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Climate change has gained traction in artists' works and exhibitions. This research aims at gaining a better understanding of visual artists who create climate-related art and are/were located in the central art market countries of the United States of America, the United Kingdom, and Germany—countries with considerable contributions to climate change and responsibility for action. Responding to calls for the further involvement of the humanities and sociology of the arts to address socio-ecological themes, this article addresses the question: How do artists' imaginations of climate change relate to aspects of climate (in)justice? To answer this question, thirty interviews were conducted with artists who addressed climate change in their works. This study finds that the artists are, among others, driven by a desire to reduce distances (spatially, temporally, human-nature) and/or to engage with causes and impacts nearby. Many artists are concerned with climate (in)justice in various ways: with not only vulnerable, remote regions, future generations, and other species, but also nearby areas in the present. This study also shows that artists face ethical questions when engaging in climate topics. The research applies and reveals insights from the environmental humanities, emphasizing the connectedness of environmental challenges to social, cultural, and human aspects. The research is also situated within the sociology of the arts, the study of aesthetic practices in times of global inequalities, but also of hope, possibilities, and learning. Further, this research adds to the increasing awareness of climate change as a domestic issue.

Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence—a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge.

How we judge the similarity between objects in the world is connected ultimately to how we represent those objects. It has been argued extensively that object representations in humans are ‘structured’ in nature, meaning that both individual features and the relations between them can influence similarity. In contrast, popular models within comparative psychology assume that nonhuman species appreciate only surface-level, featural similarities. By applying psychological models of structural and featural similarity (from conjunctive feature models to Tversky’s contrast model) to visual similarity judgements from adult humans, chimpanzees, and gorillas, we demonstrate a cross-species sensitivity to complex structural information, particularly for stimuli that combine colour and shape. These results shed new light on the representational complexity of nonhuman apes, and the fundamental limits of featural coding in explaining object representation and similarity, which emerge strikingly across both human and nonhuman species.

Most democracies seek input from scientists to inform policies. This can put scientists in a position of intense scrutiny. Here we focus on situations in which scientific evidence conflicts with people’s worldviews, preferences, or vested interests. These conflicts frequently play out through systematic dissemination of disinformation or the spreading of conspiracy theories, which may undermine the public’s trust in the work of scientists, muddy the waters of what constitutes truth, and may prevent policy from being informed by the best available evidence. However, there are also instances in which public opposition arises from legitimate value judgments and lived experiences. In this article, we analyze the differences between politically-motivated science denial on the one hand, and justifiable public opposition on the other. We conclude with a set of recommendations on tackling misinformation and understanding the public’s lived experiences to preserve legitimate democratic debate of policy.

The idea of resolving dispute through the exchange of arguments and reasons has been central to society for millennia. We exchange arguments as a way of getting at the truth in contexts as diverse as science, the court room, and our everyday lives. In democracies, political decisions should be negotiated through argument, not deception, or even worse, brute force. If argument is to lead to the truth or to good decisions, then some arguments must be better than others and 'argument strength' must have some meaningful connection with truth. Can argument strength be measured in a way that tracks an objective relationship with truth and not just mere persuasiveness? This article describes recent developments in providing such measures.

Indicative conditionals—that is, sentences typically, though not exclusively, of the form “If p, (then) q,”—belong to the most puzzling phenomena of language. One of the puzzles that has recently attracted attention of psychologists of reasoning stems from the fact that on the majority of accounts of indicative conditionals, “If p, (then) q” can be true, or at least highly acceptable, even when there is no meaningful connection between p and q. Conditionals without such a connection, dubbed missing-link conditionals, however, often seem very odd. A standard pragmatic account of their oddity rests on an observation that, whenever missing-link conditionals come out as true, these are situations in which speakers are justified in asserting stronger, more informative statements. Asserting a less informative statement is odd because it is a violation of the Maxim of Quantity. This paper reports four experiments that present a challenge to the Gricean explanation of why missing-link conditionals are odd. At the same time, we will argue that these findings can be reconciled with general principles of Gricean pragmatics, if the connection is treated as a part of a conventional, “core” meaning of a conditional.

The paper introduces, compares and contrasts formal models of source reliability proposed in the epistemology literature, in particular the prominent models of Bovens and Hartmann (Bayesian epistemology, Oxford University Press, Oxford, 2003) and Olsson (Episteme 8(02):127–143, 2011). All are Bayesian models seeking to provide normative guidance, yet they differ subtly in assumptions and resulting behavior. Models are evaluated both on conceptual grounds and through simulations, and the relationship between models is clarified. The simulations both show surprising similarities and highlight relevant differences between these models. Most importantly, however, our evaluations reveal that important normative concerns arguably remain unresolved. The philosophical implications of this for testimony are discussed.

Counterfactual (CF) explanations have been employed as one of the modes of explainability in explainable artificial intelligence (AI)-both to increase the transparency of AI systems and to provide recourse. Cognitive science and psychology have pointed out that people regularly use CFs to express causal relationships. Most AI systems, however, are only able to capture associations or correlations in data, so interpreting them as casual would not be justified. In this perspective, we present two experiments (total n = 364) exploring the effects of CF explanations of AI systems' predictions on lay people's causal beliefs about the real world. In Experiment 1, we found that providing CF explanations of an AI system's predictions does indeed (unjustifiably) affect people's causal beliefs regarding factors/features the AI uses and that people are more likely to view them as causal factors in the real world. Inspired by the literature on misinformation and health warning messaging, Experiment 2 tested whether we can correct for the unjustified change in causal beliefs. We found that pointing out that AI systems capture correlations and not necessarily causal relationships can attenuate the effects of CF explanations on people's causal beliefs.

In social-dilemma situations (e.g., public-good games), people may pursue their local self-interests, thereby lowering the overall payoff of their group and, paradoxically, even their individual payoffs as a result. Likewise, in inner-individual dilemmas, even without conflict of interest between persons, people may pursue local goals at the expense of overall utility. Our experiments investigate such dissociations of individual- and group-level effects in the context of personnel evaluation and selection. Participants were given the role of human resource managers selecting workers to optimize the overall payoff for the company. We investigated contexts where the individually best/worst 'employees' systematically caused the worst/best group performance. When workers in a team could substantially increase or decrease coworkers' performance, most participants (albeit not all) tended to focus solely on individual performance without considering their overall contribution even when instructed to maximize group performance. This undue focus on individual information meant that employees who enhanced team performance the most received the most negative evaluations. This may result in a 'tragedy of personnel evaluation' relevant to maladaptive incentive structures (personnel evaluation), job offers (personnel selection), and a substantially negative impact on organizational effectiveness. At the same time, the results suggest ways that this problem may be overcome. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

In this paper, new evidence is presented for the assumption that the reason-relation reading of indicative conditionals ('if A, then C') reflects a conventional implicature. In four experiments, it is investigated whether relevance effects found for the probability assessment of indicative conditionals (Skovgaard-Olsen, Singmann, & Klauer, 2016a) can be classified as being produced by (a) a conversational implicature, (b) a (probabilistic) presupposition failure, or (c) a con- ventional implicature. After considering several alternative hypotheses, and the accumulating evidence from other studies as well, we conclude that the evidence is most consistent with the Relevance Effect being the outcome of a conventional implicature. This finding indicates that the reason-relation reading is part of the semantic content of indicative conditionals, albeit not part of their primary truth-conditional content.

Base rate neglect refers to people's apparent tendency to underweight or even ignore base rate information when estimating posterior probabilities for events, such as the probability that a person with a positive cancer-test outcome actually does have cancer. While often replicated, almost all evidence for the phenomenon comes from studies that used problems with extremely low base rates, high hit rates, and low false alarm rates. It is currently unclear whether the effect generalizes to reasoning problems outside this "corner" of the entire problem space. Another limitation of previous studies is that they have focused on describing empirical patterns of the effect at the group level and not so much on the underlying strategies and individual differences. Here, we address these two limitations by testing participants on a broader problem space and modeling their responses at a single-participant level. We find that the empirical patterns that have served as evidence for base-rate neglect generalize to a larger problem space, albeit with large individual differences in the extent with which participants "neglect" base rates. In particular, we find a bi-modal distribution consisting of one group of participants who almost entirely ignore the base rate and another group who almost entirely account for it. This heterogeneity is reflected in the cognitive modeling results: participants in the former group were best captured by a linear-additive model, while participants in the latter group were best captured by a Bayesian model. We find little evidence for heuristic models. Altogether, these results suggest that the effect known as "base-rate neglect" generalizes to a large set of reasoning problems, but varies largely across participants and may need a reinterpretation in terms of the underlying cognitive mechanisms.

Base rate neglect refers to people9s apparent tendency to underweight or even ignore base rate information when estimating posterior probabilities for events, such as the probability that a person with a positive cancer-test outcome actually does have cancer. While many studies have replicated the effect, there has been little variation in the structure of the reasoning problems used in those studies. In particular, most experiments have used extremely low base rates, high hit rates, and low false alarm rates. As a result, it is unclear whether the effect is a general phenomenon in human probabilistic reasoning or an anomaly that applies only to a small subset of reasoning problems. Moreover, previous studies have focused on describing empirical patterns of the effect and not so much on the underlying strategies. Here, we address these limitations by testing participants on a broader problem space and modelling their response at a single-participant level. We find that the empirical patterns that have served as evidence for base-rate neglect generalize to the larger problem space. At the level of individuals, we find evidence for large variability in how sensitive participants are to base rates, but with two distinct groups: those who largely ignore base rates and those who almost perfectly account for it. This heterogeneity is reflected in the cognitive modeling results, which reveal that there is not a single strategy that best captures the data for all participants. The overall best model is a variant of the Bayesian model with too conservative priors, tightly followed by a linear-additive integration model. Surprisingly, we find very little evidence for earlier proposed heuristic models. Altogether, our results suggest that the effect known as "base-rate neglect" generalizes to a large set of reasoning problems, but may need a reinterpretation in terms of the underlying cognitive mechanisms.

Given the need for a rapid and supposed critical response from behavioural sciences during times of crisis, this study aimed to track the development of COVID-19 psychology-related preprints. We tracked the first 211 COVID related preprints on the repository PsyArXiv. Specifically, we tracked who was submitting preprints, what the preprints were investigating, and whether the preprints lead to publications and their impact (measured by Google Scholar citations). We then followed up with the preprints about a year later to determine the number of preprints that lead to publication and the number of citations they received. The results showed that males from western countries submitted most preprints. Fifty-one per cent of preprints used a survey design, and the most common topic for covid-19 related preprints was mental health. Eighty-three per cent of preprints did not meet credible open science measures. 54% of the sampled preprints had been published in peer-reviewed journals, with a median time between preprint upload and publication of 105 days. Metascience preprints were more likely to be published, and preprints with reviews had lower citation rates. Overall, the results demonstrate that some of the structural problems in research are still in play despite global efforts to mobilise research efforts during the pandemic.

In reasoning about situations in which several causes lead to a common effect, a much studied and yet still not well-understood inference is that of explaining away. Assuming that the causes contribute independently to the effect, if we learn that the effect is present, then this increases the probability that one or more of the causes are present. But if we then learn that a particular cause is present, this cause “explains” the presence of the effect, and the probabilities of the other causes decrease again. People tend to show this explaining away effect in their probability judgments, but to a lesser extent than predicted by the causal structure of the situation. We investigated further the conditions under which explaining away is observed. Participants estimated the probability of a cause, given the presence or the absence of another cause, for situations in which the effect was either present or absent, and the evidence about the effect was either certain or uncertain. Responses were compared to predictions obtained using Bayesian network modeling as well as a sensitivity analysis of the size of normative changes in probability under different information conditions. One of the conditions investigated: when there is certainty that the effect is absent, is special because under the assumption of causal independence, the probabilities of the causes remain invariant, that is, there is no normative explaining away or augmentation. This condition is therefore especially diagnostic of people’s reasoning about common-effect structures. The findings suggest that, alongside earlier explanations brought forward in the literature, explaining away may occur less often when the causes are assumed to interact in their contribution to the effect, and when the normative size of the probability change is not large enough to be subjectively meaningful. Further, people struggled when given evidence against negative evidence, resembling a double negation effect.

Conditionals and conditional reasoning have been a long-standing focus of research across a number of disciplines, ranging from psychology through linguistics to philosophy. But almost no work has concerned itself with the question of how hearing or reading a conditional changes our beliefs. Given that we acquire much-perhaps most-of what we believe through the testimony of others, the simple matter of acquiring conditionals via others' assertion of a conditional seems integral to any full understanding of the conditional and conditional reasoning. In this paper we detail a number of basic intuitions about how beliefs might change in response to a conditional being uttered, and show how these are backed by behavioral data. In the remainder of the paper, we then show how these deceptively simple phenomena pose a fundamental challenge to present theoretical accounts of the conditional and conditional reasoning - a challenge which no account presently fully meets.

Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modeling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it. But Bayesian modeling is itself complex, and has until now remained largely inaccessible to lay people. In a large scale lab experiment, we provide proof of principle that a Bayesian network modeling tool, adapted to provide basic training and guidance on the modeling process to beginners without requiring knowledge of the mathematical machinery working behind the scenes, significantly helps lay people find normative Bayesian solutions to complex problems, compared to generic training on probabilistic reasoning. We discuss the implications of this finding for the use of Bayesian network software tools in applied contexts such as security, medical, forensic, economic or environmental decision making.

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively.

Artists creatively use scientific data in artistic information visualizations (AIVs) to address climate change. Yet, it is unclear how effective they are in making viewers consider climate change a...

This paper examines the basic question of how we can come to form accurate beliefs about the world when we do not fully know how good or bad our evidence is. Here, we show, using simulations with otherwise optimal agents, the cost of misjudging the quality of our evidence. We compare different strategies for correctly estimating that quality, such as outcome- and expectation-based updating. We also identify conditions under which misjudgment of evidence quality can nevertheless lead to accurate beliefs, as well as those conditions where no strategy will help. These results indicate both where people will nevertheless succeed and where they will fail when information quality is degraded.

Our beliefs and opinions are shaped by others, making our social networks crucial in determining what we believe to be true. Sometimes this is for the good because our peers help us form a more accurate opinion. Sometimes it is for the worse because we are led astray. In this context, we address via agent-based computer simulations the extent to which patterns of connectivity within our social networks affect the likelihood that initially undecided agents in a network converge on a true opinion following group deliberation. The model incorporates a fine-grained and realistic representation of belief (opinion) and trust, and it allows agents to consult outside information sources. We study a wide range of network structures and provide a detailed statistical analysis concerning the exact contribution of various network metrics to collective competence. Our results highlight and explain the collective risks involved in an overly networked or partitioned society. Specifically, we find that 96% of the variation in collective competence across networks can be attributed to differences in amount of connectivity (average degree) and clustering, which are negatively correlated with collective competence. A study of bandwagon or “group think” effects indicates that both connectivity and clustering increase the probability that the network, wholly or partly, locks into a false opinion. Our work is interestingly related to Gerhard Schurz’s work on meta-induction and can be seen as broadly addressing a practical limitation of his approach.

Understanding the scientific consensus on anthropogenic climate change has been dubbed a ‘gateway belief’ to engaging people in sustainable behaviour. We consider the question of how the impact of a consensus communication can be maximised. Firstly, the credibility of the communicator should be maximised. One way of achieving this is to present the opinions of a sample of scientists directly to individuals. The decision-making literature suggests that such a technique will confer an additional advantage over standard descriptions of consensus (e.g. ‘97% of scientists agree’). In decision-making research, low probabilities tend to be overweighted when probabilities are described, but underweighted when probability information is experienced. Consequently, statements of high consensus may lead to an overweighting of the dissensus, a phenomenon that may be reversed were the consensus to be ‘experienced.’ We obtain some positive support for our proposal that consensus is best ‘experienced’ in one of two experiments. We suggest that the lack of stronger positive support could relate to ceiling effects for the topics studied and propose that investigation of effective methods for ‘experiencing’ the consensus is a fruitful area for future research.

Humans possess a remarkable ability to discriminate structure from randomness in the environment. However, this ability appears to be systematically biased. This is nowhere more evident than in the Gambler's Fallacy (GF)-the mistaken belief that observing an increasingly long sequence of "heads" from an unbiased coin makes the occurrence of "tails" on the next trial ever more likely. Although the GF appears to provide evidence of "cognitive bias," a recent theoretical account (Hahn & Warren, 2009) has suggested the GF might be understandable if constraints on actual experience of random sources (such as attention and short term memory) are taken into account. Here we test this experiential account by exposing participants to 200 outcomes from a genuinely random (p = .5) Bernoulli process. All participants saw the same overall sequence; however, we manipulated experience across groups such that the sequence was divided into chunks of length 100, 10, or 5. Both before and after the exposure, participants (a) generated random sequences and (b) judged the randomness of presented sequences. In contrast to other accounts in the literature, the experiential account suggests that this manipulation will lead to systematic differences in postexposure behavior. Our data were strongly in line with this prediction and provide support for a general account of randomness perception in which biases are actually apt reflections of environmental statistics under experiential constraints. This suggests that deeper insight into human cognition may be gained if, instead of dismissing apparent biases as failings, we assume humans are rational under constraints. (PsycINFO Database Record

We gain much of our knowledge from other people. Because people are fallible-they lie, mislead, and are mistaken-it seems essential to monitor their claims and their reliability as sources of information. An intuitive way to do this is to draw on our expectations about claims and sources: to perform expectation-based updating (Hahn, Merdes,von Sydow, 2018). But this updating can have damaging consequences, leading us into a kind of confirmation bias. An alternative is to keep track of outcomes and record whether a claim proves true or false: to perform outcome-based updating (Hahn et al., 2018). This form of updating does not have the negative repercussions on belief accuracy. But both forms of updating might undermine the trust and cooperation assumed to be necessary for successful communication. We explore a potential boundary condition on these types of updating. We investigate whether speakers can protect their reputation when they make claims with low prior probability, with and without knowledge of the final outcome. We explore suggestions from McCready (2015) that speakers can protect themselves by hedging with evidential language: in particular with weaker propositional attitudes ("I suspect that . . .") and so-called double hedges ("I might be wrong, but I think . . ."). We find that both forms of updating are robust to hedging with this evidential language and find no clear evidence for a protective effect. We discuss extra ingredients that may allow successful hedging. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Suppose that 2 competing norms, N₁ and N₂, can be identified such that a given person's response can be interpreted as correct according to N₁ but incorrect according to N₂. Which of these two norms, if any, should one use to interpret such a response? In this article, we seek to address this fundamental problem by studying individual variation in the interpretation of conditionals by establishing individual profiles of the participants based on their case judgments and reflective attitudes. To investigate participants' reflective attitudes, we introduce a new experimental paradigm called the scorekeeping task. As a case study, we identify the participants who follow the suppositional theory of conditionals (N₁) versus inferentialism (N₂) and investigate to what extent internally consistent competence models can be reconstructed for the participants on this basis. After extensive empirical investigations, an apparent reasoning error with and-to-if inferences was found in 1 of these 2 groups. The implications of this case study for debates on the proper role of normative considerations in psychology are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Cytotoxic T lymphocytes patrol our body in search for infected cells which they kill through the release of cytotoxic substances contained in cytotoxic granules. The fusion of cytotoxic granules occurs at a specially formed contact site, the immunological synapse, and is tightly controlled to ensure specificity. In this review, we discuss the contribution of two intracellular compartments, endosomes and cytotoxic granules, to the formation, function and disassembly of the immunological synapse. We highlight a recently proposed sequential process of fusion events at the IS upon target cell recognition. First, recycling endosomes fuse with the plasma membrane to deliver cargo required for the docking of cytotoxic granules. Second, cytotoxic granules arrive and fuse upon docking in a SNARE-dependent manner. Following fusion, membrane components of the cytotoxic granule are retrieved through endocytosis to ensure the fast, efficient serial killing of target cells that is characteristic of cytotoxic T lymphocytes.

With the advent of social media, the last decade has seen profound changes to the way people receive infor- mation. This has fueled debate about the ways (if any) changes to the nature of our information networks might be affecting voters’ beliefs about the world, voting results, and, ultimately, democracy. At the same time, much discussion in the public arena in recent years has concerned the notion that ill-informed voters have been voting against their own self-interest. The research reported here brings these two strands together: simulations involv- ing agent-based models, interpreted through the formal framework of Condorcet’s (1785) Jury Theorem, demonstrate how changes to information networks may make voter error more likely even though individual competence has largely remained unchanged.

Whether assessing the accuracy of expert forecasting, the pros and cons of group communication, or the value of evidence in diagnostic or predictive reasoning, dependencies between experts, group members, or evidence have traditionally been seen as a form of redundancy. We demonstrate that this conception of dependence conflates the structure of a dependency network, and the observations across this network. By disentangling these two elements we show, via mathematical proof and specific examples, that there are cases where dependencies yield an informational advantage over independence. More precisely, when a structural dependency exists, but observations are either partial or contradicting, these observations provide more support to a hypothesis than when this structural dependency does not exist, ceteris paribus. Furthermore, we show that lay reasoners endorse sufficient assumptions underpinning these advantageous structures yet fail to appreciate their implications for probability judgments and belief revision.

There has been much interest in group judgment and the so-called ‘wisdom of crowds’. In many real world contexts, members of groups not only share a dependence on external sources of information, but they also communicate with one another, thus introducing correlations among their responses that can diminish collective accuracy. This has long been known, but it has—to date—not been examined to what extent different kinds of communication networks may give rise to systematically different effects on accuracy. We argue that equations that relate group accuracy, individual accuracy, and group diversity (see Hogarth, 1978; Page, 2007) are useful theoretical tools for understanding group performance in the context of research on group structure. In particular, these equations may serve to identify the kind of group structures that improve individual accuracy without thereby excessively diminishing diversity so that the net positive effect is an improvement even on the level of collective accuracy. Two experiments are reported where two structures (the complete network and a small world network) are investigated from this perspective. It is demonstrated that the more constrained network (the small world network) outperforms the network with a free flow of information.

The appeal to expert opinion is an argument form that uses the verdict of an expert to support a position or hypothesis. A previous scheme-based treatment of the argument form is formalized within a Bayesian network that is able to capture the critical aspects of the argument form, including the central considerations of the expert's expertise and trustworthiness. We propose this as an appropriate normative framework for the argument form, enabling the development and testing of quantitative predictions as to how people evaluate this argument, suggesting that such an approach might be beneficial to argumentation research generally. We subsequently present two experiments as an example of the potential for future research in this vein, demonstrating that participants' quantitative ratings of the convincingness of a proposition that has been supported with an appeal to expert opinion were broadly consistent with the predictions of the Bayesian model.

Human randomness perception is commonly described as biased. This is because when generating random sequences humans tend to systematically under- and overrepresent certain subsequences relative to the number expected from an unbiased random process. In a purely theoretical analysis we have previously suggested that common misperceptions of randomness may actually reflect genuine aspects of the statistical environment, once cognitive constraints are taken into account which impact on how that environment is actually experienced (Hahn & Warren, Psychological Review, 2009). In the present study we undertake an empirical test of this account, comparing human-generated against unbiased process-generated binary sequences in two experiments. We suggest that comparing human and theoretically unbiased sequences using metrics reflecting the constraints imposed on human experience provides a more meaningful picture of lay people’s ability to perceive randomness. Finally, we propose a simple generative model of human random sequence generation inspired by the Hahn and Warren account. Taken together our results question the notion of bias in human randomness perception., Public Significance Statement The dominant perspective in experimental psychology is that human judgment and decision making are flawed. This is particularly evident in research on human perception of randomness. Here we explore this idea, presenting several analyses of data from an experiment in which participants are asked to generate a sequence of outcomes from a binary random process (like a coin toss). Although behavior does depart from the output of genuinely random source, the extent of this departure depends on how performance is characterized and whether constraints on human memory and attention span are taken into account. We find that when such constraints are considered, and appropriate performance measures are used, humans actually match the random source rather well. We argue more generally it may be problematic to emphasize errors in human judgment and decision-making without taking account of appropriate constraints.

Reasoning with conditionals is central to everyday life, yet there is long-standing disagreement about the meaning of the conditional. One example is the puzzle of so-called missing-link conditionals such as ‘‘if raccoons have no wings, they cannot breathe under water.” Their oddity may be taken to show that con- ditionals require a connection between antecedent (‘‘raccoons have no wings”) and consequent (‘‘they cannot breathe under water”), yet most accounts of conditionals attribute the oddity to natural- language pragmatics. We present an experimental study disentangling the pragmatic requirement of dis- course coherence from a stronger notion of connection: probabilistic relevance. Results indicate that mere discourse coherence is not enough to make conditionals assertable.

"… Scholarly integrity is not only the foundational bedrock of scientific inquiry, it is also the prerequisite for a positive image of scholarship … For individuals, integrity is an aspect of moral character and experience. For institutions, it is about creating an environment that promotes responsible conduct … In the first instance, research institutions must provide guidelines and codes of practice on scholarly integrity …" Read more in the Editorial by J. S. Francisco, U. Hahn, and H. Schwarz.

It is generally assumed that when making categorization judgments the cognitive system learns to focus on stimuli features that are relevant for making an accurate judgment. This is a key feature of hybrid categorization systems, which selectively weight the use of exemplar- and rule-based processes. In contrast, Hahn, Prat-Sala, Pothos, and Brumby (2010) have shown that people cannot help but pay attention to exemplar similarity, even when doing so leads to classification errors. This paper tests, through a series of computer simulations, whether a hybrid categorization model developed in the ACT-R cognitive architecture (by Anderson & Betz, 2001) can account for the Hahn et al. dataset. This model implements Nosofsky and Palmeri’s (1997) Exemplar-Based Random Walk (EBRW) model as its exemplar route, and combines it with an implementation of Nosofsky, Palmeri and McKinley’s (1994) rule-based model RULEX. A thorough search of the model’s parameter space showed that while the presence of an exemplar-similarity effect on response times was associated with classification errors it was possible to fit both measures to the observed data for an unsupervised version of the task (i.e., in which no feedback on accuracy was given). Difficulties arose when the model was applied to a supervised version of the task in which explicit feedback on accuracy was given. Modeling results show that the exemplar-similarity effect is diminished by feedback as the model learns to avoid the error-prone exemplar-route, taking instead the accurate rule-route. In contrast to the model, Hahn et al. found that people continue to exhibit robust exemplar-similarity effects even when given feedback. This work highlights a challenge for understanding how and why people combine rules and exemplars when making categorization decisions.

Cytotoxic T lymphocytes are effector CD8+ T cells that eradicate infected and malignant cells. Here we show that the transcription factor NFATc1 controls the cytotoxicity of mouse cytotoxic T lymphocytes. Activation of Nfatc1 −/− cytotoxic T lymphocytes showed a defective cytoskeleton organization and recruitment of cytosolic organelles to immunological synapses. These cells have reduced cytotoxicity against tumor cells, and mice with NFATc1-deficient T cells are defective in controlling Listeria infection. Transcriptome analysis shows diminished RNA levels of numerous genes in Nfatc1 −/− CD8+ T cells, including Tbx21, Gzmb and genes encoding cytokines and chemokines, and genes controlling glycolysis. Nfatc1 −/− , but not Nfatc2 −/− CD8+ T cells have an impaired metabolic switch to glycolysis, which can be restored by IL-2. Genome-wide ChIP-seq shows that NFATc1 binds many genes that control cytotoxic T lymphocyte activity. Together these data indicate that NFATc1 is an important regulator of cytotoxic T lymphocyte effector functions.

The statistics of small samples are often quite different from those of large samples, and this needs to be taken into account in assessing the rationality of human behavior. Specifically, in evaluating human responses to environmental statistics, it is the effective environment that matters; that is, the environment actually experienced by the agent needs to be considered, not simply long-run frequencies. Significant deviations from long-run statistics may arise through experiential limitations of the agent that stem from resource constraints and/or information-processing bounds. The article draws together recent work from a number of areas in judgment and decision making ranging from randomness perception (Hahn & Warren, ), information sampling (Hertwig & Pleskac, ; Kareev et al., ), and consequences of choice for exploration or exploitation (e.g., Denrell, ) to demonstrate how proper consideration of these deviations leads to reevaluation of behaviors that are otherwise deemed irrational.

"… Achieving our core mission, namely progress through knowledge, now requires two kinds of communication: one to our scientific peers, but another, more fraught yet critical, to the broader public. As scientists, we need to forge a better relationship between the world of research and the general public …" Read more in the Guest Editorial by K. Boele-Woelki, J. S. Francisco, U. Hahn, and J. Herz.

Reasoning researchers within cognitive psychology have spent decades examining the extent to which human inference measures up to normative standards. Work here has been dominated by logic, but logic has little to say about most everyday, informal arguments. Empirical work on argumentation within psychology and education has studied the development and improvement of argumentation skills, but has been theoretically limited to broad structural characteristics. Using the catalogue of informal reasoning fallacies established over the centuries within the realms of philosophy, Hahn and Oaksford (2007a32. Hahn, U. and Oaksford, M. 2007a. The Rationality of Informal Argumentation: A Bayesian Approach to Reasoning Fallacies. Psychological Review, 114: 704–732. (doi:10.1037/0033-295X.114.3.704) View all references) recently demonstrated how Bayesian probability can provide a normative standard by which to evaluate quantitatively the strength of a wide range of everyday arguments. This broadens greatly the potential scope of reasoning research beyond the rather narrow set of logical and inductive arguments that have been studied; it also provides a framework for the normative assessment of argument content that has been lacking in argumentation research. The Bayesian framework enables both qualitative and quantitative experimental predictions about what arguments people should consider to be weak and strong, against which people's actual judgements can be compared. This allows the different traditions of reasoning and argumentation research to be brought together both theoretically and in empirical research.