonian mindset.

Information amount is a crucial determinant of decision outcomes. But how much information one should collect before arriving at a decision depends on a cost–benefit trade-off: Is the expected benefit of increased decision accuracy that can be gained from additional information higher than the additional information costs? To investigate this trade-off with temporal costs for information, we developed a speed–accuracy trade-off paradigm with sample-based decisions, in which the total payoff was the product of the average payoff per decision and the number of decisions completed in a restricted period. Increasing n served to increase the accuracy of choices, but also to decrease the number of completed choices. Yet, whereas the number of completed choices decreases linearly with increasing n, accuracy increases in a clearly sublinear fashion. As a consequence, the sample-based choice task calls for more weight given to speed than to accuracy. However, overly conservative sampling strategies prevented almost all participants from exploiting the speed advantage despite various guiding interventions. Even when the task was enriched by the social aspect of a teammate or rival, who demonstrated the optimal trade-off, participants remained too focussed on accuracy. We also investigate the cost–benefit trade-off with financial information costs, for which participants’ performance was less biased. We propose this to be related to how evaluable the information’s costs were relative to its benefits. Issues of adaptivity in contrast with optimality are addressed in a final discussion.

The notion of metacognitive myopia refers to a conspicuous weakness of the quality control of memory and reasoning processes. Although people are often remarkably sensitive even to complex samples of information when making evaluative judgments and decisions, their uncritical and naive tendency to take the validity of sampled information for granted constitutes a major obstacle to rational behavior. After illustrating this phenomenon with reference to prominent biases (base-rate neglect, misattribution, perseverance), we decompose metacognitive myopia into two distinct but intertwined functions, monitoring and control. We offer explanations for why effectively monitoring the biases resulting from information sampling in an uncertain world is so difficult and why the control function is severely restricted by the lack of volitional control over mental actions. Because of these and other difficulties, metacognitive myopia constitutes a major obstacle to rational judgment and decision making.

Humans are evidently able to learn contingencies from the co-occurrence of cues and outcomes. But how do humans judge contingencies when observations of cue and outcome are learned on different occasions? The pseudocontingency framework proposes that humans rely on base-rate correlations across contexts, that is, whether outcome base rates increase or decrease with cue base rates. Here, we elaborate on an alternative mechanism for pseudocontingencies that exploits base rate information within contexts. In two experiments, cue and outcome base rates varied across four contexts, but the correlation by base rates was kept constant at zero. In some contexts, cue and outcome base rates were aligned (e.g., cue and outcome base rates were both high). In other contexts, cue and outcome base rates were misaligned (e.g., cue base rate was high, but outcome base rate was low). Judged contingencies were more positive for contexts in which cue and outcome base rates were aligned than in contexts in which cue and outcome base rates were misaligned. Our findings indicate that people use the alignment of base rates to infer contingencies conditional on the context. As such, they lend support to the pseudocontingency framework, which predicts that decision makers rely on base rates to approximate contingencies. However, they challenge previous conceptions of pseudocontingencies as a uniform inference from correlated base rates. Instead, they suggest that people possess a repertoire of multiple contingency inferences that differ with regard to informational requirements and areas of applicability.

Success on many tasks depends on a trade-off between speed and accuracy. In a novel variant, a speed-accuracy trade-off with sample-based decisions in which both speed and accuracy jointly depend on (self-truncated) sample size, we found strong accuracy biases. On every trial of a sequential investment game, participants chose between 2 investment funds based on binary samples of the funds' past outcomes. Participants could stop sampling and decide whenever they felt sufficiently informed. Total payoff was the product of choice accuracy and number of choices completed within the available time (speed). Participants' failure to understand the dominance of speed over accuracy-that speed decreases more than accuracy improves with increasing sample size-led to dramatic oversampling. Our research aimed to examine to what extent metacognitive functions of monitoring and control could correct for the accuracy bias. Experiments 1a through 1c demonstrated similarly strong accuracy biases and payoff losses in psychology and economics students, depressed, and control patients. In Experiments 2 through 4, the accuracy bias persisted despite several manipulations (feedback, sample limit, choice difficulty, payoff, sampling truncation as default) that underlined the speed advantage, reflecting a conspicuous metacognitive deficit. Even when participants faced no risk of losing on incorrect trials but could still win on correct trials (Experiment 3) and when sampling was contingent on the active solicitation of every new element (Experiment 4), participants continued to sample too much and failed to overcome the accuracy bias. The final discussion focuses on psychological reasons and possible remedies for the metacognitive deficit in trade-off regulation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The current debate about how to improve the quality of psychological science revolves, almost exclusively, around the subordinate level of statistical significance testing. In contrast, research design and strict theorizing, which are superordinate to statistics in the methods hierarchy, are sorely neglected. The present article is devoted to the key role assigned to manipulation checks (MCs) for scientific quality control. MCs not only afford a critical test of the premises of hypothesis testing but also (a) prompt clever research design and validity control, (b) carry over to refined theorizing, and (c) have important implications for other facets of methodology, such as replication science. On the basis of an analysis of the reality of MCs reported in current issues of the Journal of Personality and Social Psychology, we propose a future methodology for the post–p < .05 era that replaces scrutiny in significance testing with refined validity control and diagnostic research designs.

Data sets supplying the article "Speed-Accuracy Tradeoffs in Sample-Based Decisions".

Data set and code book related to the article "Quo Vadis, Methodology? The Key Role of Manipulation Checks for Validity Control and Quality of Science"