The introduction to the volume and the overview of the idea of naturalizing the mind.

Naturalism is currently the most vibrantly developing approach to philosophy, with naturalised methodologies being applied across all the philosophical disciplines. One of the areas naturalism has been focussing upon is the mind, traditionally viewed as a topic hard to reconcile with the naturalistic worldview. A number of questions have been pursued in this context. What is the place of the mind in the world? How should we study the mind as a natural phenomenon? What is the significance of cognitive science (...) research for philosophical debates? In this book, philosophical questions about the mind are asked in the context of recent developments in cognitive science, evolutionary theory, psychology, and the project of the naturalisation. Much of the focus is upon what we have learned by studying natural mental mechanisms as well as designing artificial ones. In the case of natural mental mechanisms, this includes consideration of such issues as the significance of deficits in these mechanisms for psychiatry. The significance of the evolutionary context for mental mechanisms as well as questions regarding rationality and wisdom is also explored. Mechanistic and functional models of the mind are used to throw new light on discussions regarding issues of explanation, reduction and the realisation of mental phenomena. Finally, naturalistic approaches are used to look anew at such traditional philosophical issues as the correspondence of mind to world and presuppositions of scientific research. (shrink)

The contributors to this volume engage with issues of normativity within naturalised philosophy. The issues are critical to naturalism as most traditional notions in philosophy, such as knowledge, justification or representation, are said to involve normativity. Some of the contributors pursue the question of the correct place of normativity within a naturalised ontology, with emergentist and eliminativist answers offered on neighbouring pages. Others seek to justify particular norms within a naturalised framework, the more surprising ones including naturalist takes on the (...) a priori and intuitions. Finally, yet others examine concrete examples of the application of norms within particular epistemic endeavours, such as psychopathology and design. The overall picture is that of an intimate engagement with issues of normativity on the part of naturalist philosophers – questioning some of the fundamentals at the same time as they try to work out many of the details. (shrink)

Religious beliefs, it has been noted, are often hard to disprove. While this would be a shortcoming for beliefs whose utility was connected to their accuracy, it is actually necessary in the case of beliefs whose function bears no connection to how accurate they are. In the case of religions and other ideologies that serve to promote prosocial behaviour this leads to the need to protect belief systems against potentially disruptive counterevidence while maintaining their relevance. Religions turn out to be (...) particularly adept at this because of the use they make of existing cognitive by-products to make them plausible without exposingthem overly to investigation. (shrink)

Both science and religion are human endeavours that recruit and modify pre-existing human capacity to engage in epistemic vigilance. However, while science relies upon a focus on content vigilance, religion focusses on source vigilance. This difference is due, in turn, to the function of religious claims not being connected to their accuracy – unlike the function of scientific claims. Understanding this difference helps to understand many aspects of scientific and religious institutions.

"Konrad Talmont-Kaminski offers a very thoughtful and thought-provoking critique of the field and an alternative approach to magic, religion, and science that should spark some debate and further research Talmont-Kaminski has thrown down a challenge to the mainstream of anthropological thought about religion, and it is a challenge that we necessarily and gladly pick up." -- Anthropology Review Database "A philosophical naturalist's delight, this book - crisply written and carefully argued - weaves together insights about (...) evolution, mind, and society to explain how religions work and how secularisation threatens them." - Robert N. McCauley, Emory University Is religious belief a product of the mind? This book brings to light the multifaceted relationship between reason and religion. Religion as Magical Ideology examines the relationship between rationality and supernatural beliefs arguing that such beliefs are products of evolution, cognition and culture. The book does not offer a false rapprochement between reason and religion; instead, it explores their interrelationship as a series of complex adaptations between cognitive and cultural processes. Exploring the nature of the tension between religious traditions and reason, Religion as Magical Ideology develops a dual inheritance theory of religion - which combines the cognitive byproduct and prosocial adaptation accounts - and analyses the connection between the function of a belief and the degree of protection it gets from potential counter-evidence. With discussion ranging from individual cognitive mechanisms, general functional considerations, to the limits of evolutionary and cognitive processes, the book offers readers a systematic account of how cognition shapes religious beliefs and practices. (shrink)

Formal pragmatics plays an important, though secondary, role in modern analytical philosophy of language: its aim is to explain how context can affect the meaning of certain special kinds of utterances. During recent years, the adequacy of formal tools has come under attack, often leading to one or another form of relativism or antirealism. Our aim will be to extend the critique to formal pragmatics while showing that sceptical conclusions can be avoided by developing a different approach to the issues. (...) In particular, we will show that formal pragmatics cannot provide a complete account of how context affects the meaning of utterances, both on its own terms and when faced with evidence of important aspects of natural languages. The focal issue is the relevant kind of context in which pragmatics should examine utterances. Our contention will be that the relevant context of an utterance is determined by the function of that utterance, this function being dependent upon the primary function of language – to convey information. We will argue that the functions of utterances and of language are too broad to be caught by the tools of formal pragmatics of the sort advocated by Montague (1968, 1974), which are an extension the methods of traditional model-theoretic semantics. The particular formal approach we will use as the main example is David Kaplan’s position (1979, 1989), an extension of Montague’s program. (shrink)

McKay & Dennett (M&D) look for adaptive misbeliefs that result from the normal, though fallible, functioning of human cognition. Their account can be substantially improved by the addition of two elements: (1) significance of a belief's testability for its functionality, and (2) an account of reason appropriate to understanding systemic misbelief. Together, these points show why religion probably is an adaptive misbelief.

I will put forward a short, simple argument for a pair of realist claims: metaphysical realism and what I will refer to as epistemological realism. The argument will rely upon nothing more than our apparent memories. Having presented the argument, I will go on to consider possible objections to it, of which there will be a number but none of which will do more than complicate the matter. The argument I present borrows from Peirce’s view that the world’s capacity to (...) surprise us plays a vital role in guiding us toward truth, and from the argument to the best explanation. However, it combines the two views in a way I have not seen done previously. (shrink)

Famously, Pascal described human beings as ‘thinking reeds’, weak in flesh but magnificent in mind. While it is a poetic image, it is also an ambivalent one and may suggest an inappropriately dualist view of human nature. It is important to realise that not only are we thinking reeds but that we are thinking because we are reeds. In fact – while being every bit the marvel that Pascal wondered at – rationality is reed-like itself, very much of a kind (...) with the rest of human nature. (shrink)

Pascal described human beings as ‘thinking reeds’, weak in flesh but magnificent in mind. While it is a poetic image, it is also an ambivalent one and may suggest an inappropriately dualist view of human nature. It is important to realise that not only are we thinking reeds but that we are thinking because we are reeds. In fact, rationality is reed-like itself, very much of a kind with the rest of human nature. It is now more than two and (...) half centuries since David Hume first pointed out the lack of an argument that would fully justify claims about matters of fact. Being neither made evident by our observations nor arising out of the mere considera-tion of relations of ideas, claims such as that the turkey will be fed dinner tomo-rrow – rather than being had for dinner have remained problematic ever since. Many attempts have been made to show that something of the beauty and certainty of reasoning about relations of ideas could be recaptured in our dealings with matters of fact, but all attempts have remained mere shadows of what we tried to grasp. Hume’s argument stands. An infinite being might watch countless sun-sets and yet should witness each new sun-rise with surprise, always withholding its judgement regarding what will follow. (shrink)

Communication is an essentially cooperative activity. However, cooperation only makes sense in a particular kind of environment – one in which cooperation leads to shared benefits. This can be seen once we take Grice’s Cooperative Principle and consider its implications in the general context of game theory. The effect is that something like metaphysical realism underpins normal human discourse, such discourse becoming impossible without that presumption.

To account for a perceived distinction it is necessary to postulate a real distinction. Our process of experiencing the world is one of, mostly unconscious, interpretation of observed distinctions to provide us with a partial world-picture that is sufficient to guide action. The distinctions, themselves, are acorrigible (they do not have a truth value), directly perceived, structured, and capable of being interpreted. Interpreted experience is corrigible, representational and capable of guiding action. Since interpretation is carried out mostly unconsciously and in (...) real time, the two aspects are present in experience together so that it is difficult to separate them out. (shrink)

Sellars’ argument against The Given has set the scene for much of the discussion of the role of experience in justification. Susan Haack tries to avoid the objection presented by Sellars and to give experience a role in the justification of beliefs. Her approach is to put forward a double aspect theory of justification consisting of a logical/evaluative aspect and a causal aspect. Like other double aspect theories, her approach is led astray by the possibility of deviant causal chains. Her (...) argument’s shortcomings, however, only help to underscore the false assumption behind Sellars’ original argument – that justification is purely logical in character. But, rather than arguing that justification is logical and causal, we are led toward to a view that the character of justification, while essentially normative, is much richer than can be modelled by logic. (shrink)

Prof. Wójcicki calls his position ‘radically pragmatist’. I will argue, however, that it is not nearly pragmatic enough. In particular, I will argue that his view is not pragmatist enough in three vital respects - even though it greatly improves upon how these issues have been traditionally dealt with.

In Darwin’s Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett’s analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms by physical (...) processes. For that to be true, the processes have to be part of computational systems. The basic difference between mere simulation and real computing is having proper causal structure. I will show what kind of requirements this poses for natural evolutionary processes if they are to be computational. (shrink)

Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system in terms of predictive coding.

In his recent book, Daniel Dennett defends a novel account of semantic information in terms of design worth getting (Dennett, 2017). While this is an interesting proposal in itself, my purpose in this commentary is to challenge several of Dennett’s claims. First, he argues that semantic information can be transferred without encoding and storing it. Second, this lack of encoding is what makes semantic information unmeasurable. However, the argument for both these claims, presented by Dennett as an intuition pump, is (...) invalid. (shrink)

This paper centers around the notion that internal, mental representations are grounded in structural similarity, i.e., that they are so-called S-representations. We show how S-representations may be causally relevant and argue that they are distinct from mere detectors. First, using the neomechanist theory of explanation and the interventionist account of causal relevance, we provide a precise interpretation of the claim that in S-representations, structural similarity serves as a “fuel of success”, i.e., a relation that is exploitable for the representation using (...) system. Then, we discuss crucial differences between S-representations and indicators or detectors, showing that—contrary to claims made in the literature—there is an important theoretical distinction to be drawn between the two. (shrink)

In this paper, we argue that several recent ‘wide’ perspectives on cognition (embodied, embedded, extended, enactive, and distributed) are only partially relevant to the study of cognition. While these wide accounts override traditional methodological individualism, the study of cognition has already progressed beyond these proposed perspectives towards building integrated explanations of the mechanisms involved, including not only internal submechanisms but also interactions with others, groups, cognitive artifacts, and their environment. The claim is substantiated with reference to recent developments in the (...) study of “mindreading” and debates on emotions. We claim that the current practice in cognitive (neuro)science has undergone, in effect, a silent mechanistic revolution, and has turned from initial binary oppositions and abstract proposals towards the integration of wide perspectives with the rest of the cognitive (neuro)sciences. (shrink)

This paper presents an argument for the realism about mechanisms, contents, and vehicles of mental representation at both the personal and subpersonal levels, and showcases its role in instrumental rationality and proper cognitive functioning. By demonstrating how misrepresentation is necessary for learning from mistakes and explaining certain failures of action, we argue that fallible rational agents must have mental representations with causally relevant vehicles of content. Our argument contributes to ongoing discussions in philosophy of mind and cognitive science by challenging (...) anti-realist views about the nature of mental representation, and by highlighting the importance of understanding how different agents can misrepresent in pursuit of their goals. While there are potential rebuttals to our claim, our opponents must explain how agents can be rational without having mental representations. This is because mental representation is grounded in rationality. (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)

Is the mathematical function being computed by a given physical system determined by the system’s dynamics? This question is at the heart of the indeterminacy of computation phenomenon (Fresco et al. [unpublished]). A paradigmatic example is a conventional electrical AND-gate that is often said to compute conjunction, but it can just as well be used to compute disjunction. Despite the pervasiveness of this phenomenon in physical computational systems, it has been discussed in the philosophical literature only indirectly, mostly with reference (...) to the debate over realism about physical computation and computationalism. A welcome exception is Dewhurst’s ([2018]) recent analysis of computational individuation under the mechanistic framework. He rejects the idea of appealing to semantic properties for determining the computational identity of a physical system. But Dewhurst seems to be too quick to pay the price of giving up the notion of computational equivalence. We aim to show that the mechanist need not pay this price. The mechanistic framework can, in principle, preserve the idea of computational equivalence even between two different enough kinds of physical systems, say, electrical and hydraulic ones. (shrink)

In this paper, we focus on the development of geometric cognition. We argue that to understand how geometric cognition has been constituted, one must appreciate not only individual cognitive factors, such as phylogenetically ancient and ontogenetically early core cognitive systems, but also the social history of the spread and use of cognitive artifacts. In particular, we show that the development of Greek mathematics, enshrined in Euclid’s Elements, was driven by the use of two tightly intertwined cognitive artifacts: the use of (...) lettered diagrams; and the creation of linguistic formulae. Together, these artifacts formed the professional language of geometry. In this respect, the case of Greek geometry clearly shows that explanations of geometric reasoning have to go beyond the confines of methodological individualism to account for how the distributed practice of artifact use has stabilized over time. This practice, as we suggest, has also contributed heavily to the understanding of what mathematical proof is; classically, it has been assumed that proofs are not merely deductively correct but also remain invariant over various individuals sharing the same cognitive practice. Cognitive artifacts in Greek geometry constrained the repertoire of admissible inferential operations, which made these proofs inter-subjectively testable and compelling. By focusing on the cognitive operations on artifacts, we also stress that mental mechanisms that contribute to these operations are still poorly understood, in contrast to those mechanisms which drive symbolic logical inference. (shrink)

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I (...) argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

In this paper, I argue that even if the Hard Problem of Content, as identified by Hutto and Myin, is important, it was already solved in natu- ralized semantics, and satisfactory solutions to the problem do not rely merely on the notion of information as covariance. I point out that Hutto and Myin have double standards for linguistic and mental representation, which leads to a peculiar inconsistency. Were they to apply the same standards to basic and linguistic minds, they would (...) either have to embrace representationalism or turn to semantic nihilism, which is, as I argue, an unstable and unattractive position. Hence, I conclude, their book does not offer an alternative to representation- alism. At the same time, it reminds us that representational talk in cognitive science cannot be taken for granted and that information is different from men- tal representation. Although this claim is not new, Hutto and Myin defend it forcefully and elegantly. (shrink)

Is the mathematical function being computed by a given physical system determined by the system’s dynamics? This question is at the heart of the indeterminacy of computation phenomenon (Fresco et al. [unpublished]). A paradigmatic example is a conventional electrical AND-gate that is often said to compute conjunction, but it can just as well be used to compute disjunction. Despite the pervasiveness of this phenomenon in physical computational systems, it has been discussed in the philosophical literature only indirectly, mostly with reference (...) to the debate over realism about physical computation and computationalism. A welcome exception is Dewhurst’s ([2018]) recent analysis of computational individuation under the mechanistic framework. He rejects the idea of appealing to semantic properties for determining the computational identity of a physical system. But Dewhurst seems to be too quick to pay the price of giving up the notion of computational equivalence. We aim to show that the mechanist need not pay this price. The mechanistic framework can, in principle, preserve the idea of computational equivalence even between two different enough kinds of physical systems, say, electrical and hydraulic ones. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

The purpose of this paper is to present a general mechanistic framework for analyzing causal representational claims, and offer a way to distinguish genuinely representational explanations from those that invoke representations for honorific purposes. It is usually agreed that rats are capable of navigation because they maintain a cognitive map of their environment. Exactly how and why their neural states give rise to mental representations is a matter of an ongoing debate. I will show that anticipatory mechanisms involved in rats’ (...) evaluation of possible routes give rise to satisfaction conditions of contents, and this is why they are representationally relevant for explaining and predicting rats’ behavior. I argue that a naturalistic account of satisfaction conditions of contents answers the most important objections of antirepresentationalists. (shrink)

The predictive processing account of action, cognition, and perception is one of the most influential approaches to unifying research in cognitive science. However, its promises of grand unification will remain unfulfilled unless the account becomes theoretically robust. In this paper, we focus on empirical commitments of PP, since they are necessary both for its theoretical status to be established and for explanations of individual phenomena to be falsifiable. First, we argue that PP is a varied research tradition, which may employ (...) various kinds of scientific representations, differing in the scope of empirical commitments they entail. Two major perspectives on PP qua cognitive theory may then be distinguished: generalized vs. hierarchical. The first one fails to provide empirical detail, and the latter constrains possible physical implementations. However, we show that even hierarchical PP is insufficiently restrictive to disallow incorrect models and may be adjusted to explain any neurocognitive phenomenon–including non-existent or impossible ones–through flexible adjustments. This renders PP a universal modeling tool with an unrestricted number of degrees of freedom. Therefore, in contrast with declarations of its proponents, it should not be understood as a unifying theoretical perspective, but as a computational framework, possibly informing further theory development in cognitive science. (shrink)

Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead to unification are (...) analyzed in terms of the mechanistic account of explanation. Finally, I argue that theoretical unification is not an absolute requirement from the mechanistic perspective, and that strategies aiming at unification may be premature in fields where there are multiple conflicting explanatory models. (shrink)

In this paper, we defend a novel, multidimensional account of representational unification, which we distinguish from integration. The dimensions of unity are simplicity, generality and scope, non-monstrosity, and systematization. In our account, unification is a graded property. The account is used to investigate the issue of how research traditions contribute to representational unification, focusing on embodied cognition in cognitive science. Embodied cognition contributes to unification even if it fails to offer a grand unification of cognitive science. The study of this (...) failure shows that unification, contrary to what defenders of mechanistic explanation claim, is an important mechanistic virtue of research traditions. (shrink)

We argue that Yarkoni's proposed solutions to the generalizability crisis are half-measures because he does not recognize that the crisis arises from investigators' underappreciation of the roles of theory in experimental research. Rather than embracing qualitative analysis, the research community should make an effort to develop better theories and work toward consistently incorporating theoretical results into experimental practice.

Three special issues of Entropy journal have been dedicated to the topics of “InformationProcessing and Embodied, Embedded, Enactive Cognition”. They addressed morphological computing, cognitive agency, and the evolution of cognition. The contributions show the diversity of views present in the research community on the topic of computation and its relation to cognition. This paper is an attempt to elucidate current debates on computation that are central to cognitive science. It is written in the form of a dialog between two authors (...) representing two opposed positions regarding the issue of what computation is and could be, and how it can be related to cognition. Given the different backgrounds of the two researchers, which span physics, philosophy of computing and information, cognitive science, and philosophy, we found the discussions in the form of Socratic dialogue appropriate for this multidisciplinary/cross-disciplinary conceptual analysis. We proceed as follows. First, the proponent (GDC) introduces the info-computational framework as a naturalistic model of embodied, embedded, and enacted cognition. Next, objections are raised by the critic (MM) from the point of view of the new mechanistic approach to explanation. Subsequently, the proponent and the critic provide their replies. The conclusion is that there is a fundamental role for computation, understood as information processing, in the understanding of embodied cognition. (shrink)

In this paper, an account of theoretical integration in cognitive (neuro)science from the mechanistic perspective is defended. It is argued that mechanistic patterns of integration can be better understood in terms of constraints on representations of mechanisms, not just on the space of possible mechanisms, as previous accounts of integration had it. This way, integration can be analyzed in more detail with the help of constraintsatisfaction account of coherence between scientific representations. In particular, the account has resources to talk of (...) idealizations and research heuristics employed by researchers to combine separate results and theoretical frameworks. The account is subsequently applied to an example of successful integration in the research on hippocampus and memory, and to a failure of integration in the research on mirror neurons as purportedly explanatory of sexual orientation. (shrink)

In this paper, I show how semantic factors constrain the understanding of the computational phenomena to be explained so that they help build better mechanistic models. In particular, understanding what cognitive systems may refer to is important in building better models of cognitive processes. For that purpose, a recent study of some phenomena in rats that are capable of ‘entertaining’ future paths (Pfeiffer and Foster 2013) is analyzed. The case shows that the mechanistic account of physical computation may be complemented (...) with semantic considerations, and in many cases, it actually should. (shrink)

Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, (...) serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code. (shrink)