Philosophy

Replicability and reproducibility of computational models has been somewhat understudied by âthe replication movement.â 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.

Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail Marcin Miłkowski1 & Witold M. Hensel2 & Mateusz Hohol3 Received: 28 April 2018 /Revised: 5 October 2018 /Accepted: 17 October 2018 # The Author(s) 2018 Abstract Replicability and reproducibility of computational models has been somewhat understudied by Bthe 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. Keywords Replication studies . Computational modeling . Methodology of computational neuroscience . Direct and conceptual replication . Replication and reproduction 1 Background Public controllability of research and reliability of results are the cornerstones of science, so it is no surprise that recent doubts about researchers' ability to consistently duplicate findings in a number of scientific fields have caused quite a stir in the scientific community (Button et al. 2013; Loken and Gelman 2017; Maxwell et al. 2015). The crisis of confidence provoked by critical assessments of reproducibility has affected such disciplines as psychology, neuroscience, economics and medicine. In this paper, we draw on methodological insights from psychology to assess the problems of replicability and reproducibility of computational models in neuroscience. The paper unfolds in the following manner. In the subsequent section, we introduce the notions of repeatability and reproducibility as they are used in physics, chemistry and medicine, and discuss the various types and functions of replication studies in psychology, where the recent crisis has inspired serious methodological reflection on the subject. The point of this exercise is to get a solid understanding of why exactly replicability of experimental research is so important and to what extent the functions of replication may depend on factors specific to a given field of study. We describe a basic methodological distinction between direct and conceptual replications. A direct replication aims to recreate an original experiment, whereas a conceptual replication modifies a previously used experimental procedure. Although they serve different functions, both kinds of replication are necessary for Action Editor: D. Jaeger * Mateusz Hohol email@example.com 1 Institute of Philosophy and Sociology, Polish Academy of Sciences, Nowy Świat 72, 00-330 Warsaw, Poland 2 Faculty of History and Sociology, University of Białystok, Plac NZS 1, 15-420 Białystok, Poland 3 Copernicus Center for Interdisciplinary Studies, Jagiellonian University, Szczepańska 1/5, 31-011 Kraków, Poland Journal of Computational Neuroscience https://doi.org/10.1007/s10827-018-0702-z long-term scientific progress. We also observe that psychologists participating in the replicability debate focus on empirical experiments and completely ignore computational modeling-an oversight that may lead the scientific community to underestimate the full extent of the confidence crisis. In section 3, we define the terms Brepeatability,^ Breplicability^ and Breproducibility^ as they are used in computational modeling and describe problems with the replicability and reproducibility of computational models, which we illustrate with a study by Manninen et al. (2017) who attempted to recreate models of astrocyte excitation. We review several proposals of how to improve model replicability and reproducibility and observe that it is difficult to assess them because they have not been adequately justified. Section 4 focuses on the specific functions of replications and reproductions in computational modeling. We note that the distinction between replications and reproductions drawn in the modeling literature corresponds nicely with that between direct and conceptual replications in psychology but, due to differences between modeling and empirical studies, we claim that low model replicability, though undesirable, does not pose a serious threat to long-term scientific progress. What is essential to scientific progress is model reproducibility, or the ability of other researchers to recreate a model based on its published description. It is important in our opinion to keep these two notions distinct because some measures designed to improve model replicability may actually inhibit model reproducibility. We claim that 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 sufficient solution. Papers should remain selective and include all and only relevant bits of code. By relying on a recent discussion on the normative principle of completeness, proposed by philosophers of science, we defend a version of the principle for computational modeling in general. We close by drawing attention to the limitations of standardization as a means of improving model replicability. 2 Repeatability, reproducibility and replicability of empirical studies It is clear that the ability to recreate a scientific result is closely associated with some of the most general aspects of scientific inquiry. One way to think about it is in terms of the tension between the ideal of objectivity, on the one hand, and our various cognitive limitations, on the other. It would be unreasonable, for example, to announce a new discovery on the strength of a single observation because we know that no observational technique is perfectly precise. In many sciences, such as physics, chemistry and medicine, measurements of a property are often repeated by the same researcher (group of researchers), using the same equipment and a specimen from the same source. If the values of successive measurements are close to one another, the measurements are said to be highly repeatable (Lyons 1986, pp. 3–7, Miller and Miller 2010, pp. 5–6, Connett 2008, Plesser 2018). Repeat experiments allow researchers to estimate random error inherent in any observation. Although important, repeat observations are not the focus of our paper. We are more interested in what researchers in chemistry and medicine call reproductions, i.e., experiments duplicated by an independent researcher or group of researchers, often by means of a different instrument used on a specimen drawn from a different source. The extent to which the elements of a reproduction can diverge from the original experiment vary with the field of study. It is crucial, however, that the reproduction experiment be conducted by someone other than the original investigator. The advantages of reproducible research are considerable. Besides allowing the scientific community to detect researcher fraud and address a number of biases (reproductions often serve to reduce systematic error), the idea that acceptability of scientific claims is associated with reproducibility informs the standards of scientific writing and contributes to making scientific investigation a genuinely collective and cumulative endeavor. Despite a seemingly widespread awareness of the importance of replicability-to use an umbrella term popular in the social sciences-serious methodological reflection on the roles of replication in psychology was surprisingly slow in coming. The situation has changed only recently. According to Schmidt (2009), replication studies in psychology serve a wide range of functions, such as aiming to discover false positives, controlling for artifacts, addressing researcher fraud, attempting to generalize a result to a different population and trying to confirm a previously supported hypothesis using a different experimental procedure. As no single study can fulfill all these functions, some distinctions must be made to avoid confusion. One distinction that has become influential is the contrast between direct and conceptual replications (Hüffmeier et al. 2016; Schmidt 2009; Stroebe and Strack 2014; Zwaan et al. 2018). Although each writer offers a slightly different definition, the distinction can be drawn as follows. Direct replications are intended to recreate an original study (its samples, measures, procedures, etc.) according to the current understanding of what is needed to produce the phenomenon under investigation. Conceptual replications, by contrast, deliberately modify the critical elements of an original procedure in order to test the robustness of a phenomenon or the generality of a theoretical claim. In other words, the main purpose of a conceptual replication is to investigate the target theory or hypothesis in a novel way (Zwaan et al. 2018). However, direct replications that recreate the elements of an original procedure down to the smallest detail are often impossible to perform, especially if both independent and dependent variables are mediated by social or cultural factors J Comput Neurosci (Stroebe and Strack 2014, p. 61). Rather than a strict dichotomy between direct and conceptual replications, what we have, then, is a range of cases, with studies closely resembling the original experiment located near one end of the spectrum and those using completely novel methods, near the other. What distinguishes direct replications from conceptual ones are the researcher's intentions: if the researcher attempts to produce a phenomenon according to an existing procedure then her study is a direct replication, and if her aim is to expand or refine current understanding of a phenomenon then her study is a conceptual replication. The need to conduct conceptual replications is not in question. They are seen as valuable contributions to scientific knowledge as they strike a balance between drawing on previous research and offering new insights. What is controversial is the status of direct replications, which are criticized as either impossible to perform, uninformative or potentially (and often unfairly) damaging to the reputation of researchers whose studies have failed to replicate (see Zwaan et al. 2018). Zwaan et al. (2018) defend direct replications by pointing to their role in theory testing. When researchers are unable to reproduce findings that were initially taken to confirm a theory, its advocates are forced to explain this by proposing auxiliary hypotheses. If too many such hypotheses fail, the theory may be rejected as part of a degenerative research program. If, on the other hand, some auxiliary hypothesis garners empirical support, the theory is retained as progressive. Although true, this claim presupposes rather than explains why it is that researchers who are evaluating a theory make attempts at reproducing previous findings. The main question, then, is why and under what kind of conditions are direct replication attempts justified. Hüffmeier et al. (2016) provide an answer to this question in the context of social psychology. They distinguish five types of replication and assign a set of functions to each type. Two of these types are subspecies of direct replication. Thus, the central function of exact replications, i.e., direct replications performed by the same group of researchers, is to protect the scientific community against false positives, which are likely to occur when the first study is statistically underpowered (exact replications are analogous to repeat measurements in physics, chemistry and medicine). Exact replications are strongly recommended when initial findings are either unexpected or loosely based on current theoretical models. Close replications, i.e. direct replications performed by an independent team of researchers, also reduce the likelihood of false positives, especially those stemming from experimenter effects (Rosenthal 1966) and tacit knowledge. Moreover, they provide information needed to establish the size of an effect, which the original investigators are prone to overestimate. But, above all, close replications enable the research community not only to confirm the existence of an effect but also to disconfirm it. Indeed, as Hüffmeier et al. (2016) note, sometimes a series of independent close replications is the only way to effectively undermine the reality of a prematurely accepted phenomenon. Interestingly, psychologists who are calling for more replications (Zwaan et al. 2018) seem to equate replication with reproducing experimental procedures. They fail to recognize an additional, non-empirical dimension of the replication crisis because they take it for granted that science is primarily concerned with empirical problems. This empiricist belief is shared by most experimental neuroscientists, which poses a challenge to the field of computational neuroscience (De Schutter 2008), but is deeply problematic because it forces us to view a great deal of scientific activity as irrational (Laudan 1977). Many debates in science have focused on theoretical and conceptual issues. The Copernican revolution was not about empirical adequacy (Kuhn 1957, 1962), and neither was Chomsky's critique of Skinnerian theory of language (Chomsky 1959). If we view science as an activity directed at solving both empirical and theoretical problems we immediately see that the confidence crisis does not only affect empirical studies but also computational modeling. This is all the more significant because a vast majority of theoretical studies in neuroscience and cognitive science are based on computational modeling (Busemeyer and Diederich 2010). 3 The confidence crisis in computational modeling The distinction between direct and conceptual replications has a counterpart in computational modeling. Unfortunately, there is no standard terminology to go with it (Plesser 2018). Claerbout, who was the first to call for replicability in computational modeling, marked the distinction using the terms Breproduction^ and Breplication^ (see Claerbout and Karrenbach 1992). He defined model reproduction as the procedure of obtaining the same outputs by running the same software on the same inputs (which corresponds with the psychologist's notion of direct replication) and model replication as obtaining sufficiently similar results by designing and running new code based on a published description of a model (which corresponds with conceptual replication in psychology and reproduction in physics, chemistry and medicine; Plesser 2018, Rougier et al. 2017). However, following an influential paper by Drummond (2009), many, though not all, authors in the field have switched the meanings of these terms. Thus, the Association for Computing Machinery (Delling et al. 2016) has recently recommended the following usage: repeatability involves a researcher being able to reliably repeat her computations, replicability consists in a group of researchers being able to obtain the same results using an original author's artifacts, and reproducibility means that an independent group of researchers can obtain the same results using artifacts which J Comput Neurosci they develop completely independently. This is how we use these terms whenever we discuss computational modeling. Note, however, that this terminological convention applies only to computational modeling-when discussing experimental (empirical) studies we use Breplication^ as an umbrella term, covering both repeatability (agreement between measurements taken by the original researcher) and reproducibility (involving an independent researcher). So is computational science facing a confidence crisis similar to that occurring in experimental studies? A cursory glance at the literature confirms that it is. A number of authors in the computational science community are drawing attention to problems with model replicability and reproducibility (Hutson 2018; Peng 2011; Rougier et al. 2017; Sandve et al. 2013). Model reproduction is rarely performed (Legéndi et al. 2013) because successful reproductions do not seem to deliver novel scientific results and causes of failed reproduction may be difficult to discern. Instead of reproducing a model with new data, researchers tend to compare new models with previous work. Moreover, to the best of our knowledge, no major journal accepts publications related to computational replications or reproductions. The journal ReScience (https://rescience.github. io), which aims to fill this gap, was established in 2015 but has as few as 22 papers as of August 2018. The complexity of current computational modeling means that both replications and reproductions are far from trivial. Not only are most published computational studies unaccompanied by any code, let alone the original data or custom scripts, but it is rarely documented how exactly the model was produced. One may reasonably suspect that at least some models, even those whose verbal descriptions seem plausible, owe their success to complex undiscovered bugs in the original software that is no longer available. Thus, even faithful replication of these models does not guarantee that the model actually represents the intended phenomenon (cf. Stroebe and Strack 2014). In a recent article, Manninen et al. (2017) reported their investigation into the reproducibility of four existing computational models of principal types of astrocyte activity. Two of the models deal with spontaneous Ca2+ excitability in single astrocytes (Lavrentovich and Hemkin 2008; Riera et al. 2011), while the other two simulate the neurotransmitter-evoked excitability of this element (De Pittà et al. 2009; Dupont et al. 2011). Manninen et al. evaluated the possibility of reproducing the original findings with a reimplementation of the equations offered in the original papers. Furthermore, they investigated reusability of the models with other parameters and setups. Their investigation showed a number of inaccuracies. First of all, they found that it is impossible to reimplement three of the models, those by Riera et al. (2011), De Pittà et al. (2009) and Dupont et al. (2011), due to insufficient information in published papers. Relying on the original paper and a subsequent corrigendum, the researchers were able to reproduce the outcomes of only one model of astrocyte activity, i.e., by Lavrentovich and Hemkin (2008). Manninen and colleagues found serious mistakes in the mathematical formalisms presented in two original papers (Riera et al. 2011 and Dupont et al. 2011), which made exact reproduction impossible. Only after modifying the equat ions did the reimplementation of these models begin to work correctly. Furthermore, when they set out to perform comparative assessment, the researchers observed that, although the models target the same phenomenon, their performance differs significantly. For instance, the model of spontaneous Ca2+ excitability developed by Lavrentovich and Hemkin (2008) began to give completely different results when Manninen and colleagues applied parameter values from the Riera et al. (2011) model of the same phenomenon. To sum up, prominent computational models of astrocyte excitability-one of the key biological events participating in synaptic transmission-are very hard to recreate and compare. A number of recommendations have been made to address the problem of irreplicability and irreproducibility in computational modeling. It has been suggested, for example, that all code should be shared (Buckheit and Donoho 1995; Sandve et al. 2013). However, according to a recent survey by Gundersen (see Hutson 2018), this call is largely ignored as only 6% of the 400 algorithms presented at two top AI conferences in the past few years contained the code and only a third had pseudocode, or simplified summaries of the code. Furthermore, Stodden et al. (2018) have recently investigated the effectiveness of a replicational policy adopted by Science in 2011. Since then, the journal requires authors to make the data and code sufficient to replicate their study available to other researchers upon request. Stodden and colleagues selected 204 computational studies published in Science. Out of those, 24 papers (about 12%) provided code and data via external links or supplementary material. Stodden and colleagues contacted the authors of the remaining 180 studies. To start with, 26% of the authors failed to reply altogether while the others often responded evasively-e.g., by asking for reasons, making unfulfilled promises or directing the researchers back to supplementary material. In the end, it was possible to obtain artifacts for only 36% of the papers. Overall, Stodden and colleagues estimated about 25% of the models to be replicable. Their investigation has shown that the requirement to share data on demand after publishing is not being followed. Until recently the policy of Nature journals was similar. Adopted in 2014, the policy demanded that authors explicitly express readiness to share the code and data (BDoes your code stand up to scrutiny?^ 2018). The situation in computational neuroscience is not much better. According to our survey, 32% of the 242 articles published in three prominent journals from January 1, 2016, to September 26, 2018, contained code J Comput Neurosci (available either in supplementary material or in an open repository).1 But sharing code would not by itself solve the problem of model irreplicability. As Crook et al. (2013) point out, it may not be possible to recreate a published result even when the code is available. Causes of this include differences in the version of the computational platform (or its simulator), the compiler, or of shared libraries that are used by either the simulator or the code, or even poor record-keeping on the part of the researcher who published the paper. Therefore, one may be tempted to set the bar even higher. Sandve et al. (2013) propose no fewer than ten rules to ensure model replicability. These rules involve tracking how results were produced, avoiding the manual manipulation of data sets, archiving the exact versions of external programs used, using version control to store custom scripts, recording intermediate results (preferably in standardized formats), noting random seeds for randomized analyses, storing raw data behind plots, connecting textual statements to underlying results and finally providing public access to scripts, runs and results. It is doubtful whether such guidelines will remedy the situation if not suitably motivated. Before adopting a methodological rule, researchers should understand its purpose and know its possible side-effects. They also need to decide whether the intended aim of adopting a rule is worth pursuing relative to other aims. Otherwise they run the risk of acting at cross purposes. For instance, The Journal of Neuroscience announced in 2010 that it would no longer allow authors to include supplemental material with new manuscripts or host such material on its website (Maunsell 2010). Somewhat surprisingly, the editorial board justified its decision by appeal to the requirement that scientific results must be subject to public scrutiny. The argument was simple. Although initially plausible, the idea of sharing computer code in supplemental material had soon backfired when the reviewers started to require authors to provide code and the authors reacted by providing more and more of it-so much, in fact, that the reviewers were unable to assess the code's quality. Paradoxically, then, a measure designed to improve model replicability had come into conflict with public controllability of scientific results. 4 Model replication vs. model reproduction Clearly, before implementing specific measures to improve replicability and reproducibility in computational modeling, we need to get a better idea about what functions are served by model replications on the one hand and model reproductions on the other. Needless to say, they must differ in some respects from the functions of various types of reproduction experiments in purely empirical studies. For one thing, it makes no sense to say that a computational model is statistically underpowered, so one important reason for performing exact replications of studies in social psychology becomes irrelevant where computational modeling is concerned. There are two important kinds of model evaluation: model verification and model validation. To verify a model is to make sure that it follows the specification. To validate it is to ensure that it describes the target phenomenon at a required level of detail (Zeigler 1976). As long as verification and validation can be automated, repeating and replicating a model can both contribute to them. For example, if the specification of a model is formalized, the model can also be verified automatically by re-running specification-checking scripts. It can also be validated if (1) the original data sets are also publicly available (for example, by performing randomized cross-validation) or (2) new data sets can be fed into the model to process. However, repeating the model, or running it again by the original researcher(s) on the original data, need not contribute to model verification or validation. Sometimes it only shows that the model actually operates. By contrast, as Rand and Wilensky (2006) observe, a successful replication demonstrates that the implementation of a model follows the official specification, which constitutes model verification. Replications may also be useful in detecting type I errors in scientific papers, such as typographical mistakes in numerical values in figures or tables. Whenever there is a discrepancy between the paper contents and the code, running the code may help to discover the mistakes. By following the rules proposed by Sandve et al. (2013), we safeguard model repeatability and replicability. However, compared to empirical studies, whose direct replications enable researchers to either confirm the original effect or else weed out false positives, the fact that model replications are rarely performed does not pose a serious threat to scientific progress. Only by reproducing a model, or offering what psychologists would call a conceptual replication, can we discover the model's hidden assumptions, bugs or unexpected interactions. Moreover, a successful reproduction contributes to model validation. Validation is methodologically more valuable than verification because it shows how an implemented model corresponds to empirical data. In particular, by validating a computational model through reproduction we make sure that the results of modeling are sound (see Drummond 2009 for a similar argument). 1 Note that the ratio varies according to journal. We have investigated the Journal of Computational Neuroscience (108 articles) and two other core journals listed at the official website of the Organization of Computational Neurosciences: namely, Frontiers in Neuroinformatics (79 pieces) and Biological Cybernetics (55 pieces). The results for the Journal of Computational Neuroscience and Frontiers in Neuroinformatics are comparable, with 40 out of the 108 papers (37%) published in the Journal of Computational Neuroscience and 32 out of the 79 articles (40%) published in Frontiers in Neuroinformatics providing code. By contrast, only 5 out of the 55 studies (9%) published in Biological Cybernetics included such references. Note also that our investigation was limited to original research articles in the field of computational neuroscience (we excluded papers presenting results from other fields as well as methodological or software description papers). J Comput Neurosci The upshot is that, unlike model replicability, model reproducibility is essential to long-term scientific progress. Without it, research in computational modeling can degenerate into something akin to alchemy, with every practitioner essentially working alone. Lack of replicability is a minor obstacle by comparison. Improving reproducibility should be the first order of business. Why is so much research in computational modeling irreproducible? Researchers typically attempt to reproduce a result in order to compare their new model to a previous stateof-the-art model, usually with new empirical data. Model reproductions fail not only due to type II errors but also due to type I errors: errors of omission of information that is crucial to recreating the model based on its published description or to using the model on new (kinds of) data. The normative principle that is violated when a computational model is irreproducible asserts that the ideal text describing the model should be complete, i.e., contain all and only relevant information. This principle has been extensively discussed in the recent philosophy of science, in the context of the new mechanistic approach to explanation. According to this approach, to explain a phenomenon in terms of how it is constituted is to elucidate its underlying causal structure and organized components and operations of the mechanism responsible for the phenomenon (Craver 2007; Machamer et al. 2000). The defenders of the new mechanistic approach have been criticized for assuming that ideal explanatory texts should include too much detail (Chirimuuta 2014). However, what they claim is not that one should include all kinds of detail. Only relevant detail counts (Baetu 2015; Craver and Kaplan 2018; Miłkowski 2016). In the new mechanistic approach, the focus is on causal explanatory models, i.e., models of mechanisms that are responsible for some phenomena. Thus, complete explanatory texts Brepresent all and only the relevant portions of the causal structure of the world^ (Craver 2007, p. 27). The kind of relevance in question is explanatory relevance vis-à-vis a certain phenomenon to be explained. The completeness of the mechanistic model is to be understood as specifying the whole causal model; to specify the causal model, one needs to know all and only the relevant variables and their connections in the graph that describes it. How could this be useful for evaluating papers on computational models in neuroscience? The principle of completeness may strike one as too general to yield any practical consequences but this is not true. It implies, for example, that including irrelevant detail in an explanatory text is detrimental to the quality of an explanation as it makes understanding difficult. Unfortunately, researchers do not always avoid the pitfall of writing too much. For example, Parr and Friston (2018) devote two sections of their paper on oculomotion to basic equations of the free energy framework instead of specifying exactly how their computer simulation was built, which is described extremely tersely, without any quantitative detail. This makes little sense as far as model reproducibility is concerned, given that they cite other papers that offer a fuller introduction to their framework. Scientific papers are not autonomous entities that are read in complete isolation; instead, they are parts of an explanation distributed over a collection of papers (Hochstein 2015). Thus, the basics of an approach should be relegated to introductory papers. From the perspective of reproducibility, the paper by Parr and Friston (2018) contains both too much (lengthy introductions, complex figures instead of data) and too little information (no experimental data fed into the simulation, no details of the simulation framework). Similarly, not all kinds of data in the supplemental material would be required; the ideal would be to have all and only code and data that are causally relevant to oculomotion. A crucial part of modeling, therefore, is to explicitly theorize about the phenomena in question: one has to understand exactly what is meant by oculomotion to fully evaluate whether the computational model is complete. Completeness is assessed relative to the phenomenon to be explained. This means that there are no fast-and-easy solutions to the problem of model irreproducibility, since there are no specific guidelines covering all potential target phenomena. However, if we look at the question of relevance on a case-by-case basis, we see that it is not intractable. Let us return to the study of models of astrocyte activity. Manninen et al. (2017) notice that graphical illustrations of models are often misleading or completelymissing, and sometimes not all equations are explicitly given in the publications about models but referred to with a citation to a previous model publication. These may be type I errors unless all these equations are found in the previous publication. However, there are also type II errors. As the authors say, Bit is often difficult to know exactly what the actual model components arê (ibid., p. 15). And even if these components are known, it is sometimes unclear what they are supposed to correspond to in the biological domain. Furthermore, Manninen et al. explicitly point out that the analyzed models are biologically incomplete: Bthe four studied models consider only a subset of mechanisms responsible for astrocyte Ca2+ excitability and leave out several essential mechanisms, such as the cell membrane ionic currents and various intracellular signaling cascades.^ (p. 15). Of course, not all omissions and simplifications violate the completeness principle, for no neuroscientific work can or should avoid idealizations (Miłkowski 2016). It should be obvious that these are idealizations introduced to make the paper or the model more perspicuous. The model cannot reflect the full complexity of a physical mechanism on pain of becoming explanatorily obscure. However, a model of an idealized phenomenon does not violate the mechanistic norm of completeness as long as it includes all and only causal J Comput Neurosci interactions relevant to the phenomenon, defined in an idealizing fashion. Hence, for example, one might idealize away the influence of memory tasks (which may rely on eye movements; cf. Johansson and Johansson 2014) in theorizing about the mechanism of oculomotion. Note that memory tasks may themselves cause certain kinds of eye movements but oculomotion is understood mostly to be related to five basic kinds of eye movement (Dodge 1903). Causal explanatory models are but one of many kinds of models used in computational neuroscience. For example, computational models can be used for predictive purposes, and some models are used for purely exploratory reasons, to understand complex interactions between certain variables in a class of models. There might also be models of purposively simplified artificial animals in neuroethology (Arbib 2003; Braitenberg 1984). In all such cases, however, there is a certain, usually formally definable, relationship between the model and its target, which underlies the validity of a model. This relationship specifies the relevance in question: the paper describing the model should contain all and only information considered to be relevant in assessing the relationship in question. As far as model reproducibility is concerned, specific rules such as those introduced by Sandve et al. (2013) are of little help unless they contribute to an understanding of how theoretical principles are translated into modeling practice. Indeed, they can improve replicability at the cost of affecting reproducibility. Specifying the exact computational platform should not be required as long as software is designed to be portable for interoperability. With the exception of the use of supercomputers, specialized hardware or very sensitive realtime requirements, if the details of the computational architecture count then the model may be too sensitive to improper background conditions, and its scientific value is limited. It is simply poor-quality code. Thus, while avoiding prototype code and quick hacks is difficult in actual scientific work, this kind of code should be discardable. It should be possible to reconstruct the model with no knowledge of such detail, from a paper only. What we suggest, then, is that a paper should above all contain all and only information needed to reproduce a model and assess the model's intended relationship to its target. By contrast, information necessary to replicate a model, including code and experimental data sets, should be deposited in open repositories (Migliore et al. 2003). Research papers and repositories serve different functions, though ultimately both should contribute to the same overarching goal of making science cumulative. If science communication continues to rely on journal papers then too much irrelevant detail will make reading (and reviewing) them impossible. Open model repositories facilitate replication and code reuse. A similar division of function has been defended by Nordlie et al. (2009), who also stress that publication serves a different function from code. Their proposal also helps to deal with highly complexmodels, such as detailed simulations of neocortical microcircuitry (Markram et al. 2015) or functional simulations of the whole brain (Eliasmith et al. 2012). The authors suggest that papers that describe such models should be, if needed, split: one paper should contain the model description, and another its analysis. In the worst case, the description could be relegated to supplementary materials, but they notice-as we did-that these may not be properly peer-reviewed, and authors may not receive proper credit because these are not citable. At the same time, Nordlie et al. (2009) offer detailed guidelines, based on the analysis of 14 papers on neuronal networks, aimed at making future papers complete. Thus, while their contribution is more detailed and specifically tailored to neuronal network models, it is quite close to ours in spirit. Let us close by comparing and contrasting our proposal with the best practices defended by Manninen et al. (2011): We propose that all models should (1) be formulated using common description language, (2) have adequate metadata related to model and experimental data used, (3) explain set of features describing the overall behavior of the modeled system, and (4) be compared to previous models. In other words, all new models should be constructed according to clearly defined general rules (p. 9). We wholeheartedly agree with (3), which we try to clarify in terms of the principle of completeness. As to (4), it is a necessary step in defending the quality of a model, and such defenses are always contrastive. We believe that it is essentially correct, though not directly associated with replicability or reproducibility. What about a common description language and metadata? Standardizing scientific work makes interoperability easier, which is obviously required for model replication. But standardizing prematurely will inevitably lead to a proliferation of standards instead of increasing interoperability. The problem is that we still do not know what should be included in the ideal future computational description of a model. Thus, any cutting-edge work is in danger of not being expressible in a common description language, which will require small adjustments of such languages, whose variety and number-but not necessarily their interoperability-will increase over time. There are already several such languages, such as SBML, CellML, FieldML, NineML, LEMS/NeuroML and PyNN (Davison et al. 2008; Gleeson et al. 2010; Cannon et al. 2014; McDougal et al. 2016). It should not be considered a serious violation of scientific norms to publish a paper describing new work without conforming to a previous standard. J Comput Neurosci The same goes for open source software, which, as some argue, should be used whenever possible to generate publishable results (Easterbrook 2014; Gleeson et al. 2017). However, open source software is required only for replicability, and is not necessary for reproducibility. In other words, adherence to a common description language, common metadata description format and using open source software are all desirable but not strictly necessary features of correct scientific reporting. 5 Concluding remarks In this opinion paper, we have argued that model reproducibility and replicability are different goals. In a nutshell, effective scientific communication requires that all and only relevant information is shared, and this is the basic guideline for model reproducibility. Model replicability and repeatability require that all details relevant to running the code be shared even if they are scientifically inessential. We hope that understanding that model repeatability, replicability, and reproducibility require different solutions will, in the long run, alleviate the current problems in the computational modeling community. Acknowledgements We would like to kindly thank Piotr Litwin (Department of Psychology, University of Warsaw, & Institute of Philosophy and Sociology, Polish Academy of Sciences) for helping us with the survey on code availability. We are also grateful to Carlos Zednik and two Anonymous Reviewers for their valuable remarks. Funding MM is supported by the research grant 2014/14/E/HS1/00803 funded by the National Science Centre, Poland. MH is supported by the research grant 2015/19/B/HS1/03310 funded by the National Science Centre, Poland and the START scholarship funded by the Foundation for Polish Science (FNP). 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Winnicott and Lacan and The Lack within Subjectivity In the Context of Dzogchen

Philosophy

This paper focuses on Winnicott and Lacan and the lack within subjectivity in light of dzogchen.

Self Liberation in Phenomenology and Dzogchen, Essays WINNICOTT AND LACAN AND THE LACK WITHIN SUBJECTIVITY IN THE CONTEXT OF DZOGCHEN By Rudolph Bauer, Phd Wed, Oct 23, 2013 Rudolph Bauer Ph.D., Author I will discuss Subjectivity as Lack and the Realm of Ontological Insecurity in Lacan and Winnicott from within the Context of Dzogchen. Primordial awareness manifests within us as our own subjectivity. This is the understanding both of Dzogchen and certain contemporary forms of Western phenomenology. This theme of primordial awareness manifesting as subjectivity is also the focus of the eight century Dzogchen source text the The Sovereign All-Creating Mind The Motherly Buddha. Psychoanalyst such as Lacan and Winnicott focus on the ontological insecurity of being a subject, being a self, being a self constituted by awareness. In this paper the base of awareness as emptiness or space, or void or openness, or potential space or nothingness is described in light of Winnicott and Lacan's work on subjectivity and lack. Lacan and Winnicott were profoundly aware of the lack that lies at the core of subjectivity, this missingness in the sense of self. In their clinical work around this lack, Lacan and Winnicott used different clinical approaches to study this existential actuality. Winnnicot and Lacan knew that a person has to encounter the void within themselves as a self, as a subject, as a person. Both, Lacan and Winnicott, knew that at the innermost core of a person there is this innermost space, this incommunicado space that is to be experienced and that is to be held, and contained, and sustained. This space should not be impinged or penetrated upon and this space is beyond words and language, beyond mind. There is this hole, or emptiness, or void, or space at the innermost core of subjectivity; this emptiness is the essence of subjectivity . For Lacan and Winnicott the dynamic of the unconscious was this opening, this emptiness, this gap of this incommunicable dimension. Known but not easily thought, known but not thinkable, unthought but known is the challenge of understanding the invisible and bringing the invisible into visibility. Their descriptions of human subjectivity allowed Lacan and Winnicott to work with and describe in their own way the fundamental agonies and anxieties associated with the person's foundational existence as a person, as subjectivity, as self, as the awareness field. Although Wiinicott and Lacan realized that it is the lack of a sense of self that lies at the core of subjectivity, their clinical psychotherapy approaches differed. For Winnicott, the capacity was to be with and to be attuned to the innermost awareness as subjectivity. To hold awareness with and within the matrix of this mutually held field brings forth the container and support for the sense of subjectivity. The emerging sense of self must move through void states of experience within the very void arises the unfolding of ongoing continuity of aliveness. The continuity of beingness, the continuity of the aliveness, the continuity of presence, the aliveness of innermost self, is the aliveness of the "True Self" to use Winnicott's wonderful phrase. Within the void, by entering into the void, there can be experience of self arising presence of the beingness of being of the person. Void as source, void as primordial openness from which and from within which continuity of being and the very experience of awareness of being takes place as presence, as aliveness, as continuity, as ongoing subjectivity, as the continuity of being,as the knower of knowingness. For Winnicot, void and beingness are in oneness. As Winnicott would say "emptiness is waiting to fill up". In this way, Winnicott is a Dzogchen master that is a master of awareness. For Lacan, the focus of the healing relationship was to assist the person in experiencing the void or emptiness and from within this void of awareness (metabolism of awareness) experience the liquidation of identificatory relationships. In the language of semiology this is called the dissolving of signifiers that were attributions of others. For Lacan many of the mirroring relationships are mirrored attributions of the Others' opinions of us internalized by us. Many of the mirroring relationships are mirrored signifiers that were the attribution of otherness. This internalized otherness fosters a sense of false subjectivity. The sense of lack arises as the sense of the other opinions of one's self are internalized by one's self. One's desire becomes the function of the desire of another. One's imaginary identifications with the Others' opinions takes place rather than one's own identification with one's subjective essence which is emptlness, or openness, or no-thingness. Therefore one's own voidness, spaciousness, emptiness, and openness are the source of desire. Primordial awareness itself is the source of desire; one's own innermost desire arises out of primordial awareness. This primordial awareness which is no-thingness and yet from which everything and anything can arise. And therefore Lacan was also a Dzogchen master. By Dzogchen master I mean the master of awareness. As Namkai Norbu, the great contemporary master of Dzogchen, says "Dzogchen does not belong to any religion and neither is it simply a philosophy nor a cultural tradition rather Dzogchen is the capacity to experience our real condition freed from self deceptions". Lacan focused instead on our misfilling the void with imaginary identifications, he focused on void or lack as source. Lacan understood that subjective freedom, or freedom of subjectivity could manifest because of the courage to face the truth that the sense of fundamental lack was necessary for the experience of subjectivity to enter completion. Subjectivity as void, as abyss, as spaceousness, as openness, as light. These are existential development passages that many people pass through. The innermost abyss is the doorway to liberation. But one has to be up for the play of awareness and not overly psychologize this endeavor with cloudy affectivity and wobbly cognitions and relentless linguistic mental representations which are imposing mind identifications on the void of awareness...the sea of awareness. Within the sea of qi courage and affection are necessary. Feeling sorry for oneself is a poor strategy for self liberation. Both, Lacan and Winnicott, understood that mirroring was of great importance. Winnicott understood that for a person to be the attuned mirror of the other, to be attuned to the innermost potentiality (space) which is subjectivity of the other is a healing possibility. The place of potentialness in one's self is the instrument of healing. Potentiality experiencing the potential place in the other. This non verbal attunement of potential self to potential self, this inside to inside resonance is the force of aliveness that continuously brings forth manifestation of subjectivity, of innermost presence. This attunement of awareness within awareness reflects the wondeful capacity to be in the intermediate area of experience in mutual, transitional space. To be held in the oneness of the awareness field is liberation that allows for the dissolving of misidenitifications and supports the experience of the natural self arising of presence, aliveness, vast openness, a continuity of being. For Winnicott being and nothingness were in oneness. For Winnicott Mirroring was positive and for Lacan mirroring can be both positive and fragmenting. For Winnicott the attunement or resonance or extension takes place at a nonverbal prereflective level of awareness, and not within mind alone. For Lacan when the mirroring is not simply that of awareness' attunement to awareness, but rather the mirror's becoming one person's mind, attuning to the others person's mind alone. This is when this mirroring actually is the reflection of mental opinion and phantasm about the subjectivity of the other. The internalization of these mind opinions becomes the externalized source of subjectivity, becomes the source of excentric subjectivity and falsely rescues one from the void of subjecitivity. The cost is that the Other becomes the source of subjectivity. The big Other's opinions stand behind one's subjectivy and actually the Other's opinions do not really exist, but are purely imaginary. Such internalizations of others' signifiers about them rest in the imaginary register of mind. Internalized Opinion's or signifiers only exist in this imaginary dimension of mind alone. In time these internalized opinions of the other become a form of the master-slave relationship]. In this way an imaginary self is formed from imaginary opinions, from an imaginary mirror to an imaginary sense of wellbeing. Behind the imaginary adaptation is the actual suffering of lack or void, and within this configuration annihilation anxiety and deprivation become a felt sense and the person is ontologically terrified. Now, of course, the primitive superego comes to the rescue and so the superego, the judgmentalness of mind alone becomes another substitute for true subjectivity and true personhood. The sense of potentiality is ursurped by the primitive superego and so now the person has the substitution of a cruel master who provides imaginary security. Lacanian Superego as the Substitution for Subjectivity and Lack. For Lacan, the superego minimizes awareness, minimizes whoness. Lacan re-understood the Freudian superego as judgmentalness and re-introduced judgmentalness into the French philosophical tradition. His translation was from an entity or super structure of the mind of the classical psychoanalytic frame to the experiential symbolic frame of a spectacle and spectator. From within the perceptual field judgmentalness arises, the spectator of judgment arises, and as the saying goes "Here Comes the Judge!" This judgmentalness, this judgment is both considered to be the absolute truth and beyond doubt. Thus the person becomes the one who knows. And the sense of subjectivity is sealed over and concealed in this spectacle of the spectator sphere. Thus there is a relentless criticalness, sado-mashochistic enactments, and the giving of torment to one's self and others. This sense of basic lack, this nothingness invokes these deprivation and annihilation anxieties. With all this there is the sense of powerlessness in action, and the impotency of rage, murderous rage. Polite people call it anger. This spectacle self is itself at times a comedy and at times a caricature and may be funny and bizarre and even entertaining. Yet the scenes are terrifying. The person's entire being and sense of being is organized within this spectacle as the spectator, the viewer. The spectator makes endless judgments, endless observations and notations as the one who knows what is right and what is wrong, what is good and what is evil, what is true and what is false, what is better and best. This is the mind of the "Pharisee". The "Pharisee" is the one who knows,and knows, and knows, and knows. This person's life is somewhat like the two characters in "Waiting for Godot" in that nothing actually happens other than endless, relentless judgments about what is happening and what is not happening or what should be happening. Eternal commenting, relentless commenting! There are of course sado-masochistic interludes where the person suffers for the sake of something or suffers for the sake of the other or relentlessly imposes suffering on another for something or other. Every event is the opportunity for judgment. Children of course are excellent targets for such a mind, such a moralistic judgmental mind. A would-be superior mind of absolute knowledge! Opinion as the truth! Gangsters are moralistic, perhaps having the most moralistic sadistic view of us all. Even psychopathic personalities have their own demented form of superego, their own form of judgmentalness, there own form of moral-sadism. The absolute moral judgment is most importantly acted out as the last judgment, the final judgment of the imposition of death on the other. Religious people also revel in the moments of moral judgmentalness, orgasmic judgmentalness. For the religious mind, even death is contrived or configurated within the torture of judgmentalness, final judgmentalness. Wow, you miss the glimpse of the light, or wow, you let the archetypes terrify you and now you will find yourself being a dog in someone's back yard. Wow, you did not recognize Jesus and so you are condemned forever. Wow, outside the church there is no salvation. Wow, you did not keep the samaya, so you are lost for eons. You said something nasty about the teacher and so you will suffer forever. The liberation of subjectivity, the bliss of freedom comes about by directly knowing and knowning experientially your fundamental awareness which you can experience as spaceousness, openness, voidness, emptiness, spontaneous presence, luminous clarity. By knowning these qualities that constitute your self as subjectivity, liberation arises. Since your subjectivity and another's person subjectivity are constituted by primordial awareness, paradoxically, this primordial awareness can be experienced as the abyss of emptiness or abyss of lack, the abyss of annihilation and the abyss of deprivation. This primordial awareness which is your subjectivity can be experienced as void or nothingness. This primordial awareness which is your very own subjectivity can also be experienced as vast unbound spaceousness infinite in its horizons. Primordial awareness, your very own subjectivity, can be experienced as vast openness, just openness beyond words and letters. Primordial awareness, your very own subjectivity, can be experienced as spontaneous presence, pure presence beyond your mind. Your innermost awareness which is subjectivity can be experienced as innermost luminosity, light, clear radiant light. Your own innermost awareness subjectivity can be experienced as compassion, as the sense of oneness, knowingand loving-oneness, potentiality knowingpotentiality. In the emptiness of awareness, one's mind can inscribe within the emptiness of awareness words, letters, and signifiers. This act is a bit like writing with one's finger tips on water, water in a bucket, water in a stream, and water in the ocean. Writing and inscribing interpretations from one's mind within the sea of awareness. Taking one's mind's affect and placing it on the sea of awareness there is a dissolving. One will not know awareness by mind alone. In fact, one will be passing on into the field of awareness explanations of mind and the attributions of other opinions which one has internalized in one's imaginary mind and transmitting these opinions into the field of primordial awareness, this field of emptiness, spaceouss one's own subjectivity. These placements of mind all disappear, they all dissolve within awareness. Only no-thingness remains. This often happens when masters of awareness speak. One remembers nothing. A person can enter the depth of the awareness field and within the spaceousness of potentiality experience pure potentiality. Potentiality is not the probability estimates of the mind which result in hope or hopelessness. Potential space is open space and unbound space, non conceptual space beyond words and letters. Presence, the pure presence of primordial awareness is the pervasive experience of this dimension of reality as potential space. To experience the pervasiveness of potential space is to experience the freedom of the innermost awareness. This vast awareness as experience can both be in time and in timeless awareness. Timeless awareness is sustained, it is beyond past, beyond present and beyond future. In this way timeless awareness is unborn and undying. The Configuration of the Superego Being Dissolved within the Space of Potentiality To deconstruct the judgmentalness of the super ego one can from within the inner heart essence of potential space, of innermost awareness extend the power of the awareness field into the mind cutting through the configuration of the judgmentalness as well as the results of the judgmentalness on one's own self, such as doubt. This cutting through is a key skill in the tradition of Dzogchen and is a powerfull, direct infusion and liquifies both the signifiers as well as the function of judgmentalness itself. This cutting through of awareness into mind is symbolized by the phurba, or danger, or the sword. Phat is the mantra that symbolizes this direct cutting through action of luminous energy arising from the inner place of awareness. The configuration of judgmentalness itself can be pulled into the heart essence of awareness, that place of awareness within the heart expanse, and in doing so dissolving can take place as well as the undoing of the signifiers that have been internalized. This is a function of the great metabolism power of the awareness field. When two people are holding the matrix of the field together in the field of awareness then the mind and the construction of the superego are emerged into the energetic vortexual winds of innermost awareness. The signifiers and functions of judgmentalness can be dissolved in the mutually held vase of awareness. The luminous energy dissolves the mind functions and signifiers. Objects Behind the Superego Judgementalness the Object behind the Mirror Lacan suggests that behind the internalized opinions is an object, a person. Behind the mirrored attributions is the mirroring object. Behind the self imposed judgements is the mirror of the judgmental person. Besides the mirrored attributions is the mirror itself. The mirroring object is the object behind the attributions. First the attributions are dissolved, then the mirror itself is dissolved. The mirror as an object is dissolved. This is not a question of hating the mirroring object or loving the mirroring object. But, the mirror as objectifying others must be dissolved and metabolized by the power of awareness field. The objectifying mirror results in a sense of objectified subjectivity. The objectified sense of subjectivity results in a cohorted sense of self and a contracted state of primordial awareness. This dissolving of the objectifying mirroring object is easy to understand. The bad object that stands behind the superego must be dissolved. But, the good enough mirroring object must also be dissolved. The object in the mind, even the good object, serves an important function for a period of time, but in times the good object in the mind must be dissolved so that the subjectivity of awareness as awareness as the true subjectivity, true base, can emerge. Up to a point the good object plays a great role in the mirroring and stability of subjectivity. But the good object itself must be dissolved into awareness itself. The support of primordial awareness is the base for subjectivity without substitution of mental images. This dissolving of mirroring images allows for the symbolic fu nction of awareness itself to be the innate support. The good enough object behind the sense of self is great and necessary in the beginning, but not good enough in the long run. Just like meeting the guru is great, but one must experience the light of awareness as one's own. The radiance and expanse of awareness as one's own. The guru mirrors the light of your own innermost awareness and is actually not a real object, but exercises a symbolic function. The good enough object can stand behind us. Eventually the good enough object dissolves and what remains is awareness. The gift of the good enough object is love, and love is experienced and within the graditude of love, love remains. The mirroring object whether good or not-good must be dissolved so that pure subjectivity can manifest in clarity and openness and unboundedness. The mirroring object plays a necessary and supportive role in the manifestation of personal awareness. And in time, these mirroring objects are dissolved and become memory, memory as source of gratitude. As the object of mind dissolves, the actual existential actuality of the supporting beings remain in the actual field of the being being supported. The mental dissolving of image allows the direct support to be experienced. There is the difference between having the object in the mind, and the object as the actual, the existential actuality stand behind the person. This is a living trace of relational experience. These traces are traces within the field of awareness and are easily activated and amplified throughout life by extension and profound resonance. Such process results in an empty mind and fullness of awareness field! Written by Rudolph Bauer, Ph.D A.B.P.P. The Washington Center for Consciousness Studies


Epistemic Injustice

Philosophy

Suppose a jury rejects a Black defendantâs testimony because they believe that Black people are often untrustworthy. Or suppose the male members of a board reject a female colleagueâs suggestions because they believe that women are too often irrational. Imagine also a woman whose postpartum depression is dismissed by her doctor as mere âbaby blues.â All these three people suffer what contemporary English philosopher Miranda Fricker calls epistemic injustice. Epistemic injustice refers to a wrong done to someone as a knower or transmitter of knowledge: due to unjustified prejudice, someone is unfairly judged to not have the knowledge or reasonable beliefs that they actually have. Fricker identifies two forms of epistemic injustice: testimonial and hermeneutical. This essay explains these forms of epistemic injustice and surveys some suggestions for how to prevent them.

1000wordphilosophy.com/2020/07/21/epistemi c-injustice/ Epistemic Injustice Author: Huzeyfe Demirtas Categories: Epistemology, Ethics, Social and Political Philosophy, Philosophy of Sex and Gender, Philosophy of Race, Logic and Reasoning Wordcount: 996 Suppose a jury rejects a Black defendant's testimony because they believe that Black people are often untrustworthy. Or suppose the male members of a board reject a female colleague's suggestions because they believe that women are too often irrational. Imagine also a woman whose postpartum depression is dismissed by her doctor as mere 'baby blues.'[1] All three people above suffer what contemporary English philosopher Miranda Fricker calls epistemic injustice.[2] "Epistemic" means relating to knowledge. Epistemic injustice refers to a wrong done to someone as a knower or transmitter of knowledge: due to unjustified prejudice, someone is unfairly judged to not have the knowledge or reasonable beliefs that they actually have. Fricker identifies two forms of epistemic injustice: testimonial and hermeneutical. This essay explains these forms of epistemic injustice and surveys some suggestions for how to prevent them. 1. Testimonial Injustice Testimony involves someone sharing their knowledge with someone else. Testimony typically happens when people communicate: e.g., telling directions, reporting the news, writing a research paper. Testimony isn't just statements in a courtroom: whenever we gain knowledge from other people, we rely on testimony. Testimony is credible when there's good reason to believe that what's claimed is true, when the person testifying knows what they claim to know.[3] A speaker suffers testimonial injustice when their testimony is judged to be not or less credible because of prejudice and not because the testimony itself is unreasonable.[4] These prejudices can be related to race, gender, accent, age, and others and impact people in many areas of life: economic, educational, professional, sexual, legal, political, religious, and more. Testimonial injustices caused by such prejudices constitute the central cases of testimonial injustice. Testimonial injustice can take various forms. For instance, a female board member's prediction might be rejected due to the prejudices of her male colleagues: this is called testimonial quieting. Alternatively, that female board member might instead silence or limit her assertions beforehand knowing that she'll receive inappropriately low credibility: this is called testimonial smothering.[5] 2. Hermeneutical Injustice A second type of epistemic injustice is known as hermeneutical injustice. "Hermeneutical" relates to interpretation and understanding. Consider sexual harassment. Although this might be surprising, that concept hasn't always existed. So imagine a woman who is sexually harassed at her workplace before the introduction of the concept in the 1970s.[6] She lives in a society where unwanted sexual propositions are typically seen as a form of 'flirting,' and rejection of them as a 'lack of a sense of humor.' Since she lacks the concept of sexual harassment, she'll have difficulty understanding her experience adequately or finding the appropriate words to communicate it to others. Hermeneutical injustice then is about cases of lacking the concepts to adequately understand or communicate an experience. Hermeneutical injustice occurs when this failure is due ultimately to prejudices that result in people lacking the concepts needed to understand or communicate their experiences.[7] The injustice above was due to women's (partial) exclusion from certain professions-e.g., journalism, politics, academics, law-which tended to result in biased interpretations of women's experiences, as in interpreting sexual harassment as merely a form of flirting. Some argue that hermeneutical injustice doesn't require a lack of concepts since it can occur when interpretations of a certain group's experiences and the resulting concepts are unduly dismissed by others. For instance, postpartum depression might be dismissed as a personal deficiency rather than a 2 genuine medical condition: this is called hermeneutical dissent.[8] Unlike testimonial injustice, hermeneutical injustice is not the result of an individual hearer's prejudice: no single agent perpetrates hermeneutical injustice.[9] In the sexual harassment example, it's not any individual's prejudice, but prejudicial practices in society that resulted in people lacking the concept of sexual harassment. 3. The Wrong of Epistemic Injustice Many people closely associate producing and spreading knowledge with being human. Epistemic injustice results in victims' being at least partially excluded from participation in these activities. Epistemic injustice can lead to grave practical consequences for both victims and wrongdoers. A wrongly disbelieved defendant might lose their freedom or life. Not listening to a board member who might have crucial knowledge for the future of the company may have dire consequences. Epistemic injustice also harms the victim's identity. We typically define ourselves partly by our group identities: racial, sexual, religious, political, and more. We construct this part of our identity largely by communicating with others or using the available conceptual resources. So, being considered untrustworthy or lacking the conceptual resources to make sense of our experiences potentially prevents us from constructing our own social identity-from becoming who we are. If a woman who is interested in and has a talent for politics is constantly excluded from political dialogue, she can hardly fulfill her potential to become a politician.[10] 4. How to Prevent Epistemic Injustice To prevent epistemic injustice, Fricker suggests that we cultivate reliable character traits-virtues-that neutralize prejudice.[11] We should aim to be aware of the potential prejudice in who we judge as credible. If we find that the low credibility we assigned to a speaker is due to prejudice, we should work to change that attitude.[12] Since victims of prejudice can have undue difficulty articulating their experiences, we should try to make out how their experiences would be understood in a prejudice-free climate. Some argue that individual efforts, though they should prove useful, may not be enough to defeat epistemic injustice. For instance, education is a marker of credibility. But the lack of proper education might be due to the absence of fair opportunities for education. So that some people are unfairly considered not credible and excluded from participation in knowledge-related activities is due to underlying injustice in social institutions. The solution then requires changes in social institutions, not just individual efforts.[13] 5. Conclusion It's difficult to overestimate the value of being (recognized as) someone with knowledge. So it's difficult to overestimate the badness of knowledgerelated unfairness or epistemic injustice. Avoiding such injustice, however, might require both serious personal efforts and social and political change. Notes [1] See Medina and Pohlhaus (2017) for further discussion on, and examples of, varieties of epistemic injustice. [2] Fricker (2007, p. 7). [3] See Green, Epistemology of Testimony, for further discussion on the epistemology of testimony. [4] More specifically, a speaker suffers testimonial injustice when they are given a level of credibility different from what's appropriate due to prejudice. Hence, a speaker might suffer testimonial injustice also as a result of their testimony being judged as more credible than it actually is. Fricker argues that individual cases of one's testimony being judged more credible doesn't constitute testimonial injustice. However, she accepts that these cases seldom might add up to testimonial injustice (Fricker 2007, pp. 20-21). Davis (2016) argues that a speaker's testimony being judged more credible is a bigger problem than Fricker thought. This is because, Davis argues, testimonial injustice is wrong primarily because the victim is treated merely as a member of a group and not as the unique person that she is. And when a speaker is judged as more credible due to (positive) prejudices that are based on (positive) stereotypes, she's treated merely as a member of a group and not as the unique person that she is. [5] Doston (2011). Beyond statements and explanations, asking questions can also be treated unjustly. For instance, questions asked by women might be unjustly dismissed as irrelevant (Hookway 2010). 3 [6] See Fricker (2007, p. 149-152) for a history of how the concept of sexual harassment was introduced. [7] Fricker (2007, p. 158). [8] Goetze (2018). [9] Fricker (2007, p. 159). However, as Fricker notes, "[w]hile hermeneutical injustice is not perpetrated by individuals, it will normally make itself apparent in discursive exchanges between individuals" (Fricker, 2007, p. 18). [10] Fricker (2007, p. 54). [11] Corresponding to the two forms of epistemic injustice, Fricker identifies two different character traits and calls them the virtue of testimonial justice, and the virtue of hermeneutical justice. A virtue, roughly, is a relatively stable character trait-like honesty, generosity, or compassion. [12] This is one way the aim of the virtue of testimonial justice could be achieved. Another way it could be achieved is by plain personal familiarity. Suppose a certain accent presents an obstacle to one's fair credibility judgment. One could converse with people having that accent, or socialize with them, which can in time eradicate this obstacle. With plain habituation, people's race, age, accent, etc. can stop being relevant to their credibility (Fricker 2007, p. 96). Fricker also suggests that if "the whole business of judging credibility becomes too indeterminate, we may need to suspend judgment altogether" (Fricker 2007, p. 92). [13] Anderson (2012). References Anderson, Elizabeth (2012) "Epistemic Justice as a Virtue of Social Institutions," Social Epistemology: A Journal of Knowledge, Culture and Policy, 26(2): 163– 173. Davis, Emmalon (2016) "Typecasts, Tokens, and Spokespersons: A Case for Credibility Excess as Testimonial Injustice," Hypatia, 31(3): 485-501. Dotson, Kristie (2011) "Tracking Epistemic Violence, Tracking Practices of Silencing," Hypatia: A Journal of Feminist Philosophy, 26(2): 236–257. Fricker, Miranda (2007) Epistemic Injustice: Power and the Ethics of Knowing. Oxford New York: Oxford University Press. Green, Christopher R. "Epistemology of Testimony," The Internet Encyclopedia of Philosophy, ISSN 2161-0002, https://www.iep.utm.edu/, accessed 06 June 2020. Goetze, Trystan S. (2018) "Hermeneutical Dissent and the Species of Hermeneutical Injustice," Hypatia: A Journal of Feminist Philosophy, 33: 73–90. Hookway, Christopher (2010) "Some Varieties of Epistemic Injustice: Reflections on Fricker," Episteme, 7: 151–63. Kidd, I., Medina, J. & Pohlhaus, G. (2017) The Routledge Handbook of Epistemic Injustice. London New York: Routledge, Taylor & Francis Group. Related Essays Moral Testimony by Annaleigh Curtis Take My Word for It: On Testimony by Spencer Case The Epistemology of Disagreement by Jonathan Matheson Acknowledgements I am greatly indebted to Shane Gronholz, Chelsea Haramia, Kellan D L Head, Sanggu Lee, Dan Lowe, YaoJun Lu, Thomas Metcalf and Nathan Nobis for their invaluable comments on earlier versions of this article. About the Author Huzeyfe Demirtas is a PhD candidate and teaching associate in the Department of Philosophy at Syracuse University. He mainly works in ethics, but also has interests in epistemology and metaphysics. His dissertation investigates the relationship between moral responsibility and causal responsibility: must you have caused anything for you to be morally praised or blamed? He has taught Theories of Knowledge and Reality, Introduction to Moral Theory, and Logic. https://philpeople.org/profiles/huzeyfedemirtas-1 Follow 1000-Word Philosophy on Facebook, Twitter and subscribe to receive email notifications of new essays at the bottom of 1000WordPhilosophy.com