This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, single-cell (...) recording, and electroencephalography. What sets computational cognitive neuroscience apart, however, is the explanatory role of analytic techniques from disciplines as varied as computer science, statistics, machine learning, and mathematical physics. These techniques serve to describe neural mechanisms computationally, but also to drive the process of scientific discovery by influencing which kinds of mechanisms are most likely to be identified. For this reason, understanding the nature and unique appeal of computational cognitive neuroscience requires not just an understanding of the basic research strategies that are involved, but also of the formal methods and tools that are being deployed, including those of probability theory, dynamical systems theory, and graph theory. (shrink)

Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...) are often deployed unsystematically, Bayesian reverse-engineering avoids several important worries that have been raised about the explanatory credentials of Bayesian cognitive science: the worry that the lower levels of analysis are being ignored altogether; the challenge that the mathematical models being developed are unfalsifiable; and the charge that the terms ‘optimal’ and ‘rational’ have lost their customary normative force. But while Bayesian reverse-engineering is therefore a viable and productive research strategy, it is also no fool-proof recipe for explanatory success. (shrink)

According to Marr, explanations of perceptual behavior should address multiple levels of analysis. Rahnev & Denison (R&D) are perhaps overly dismissive of optimality considerations at the computational level. Also, an exclusive reliance on standard observer models may cause neglect of many other plausible hypotheses at the algorithmic level. Therefore, as far as explanation goes, standard observer modeling is no panacea.

The information age can be dated to the work of Norbert Wiener and Claude Shannon in the 1940s. Their work on cybernetics and information theory, and many subsequent developments, had a profound influence on reshaping the field of psychology from what it was prior to the 1950s. Contemporaneously, advances also occurred in experimental design and inferential statistical testing stemming from the work of Ronald Fisher, Jerzy Neyman, and Egon Pearson. These interdisciplinary advances from outside of psychology provided the conceptual and (...) methodological tools for what is often called the cognitive revolution but is more accurately described as the information-processing revolution. Cybernetics set the stage with the idea that everything ranging from neurophysiological mechanisms to societal activities can be modeled as structured control systems with feedforward and feedback loops. Information theory offered a way to quantify entropy and information, and promoted theorizing in terms of information flow. Statistical theory provided means for making scientific inferences from the results of controlled experiments and for conceptualizing human decision making. With those three pillars, a cognitive psychology adapted to the information age evolved. The growth of technology in the information age has resulted in human lives being increasingly interweaved with the cyber environment, making cognitive psychology an essential part of interdisciplinary research on such interweaving. Continued engagement in interdisciplinary research at the forefront of technology development provides a chance for psychologists not only to refine their theories but also to play a major role in the advent of a new age of science. (shrink)

In response to the increasingly acknowledged power of metaphor upon everyday and scientific thinking, the present essay aims to revitalize the metaphoric process in commonsense psychology from the interaction view perspective. As prerequisites, a historical review of the "man-the-scientist" metaphor inherited in commonsense psychology, and a situation analysis of its dormant state are attempted. With metaphorical imagination, a holistic-paradigmatic view of personal theories is postulated on the basis of new knowledge in the philosophy and history of science, namely, the Duhem (...) - Quine (holism) thesis and Kuhn's paradigmatic perspective of scientific activities. To optimize the metaphoric process, deliberations are made on the similarities as well as the differences between the principal subject (i.e. everyday activities) and the subsidiary subject (i.e. scientific activities). It is shown that the "man-the-scientist" metaphoric process can be far-reaching and dialectic in nature, resulting in fresh perspectives for understanding both everyday and scientific activities. The focus of the present essay lies in elaborating a more sophisticated view on personal theories, and in calling for a non-reductionist approach to the investigation of commonsense psychology. (shrink)

Glenberg's account falls short in several respects. Besides requiring clearer explication of basic concepts, his account fails to recognize the autonomous nature of perception. His account of what is remembered, and its description, is too static. His strictures against connectionist modeling might be overcome by combining the notions of psychological space and principled learning in an embodied and situated network.

In this response, we clarify several misunderstandings of the understanding/acceptance principle and defend our specific operationalization of that principle. We reiterate the importance of addressing the problem of rational task construal and we elaborate the notion of computational limitations contained in our target article. Our concept of thinking dispositions as variable intentional-level styles of epistemic and behavioral regulation is explained, as is its relation to the rationality debate. Many of the suggestions of the commentators for elaborating two-process models are easily (...) integrated into our generic dual-process account. We further explicate how we view the relation between System 1 and System 2 and evolutionary and normative rationality. We clarify our attempt to fuse the contributions of the cognitive ecologists with the insights of the original heuristics and biases researchers. (shrink)

A major, but too-little-considered problem for Software Engineering is a lack of consensus concerning Computer Science and how this relates to developing unpredictable computing technology. We consider some implications for SE of computer systems differing scientific basis, exemplified with the International Standard Organisations Open Systems Interconnection layered architectural model. An architectural view allows comparison of computing technology components facilitating a view of computing as a continuum. For example, at one layer of computer architecture, components written in Turing-complete machine language can (...) be seen as deterministic and consistent with a theoretical paradigm of CS. At another layer, components closer to the human sphere have been seen as non-deterministic and inconsistent with theoretical CS. We compare unpredictable development of computing technology against the cyclic legacy of technological advance and scientific discovery, and suggest that SE indicates an enabling cycle, discernible in previous scientific revolution, is stalled or possibly hidden. The CS consequence of divorcing technological advance from scientific consensus is particularly concerning. For example human/computing events could be seen as unpredictable virtual phenomena that somehow extend the ontology of CS. Our approach challenges practical and philosophical boundaries by investigating if applying scientific method resolves any SE/Science dichotomy. (shrink)

The analytical notions of ‘thought style’, ‘paradigm’, ‘episteme’ and ‘style of reasoning’ are some of the most popular frameworks in the history and philosophy of science. Although their proponents, Ludwik Fleck, Thomas Kuhn, Michel Foucault, and Ian Hacking, are all part of the same philosophical tradition that closely connects history and philosophy, the extent to which they share similar assumptions and objectives is still under debate. In the first part of the paper, I shall argue that, despite the fact that (...) these four thinkers disagree on certain assumptions, their frameworks have the same explanatory goal – to understand how objectivity is possible. I shall present this goal as a necessary element of a common project -- that of historicising Kant's a priori. In the second part of the paper, I shall make an instrumental use of the insights of these four thinkers to form a new model for studying objectivity. I shall also propose a layered diagram that allows the differences between the frameworks to be mapped, while acknowledging their similarities. This diagram will show that the frameworks of style of reasoning and episteme illuminate conditions of possibility that lie at a deeper level than those considered by thought styles and paradigm. (shrink)

We live, according to some, in the century of biology, where we now understand ourselves in radically new ways as the insights of genomics and neuroscience have opened up the workings of our bodies and our minds to new kinds of knowledge and intervention. Is a new figure of the human, and of the social, taking shape in the 21st century? With what consequences for the politics of life today? And with what implications, if any, for the social, cultural and (...) human sciences? These are the issues that are discussed in this article, which argues that a new relation is requred with the life sciences, beyond commentary and critique, if the social and human sciences are to revitalize themselves for the 21st century. (shrink)

Although radical forms of relativism are perhaps beyond the epistemological pale, I argue here that a more moderate form may be plausible, and articulate the conditions under which moderate epistemic relativism could well serve our epistemic goals. In particular, as a result of our limitations as human cognizers, we find ourselves needing to investigate the dappled and difficult world by means of competing communities of highly specialized researchers. We would do well, I argue, to admit of the existence of unresolvable (...) disputes between such communities, but only so long as there is a sufficient amount of fruitful exchange between them as well. I close with some speculation about when it is or is not legitimate to make an “appeal to discipline”: responding to another’s argument by saying something like, “we should do it this way, because we are philosophers (/linguists/psychologists/…), and that’s just what we do”. (shrink)

This study presents a theory of causally complex configurations of antecedent conditions influencing the adoption versus non-adoption of international supplier ethical certification-standards. Using objective measures of antecedents and outcomes, a large-scale study of exporting firms in the cut-flower industry in two South American countries supports the theory. The theory includes the following and additional propositions. No single -antecedent condition is sufficient for accurately predicting a high membership score in outcome conditions; the outcome conditions include a firm’s adoption or rejection of (...) a product certification. No single -antecedent condition is necessary for accurately predicting high scores in the outcome condition. A few complex antecedent conditions are sufficient but the occurrence of each is not necessary for accurately predicting high scores in an outcome condition. Causal asymmetry of antecedent conditions indicating adoption versus non-adoption of specific ethical standards occurs—that is, causal conditions leading to rejection are not the mirror opposites of causal conditions leading to adoption. (shrink)

This article offers a historical analysis of the relationship between the practice of participant-observation among American sociologists and Erving Goffman’s dramaturgical model of the self. He was a social scientist who privileged ethnography in the field over the laboratory experiment, the survey questionnaire, or the mental test. His goal was a natural history of communication among humans. Rather than rely upon standardizing technologies for measurement, Goffman tried to obtain accurate recordings of human behavior through secretive observations. During the 1950s, he (...) conducted three major studies as a participant-observer, disguised from those studied through insincere performances. As originally presented, his dramaturgical theory did not draw upon the theater as the governing metaphor, but rather the confidence game. It is suggested that Goffman’s writings exemplify what Gerd Gigerenzer calls the tools-to-theories heuristic. Goffman’s depiction of the confidence man’s behavior closely mirrored how he and his fellow sociologists described the practice of participant-observation. Both were represented as embedded and attentive yet coolly detached observers skilled at playing different roles as the situation necessitated. The similarities between his own professional behavior and the activities of the confidence man may have suggested to Goffman the latter as a model for human nature. (shrink)

In this article, I show the role that the philosopher of cognitive science can cur-rently play in cognitive science research. I argue for the important, and not yet considered, role of the philosophy of cognitive science in cognitive science, that is, the importance of cooperation between philosophers of science with cogni-tive scientists in investigating the research methods and theoretical assump-tions of cognitive science. At the beginning of the paper I point out, how the philosopher of science, here, the philosopher of (...) cognitive science, can participate in interdisciplinary research. I am opting of the cooperation in investigating the so-called reflective problems. Then, I discuss four examples of issues important for the cognitive science, in which the competences possessed by the philosopher are useful. At the ending I point out wider landscape of possible cooperation of philosophers with cognitive scientists. (shrink)

The paper locates, appreciates, and extends several dimensions of Simon’s work in the direction of more recent contributions by people such as Gigerenzer and Dennett. The author’s “crowbar model of method” is compared to Simon’s scissors metaphor. Against an evolutionary background, both support a pragmatic rather than strong realist approach to theoretically deep and complex problems. The importance of implicit knowledge is emphasized, for humans, as well as nonhuman animals. Although Simon was a realist in some respects, his work on (...) bounded rationality, satisficing, problem solving, heuristics, models, and scientific discovery mark him as a pragmatist. Indeed, he should be regarded as one of the great American pragmatists, alongside Peirce, James, Dewey, and a few others. (shrink)

Cognitive relativists‐pragmatists (Stich, Churchland) claim that human cognitive strategies, lacking a common goal, are in addition divergent to the point of incommensurability. They appeal to the study of reasoning heuristics for evidence on cognitive diversity and incorrigibility. It is here argued that no such evidence is offered by the research, which, on the contrary (1) presents heuristics as uniform across great variations; (2) offers advice for correcting and improving human reasoning; and (3) very often postulates a uniformity of core logical (...) strategies, built into reasoning competence. Cognitive research thus supports a moderate rationalism rather than relativism‐pragmatism. (shrink)

Le psychologue français Alfred Binet est à l'origine du développement de tests mentaux destinés à diagnostiquer le « niveau intellectuel » des enfants. Initialement conçus comme des tests cliniques, leur importation aux États-Unis dans les années 1910 a considérablement modifié leur usage, leur portée pratique et leur interprétation. Devenus les instruments de politiques eugéniques ou héréditaristes, utilisés dans des processus de sélection de grande échelle, les tests ont transformé la conception que les psychologues se faisaient de l'intelligence: initialement conçue comme (...) multiforme et qualitative, elle est devenue une grandeur unique quantifiable. C'est dans ce contexte que le psychologue américain Louis Léon Thurstone, critiquant le manque de rigueur des méthodes statistiques utilisées dans les tests, a développé une nouvelle méthode permettant de disposer d'une unité et d'un zéro absolu pour la mesure (échelonnement) de l'intelligence. Une fois objectivée, sa méthode est devenue une « loi » générale permettant de rendre compte de tous les processus mentaux, de disposer d'un outil d'analyse des opinions et des attitudes. (shrink)

Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same set of (...) principles, we observe systematic equivocations in PP‐based models, or outright contradictions with its avowed principles. To make matters worse, PP‐based models are seldom empirically validated, and they are frequently offered as mere just‐so stories. The large number of PP‐based models is thus not evidence of theoretical progress in unifying perception, action, and cognition. On the contrary, we maintain that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory. Thus, we urge the defenders of PP to focus on its critical problems instead of offering mere re‐descriptions of known phenomena, and to validate their models against possible alternative explanations that stem from different theoretical assumptions. Otherwise, PP will ultimately fail as a unified theory of cognition. (shrink)

A central question in many debates on paternalism is whether a decision analyst can ever go against the stated preference of a client, even if merely intending to improve the decisions for the client. Using four gedanken-experiments, this paper shows that this central question, so cleverly and aptly avoided by libertarian paternalism (nudge), cannot always be avoided. The four thought experiments, while purely hypothetical, serve to raise and specify the critical arguments in a maximally clear and pure manner. The first (...) purpose of the paper is, accordingly, to provide a litmus test on the readers’ stance on paternalism. We thus also survey and organize the various stances in the literature. The secondary purpose of this paper is to argue that paternalism cannot always be avoided and consumer sovereignty cannot always be respected. However, this argument will remain controversial. (shrink)

Psychologists' work on conversational pragmatics and judgment suggests a refreshing approach to charitable interpretation and theorizing. This charitable approach—what I call Gricean charity —recognizes the role of conversational assumptions and norms in subject-experimenter communication. In this paper, I outline the methodological lessons Gricean charity gleans from psychologists' work in conversational pragmatics. In particular, Gricean charity imposes specific evidential standards requiring that researchers collect empirical information about (1) the conditions of successful and unsuccessful communication for specific experimental contexts, and (2) the (...) conversational norms governing communication in experimental contexts. More generally, the Gricean turn in psychological research shifts focus from attributional to reflexive, situational explanations. Gricean charity does not primarily seek to rationalize subject responses. Rather, it imposes evidential requirements on psychological studies for the purpose of gaining a more accurate picture of the surprising and muddled ways in which we weigh evidence and draw. Key Words: Gricean charity • methodological rationalism • interpretation • principle of charity • cognitive psychology • conversational pragmatics • heuristics and biases • reflexive analysis. (shrink)

Latent variable modeling has revealed important conundrums in the DSM classification system. We agree that the network perspective has potential to inspire new insights and resolve some of these conundrums. We note, however, that alone it cannot really help us understand etiology. Etiology, not comorbidity, is the fundamental question.

Research on the human microbiome has generated a staggering amount of sequence data, revealing variation in microbial diversity at the community, species (or phylotype), and genomic levels. In order to make this complexity more manageable and easier to interpret, new units—the metagenome, core microbiome, and enterotype—have been introduced in the scientific literature. Here, I argue that analytical tools and exploratory statistical methods, coupled with a translational imperative, are the primary drivers of this new ontology. By reducing the dimensionality of variation (...) in the human microbiome, these new units render it more tractable and easier to interpret, and hence serve an important heuristic role. Nonetheless, there are several reasons to be cautious about these new categories prematurely ‘‘hardening’’ into natural units: a lack of constraints on what can be sequenced metagenomically, freedom of choice in taxonomic level in defining a ‘‘core microbiome,’’ typological framing of some of the concepts, and possible reification of statistical constructs. Finally, lessons from the Human Genome Project have led to a translational imperative: a drive to derive results from the exploration of microbiome variation that can help to articulate the emerging paradigm of personalized genomic medicine (PGM). There is a tension between the typologizing inherent in much of this research and the personal in PGM. (shrink)

Research on the human microbiome has gen- erated a staggering amount of sequence data, revealing variation in microbial diversity at the community, species (or phylotype), and genomic levels. In order to make this complexity more manageable and easier to interpret, new units—the metagenome, core microbiome, and entero- type—have been introduced in the scientific literature. Here, I argue that analytical tools and exploratory statisti- cal methods, coupled with a translational imperative, are the primary drivers of this new ontology. By reducing the (...) dimensionality of variation in the human microbiome, these new units render it more tractable and easier to interpret, and hence serve an important heuristic role. Nonetheless, there are several reasons to be cautious about these new categories prematurely ‘‘hardening’’ into natural units: a lack of constraints on what can be sequenced metagenomically, freedom of choice in taxonomic level in defining a ‘‘core microbiome,’’ typological framing of some of the concepts, and possible reification of statistical constructs. Finally, lessons from the Human Genome Project have led to a translational imperative: a drive to derive results from the exploration of microbiome variation that can help to articulate the emerging paradigm of per- sonalized genomic medicine (PGM). There is a tension between the typologizing inherent in much of this research and the personal in PGM. (shrink)

[Correction Notice: An erratum for this article was reported in Vol 109 of Psychological Review. Due to circumstances that were beyond the control of the authors, the studies reported in "Models of Ecological Rationality: The Recognition Heuristic," by Daniel G. Goldstein and Gerd Gigerenzer overlap with studies reported in "The Recognition Heuristic: How Ignorance Makes Us Smart," by the same authors and with studies reported in "Inference From Ignorance: The Recognition Heuristic". In addition, Figure 3 in the Psychological Review article (...) was originally published in the book chapter and should have carried a note saying that it was used by permission of Oxford University Press.] One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counter-intuitive less-is-more effect in which less knowledge is better than more for making accurate inferences. (shrink)

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (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)

Unlike behaviorism, cognitive psychology relies on mental concepts to explain behavior. Yet mental processes are not directly observable and multiple explanations are possible, which poses a challenge for finding a useful framework. In this article, I distinguish three new frameworks for explanations that emerged after the cognitive revolution. The first is called tools‐to‐theories: Psychologists' new tools for data analysis, such as computers and statistics, are turned into theories of mind. The second proposes as‐if theories: Expected utility theory and Bayesian statistics (...) are turned into theories of mind, describing an optimal solution of a problem but not its psychological process. The third studies the adaptive toolbox (formal models of heuristics) that describes mental processes in situations of uncertainty where an optimal solution is unknown. Depending on which framework researchers choose, they will model behavior in either situations of risk or of uncertainty, and construct models of cognitive processes or not. The frameworks also determine what questions are asked and what kind of data are generated. What all three frameworks have in common, however, is a clear preference for formal models rather than explanations by general dichotomies or mere verbal concepts. The frameworks have considerable potential to inform each other and to generate points of integration. (shrink)

The ArgumentScientific tools—measurement and calculation instruments, techniques of inference—straddle the line between the context of discovery and the context of justification. In discovery, new scientific tools suggest new theoretical metaphors and concepts; and in justification, these tool-derived theoretical metaphors and concepts are morelikely to be accepted by the scientific community if the tools are already entrenched in scientific practice.Techniques of statistical inference and hypothesis testing entered American psychology first as tools in the 1940s and 1950s and then as cognitive theories (...) in the 1960s and 1970s. Not only did psychologists resists statistical metaphors of mind prior to the institutionalization of inference techniques in their own practice; the cognitive theories they ultimately developed about “the mind as intuitive statistician” still bear the telltale marks of the practical laboratory context in which the tool was used. (shrink)