Recently Feminist New Materialism has emerged as a field that questions the capability of critique to offer substantive change and calls for more affirmative forms of criticality which add to, rather than subtract from, alternate ways of living in the world. This ‘affirmative turn’ is an emerging influence in social work where it is taken up to disrupt human-centred notions of agency and engage with the non-human and more-than-human relations that make up the material-social world. This paper adds to this (...) work, utilizing Karen Barad’s concept and method of diffraction to critically engage with trauma-informed practice, a current popular approach in social work that draws on neuroscience and social theory. Specifically, diffraction is used to put neuro-trauma theory into conversation with Extended Emotion theory, and through reading the insights they offer, re-configure trauma-informed social work as situated, embodied, relational practices for making differences that matter in the world. This example also suggests what diffraction makes possible for social work as an onto-ethical mode of affirmative critique. (shrink)

Rich and sophisticated analyses of gender have been produced around the postcolonial world. But the theory in this work gets little recognition in the current global economy of knowledge. Feminist theory needs an understanding of the coloniality of gender, seeing the gender dynamic in imperialism and the significance of global processes for the meaning of gender itself. The agendas of feminist theory are being re-shaped on issues that include violence, power and the state, identity, methodology, and the land. An alternative (...) structure of knowledge is emerging that can re-shape the global terrain of feminist theory and its connections with practice. (shrink)

The relation between hegemony and masculinity needs reassessment in the light of postcolonial critique. A fully historical understanding of hegemony is required. The violence of colonization set up a double movement, disrupting gender orders and launching new hegemonic projects. This dynamic can be traced in changing forms through the eras of decolonization, postcolonial development, and neoliberal globalization. Specific configurations of masculinity in the contemporary metropole-apparatus can be traced, together with their relations with local power. A gender order is emerging in (...) transnational space and minimal conditions for hegemony within it can be defined. Counter-hegemonic projects among men have multiplied but have limited reach. Hegemony under construction, rather than achieved hegemony, is the key concept. (shrink)

Recent extensions of mechanistic explanation into psychology suggest that cognitive models are only explanatory insofar as they map neatly onto, and serve as scaffolding for more detailed neural models. Filling in those neural details is what these accounts take the integration of cognitive psychology and neuroscience to mean, and they take this process to be seamless. Critics of this view have given up on cognitive models possibly explaining mechanistically in the course of arguing for cognitive models having explanatory value independent (...) of how well they align with neural mechanisms. We can have things both ways, however. The problem with seamless integration accounts is their seamlessness, not that they take cognitive models to be mechanistic. A non-componential view of mechanisms allows for cognitive and neural models that cross cut one another, and for cognitive models that don’t decompose into parts. I illustrate the inadequacy of seamless accounts of integration by contrasting how “filter” models of attention in psychology and of sodium channels in neuroscience developed; by questioning whether the mappings generated by neuroimaging subtraction studies achieve integration; and by reinterpreting the evidence for cognitive models of memory having been successfully integrated with neural models. I argue that the integrations we can realistically expect are more partial, patchy, and full of loose threads than the mosaic unity Craver describes. (shrink)

Corrigendum to “Border thinking and disidentification: Postcolonial and postsocialist feminist dialogues”, by Madina Tlostanova, Redi Koobak, Suruchi Thapar-Björkert, Feminist Theory, DOI: 10.1177/1464700116645878.

There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and (...) targets are picked out, how modeling inferences are justified, and the metaphysical status of models. (shrink)

Discussions of psychiatric nosology focus on a few popular examples of disorders, and on the validity of diagnostic criteria. Looking at Anorexia Nervosa, an example rarely mentioned in this literature, reveals a new problem: the DSM has a strict taxonomic structure, which assumes that disorders can only be located on one branch. This taxonomic assumption fails to fit the domain of psychopathology, resulting in obfuscation of cross-category connections. Poor outcomes for treatment of Anorexia may be due to it being pigeonholed (...) as an Eating Disorder, when a disturbance of body perception may be a more central symptom than food restriction. This paper explores the possibility of restructuring the DSM taxonomy to allow for a pluralist classification of disorders. This change could improve treatment and research without requiring any changes to diagnostic criteria. (shrink)

When Artificial Intelligence (AI) is applied in decision-making that affects people’s lives, it is now well established that the outcomes can be biased or discriminatory. The question of whether algorithms themselves can be among the sources of bias has been the subject of recent debate among Artificial Intelligence researchers, and scholars who study the social impact of technology. There has been a tendency to focus on examples, where the data set used to train the AI is biased, and denial on (...) the part of some researchers that algorithms can also be biased. Here we illustrate the point that algorithms themselves can be the source of bias with the example of collaborative filtering algorithms for recommendation and search. These algorithms are known to suffer from cold-start, popularity, and homogenizing biases, among others. While these are typically described as statistical biases rather than biases of moral import; in this paper we show that these statistical biases can lead directly to discriminatory outcomes. The intuitive idea is that data points on the margins of distributions of human data tend to correspond to marginalized people. The statistical biases described here have the effect of further marginalizing the already marginal. Biased algorithms for applications such as media recommendations can have significant impact on individuals’ and communities’ access to information and culturally-relevant resources. This source of bias warrants serious attention given the ubiquity of algorithmic decision-making. (shrink)

Mechanism schemata are one of the least understood parts of MDC’s account of mechanistic explanation. Relatedly, there is a common misconception that there is no place for abstraction in MDC mechanisms. These two problems can be remedied by looking more carefully at what MDC say both in their 2000 paper and elsewhere about schemata and abstraction, and by following up on a comment of Machamer’s indicating that Piaget was the inspiration for schemata. Darden’s work on mechanism discovery reveals an important (...) role for abstract mechanism schemata, not only as concise representations of known mechanisms, but also as hypotheses for how unknown mechanisms might work. Piaget’s schemata likewise both capture the developing child’s current state of knowledge, and serve as templates into which new experiences are fit. If we assume that mechanistic explanations should pick out real difference-makers, and that what makes a difference needn’t always be fine-grained details, we open up an explanatory role for abstract schemata to play. Schemata represent the coarser-grained features of mechanisms that are often among the most important difference-makers. In many contexts they might need to be supplemented with finer grained details in order to give an adequate explanation, but independent of those details, schemata remain explanatory in virtue of being part of the explanation. Detailed accounts of real difference-makers need not be seen as in competition with abstract models or generalizations. They can be combined in mechanistic explanations. (shrink)

This chapter explores the epistemic roles played by connectionist models of cognition, and offers a formal analysis of how connectionist models explain. It looks at how other types of computational models explain. Classical artificial intelligence (AI) programs explain using abductive reasoning, or inference to the best explanation; they begin with the phenomena to be explained, and devise rules that can produce the right outcome. The chapter also looks at several examples of connectionist models of cognition, observing what sorts of constraints (...) are used in their design, and how their results are evaluated. It argues that the point of implementing networks roughly analogous to neural structures is to discover and explores the generic mechanisms at work in the brain, not to deduce the precise activities of specific structures. The chapter explores a formal analysis of the explanations offered, which interprets connectionist models and the cognitive theories they represent as sharing membership in a type of mechanism. (shrink)

William James presaged, and Alan Allport voiced criticisms of cause theories of executive attention for involving a homunculus who directs attention. I review discussions of this problem, and argue that existing philosophical denials of the problem depend on equivocations between different senses of “Cartesian error”. Another sort of denial tries to get around the problem by offering empirical evidence that such an executive attention director exists in prefrontal cortex. I argue that the evidence does not warrant the conclusion that an (...) executive director can be localized in prefrontal cortex unless dubious assumptions are made, and that computational models purporting to support these assumptions either beg the question, or fail to model executive attention in terms of cause theories. (shrink)

Diversity is often announced as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by diversity and how it can solve those problems is seldom spelled out. This lack of clarity is one hurdle to motivating diversity in AI. Another hurdle is that while the most common perceptions about what diversity is are too weak to do the work set out for them, stronger notions of diversity are often defended on normative grounds that fail to (...) connect to the values that are important to decision-makers in AI. However, there is a long history of research in feminist philosophy of science and a recent body of work in social epistemology that taken together provide the foundation for a notion of diversity that is both strong enough to do the work demanded of it, and can be defended on epistemic grounds that connect with the values that are important to decision-makers in AI. We clarify and defend that notion here by introducing emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise. We illustrate the connected epistemic and ethical benefits of designing technology with diverse groups of workers using the examples of an infamous racist soap dispenser, and the millimeter wave scanners used in US airport security. (shrink)

Andrew was a desperately premature baby weighing under two pounds. He died after months of "heroic' efforts in an intensive care facility. The story of his short cruel institutionalised life is a case study in the limits and excesses of modern medicine. The night he told us our son Andrew was about to die the doctor who had taken charge of him six months before also told us we were "intellectually tight' that we had "no feelings only thoughts and words (...) and strategies'. We were "bad parents'. As the parents of a five-year-old daughter we knew the love a mother and father feels for children. Yet as Andrew's parents we were used to condemnation and insult. Andrew was a baby born 15 weeks prematurely weighing only 1lb 12oz and in a state of painful deterioration almost from the start. We wanted him to be allowed to die a natural death. Andrew's story is the story of what can happen when a baby becomes hopelessly entrapped in an intensive care unit where the machinery is more sophisticated than the code of law and ethics governing its use. The letter printed below was sent to the administrator and numerous personnel of the hospital that controlled the life and death of our son. The physician-in-chief of that hospital characterised it as a "carefully documented critique'. The letter appear here somewhat edited and abridged and the names of people and institutions have been changed all but our own. It is the personal record of what happened to our baby and to us. (shrink)

Synchronicity has long been described as an ‘acausal’ connecting principle. However, the use of this descriptor is not only misleading, but also outright false on any seriously considered picture of synchronicity due to admissions of multiple types of causes. Furthermore, previous attempts to clarify the ‘acausal’ label have served only to further muddy the waters of discussion. A ‘multi-causal’ conception of synchronicity is proposed to ease and encourage future discussion in many disciplines.

The past seven centuries of scholarly attention to and debate over the Roman de la Rose bear strong witness to the fact that the allegorical figure Faus Semblant presents us with an interpretive crux—one of many such in the poem—that we are not likely to resolve in the coming centuries. Perhaps it should come as no surprise that a character who so embodies paradox—a profane friar who is openly honest about his intent to deceive—should be so difficult to pin down; (...) it is his singular talent, after all, to dissemble and confuse. The last century has seen a significant increase in criticism centered upon Faus Semblant that seeks to understand what he signifies within the larger allegory of the poem and how he relates to Jean de Meun's purported satire and antifraternalism. But this struggle to understand and explain the character began centuries earlier and, like him, has taken many forms. As early as the thirteenth century, the passage of Jean's poem in which Faus Semblant explains his craft and guile had begun to attract a diverse group of revisers: scribes, who added or deleted passages in order to shape a reading of the poem or avoid offending readers; remanieurs, such as Gui de Mori, who substantially rewrote the passage in efforts to reshape the poem into a more cohesive form; readers, whose marginal inscriptions and notae often accompany the passage; and illustrators , as images of Faus Semblant are frequently included in illuminated copies of Rose manuscripts. Even Jean himself seemed to feel the need to gloss and contain Faus Semblant, offering an apology and clarification in lines 15213–30. (shrink)

This chapter explores some of the ways that mechanisms are invoked in neuroscience and looks at a selection of the philosophical problems that arise when trying to understand mechanistic explanations. It introduces a series of historical case studies that illustrate how neuroscientists have depended on mechanistic metaphors in their efforts to understand the mind and brain, and how their mechanistic explanations have developed over time. The chapter highlights what contemporary philosophers have identified as the fundamental features of mechanisms and mechanistic (...) explanation. It considers some of the methodological issues that arise in neuroscience including how to integrate psychological with neural models, how to generalize findings in model organisms like the sea slug Aplysia to human learning and memory, and whether to favor top-down or bottom-up methods. Constructing multi-level mechanistic explanations involves intensive collaboration across different branches of science, and involves many challenges, both pragmatic and methodological. (shrink)

Design and analysis of algorithms can be a difficult subject for students due to its sometimes-abstract nature and its use of a wide variety of mathematical tools. Here the author, an experienced and successful textbook writer, makes the subject as straightforward as possible in an up-to-date textbook incorporating various new developments appropriate for an introductory course. This text presents the main techniques of algorithm design, namely, divide-and-conquer algorithms, greedy algorithms, dynamic programming algorithms, and backtracking. Graph algorithms are studied in detail, (...) and a careful treatment of the theory of NP-completeness is presented. In addition, the text includes useful introductory material on mathematical background including order notation, algorithm analysis and reductions, and basic data structures. This will serve as a useful review and reference for students who have covered this material in a previous course. Features The first three chapters provide a mathematical review, basic algorithm analysis, and data structures. Detailed pseudocode descriptions of the algorithms along with illustrative algorithms are included. Proofs of correctness of algorithms are included when appropriate. The book presents a suitable amount of mathematical rigor. After reading and understanding the material in this book, students will be able to apply the basic design principles to various real-world problems that they may encounter in their future professional careers. (shrink)

The problem of why we identify with Barney Stinson on the show How I Met Your Mother.

Deep learning (DL) is a statistical technique for pattern classification through which AI researchers train artificial neural networks containing multiple layers that process massive amounts of data. I present a three-level account of explanation that can be reasonably expected from DL models in cognitive neuroscience and that illustrates the explanatory dynamics within a future-biased research program (Feest Philosophy of Science 84:1165–1176, 2017 ; Doerig et al. Nature Reviews: Neuroscience 24:431–450, 2023 ). By relying on the mechanistic framework (Craver Explaining the (...) brain: Mechanisms and the mosaic unity of neuroscience. Clarendon, 2007 ; Stinson Eppuor si muove: Doing history and philosophy of Science with Peter Machamer. Springer, 2017, The Routledge Handbook of the computational mind. Routledge, 2018 ), I develop an account that corresponds to the stages of mechanism discovery, i.e., our shifting epistemic position and epistemic goals, and propose a representative model for each level. Generic, theoretical DL models at _Level 1_ address the general features of a cognitive phenomenon through exploration and provide us with how-possibly explanations. On the other hand, DL models at _Level 2_ either identify the interaction between the features or represent the epistemic stage when the researcher is still unsure if the modeled features are crucial or arbitrary. These models should provide us with how-plausibly explanations. Finally, specific DL models of specific brain areas, i.e., ersatz models filled with relevant cognitive and neuroscientific details, are at _Level 3_. At this level, a researcher can advance how-actually explanations of cognitive phenomena. The main strength of this account is that it elucidates both _global explanatory dynamics_ and _local explanatory dynamics_ (cf. Dresow Synthese 199:10441–10474, 2021 ). The former occurs when the transition between levels happens in accordance with the process of obtaining more details about a particular cognitive phenomenon through multiple DL models. The latter, meanwhile, involves cases in which the transition between levels takes place within a single DL model by elucidating internal mechanisms (e.g., using Explainable AI techniques for rendering models more transparent). (shrink)

The recent process of deinstitutionalization of the psychiatric treatment system, in both Denmark and other European countries, has relied heavily on the involvement in treatment and recovery of cohabitant relatives of diagnosed people. However, political objectives regarding depression and involvement rely on a limited body of knowledge about people’s ways of managing illness-related problems in everyday life. Drawing on a discursive notion of gender laid out by Raewyn Connell, the aim of the article is to elucidate how the involvement (...) of relatives is guided by an extra- individual rationale about gender and caregiving, and how this gendered discourse might frame different challenges and burdens, depending on the gender of the diagnosed person and the cohabitant relative. Drawn from a larger, multisited field study on involvement processes in Danish psychiatry, t he article is based on field notes and 21 interviews with seven heterosexual couples. The analysis shows that gender works as a decisive premise for the division of caregiving labour among the couples, and clarifies how the couples’ gendered institution is disrupted after the onset of depression. The article argues that gender-blind involvement strategies could produce divergent treatment outcomes and varying social effects in relation to couples’ everyday family lives. (shrink)

Our paper focuses on the philosophical perspective of constructing active caring masculinities agencies in the contemporary feminist discourse. Since contemporary feminisms are not simply anti-essentialist, but more importantly, polyphonic, we believe that it is far more appropriate to talk about ‘masculinities’ as opposed to ‘masculinity’. We are proposing a revised understanding of the self in which the self is not defined primarily in the dichotomous, categorical one-other relationship. We use Paul Ricoeur’s anthropology to describe the self as relational, as well (...) as Joan Tronto’s recent perspective on care which fits well with a Ricoeurian reconstruction of the self. We also engage with Raewyn Connell’s discourse on masculinity and more specifically, hegemonic masculinity. By using ‘caring masculine agencies’ as an alternative to ‘masculinity as reactive anti-femininity’, we are proposing a paradigm shift that hopefully is flexible enough to respect the dynamism inherent to any act of gender-identification. (shrink)

In her excellent and thought-provoking essay ";Searching for the Source of Executive Attention";; Catherine Stinson argues that many accounts of executive attention threaten to involve some kind of conceptual confusion. While agreeing with many of the key criticisms, I explore some possible responses, which retain some of the flavor of the notion of executive attention.

_The Opportunity Gap_ aims to shift attention from the current overwhelming emphasis on schools in discussions of the achievement gap to more fundamental questions about social and educational opportunity. The achievement gap looms large in the current era of high-stakes testing and accountability. Yet questions persist: Has the accountability movement—and attendant discussions on the achievement gap—focused attention on the true sources of educational failure in American schools? Do we need to look beyond classrooms and schools for credible accounts of disparities (...) in educational outcomes? The essays in this book reintroduce the overlooked central issue in educational inequity: the lack of opportunity that many social groups face in our common quest for educational attainment. In a series of wide-ranging and carefully nuanced essays, _The Opportunity Gap_ casts much-needed light on the vexed relationship between society and education—and on the crucial, persistent role that education plays in addressing social ills. Contributors include Gilberto Q. Conchas, Raewyn Connell, Pat English-Sand, Linda May Fitzgerald, Patresa Hartman, Jeff Howard, Mieko Kamii, Rafa M. Kasim, Christopher Kliewer, Robert A. LeVine, Sarah E. LeVine, Jodi Meyer-Mork, Robert Parris Moses, Sonia Nieto, Donna Raschke, Stephen W. Raudenbush, Ray C. Rist, Beatrice Schnell-Anzola, Irene Serna, Susan McAllister Swap, and Amy Stuart Wells; with an afterword by Ronald F. Ferguson. (shrink)