Our programmatic article on Homo heuristicus (Gigerenzer & Brighton, 2009) included a methodological section specifying three minimum criteria for testing heuristics: competitive tests, individual-level tests, and tests of adaptive selection of heuristics. Using Richter and Späth’s (2006) study on the recognition heuristic, we illustrated how violations of these criteria can lead to unsupported conclusions. In their comment, Hilbig and Richter conduct a reanalysis, but again without competitive testing. They neither test nor specify the compensatory model of inference they argue for. (...) Instead, they test whether participants use the recognition heuristic in an unrealistic 100% (or 96%) of cases, report that only some people exhibit this level of consistency, and conclude that most people would follow a compensatory strategy. We know of no model of judgment that predicts 96% correctly. The curious methodological practice of adopting an unrealistic measure of success to argue against a competing model, and to interpret such a finding as a triumph for a preferred but unspecified model, can only hinder progress. Marewski, Gaissmaier, Schooler, Goldstein, and Gigerenzer (2010), in contrast, specified five compensatory models, compared them with the recognition heuristic, and found that the recognition heuristic predicted inferences most accurately. (shrink)

This paper discusses the ecological case for epistemic innocence: does biased cognition have evolutionary benefits, and if so, does that exculpate human reasoners from irrationality? Proponents of ‘ecological rationality’ have challenged the bleak view of human reasoning emerging from research on biases and fallacies. If we approach the human mind as an adaptive toolbox, tailored to the structure of the environment, many alleged biases and fallacies turn out to be artefacts of narrow norms and artificial set-ups. However, we argue that (...) putative demonstrations of ecological rationality involve subtle locus shifts in attributions of rationality, conflating the adaptive rationale of heuristics with our own epistemic credentials. By contrast, other cases also involve an ecological reframing of human reason, but do not involve such problematic locus shifts. We discuss the difference between these cases, bringing clarity to the rationality debate. (shrink)

Decision making often takes place in social environments where other actors influence individuals' decisions. The present article examines how advice affects individual learning. Five social learning models combining advice and individual learning‐four based on reinforcement learning and one on Bayesian learning‐and one individual learning model are tested against each other. In two experiments, some participants received good or bad advice prior to a repeated multioption choice task. Receivers of advice adhered to the advice, so that good advice improved performance. The (...) social learning models described the observed learning processes better than the individual learning model. Of the models tested, the best social learning model assumes that outcomes from recommended options are more positively evaluated than outcomes from nonrecommended options. This model correctly predicted that receivers first adhere to advice, then explore other options, and finally return to the recommended option. The model also predicted accurately that good advice has a stronger impact on learning than bad advice. One‐time advice can have a long‐lasting influence on learning by changing the subjective evaluation of outcomes of recommended options. (shrink)

When we solve problems there are usually a number of different strategies that we can use. It is a hallmark of intelligence that we, as humans, are able to flexibly choose between them. Siegler and...

The phenomenon of base-rate neglect has elicited much debate. One arena of debate concerns how people make judgments under conditions of uncertainty. Another more controversial arena concerns human rationality. In this target article, we attempt to unpack the perspectives in the literature on both kinds of issues and evaluate their ability to explain existing data and their conceptual coherence. From this evaluation we conclude that the best account of the data should be framed in terms of a dual-process model of (...) judgment, which attributes base-rate neglect to associative judgment strategies that fail to adequately represent the set structure of the problem. Base-rate neglect is reduced when problems are presented in a format that affords accurate representation in terms of nested sets of individuals. (shrink)

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we (...) illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality. (shrink)

Do we apply higher epistemic standards to subjects with high stakes? This paper argues that we expect different outward behavior from high-stakes subjects—for example, we expect them to collect more evidence than their low-stakes counterparts—but not because of any change in epistemic standards. Rather, we naturally expect subjects in any condition to think in a roughly adaptive manner, balancing the expected costs of additional evidence collection against the expected value of gains in accuracy. The paper reviews a body of empirical (...) work on the automatic regulation of cognitive effort in response to stakes, and argues that we naturally see high- and low-stakes subjects as experiencing different levels of ‘epistemic anxiety’, and anticipate different levels of cognitive effort from them for this reason. If unresolved epistemic anxiety always bars an ascription of knowledge, then we can explain our responses to cases involving shifting stakes without positing any variation in the standards of intuitive knowledge ascription. (shrink)

One important distinction in psychology is between inferences based on associative memory and inferences based on analysis and rules. Much previous empirical work conceive of associative and analytical processes as two exclusive ways of addressing a judgment task, where only one process is selected and engaged at a time, in an either-or fashion. However, related work indicate that the processes are better understood as being in interplay and simultaneously engaged. Based on computational modeling and brain imaging of spontaneously adopted judgment (...) strategies together with analyses of brain activity elicited in tasks where participants were explicitly instructed to perform similarity-based associative judgments or rule-based judgments (n = 74), we identified brain regions related to the two types of processes. We observed considerable overlap in activity patterns. The precuneus was activated for both types of judgments, and its activity predicted how well a similarity-based model fit the judgments. Activity in the superior frontal gyrus predicted the fit of a rule-based judgment model. The results suggest the precuneus as a key node for similarity-based judgments, engaged both when overt responses are guided by similarity-based and rule-based processes. These results are interpreted such that similarity-based processes are engaged in parallel to rule-based-processes, a finding with direct implications for cognitive theories of judgment. (shrink)

Research on adaptive rationality has focused principally on inference, judgment, and decision-making that lead to behaviors and actions. These processes typically require cognitive representations as input, and these representations must presumably be acquired via learning. Nonetheless, there has been little work on the nature of, and justification for, adaptively rational learning processes. In this paper, we argue that there are strong reasons to believe that some learning is adaptively rational in the same way as judgment and decision-making. Indeed, overall adaptive (...) rationality can only properly be assessed for pairs of learning and decision processes. We thus present a formal framework for modeling such pairs of cognitive processes, and thereby assessing their adaptive rationality relative to the environment and the agent’s goals. We then use this high-level formal framework on specific cases by demonstrating how natural formal constraints on decision-making can lead to substantive predictions about adaptively rational learning and representation; and characterizing adaptively rational learning for fast-and-frugal one-reason decision-making. (shrink)

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness (...) in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition. (shrink)

In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the (...) names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., “Does this city have a university?”) before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take–the–best or the Weighted Additive heuristics. (shrink)

While theories of rationality and decision making typically adopt either a single-powertool perspective or a bag-of-tricks mentality, the research program of ecological rationality bridges these with a theoretically-driven account of when different heuristic decision mechanisms will work well. Here we described two ways to study how heuristics match their ecological setting: The bottom-up approach starts with psychologically plausible building blocks that are combined to create simple heuristics that fit specific environments. The top-down approach starts from the statistical problem facing the (...) organism and a set of principles, such as the bias– variance tradeoff, that can explain when and why heuristics work in uncertain environments, and then shows how effective heuristics can be built by biasing and simplifying more complex models. We conclude with challenges these approaches face in developing a psychologically realistic perspective on human rationality. (shrink)

Both the fast and frugal heuristics and the naturalistic decision making research programmes have identified important areas of inquiry previously neglected in the traditional study of human judgment and decision making, and have greatly contributed to the understanding of people's real-world decision making under environmental constraints. The two programmes share similar theoretical arguments regarding the rationality, optimality, and role of experience in decision making. Their commonalities have made them appealing to each other, and efforts have been made, by their leading (...) academics, to promote synergy and integration. However, there has been little progress towards this during the last decade. This paper seeks to address this gap by seeking to better understand their commonalities and differences. To do so, literature relating to the two programmes is reviewed. The findings of the review indicated that an integration of the two could enhance FFHs' field research in applied settings, facilitate its investigation on boundary conditions of people's decision strategy selection, enable NDM to embrace emerging research opportunities in the age of big data, as well as permit each programme to enlighten the research topics and to validate the research findings of the other. (shrink)

According to the paradigm of adaptive rationality, successful inference and prediction methods tend to be local and frugal. As a complement to work within this paradigm, we investigate the problem of selecting an optimal combination of prediction methods from a given toolbox of such local methods, in the context of changing environments. These selection methods are called meta-inductive strategies, if they are based on the success-records of the toolbox-methods. No absolutely optimal MI strategy exists—a fact that we call the “revenge (...) of ecological rationality”. Nevertheless one can show that a certain MI strategy exists, called “AW”, which is universally long-run optimal, with provably small short-run losses, in comparison to any set of prediction methods that it can use as input. We call this property universal access-optimality. Local and short-run improvements over AW are possible, but only at the cost of forfeiting universal access-optimality. The last part of the paper includes an empirical study of MI strategies in application to an 8-year-long data set from the Monash University Footy Tipping Competition. (shrink)

Many theories on cognition assume that people adapt their decision strategies depending on the situation they face. To test if and how affect guides the selection of decision strategies, we conducted an online study (N = 166), where different mood states were induced through video clips. Results indicate that mood influenced the use of decision strategies. Negative mood, in particular anger, facilitated the use of non-compensatory strategies, whereas positive mood promoted compensatory decision rules. These results are in line with the (...) idea that positive mood broadens the focus of attention and thus increases the use of compensatory decision strategies that take many pieces of information into account, whereas negative mood narrows the focus of attention and thus fosters non-compensatory strategies that rely on a selective use of information. The results further indicate that gaining a deeper theoretical understanding of the cognitive mechanisms that govern decision processes requires taking emotions into account. (shrink)

The nudge approach seeks to improve people’s decisions through small changes in their choice environments. Nudge policies often work through psychological mechanisms that deviate from traditional notions of rationality. Because of that, some critics object that nudging treats people as irrational. Such treatment might be disrespectful in itself and might crowd out more empowering policies. I defend nudging against these objections. By defending a nonstandard, ecological model of rationality, I argue that nudging not only is compatible with rational agency but (...) can even support it. Accordingly, a concern with rationality speaks for more rather than less public policy nudging. (shrink)

People often face preferential decisions under risk. To further our understanding of the cognitive processes underlying these preferential choices, two prominent cognitive models, decision field theory (DFT; Busemeyer & Townsend, 1993) and the proportional difference model (PD; González‐Vallejo, 2002), were rigorously tested against each other. In two consecutive experiments, the participants repeatedly had to choose between monetary gambles. The first experiment provided the reference to estimate the models’ free parameters. From these estimations, new gamble pairs were generated for the second (...) experiment such that the two models made maximally divergent predictions. In the first experiment, both models explained the data equally well. However, in the second generalization experiment, the participants’ choices were much closer to the predictions of DFT. The results indicate that the stochastic process assumed by DFT, in which evidence in favor of or against each option accumulates over time, described people’s choice behavior better than the trade‐offs between proportional differences assumed by PD. (shrink)

Why do people shift their strategies for solving problems? Past work has focused on the roles of contextual and individual factors in explaining whether people adopt new strategies when they are exposed to them. In this study, we examined a factor not considered in prior work: people's evaluations of the strategies themselves. We presented undergraduate participants from a moderately selective university (N = 252; 64.8% women, 65.6% White, 67.6% who had taken calculus) with two strategies for solving algebraic word problems (...) and asked them to rate these strategies and their own strategy on a variety of dimensions. Participants’ ratings loaded onto two factors, which we label quality and difficulty. Participants’ initial evaluations of the quality of the strategies were associated with whether they used the strategies at posttest, and this effect held even when controlling for individual and contextual factors. However, people's evaluations of the difficulty of the strategies were not consistently associated with their later adoption of those strategies. We also examined individual and contextual predictors of strategy ratings and strategy adoption. Participants’ need for cognition and their spatial visualization ability were associated with their strategy evaluations, and the framing of the story problems was associated with their strategy adoption. The findings highlight that strategy adoption depends on multiple interacting factors, and that to understand strategy change, it is critical to examine how people evaluate strategies. (shrink)

This study examined the relationship between two types of mathematical flexibility – potential flexibility, which indicates individuals’ knowledge of multiple strategies and strategy efficiency, and practical flexibility, which refers to individuals’ flexible performances when solving math problems. Both types of flexibility were assessed in the domain of linear equation solving. Furthermore, two types of beliefs – self-efficacy and use of flexible cognition – were investigated as potential moderators between potential and practical flexibility. 121 8th grade students from China took part (...) in this study. Results indicate that potential flexibility positively predicted practical flexibility. Additionally, self-efficacy and use of flexible cognition might moderate the relationship between these two types of flexibility, suggesting that potential flexibility may lead to different degrees of practical flexibility depending on different levels of beliefs. Implications of these findings for research on mathematical flexibility and for educational practice are discussed. (shrink)

Applying the framework of ecological rationality, the authors studied the adaptivity of group decision making. In detail, they investigated whether groups apply decision strategies conditional on their composition in terms of task‐relevant features. The authors focused on the recognition heuristic, so the task‐relevant features were the validity of the group members' recognition and knowledge, which influenced the potential performance of group strategies. Forty‐three three‐member groups performed an inference task in which they had to infer which of two German companies had (...) the higher market capitalization. Results based on the choice data support the hypothesis that groups adaptively apply the strategy that leads to the highest theoretically achievable performance. Time constraints had no effect on strategy use but did have an effect on the proportions of different types of arguments. Possible mechanisms underlying the adaptive use of recognition in group decision making are discussed. (shrink)

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...) and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)

Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magnitude: with binary, categorical, or continuous values). In this article, (...) we explore the problem space of these three parameters in the context of a task whose completion entails some combination of 36 state–action pairs, where all intermediate states (i.e., after the initial state and prior to the end state) represent progressive but partial completion of the task. Different choices produce profoundly different learning paths and outcomes, with the strongest effect for moment. Unfortunately, there is little discussion in the literature of the effect of such choices. This absence is disappointing, as the choice of when, what, and how much needs to be made by a modeler for every learning model. (shrink)

Topics in Cognitive Science, Volume 14, Issue 3, Page 467-491, July 2022.

:In this paper, we analyse the difference between two types of behavioural policies – nudges and boosts. We distinguish them on the basis of the mechanisms through which they are expected to operate and identify the contextual conditions that are necessary for each policy to be successful. Our framework helps judging which type of policy is more likely to bring about the intended behavioural outcome in a given situation.

If citizens’ behavior threatens to harm others or seems not to be in their own interest, it is not uncommon for governments to attempt to change that behavior. Governmental policy makers can apply established tools from the governmental toolbox to this end. Alternatively, they can employ new tools that capitalize on the wealth of knowledge about human behavior and behavior change that has been accumulated in the behavioral sciences. Two contrasting approaches to behavior change are nudge policies and boost policies. (...) These policies rest on fundamentally different research programs on bounded rationality, namely, the heuristics and biases program and the simple heuristics program, respectively. This article examines the policy–theory coherence of each approach. To this end, it identifies the necessary assumptions underlying each policy and analyzes to what extent these assumptions are implied by the theoretical commitments of the respective research program. Two key results of this analysis are that the two policy approaches rest on diverging assumptions and that both suffer from disconnects with the respective theoretical program, but to different degrees: Nudging appears to be more adversely affected than boosting does. The article concludes with a discussion of the limits of the chosen evaluative dimension, policy–theory coherence, and reviews some other benchmarks on which policy programs can be assessed. (shrink)

The terms nested sets, partitive frequencies, inside-outside view, and dual processes add little but confusion to our original analysis (Gigerenzer & Hoffrage 1995; 1999). The idea of nested set was introduced because of an oversight; it simply rephrases two of our equations. Representation in terms of chances, in contrast, is a novel contribution yet consistent with our computational analysis System 1.dual process theory” is: Unless the two processes are defined, this distinction can account post hoc for almost everything. In contrast, (...) an ecological view of cognition helps to explain how insight is elicited from the outside (the external representation of information) and, more generally, how cognitive strategies match with environmental structures. (shrink)

The paper shows why and how an empirical study of fast-and-frugal heuristics can provide norms of good reasoning, and thus how (and how far) rationality can be naturalized. We explain the heuristics that humans often rely on in solving problems, for example, choosing investment strategies or apartments, placing bets in sports, or making library searches. We then show that heuristics can lead to judgments that are as accurate as or even more accurate than strategies that use more information and computation, (...) including optimization methods. A standard way to defend the use of heuristics is by reference to accuracy-effort trade-offs. We take a different route, emphasizing ecological rationality (the relationship between cognitive heuristics and environment), and argue that in uncertain environments, more information and computation are not always better (the “less-can-be-more” doctrine). The resulting naturalism about rationality is thus normative because it not only describes what heuristics people use, but also in which specific environments one should rely on a heuristic in order to make better inferences. While we desist from claiming that the scope of ecological rationality is unlimited, we think it is of wide practical use. (shrink)

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: the discovery of less-is-more effects; the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; an advancement from vague labels to computational models of heuristics; the (...) development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an “adaptive toolbox;” and the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies. (shrink)

What is the nature of moral behavior? According to the study of bounded rationality, it results not from character traits or rational deliberation alone, but from the interplay between mind and environment. In this view, moral behavior is based on pragmatic social heuristics rather than moral rules or maximization principles. These social heuristics are not good or bad per se, but solely in relation to the environments in which they are used. This has methodological implications for the study of morality: (...) Behavior needs to be studied in social groups as well as in isolation, in natural environments as well as in labs. It also has implications for moral policy: Only by accepting the fact that behavior is a function of both mind and environmental structures can realistic prescriptive means of achieving moral goals be developed. (shrink)

Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limitations in computational capacity, time, and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.]. These heuristics are effective to the extent that they can exploit the structure of information in the (...) environment in which they operate. Specifically, they require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. This problem becomes even more critical if compound cues are considered. We submit that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. We review experimental evidence that tested this hypothesis and report the results of a simulation study. We conclude that causal knowledge can act as a meta-cue for identifying highly valid cues, either individual or compound, and helps in the estimation of their validities. (shrink)

This paper focuses on the relation of heuristic search and level of intelligence in cognitive computation systems. The paper begins with a review of the fundamental properties of a cognitive computation system, which is defined generally as a control system that generates goal-directed actions in response to environmental inputs and constraints. An important property of cognitive computations is the need to process local cues in symbol structures to access and integrate distal knowledge to generate a response. To deal with uncertainties (...) involved in this local-to-distal processing, the system needs to perform heuristic search to locate and integrate the right set of distal structures. The level of intelligence of the system depends critically on the efficiency of the heuristic search process. It is argued that, for a bounded rationality system, the level of intelligence does not depend on how much search it needs to do to accomplish a task. Rather, the level of intelligence depends on how much search it does not need to do to achieve the same level of performance. Examples were discussed to illustrate this idea. The first two examples show how machines that play games like tic-tac-toe and chess rely heavily on the efficiency of the heuristic search algorithm to achieve better performance, demonstrating the relation of heuristic search and intelligence in a bounded rationality system. The second example shows how humans adapt to different information ecologies to perform information search on the Internet and how their performance improves over time, demonstrating how heuristic search can be improved in an adaptive rationality system. The two examples demonstrate how better search control knowledge and representations of task environment can improve the efficiency of heuristic search, thereby improving the intelligence of the system. (shrink)

Most models of response time (RT) in elementary cognitive tasks implicitly assume that the speed-accuracy trade-off is continuous: When payoffs or instructions gradually increase the level of speed stress, people are assumed to gradually sacrifice response accuracy in exchange for gradual increases in response speed. This trade-off presumably operates over the entire range from accurate but slow responding to fast but chance-level responding (i.e., guessing). In this article, we challenge the assumption of continuity and propose a phase transition model for (...) RTs and accuracy. Analogous to the fast guess model (Ollman, 1966), our model postulates two modes of processing: a guess mode and a stimulus-controlled mode. From catastrophe theory, we derive two important predictions that allow us to test our model against the fast guess model and against the popular class of sequential sampling models. The first prediction—hysteresis in the transitions between guessing and stimulus-controlled behavior—was confirmed in an experiment that gradually changed the reward for speed versus accuracy. The second prediction—bimodal RT distributions—was confirmed in an experiment that required participants to respond in a way that is intermediate between guessing and accurate responding. (shrink)

Individuals frequently make use of the body and environment when engaged in a cognitive task. For example, individuals will often spontaneously physically rotate when faced with rotated objects, such as an array of words, to putatively offload the performance costs associated with stimulus rotation. We looked to further examine this idea by independently manipulating the costs associated with both word rotation and array frame rotation. Surprisingly, we found that individuals’ patterns of spontaneous physical rotations did not follow patterns of performance (...) costs or benefits associated with being physically rotated, findings difficult to reconcile with existing theories of strategy selection involving external resources. Individuals’ subjective ratings of perceived benefits, rather, provided an excellent match to the patterns of physical rotations, suggesting that the critical variable when deciding on-the-fly whether to incorporate an external resource is the participant's metacognitive beliefs regarding expected performance or the effort required for each approach. Implications for metacognition's future in theories of cognitive offloading are discussed. (shrink)

In previous published research (“Conditionals and Inferential Connections: A Hypothetical Inferential Theory,” Cognitive Psychology, 2018), we investigated experimentally what role the presence and strength of an inferential connection between a conditional’s antecedent and consequent plays in how people process that conditional. Our analysis showed the strength of that connection to be strongly predictive of whether participants evaluated the conditional as true, false, or neither true nor false. In this article, we re-analyse the data from our previous research, now focussing on (...) the semantics of conditionals rather than on how they are processed. Specifically, we use those data to compare the main extant semantics with each other and with inferentialism, a semantics according to which the truth of a conditional requires the presence of an inferential connection between the conditional’s component parts. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

In this dissertation, I construct scientifically and practically adequate moral analogs of cognitive heuristics and biases. Cognitive heuristics are reasoning “shortcuts” that are efficient but flawed. Such flaws yield systematic judgment errors—i.e., cognitive biases. For example, the availability heuristic infers an event’s probability by seeing how easy it is to recall similar events. Since dramatic events, such as airplane crashes, are disproportionately easy to recall, this heuristic explains systematic overestimations of their probability (availability bias). The research program on cognitive heuristics (...) and biases (e.g., Daniel Kahneman’s work) has been scientifically successful and has yielded useful error-prevention techniques—i.e., cognitive debiasing. I attempt to apply this framework to moral reasoning to yield moral heuristics and biases. For instance, a moral bias of unjustified differences in the treatment of particular animal species might be partially explained by a moral heuristic that dubiously infers animals’ moral status from their aesthetic features. While the basis for identifying judgments as cognitive errors is often unassailable (e.g., per violating laws of logic), identifying moral errors seemingly requires appealing to moral truth, which, I argue, is problematic within science. Such appeals can be avoided by repackaging moral theories as mere “standards-of-interest” (a la non-normative metrics of purportedly right-making features/properties). However, standards-of-interest do not provide authority, which is needed for effective debiasing. Nevertheless, since each person deems their own subjective morality authoritative, subjective morality (qua standard-of-interest and not moral subjectivism) satisfies both scientific and practical concerns. As such, (idealized) subjective morality grounds a moral analog of cognitive biases—namely, subjective moral biases (e.g., committed anti-racists unconsciously discriminating). I also argue that "cognitive heuristic" is defined by its contrast with rationality. Consequently, heuristics explain biases, which are also so defined. However, such contrasting with rationality is causally irrelevant to cognition. This frustrates the presumed usefulness of the kind, heuristic, in causal explanation. As such, in the moral case, I jettison the role of causal explanation and tailor categories solely for contrastive explanation. As such, “moral heuristic” is replaced with "subjective moral fallacy," which is defined by its contrast with subjective morality and explains subjective moral biases. The resultant subjective moral biases and fallacies framework can undergird future empirical research. (shrink)