49 publications classées par:
type de publication
: Revue avec comité de lecture
Articles Hafenbrädl S., Wäger D., Marewski J. N. & Gigerenzer G. (in press). Applied decision making with fast-and-frugal heuristics. Journal of Applied Research in Memory and Cognition.
Stevens J. R., Marewski J. N., Schooler L. J. & Gilby I. C. (in press). Reflections of the social environment in chimpanzee memory: Applying rational analysis beyond humans. Royal Society Open Science.
Gaschler R., Marewski J. N. & Frensch P. A. (2015). Once and for all - How people change strategy to ignore irrelevant information in visual tasks. Quarterly Journal of Experimental Psychology, 68(3), 543-67. [doi] [abstract]
Ignoring irrelevant visual information aids efficient interaction with task environments. We studied how people, after practice, start to ignore the irrelevant aspects of stimuli. For this we focused on how information reduction transfers to rarely practised and novel stimuli. In Experiment 1, we compared competing mathematical models on how people cease to fixate on irrelevant parts of stimuli. Information reduction occurred at the same rate for frequent, infrequent, and novel stimuli. Once acquired with some stimuli, it was applied to all. In Experiment 2, simplification of task processing also occurred in a once-for-all manner when spatial regularities were ruled out so that people could not rely on learning which screen position is irrelevant. Apparently, changes in eye movements were an effect of a once-for-all strategy change rather than a cause of it. Overall, the results suggest that participants incidentally acquired knowledge about regularities in the task material and then decided to voluntarily apply it for efficient task processing. Such decisions should be incorporated into accounts of information reduction and other theories of strategy change in skill acquisition.
Gigerenzer G. & Marewski J. N. (2015). Surrogate science: The idol of a universal method for scientific inference. Journal of Management, 41(2), 421-440. [doi] [url] [abstract]
The application of statistics to science is not a neutral act. Statistical tools have shaped and were also shaped by its objects. In the social sciences, statistical methods fundamentally changed research practice, making statistical inference its centerpiece. At the same time, textbook writers in the social sciences have transformed rivaling statistical systems into an apparently monolithic method that could be used mechanically. The idol of a universal method for scientific inference has been worshipped since the "inference revolution" of the 1950s. Because no such method has ever been found, surrogates have been created, most notably the quest for significant p values. This form of surrogate science fosters delusions and borderline cheating and has done much harm, creating, for one, a flood of irreproducible results. Proponents of the "Bayesian revolution" should be wary of chasing yet another chimera: an apparently universal inference procedure. A better path would be to promote both an understanding of the various devices in the "statistical toolbox" and informed judgment to select among these.
Hoffrage U. & Marewski J. N. (2015). Unveiling the Lady in Black: Brunswikian (and other) approaches to intuition. The Brunswik Society Newsletter, 30, 15-19. [abstract]
In September 2015, the Journal of Applied Research in Memory and Cognition (JARMAC) published a special issue on "Modeling and Aiding Intuition in Organizational Decision Making", edited by Julian Marewski and Ulrich Hoffrage (2015). The issue contained 17 articles - all are open access and can be downloaded at http://www.sciencedirect.com/science/journal/22113681/4/3. In the present article, we will give an overview of our introduction to this special issue. We will focus on those parts (and on the discussion of those papers in the special issue) that are related to Brunswikian approaches. There are three such links to the work of Egon Brunswik: (1) the conceptualization of intuition as inference, (2) the notion of quasirationality, and (3) the methodological imperative of using a representative design when studying intuition where it can be been built, namely in natural environments.
Hoffrage U. & Marewski J. N. (2015). Unveiling the Lady in Black: Modeling and aiding intuition. Journal of Applied Research in Memory and Cognition, 4(3), 145-163. [doi] [web of science] [abstract]
The cognitive and decision science literature on modeling and aiding intuitions in organizations is rich, but segregated. This special issue offers a sample of that literature, stimulating exchange and inspiring intuitions about intuition. A total of 16 articles bring together diverse approaches, such as naturalistic-decision-making, heuristics-and-biases, dual-processes, ACT-R, CLARION, Brunswikian, and Quantum-Probability-Theory, many of them co-authored by their founders. The articles cover computational models and verbal theories; experimental and observational work; laboratory and naturalistic research. Comprising various domains, such as consulting, investment, law, police, and morality, the articles relate intuition to implicit cognition, emotions, scope insensitivity, expertise, and representative experimental design. In this article, we map intuition across poles such as Enlightenment/Romanticism, reason/emotion, objectivity/subjectivity, inferences/qualia, Taylorism/universal scholarship, System 2/System 1, dichotomies/dialectics, and science/art. We discuss intuitions as inspirations, instincts, inferences, and insights. Finally, we review the contributions to this special issue, placing them into historical, philosophical, and societal contexts.
Marewski J. N. & Hoffrage U. (2015). Special issue and call for commentaries: Modeling and aiding intuition in organizational decision making. The Brunswik Society Newsletter, 30, 28-31. [abstract]
With the present contribution to the Brunswik Society Newsletter, we like to draw attention to a special issue on "Modeling and Aiding Intuition in Organizational Decision Making" (Marewski & Hoffrage, 2015) that recently appeared in the Journal of Applied Research in Memory and Cognition (JARMAC); http://www.sciencedirect.com/science/journal/22113681/4/3), and we solicit commentaries on the articles and opinion pieces published in this issue.
Marewski J. N. & Hoffrage U. (2015). Modeling and aiding intuition in organizational decision making [Special Issue]. Journal of Applied Research in Memory and Cognition, 4(3), 145-312.
Olds J. & Marewski J. N. (2015). Commentary on "The Cognitive-Emotional Brain: From Interactions to Integration" by Luiz Pessoa - "On theory integration: Toward developing affective components within cognitive architectures". Behavioral and Brain Sciences, 38, 32-33. [doi] [abstract]
In The Cognitive-Emotional Brain, Pessoa (2013) suggests that cognition and emotion should not be considered separately. We agree with this and argue that cognitive architectures can provide steady ground for this kind of theory integration and for investigating interactions among underlying cognitive processes. We briefly explore how affective components can be implemented and how neuroimaging measures can help validate models and influence theory development.
Dietz J., Antonakis J., Hoffrage U., Krings F., Marewski J. N. & Zehnder C. (2014). Teaching evidence-based management with a focus on producing local evidence. Academy of Management Learning and Education, 13(3), 397-414. [doi] [pdf] [abstract]
We present an approach to teaching evidence-based management (EBMgt) that trains future managers how to produce local evidence. Local evidence is causally interpretable data, collected on-site in companies to address a specific business problem. Our teaching method is a variant of problem-based learning, a method originally developed to teach evidence-based medicine. Following this method, students learn an evidence-based problem-solving cycle for addressing actual business cases. Executing this cycle, students use and produce scientific evidence through literature searches and the design of local, experimental tests of causal hypotheses. We argue the value of teaching EBMgt with a focus on producing local evidence, how it can be taught, and what can be taught. We conclude by outlining our contribution to the literature on teaching EBMgt and by discussing limitations of our approach.
Gaschler R., Marewski J. N., Wenke D. & Frensch P. A. (2014). Transferring control demands across incidental learning tasks - Stronger sequence usage in serial reaction task after shortcut option in letter string checking. Frontiers in Psychology (section Cognition), 5, 1-11. [doi] [url] [abstract]
After incidentally learning about a hidden regularity, participants can either continue to solve the task as instructed or, alternatively, apply a shortcut. Past research suggests that the amount of conflict implied by adopting a shortcut seems to bias the decision for vs. against continuing instruction-coherent task processing. We explored whether this decision might transfer from one incidental learning task to the next. Theories that conceptualize strategy change in incidental learning as a learning-plus-decision phenomenon suggest that high demands to adhere to instruction-coherent task processing in Task 1 will impede shortcut usage in Task 2, whereas low control demands will foster it. We sequentially applied two established incidental learning tasks differing in stimuli, responses and hidden regularity (the alphabet verification task followed by the serial reaction task, SRT). While some participants experienced a complete redundancy in the task material of the alphabet verification task (low demands to adhere to instructions), for others the redundancy was only partial. Thus, shortcut application would have led to errors (high demands to follow instructions). The low control demand condition showed the strongest usage of the fixed and repeating sequence of responses in the SRT. The transfer results are in line with the learning-plus-decision view of strategy change in incidental learning, rather than with resource theories of self-control.
Marewski J. N., Bröder A. & Glöckner A. (2014). Call for papers: Strategy selection: A theoretical and methodological challenge. Journal of Behavioral Decision Making (Special Issue: Strategy Selection), NA. [url]
Marewski J. N. & Link D. (2014). Strategy selection: An introduction to the modeling challenge. Wiley Interdisciplinary Reviews: Cognitive Science, 5(1), 39-59. [doi] [abstract]
Modeling the mechanisms that determine how humans and other agents choose among different behavioral and cognitive processes-be they strategies, routines, actions, or operators-represents a paramount theoretical stumbling block across disciplines, ranging from the cognitive and decision sciences to economics, biology, and machine learning. By using the cognitive and decision sciences as a case study, we provide an introduction to what is also known as the strategy selection problem. First, we explain why many researchers assume humans and other animals to come equipped with a repertoire of behavioral and cognitive processes. Second, we expose three descriptive, predictive, and prescriptive challenges that are common to all disciplines which aim to model the choice among these processes. Third, we give an overview of different approaches to strategy selection. These include cost‐benefit, ecological, learning, memory, unified, connectionist, sequential sampling, and maximization approaches. We conclude by pointing to opportunities for future research and by stressing that the selection problem is far from being resolved.
Marewski J. N. & Hoffrage U. (2013). Processes models, environmental analyses, and cognitive architectures: Quo vadis quantum probability theory? (Commentary on Pothos and Busemeyer). Behavioral and Brain Sciences, 36, 297-298. [doi] [abstract]
A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures.
Hicks J. S., Burgman M. A., Marewski J. N., Fidler F. & Gigerenzer G. (2012). Decision making in a human population living sustainably. Conservation Biology, 26, 760-768. [doi] [abstract]
The Tiwi people of northern Australia have managed natural resources continuously for 6000-8000 years. Tiwi management objectives and outcomes may reflect how they gather information about the environment. We qualitatively analyzed Tiwi documents and management techniques to examine the relation between the social and physical environment of decision makers and their decision-making strategies. We hypothesized that principles of bounded rationality, namely, the use of efficient rules to navigate complex decision problems, explain how Tiwi managers use simple decision strategies (i.e., heuristics) to make robust decisions. Tiwi natural resource managers reduced complexity in decision making through a process that gathers incomplete and uncertain information to quickly guide decisions toward effective outcomes. They used management feedback to validate decisions through an information loop that resulted in long-term sustainability of environmental use. We examined the Tiwi decision-making processes relative to management of barramundi (Lates calcarifer) fisheries and contrasted their management with the state government's management of barramundi. Decisions that enhanced the status of individual people and their attainment of aspiration levels resulted in reliable resource availability for Tiwi consumers. Different decision processes adopted by the state for management of barramundi may not secure similarly sustainable outcomes.
Marewski J. N. & Gigerenzer G. (2012). Heuristic decision making in medicine. Dialogues in Clinical Neuroscience, 14, 77-89.
Marewski J. N. & Hoffrage U. (2012). Call for papers: Modeling and aiding intuitions in organizational decision making. Journal of Applied Research in Memory and Cognition, 1(4), 267-268. [url]
Gaissmaier W. & Marewski J. N. (2011). Forecasting elections with mere recognition from small, lousy samples: A comparison of collective recognition, wisdom of crowds, and representative polls. Judgment and Decision Making, 6, 73-88.
Marewski J. N. & Mehlhorn K. (2011). Using the ACT-R architecture to specify 39 quantitative process models of decision making. Judgment and Decision Making, 6, 439-519.
Marewski J. N., Pohl R. F. & Vitouch O. (2011). Recognition-based judgments and decisions: What we have learned (so far). Judgment and Decision Making, 6, 359-380.
Marewski J. N., Pohl R. F. & Vitouch O. (2011). Recognition-based judgments and decisions: Introduction to the special issue (II). Judgment and Decision Making, 6, 1-6.
Marewski J. N. & Schooler L. J. (2011). Cognitive niches: An ecological model of strategy selection. Psychological Review, 118(3), 393-437. [doi]
Tomlinson T., Marewski J. N. & Dougherty M. (2011). Four challenges for cognitive research on the recognition heuristic and a call for a research strategy shift. Judgment and Decision Making, 6, 89-99.
Marewski J. N. (2010). On the theoretical precision and strategy selection problem of a single-strategy approach: A comment on Glöckner, Betsch, and Schindler. Journal of Behavioral Decision Making, 23, 463-467. [doi] [abstract]
According to the fast and frugal heuristics (FFH) program, humans make decisions by selecting from a repertoire of strategies. Glöckner, Betsch, and Schindler criticize such multi-strategy frameworks for not explaining how people select between the different strategies. As an alternative, they propose a parallel constraint satisfaction (PCS) model that assumes a single strategy for all tasks. However, contrary to Glöckner et al.'s assertions, the FFH and other multi-strategy frameworks have developed a number of approaches to strategy selection, tackling a difficult modeling problem that the PCS model disguises but cannot solve itself. Moreover, in contrast to the PCS model, which has not been completely spelled out, the repertoire of strategies assumed by the FFH framework is precisely defined, enabling researchers to make quantitative predictions about behavior. Copyright © 2010 John Wiley & Sons, Ltd.
Marewski J. N., Gaissmaier W. & Gigerenzer G. (2010). Good judgments do not require complex cognition. Cognitive Processing, 11(2), 103-121. [doi] [abstract]
What cognitive capabilities allow Homo sapiens to successfully bet on the stock market, to catch balls in baseball games, to accurately predict the outcomes of political elections, or to correctly decide whether a patient needs to be allocated to the coronary care unit? It is a widespread belief in psychology and beyond that complex judgment tasks require complex solutions. Countering this common intuition, in this article, we argue that in an uncertain world actually the opposite is true: Humans do not need complex cognitive strategies to make good inferences, estimations, and other judgments; rather, it is the very simplicity and robustness of our cognitive repertoire that makes Homo sapiens a capable decision maker.
Marewski J. N., Gaissmaier W. & Gigerenzer G. (2010). We favor formal models of heuristics rather than lists of loose dichotomies: A reply to Evans and Over. Cognitive Processing, 11(2), 177-179. [doi] [abstract]
In their comment on Marewski et al. (good judgments do not require complex cognition, 2009) Evans and Over (heuristic thinking and human intelligence: a commentary on Marewski, Gaissmaier and Gigerenzer, 2009) conjectured that heuristics can often lead to biases and are not error free. This is a most surprising critique. The computational models of heuristics we have tested allow for quantitative predictions of how many errors a given heuristic will make, and we and others have measured the amount of error by analysis, computer simulation, and experiment. This is clear progress over simply giving heuristics labels, such as availability, that do not allow for quantitative comparisons of errors. Evans and Over argue that the reason people rely on heuristics is the accuracy-effort trade-off. However, the comparison between heuristics and more effortful strategies, such as multiple regression, has shown that there are many situations in which a heuristic is more accurate with less effort. Finally, we do not see how the fast and frugal heuristics program could benefit from a dual-process framework unless the dual-process framework is made more precise. Instead, the dual-process framework could benefit if its two "black boxes" (Type 1 and Type 2 processes) were substituted by computational models of both heuristics and other processes.
Marewski J. N., Gaissmaier W., Schooler L. J., Goldstein D. G. & Gigerenzer G. (2010). From recognition to decisions: Extending and testing recognition-based models for multialternative inference. Psychonomic Bulletin & Review, 17(3), 287-309. [abstract]
The recognition heuristic is a noncompensatory strategy for inferring which of two alternatives, one recognized and the other not, scores higher on a criterion. According to it, such inferences are based solely on recognition. We generalize this heuristic to tasks with multiple alternatives, proposing a model of how people identify the consideration sets from which they make their final decisions. In doing so, we address concerns about the heuristic's adequacy as a model of behavior: Past experiments have led several authors to conclude that there is no evidence for a noncompensatory use of recognition but clear evidence that recognition is integrated with other information. Surprisingly, however, in no study was this competing hypothesis-the compensatory integration of recognition-formally specified as a computational model. In four studies, we specify five competing models, conducting eight model comparisons. In these model comparisons, the recognition heuristic emerges as the best predictor of people's inferences.
Marewski J. N. & Krol K. (2010). Fast, frugal, & moral: Uncovering the heuristics of morality. Journal of Organizational Moral Psychology, 1(3), 1-20.
Marewski J. N., Pohl R. F. & Vitouch O. (2010). Recognition-based judgments and decisions: Introduction to the special issue (Vol. 1). Judgment and Decision Making, 5(4), 207-215.
Marewski J. N., Schooler L. J. & Gigerenzer G. (2010). Five principles for studying people's use of heuristics. Acta Psychologica Sinica, 42(1), 72-87. [doi] [abstract]
The fast and frugal heuristics framework assumes that people rely on an adaptive toolbox of simple decision strategies-called heuristics-to make inferences, choices, estimations, and other decisions. Each of these heuristics is tuned to regularities in the structure of the task environment and each is capable of exploiting the ways in which basic cognitive capacities work. In doing so, heuristics enable adaptive behavior. In this article, we give an overview of the framework and formulate five principles that should guide the study of people's adaptive toolbox. We emphasize that models of heuristics should be (i) precisely defined; (ii) tested comparatively; (iii) studied in line with theories of strategy selection; (iv) evaluated by how well they predict new data; and (vi) tested in the real world in addition to the laboratory.
Marewski J. N. & Olsson H. (2009). Beyond the null ritual: Formal modeling of psychological processes. Zeitschrift für Psychologie/Journal of Psychology, 217(1), 49-60. [doi] [abstract]
Rituals shape many aspects of our lives, and they are no less common in scientific research than elsewhere. One that figures prominently in hypothesis testing is the null ritual, the pitting of hypotheses against chance. Although known to be problematic, this practice is still widely used. One way to resist the lure of the null ritual is to increase the precision of theories by casting them as formal models. These can be tested against each other, instead of against chance, which in turn enables a researcher to decide between competing theories based on quantitative measures. This article gives an overview of the advantages of modeling, describes research that is based on it, outlines the difficulties associated with model testing, and summarizes some of the solutions for dealing with these difficulties. Pointers to resources for teaching modeling in university classes are provided.
Pachur T., Bröder A. & Marewski J. N. (2008). The recognition heuristic in memory-based inference: Is recognition a non-compensatory cue?. Journal of Behavioral Decision Making, 21, 183-210. [doi] [abstract]
The recognition heuristic makes the strong claim that probabilistic inferences in which a recognized object is compared to an unrecognized one are made solely on the basis of whether the objects are recognized or not, ignoring all other available cues. This claim has been seriously challenged by a number of studies that have shown a clear effect of additional cue knowledge. In most of these studies, either recognition knowledge was acquired during the experiment, and/or additional cues were provided to participants. However, the recognition heuristic is more likely to be a tool for exploiting natural (rather than induced) recognition when inferences have to be made from memory. In our study on natural recognition and inferences from memory, around 85% of the inferences followed recognition information even when participants had learned three cues that contradicted recognition and when some of the contradictory cues were deemed more valid than recognition. Nevertheless, there were strong individual differences in the use of recognition. Whereas about half of the participants chose the recognized object regardless of the number of conflicting cues-suggestive of the hypothesized noncompensatory processing of recognition-the remaining participants were influenced by the additional knowledge. The former group of participants also tended to give higher estimates of recognition's validity. In addition, we found that the use of recognition for an inference may be affected by whether additional cue knowledge has been learned outside or within the experimental setting. Copyright © 2007 John Wiley & Sons, Ltd.
Parties de livre
Chapitre Olds J. M. & Marewski J. N. (in press). Heuristics for Memory and Decision Making. In Miller H. L. (Ed.), The SAGE Encyclopedia of Theory in Psychology. SAGE.
Marewski J. N. & Gigerenzer G. (2013). Entscheiden [Decision Making]. In Sarges W. (Ed.), Management-Diagnostik [Management diagnostics] (4th edition, pp. 228-240). Hogrefe, Göttingen, Germany.
Pachur T., Bröder A. & Marewski J. N. (2011). The recognition heuristic in memory-based inference: Is recognition a non-compensatory cue?. In Gigerenzer G., Hertwig R. & Pachur T. (Eds.), Heuristics: The foundations of adaptive behavior (pp. 503-522). Oxford University Press, New York.
Marewski J. N. & Krol K. (2010). Modelle ökologischer Rationalität: Auf dem Weg zu einer Theorie der Moralheuristiken [Models of ecological rationality: Towards studying the heuristics of morality]. In Iorio M. & Reisenzein R. (Eds.), Regel, Norm, Gesetz: Eine interdisziplinäre Bestandsaufnahme [Rules, norms, and laws: An interdisciplinary review] (pp. 231-256). Lang, Frankfurt am Main, Germany.
Marewski J. N., Galesic M. & Gigerenzer G. (2009). Fast and frugal media choices. In Hartmann T. (Ed.), Media choice: A theoretical and empirical overview (pp. 102-127). Routledge, New York & London.
Actes de conférence (partie) Link D. & Marewski J. N. (2015). Populating ACT-R's Declarative Memory with Internet Statistics. In Taatgen N. A., van Vugt M. K., Borst J. P. & Mehlhorn K. (Eds.), Proceedings of the 13th International Conference on Cognitive Modeling (ICCM) (pp. 69-70). University of Groningen, the Netherlands. [url] [abstract]
Decision situations are often characterized by uncertainty: we do not know the values of the different options on all attributes and have to rely on information stored in our memory to decide. Several strategies have been proposed to describe how people make inferences based on knowledge used as cues. The present research shows how declarative memory of ACT-R models could be populated based on internet statistics. This will allow to simulate the performance of decision strategies operating on declarative knowledge based on occurrences and co-occurrences of objects and cues in the environment.
Dimov C., Marewski J. N. & Schooler L. J. (2013). Constraining ACT-R models of decision strategies: An experimental paradigm. In Knauff M., Pauen M., Sebanz N. & Wachsmuth I. (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 2201-2206). Austin, TX: Cognitive Science Society. [url] [url] [abstract]
It has been repeatedly debated which strategies people rely on in inference. These debates have been difficult to resolve, partially because hypotheses about the decision processes assumed by these strategies have typically been formulated qualitatively, making it hard to test precise quantitative predictions about response times and other behavioral data. One way to increase the precision of strategies is to implement them in cognitive architectures such as ACT-R. Often, however, a given strategy can be implemented in several ways, with each implementation yielding different behavioral predictions. We present and report a study with an experimental paradigm that can help to identify the correct implementations of classic compensatory and non-compensatory strategies such as the take-the-best and tallying heuristics, and the weighted-linear model.
Mehlhorn K. & Marewski J. N. (2011). Using a cognitive architecture to specify and test process models of decision making. In Özyurt J., Anschütz A., Bernholt S. & Lenk J. (Eds.), Interdisciplinary Perspectives on Cognition, Education and the Brain (pp. 77-85). BIS-Verlag, Oldenburg, Germany.
Mehlhorn K. & Marewski J. N. (2011). Racing for the city: The recognition heuristic and compensatory alternatives. In Carlson L., Hölscher C. & Shipley T. (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 360-365). Austin, TX: Cognitive Science Society.
Coenen A. & Marewski J. N. (2009). Predicting moral judgments of corporate responsibility with formal decision heuristics. In Taatgen N. A. & van Rijn H. (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 1524-1528). Austin, TX: Cognitive Science Society.
Marewski J. N., Gaissmaier W., Schooler L. J., Goldstein D. G. & Gigerenzer G. (2009). Do voters use episodic knowledge to rely on recognition?. In Taatgen N. A. & van Rijn H. (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2232-2237). Austin, TX: Cognitive Science Society.
Van Maanen L. & Marewski J. N. (2009). Recommender systems for literature selection: A competition between decision making and memory models. In Taatgen N. A. & van Rijn H. (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2914-2919). Austin, TX: Cognitive Science Society.
Marewski J. N. & Schooler L. J. (2008). How memory aids strategy selection. The Brunswik Society Newsletter, 23 (pp. 27-28).
Marewski J. N., Gaissmaier W., Dieckmann A., Schooler L. J. & Gigerenzer G. (2005). Is ignorance useful and used? Applying the recognition heuristic to political elections. The Brunswik Society Newsletter, 20 (pp. 12-13).
Marewski J. N., Gaissmaier W., Dieckmann A., Schooler L. J. & Gigerenzer G. (2005). Don't vote against the recognition heuristic. In Bara B. G., Barsalou L. & Bucciarelli M. (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 2524). Erlbaum, Mahwah, NJ.
Rapports Powalla C., Bresser R. K. F. & Marewski J. N. (2009). Performance forecasts in uncertain environments: Comparing the VRIO-framework with the recognition heuristic and analyst ratings. Discussion Papers in Strategic Management, in R. K. F. Bresser & T. Mellewigt, Institute for Management, Free University Berlin, Germany.
Thèses Marewski J. N., Gigerenzer G. & Schooler L. J. (Dir.) (2009). Ecologically rational strategy selection. Free University, Berlin.