Explanation in Neuroscience

Today research on prosocial behaviour is very much shaped by the success of social neuroscience. However, some philosopher's criticise neuroscience as reductionist. The purpose of this paper is to analyse this critique. With a philosophical background in Charles Taylor's hermeneutic thesis "man as a self-interpreting animal", the paper shows that neuroscientists' attempt to describe prosocial behaviour in science through brain imaging technologies (MRI) constitute a neurochemical self that resonates a modern ‘paradigm of clarity and objectivity’ as presented by Taylor. It (...) is argued that this scientific explanation model challenges Taylor's hermeneutic self. Where human behaviour previous was mapped on a psychological level, prosocial behaviour is described in the body - the human brain - on a neurophysiological level. The author applies an autism analogy metaphor, which paradoxical shows that the neurochemical self’s emphasis on structures and observations epistemological facilitate an autistic understanding of prosocial behaviour alien to the situated and interpreted feelings of Taylor's self-understanding(s), and the logic of our everyday language. (shrink)

Neuroscience hasn’t shown how quite similar sensory circuits encode quite different colors and other qualia, nor how the unified pictorial form of images is encoded, nor how these codes yield conscious images. Neuroscience’s fixation here on cortical codes may be the culprit. Treating conscious images partly as retinal substances may avoid these problems. The evidence for conscious retinal images is that (a) the cortical codes for images are quite problematic, (b) injecting retinas with certain genes turns dichromats into trichromats without (...) cortical help, (c) retinas can discern colors by themselves without cortex, (d) retinal distortions appear in images but cortical distortions don’t, (e) retinal detectors support finely detailed colors, but V4 detectors don’t, due to their big receptive fields, (f) retinal activity has pictorial form, but viable cortical codes for pictorial form don’t exist. So, the colors and unified pictorial form of images arguably arise mainly across arrays of retinal detectors—not from abstract cortical codes. Images may be hidden in retinal activity beyond what EEGs show of it (cf. Strawson’s monism). Also, cortex refines these retinal images but cortical impairment (anesthesia, lesions, etc.) blocks awareness of them, for they’re no longer accessible to the mind’s subject-centered consciousness, resident in cortex. This proposal is largely testable and based in recent evidence. It avoids neuroscience’s elusive, problematic coded images and enigmatic explanations of how they become conscious. (shrink)

Many of us consider it uncontroversial that information processing is a natural function of the brain. Since functions in biology are only won through empirical investigation, there should be a significant body of unambiguous evidence that supports this functional claim. Before we can interpret the evidence, however, we must ask what it means for a biological system to process information. Although a concept of information is generally accepted in the neurosciences without critique, in other biological sciences applications of information, despite (...) careful analysis, remain controversial. In this work I will review classical stimulus-response studies in neuroscience and use Claude Shannon’s mathematical information theory as a starting point to interpret information processing as a function of the brain. I will illustrate a disanalogy between Shannon’s communication model (source, encode, channel, receiver, decode) and neural systems, and will argue that the neural code is not very code-like in comparison to genetic and engineered codes. I suggest that we have conflated the act of representing neuroscientific facts—which we do to summarize and communicate our findings with others—with taking experimental facts to be representations. (shrink)

Many cognitive scientists, neuroscientists, and philosophers of science consider it uncontroversial that the brain processes information. In this work we broadly consider the types of experimental evidence that would support this claim, and find that although physical features of specific brain areas selectively covary with external stimuli or abilities, there is no direct evidence supporting an information processing function of any particular brain area.

University Abstract Philosophers have sought to improve upon the logical empiricists’ model of scientific reduction. While opportunities for integration between the cognitive and the neural sciences have increased, most philosophers, appealing to the multiple realizability of mental states and the irreducibility of consciousness, object to psychoneural reduction. New Wave reductionists offer a continuum of comparative goodness of intertheoretic mapping for assessing reductions. Their insistence on a unified view of intertheoretic relations obscures epistemically significant crossscientific relations and engenders dismissive conclusions about (...) psychology. Richer, more sensitive accounts of explanatory pluralism and mechanistic explanation in science advocate multi-level approaches in cross-scientific settings and criticize the distance of the standard philosophical objections from working scientists’ practices and discoveries. The Heuristic Identity Theory, a new, scientifically informed version of the psycho-physical identity theory, incorporates these insights, showing how multiple realizability is an argument for (not against) identities in science and why, therefore, consciousness is not irreducible. (shrink)

The traditional thesis of the autonomy of psychology from neuroscience is grounded in a metaphysical thesis; the irreducibility thesis. Anti-reductionism is still dominant nowadays, and, as a consequence, autonomists arguably stand on firmer grounds. Still, an anti-autonomist might have the intuition that even if psychology is not reducible to neuroscience, there is a sense, not metaphysically grounded, but epistemologically grounded, in which psychology clearly is not autonomous from neuroscience. The challenge is then to offer a concept of autonomy which allows (...) for the articulation of a new and significant autonomy thesis, and to show that the thesis is false. The thesis that psychologists need not consider neuroscientific explanations when putting forward psychological explanations is such a thesis. I take it that both this characterization of autonomy and an antiautonomist argument in this sense can already be found in a paper co-authored by Gualtiero Piccinini and CarlCraver. Piccinini and Craver’s attack on this thesis is grounded on the claim that psychologists should support their functional analyses by means ofmechanistic explanations for the former to be justified at all. I also take it that, as Lawrence Shapiro has shown, their argument ultimately fails. But this was a very valuable attempt, and, in this paper, I intend to build on it with the aim of offering a new and hopefully effective anti-autonomist argument. I contend that psychologists do need to consider neuroscientific explanations when putting forward psychological explanations, and this stems from the fact that they provide the same type of explanations of the same kind of phenomena as neuroscientist, or so I argue. (shrink)

Functional robustness refers to a system’s ability to maintain a function in the face of perturbations to the causal structures that support performance of that function. Modularity, a crucial element of standard methods of causal inference and difference-making accounts of causation, refers to the independent manipulability of causal relationships within a system. Functional robustness appears to be at odds with modularity. If a function is maintained despite manipulation of some causal structure that supports that function, then the relationship between that (...) structure and function fails to be manipulable independent of other causal relationships within the system. Contrary to this line of reasoning, I argue that functional robustness often attends feedback control, rather than failures of modularity. Feedback control poses its own challenges to causal explanation and inference, but those challenges do not undermine modularity—and indeed, modularity is crucial to grappling with them. (shrink)

The traditional approach to explanation in cognitive neuroscience is realist about psychological constructs, and treats them as explanatory. On the “standard framework,” cognitive neuroscientists explain behavior as the result of the instantiation of psychological functions in brain activity. This strategy is questioned by results suggesting the distribution of function in the brain, the multifunctionality of individual parts of the brain, and the overlap in neural realization of purportedly distinct psychological constructs. One response to this in the field has been to (...) employ the tools of databasing and machine learning to attempt to find and quantify specific correlations between psychological kinds such as ‘memory’ or ‘attention’ (or sub-kinds thereof) and patterns of activity in the brain. I assess the status and prospects of these projects. I argue that current proponents of the project are vague about their aims, vis-à-vis the standard framework, sometimes suggesting substantiation of the framework, sometimes suggesting retaining the framework but revising the ontology of mental constructs, and sometimes suggesting abandonment of the framework. I argue that extant results from within the projects fail to substantiate the standard framework, and propose an alternative. On my view, psychological constructs should not be viewed as explanantia, but instead as heuristic concepts that help us uncover ways that behaviors can vary and the ways that the brain implements those distinctions. I then discuss the normative upshot of these views for databasing and brain mapping projects. (shrink)

A common kind of explanation in cognitive neuroscience might be called function-theoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals (...) the causal structure of the mechanism underlying the capacity. If they are right, then a cognitive model that resists a transparent mapping to known neural mechanisms fails to be explanatory. I argue that a function-theoretic char-acterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware. (shrink)

At its core this book is concerned with logic and computation with respect to the mathematical characterization of sentient biophysical structure and its behavior. -/- Three related theories are presented: The first of these provides an explanation of how sentient individuals come to be in the world. The second describes how these individuals operate. And the third proposes a method for reasoning about the behavior of individuals in groups. -/- These theories are based upon a new explanation of experience in (...) nature, the construction of senses, and motile behavior. This new approach is developed from first principles to enable a rigorous and systematic explanation of the variety of associated intelligent behaviors. -/- Alongside this development is a further account that focuses upon the nature of our work. It discusses the existential aspects of scientific inquiry, its epistemology and logic. It seeks to clarify the nature of the mathematical characterization and computation of natural behaviors, dealing with questions in the foundations of logic. It explores methodological issues related to reduction and the refinement of ideas from intuition to formal logical structure. -/- In support of this inquiry we work toward the development of a calculus for biophysical construction and its dynamics. If successful this mechanics mathematically characterizes sensory and motile behavior. -/- Upon this foundation we propose a model of apprehension and explore how its products are processed by the organism. Finally, we develop a probabilistic theory that enables us to reason about inaccessible factors in group behavior. -/- The mechanics we propose suggests the design and physical realization of a new model of computation; one in which structure and the concurrency of action are a first-order consideration. -/- We identify opportunities for experimental verification of the theory and we suggest a proof of our results in practice by the identification of this mechanism, allowing the construction of machines that experience. (shrink)

Alex Rosenberg’s latest book purports to establish that narrative history cannot have any epistemic value. Rosenberg argues not for the replacement of narrative history by something more science-like, but rather the end of histories understood as an account of human doings under a certain description. This review critiques three of his main arguments: 1) narrative history must root its explanations in folk psychology, 2) there are no beliefs nor desires guiding human action, and 3) historical narratives are morally and ethically (...) pernicious. Rosenberg’s book reprises themes about action explanation he first rehearsed 40 years ago, albeit with neuroscience rather than sociobiology now “preempting” explanations that trade on folk psychological notions. Although Rosenberg’s argument strategy has not altered, the review develops a number of reasons as to why his approach now lacks any plausibility as a strategy for explaining histories, much less a successful one. (shrink)

Recent breakthroughs in artificial intelligence have primarily resulted from training deep neural networks (DNNs) with vast numbers of adjustable parameters on enormous datasets. Due to their complex internal structure, DNNs are frequently characterized as inscrutable ``black boxes,'' making it challenging to interpret the mechanisms underlying their impressive performance. This opacity creates difficulties for explanation, safety assurance, trustworthiness, and comparisons to human cognition, leading to divergent perspectives on these systems. This chapter examines recent developments in interpretability methods for DNNs, with a (...) focus on interventionist approaches inspired by causal explanation in philosophy of science. We argue that these methods offer a promising avenue for understanding how DNNs process information compared to merely behavioral benchmarking and correlational probing. We review key interventionist methods and illustrate their application through practical case studies. These methods allow researchers to identify and manipulate specific computational components within DNNs, providing insights into their causal structure and internal representations. We situate these approaches within the broader framework of causal abstraction, which aims to align low-level neural computations with high-level interpretable models. While acknowledging current limitations, we contend that interventionist methods offer a path towards more rigorous and theoretically grounded interpretability research, potentially informing both AI development and computational cognitive neuroscience. (shrink)

A neuroscience-based approach has recently been proposed for the relation between the mind and the brain. The proposal is that events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality (...) can best be described in brains as operating within but not between levels. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. Here, some implications are developed. It is proposed that causality, at least as it applies to the brain, should satisfy three conditions. First, interventionist tests for causality must be satisfied. Second, the causally related events should be at the same level of explanation. Third, a temporal order condition must be satisfied, with a suitable time scale in the order of 10 ms. Next, although it may be useful for different purposes to describe causality involving the mind and brain at the mental level, or at the brain level, it is argued that the brain level may sometimes be more accurate, for sometimes causal accounts at the mental level may arise from confabulation by the mentalee, whereas understanding exactly what computations have occurred in the brain that result in a choice or action will provide the correct causal account for why a choice or action was made. Next, it is argued that possible cases of “downward causation” can be accounted for by a within-levels-of-explanation account of causality. This computational neuroscience approach provides an opportunity to proceed beyond Cartesian dualism and physical reductionism in considering the relations between the mind and the brain. (shrink)

The relation between mental states and brain states is important in computational neuroscience, and in psychiatry in which interventions with medication are made on brain states to alter mental states. The relation between the brain and the mind has puzzled philosophers for centuries. Here a neuroscience approach is proposed in which events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content (...) about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This allows the supervenient properties to be emergent, though once understood at the mechanistic levels they may seem less emergent, and expected. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. This approach may provide a way of thinking about brains and minds that is different from dualism and from reductive physicalism, and which is rooted in the computational processes that are fundamental to understanding brain and mental events, and that mean that the mental and mechanistic levels are linked by the computational process being performed. Explanations at the different levels of operation may be useful in different ways. For example, if we wish to understand how arithmetic is performed in the brain, description at the mental level of the algorithm being computed will be useful. But if the brain operates to result in mental disorders, then understanding the mechanism at the neural processing level may be more useful, in for example, the treatment of psychiatric disorders. (shrink)

According to mainstream philosophical views causal explanation in biology and neuroscience is mechanistic. As the term ‘mechanism’ gets regular use in these fields it is unsurprising that philosophers consider it important to scientific explanation. What is surprising is that they consider it the only causal term of importance. This paper provides an analysis of a new causal concept—it examines the cascade concept in science and the causal structure it refers to. I argue that this concept is importantly different from the (...) notion of mechanism and that this difference matters for our understanding of causation and explanation in science. (shrink)

This paper defines and defends a notion of teleological function which is fit to figure in explanations concerning how organic systems, and the items which compose them, are able to perform certain activities, such as surviving and reproducing or pumping blood. According to this notion, a teleological function of an item (such as the heart) is a typical way in which items of that type contribute to some containing system's ability to do some activity. An account of what it is (...) for an item to contribute to a containing system's ability to perform an activity is provided. I argue that the view acquires its normative status in virtue of the fact that it obeys a function-accident distinction and obeys a function-dysfunction distinction; that the view is ahistorical; and, that its ahistoricity provides it with an advantage over one of its main competitors. (shrink)

A foundational issue for the science and philosophy of consciousness concerns the function(s) of consciousness – what consciousness does for any particular aspect of psychological or neural processing. In spite of progress in consciousness science, false assumptions and a lack of clarity regarding how best to approach the functions of consciousness represent an ongoing and serious roadblock to progress. Misguided approaches to the function(s) of consciousness have the potential to mangle explanatory priorities, and divert attention, effort, and funding away from (...) useful questions and experimental paradigms. In this paper we offer a way forward: the capacity-based approach to the function(s) of consciousness. This approach flows out of a general explanatory approach that is influential in the philosophy of science and psychology (but not consciousness studies), according to which the mind is understood in terms of a structured collection of capacities. And capacities are explained by empirically discovered facts that identify functions (causal roles) played by empirically identified parts of a system. After elucidating this capacity-based approach to the mind, we show how consciousness fits within it. We then argue that this approach avoids problems that plague theory-based approaches to identifying the function(s) of consciousness, avoids mistakes endemic to the common strategy of looking for the function(s) of consciousness by asking what consciousness is necessary for, and re-orients explanatory priorities in a way that better focuses consciousness science, and that suggests fruitful avenues for experimentation. (shrink)

Abstract: Research on the nature and varieties of the format of cognitive representations in philosophy and cognitive science have been partly shaped by analogies to external, public representations. In this paper, we argue that relying on such analogies contributes to framing the question of cognitive formats in problematic, potentially counterproductive ways. We show that cognitive and public representations differ in many of their central features, making analogies to public representations ill-suited to improving our understanding of cognitive formats. We illustrate these (...) points by examining two case studies in which analogies to public representations may have had a negative impact on research: the 80’s-90’s debate about compositionality and cognitive architecture between symbolicists and connectionists; and contemporary discussions about the nature of visual demonstratives. Finally, we outline an alternative, computational account of formats that does not share the shortcomings of appeal to public representations. (shrink)

Abstract: Research on the nature and varieties of the format of cognitive representations in philosophy and cognitive science have been partly shaped by analogies to external, public representations. In this paper, we argue that relying on such analogies contributes to framing the question of cognitive formats in problematic, potentially counterproductive ways. We show that cognitive and public representations differ in many of their central features, making analogies to public representations ill-suited to improving our understanding of cognitive formats. We illustrate these (...) points by examining two case studies in which analogies to public representations may have had a negative impact on research: the 80’s-90’s debate about compositionality and cognitive architecture between symbolicists and connectionists; and contemporary discussions about the nature of visual demonstratives. Finally, we outline an alternative, computational account of formats that does not share the shortcomings of appeal to public representations. (shrink)

Psychiatric electroceutical interventions (PEIs) use electrical or magnetic stimulation to treat psychiatric conditions. For depression therapy, PEIs include both approved treatment modalities, such as electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS), and experimental neurotechnologies, such as deep brain stimulation (DBS) and adaptive brain implants (ABIs). We present results from a survey-based experiment in which members of four relevant stakeholder groups (psychiatrists, patients with depression, caregivers of adults with depression, and the general public) assessed whether treatment with one of (...) four PEIs (ECT, rTMS, DBS, or ABIs) was better or worse than living with treatment-resistant depression (TRD) and then provided a narrative explanation for their assessment. Overall, the prevalence of many narrative themes differed substantially by stakeholder group—with psychiatrists typically offering different reasons for their assessment than non-clinicians—but much less so by PEI modality. A large majority of all participants viewed their assigned PEI as better than living with TRD, with their reasons being a mix of positive views about the treatment and negative views about TRD. The minority of all participants who viewed their assigned PEI as worse than living with TRD tended to express negative affect toward it as well as emphasize its riskiness, negative side effects, and, to a lesser extent, its invasiveness. The richness of these narrative explanations enabled us to put in context and add depth to key patterns seen in recent survey-based research on PEIs. (shrink)

Explanations in behavioural neuroscience are often said to be mechanistic in the sense that they explain an organism’s behaviour by describing the activities and organisation of the organism’s parts that are “constitutively relevant” to organism behaviour. Much has been said about the constitutive relevance of working parts (in debates about the so-called “mutual manipulability criterion”), but relatively little has been said about the constitutive relevance of the organising relations between working parts. Some New Mechanists seem to endorse a simple causal-linking (...) account: organising relations are constitutively relevant to organism behaviour if and only if (and because) they are causal relations that link the working parts that are constitutively relevant to organism behaviour. In this paper, I argue that the causal-linking account is inadequate because it neglects the constitutive relevance of anatomical relations that organise the working parts of a behaving organism. I demonstrate this by considering a case study where the anatomical organisation of the barn owl (Tyto alba) is constitutively relevant to their mechanism for sound localization. The anatomical organisation of this mechanism is best understood as the back-and-forth flow of task information across 7 “levels of anatomy” (a notion that I distinguish from levels of mechanism). A further implication, I conclude, is that at least some of the interlevel structure of neuroscientific explanation is accounted for by levels of anatomy, not levels of mechanism. (shrink)

This article briefly reviews some classical arguments in the debate on reductionism, both in support and against, emphasising the importance of not conflating epistemological limitations with ontological issues—a distinction that, in some cases, appears to be overlooked. To achieve this, we focus on another classic text in the reductionism debate, in addition to Anderson’s contribution in this monograph: ‘A neuron doctrine in the philosophy of neuroscience’ by Gold and Stoljar (1999). Drawing upon analogies intended to enhance comprehension of the debate, (...) we question the philosophical relevance of the impossibility of a reductionist _explanation_ for the ontological dependency relationship. (shrink)

Bringing together recent case studies and insights into current developments, this collection introduces philosophers to a range of experimental methods from neuroscience. Chapters provide a comprehensive survey of the discipline, covering neuroimaging such as EEG and MRI, causal interventions like brain stimulation, advanced statistical methods, and approaches drawing on research into the development of human individuals and humankind. -/- A team of experts combine clear explanations of complex methods with reports of cutting-edge research, advancing our understanding of how these tools (...) can be applied to further philosophical inquiries into agency, emotions, enhancement, perception, personhood and more. With contributions organised by neuroscientific method, this volume provides an accessible overview for students and scholars coming to neurophilosophy for the first time, presenting a range of topics from responsibility to metacognition. (shrink)

Cognitive ontology has become a popular topic in philosophy, cognitive psychology, and cognitive neuroscience. At its center is the question of which cognitive capacities should be included in the ontology of cognitive psychology and cognitive neuroscience. One common strategy for answering this question is to look at brain structures and determine the cognitive capacities for which they are responsible. Some authors interpret this strategy as a search for neural mechanisms, as understood by the so-called new mechanistic approach. In this article, (...) I will show that this new mechanistic answer is confronted with what I call the triviality problem. A discussion of this problem will show that one cannot derive a meaningful cognitive ontology from neural mechanisms alone. Nonetheless, neural mechanisms play a crucial role in the discovery of a cognitive ontology because they are epistemic proxies for best systematizations. (shrink)

The Ecological Brain is the first book of its kind, using complexity science to integrate the seemingly disparate fields of ecological psychology and neuroscience. The book develops a unique framework for unifying investigations and explanations of mind that span brain, body, and environment: the NeuroEcological Nexus Theory (NExT). Beginning with an introduction to the history of the fields, the author provides an assessment of why ecological psychology and neuroscience are commonly viewed as irreconcilable methods for investigating and explaining cognition, intelligent (...) behavior, and the systems that realize them. The book then progresses to its central aim: presenting a unified investigative and explanatory framework offering concepts, methods, and theories applicable across neural and ecological scales of investigation. By combining the core principles of ecological psychology, neural population dynamics, and synergetics under a unified complexity science approach, NExT offers a compressive investigative framework to explain and understand neural, bodily, and environmental contributions to perception-action and other forms of intelligent behavior and thought. The book progresses the conversation around the role of brains in ecological psychology, as well as bodies and environments in neuroscience. It is essential reading for all students of ecological psychology, perception, cognitive sciences and neuroscience, as well as anyone interested in the history and philosophy of the brain/mind sciences and their state-of-the-art methods and theories. (shrink)

According to a standard view in psychology and neuroscience, there are multiple memory systems in the brain. Philosophers and scientists of memory rely on the idea that there are multiple memory systems in the brain to infer that procedural memory is not a cognitive form of memory. As a result, memory is considered to be a disunified capacity. In this article, I evaluate two criteria used by Michaelian to demarcate between cognitive and non-cognitive memory systems: appeal to stored content and (...) retrieval flexibility. By considering several empirical cases I argue that the criteria offered ultimately fail to distinguish between memory systems. The procedural memory system is neither contentless nor inflexible. (shrink)

Mental disorder (earlier entitled “illness” or “disease”) is ascribed to deviations from normal thoughts, reasoning, feelings, attitudes, and actions that are considered socially or personally dysfunctional and apt for treatment. Schizophrenia, depression, and bipolar disorder are core examples. The concept of mental disorder plays a role in many domains, including medicine, social sciences such as psychology and anthropology, and the humanities, including literature and philosophy. Philosophical discussions are the primary focus of the present entry, which differs from the entry on (...) Philosophy of Psychiatry in noting several different approaches—not only those of the philosophy of science and mind, but also those arising from phenomenology and social theory. -/- Mental disorder is a widely contested concept and there are longstanding debates on whether disorders are biological or social in nature. Even uncontroversial disorders, such as those listed above, were not always regarded as disorders in the past (Porter 2002). Moreover, the status of mental disorder has frequently indicated ethical, as much as other deficiency (Irwin 2013). Such reminders direct our attention to contemporary medical psychiatry’s sometimes uncompromisingly neurobiological, and avowedly value-free “medical model”, with its incumbent analogies and assumptions, and to the general question of how the taxa of psychiatric classifications are to be viewed as objects of natural science. This model has received intensive philosophical scrutiny, including arguments that deny these conditions are deficiencies at all. Along with issues arising from cognitive psychology and the neurosciences, such foundational matters make up much of the following entry. Because of their focus on issues central to the philosophy of mind and ethics (e.g., the mind-body problem, moral responsibility, agency and identity), analyses of mental disorder have extensive implications for those fields. As clinical phenomena, they also reflect an inescapable, everyday, social reality, giving theoretical inquiries additionally urgent practical, moral, and legal implications. (shrink)

This paper asks how representational notions figure into cognitive science, especially neuroscience. Philosophers have a way of skipping over that question and going straight to another: _what is neural representation?_ What is the property or relation that representational notions pick out? I argue that this is a mistake. Our ultimate questions, as philosophers of cognitive science, are about the function and epistemology of cognitive scientific explanations—in this case, explanations that use representational notions. To answer those questions we must understand what (...) representational notions contribute to science: what they enable scientists to do or explain, and how. But I show that we can do this without raising traditional and vexing questions about the definition of neural representation, or the nature of a property or relation that notion picks out. Taking this approach, I defend a realist account of representational explanation that underwrites important connections between philosophy and neuroscience. (shrink)

A fundamental goal of research in neuroscience is to uncover the causal structure of the brain. This focus on causation makes sense, because causal information can provide explanations of brain function and identify reliable targets with which to understand cognitive function and prevent or change neurological conditions and psychiatric disorders. In this research, one of the most frequently used causal concepts is ‘mechanism’ — this is seen in the literature and language of the field, in grant and funding inquiries that (...) specify what research is supported, and in journal guidelines on which contributions are considered for publication. In these contexts, mechanisms are commonly tied to expressions of the main aims of the field and cited as the ‘fundamental’, ‘foundational’ and/or ‘basic’ unit for understanding the brain. Despite its common usage and perceived importance, mechanism is used in different ways that are rarely distinguished. Given that this concept is defined in different ways throughout the field — and that there is often no clarification of which definition is intended — there remains a marked ambiguity about the fundamental goals, orientation and principles of the field. Here we provide an overview of causation and mechanism from the perspectives of neuroscience and philosophy of science, in order to address these challenges. (shrink)

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)

Epigenetics investigates the dynamics of gene expression in various cells, and the signals from the internal and external environment affecting these dynamics. Neuroepigenetics extends this research into neurons and glia cells. Environmental-induced changes in gene expression are not only associated with the emerging structure and function of the nervous system during ontogeny, but are also fundamental to the wiring of neural circuitries responsible for learning and memory. Yet philosophers of science and neuroscience have so far paid little attention to these (...) findings. In this article, we describe some recent experimental work on the neuroepigenetics of fear and stress responses in rodents, its ingenious experimental translation into humans, and how this work seems not to be accurately described by popular “mechanist” accounts. Our final goal is to motivate broader philosophical attention to neuroepigenetics practices and findings, especially for how it considers environmentally driven and developmental plasticity in psychological explanations. (shrink)

This article examines the problem of the specificity and functions of mental control from two main perspectives: (1) from the standpoint of natural scientific explanation; (2) within a socio-psychological and socio-humanitarian context. The first approach employs an information-based framework to address the question of how phenomena of subjective reality can serve as causes of physical changes. The distinction between informational and physical causality is elucidated, providing a justification for psychic (mental) causality as a form of informational causality. Within this context, (...) the article discusses issues of information encoding and decoding, recent advances in neuroscience regarding the deciphering of brain codes for mental phenomena, and pertinent results from genomics. The author explores the significance of these developments for substantiating free will and self-determination processes, as well as for developing new artificial intelligence systems capable of emulating natural intelligence functions. Building upon these foundations, the second aspect of the discussion examines the role of mental control in interpersonal and mass communications, as well as in the functioning of institutional entities. To this end, the article investigates the relationships between individual consciousness and mass, institutional consciousness, the importance of national leadership in extreme situations, the phenomenon of polysubjectivity, and related questions concerning the optimal balance between centralization and autonomization of control mechanisms. The author demonstrates the inevitability of overcoming the principle of monopolarity, which inhibits autonomization functions and thereby undermines the foundations of global social self-organization. Finally, the article briefly addresses a number of salient issues pertaining to the meaning, objectives, and outcomes of mental control acts, as well as their socio-humanitarian implications and evaluation. (shrink)

This paper draws on the resources of computational neuroscience (an account of active inference under the free energy principle) to address Bas van Fraassen's bad lot objection to the inference to the best explanation (IBE). The general assumption of this paper is that IBE is a finessed form of active inferences that self-organising systems perform to maximise the chance of their survival. Under this assumption, the paper aims to establish the following points: first, the capacity to learn to perform explanatory (...) inferences comes with evolutionary privileges; second, adaptive actions guide beliefs and beliefs are action-oriented; and third, IBE is not inconsistent with (approximate) Bayesianism but plays a heuristic role to it. (shrink)

The topic of mechanistic explanation in neuroscience has been a subject of recent discussion. There is a lot of interest in understanding what these explanations involve. Furthermore, there is disagreement about whether neurological mechanisms themselves should be viewed as reductionist in nature. In this paper I will explain how these two issues are related. I will, first, describe how mechanisms support a form of antireductionism. This is because the mechanisms that exist should be seen as involving part-whole relations, where the (...) behavior of a whole is more than the sum of its parts. After this, I will consider mechanistic explanations and how they can be understood. While some people think the explanations concern existing entities in the world, I will argue that we can understand the explanations by viewing them in terms of arguments. Despite the fact that it is possible to understand mechanistic explanations in this manner, the antireductionist point remains. (shrink)

Physicalists typically believe that neurophysiology has refuted the thesis that non-physical minds can interact with the brain. In this paper, I argue that it is precisely a closer look at the neurophysiology of volitional actions that suggests otherwise. I start with a clarification of how the present inquiry relates to the main argument for physicalism, and how the most common alternative views relate to the findings of my study. I then give a brief overview of the neurophysiological research about volitional (...) actions, finding that there is no research specifically directed at the pertinent question. I proceed by pointing out what it would take for a complete physical explanation of volitional actions to be true: namely a complete physical explanation of the increase in the firing rate of the neurons with which the sequence leading up to volitional actions starts. Since no dedicated research about this question is available, I offer a study of the known mechanisms of neuronal excitation as a substitute, finding that there is no plausible biochemical or physical mechanism that could explain the causal initiation of volitional actions – at least none that upholds energy conservation. But non-conservation is precisely what interactive dualism, in its most plausible version, predicts. Thus, rather than buttressing physicalism, our empirical knowledge of volitional actions points toward interactive dualism. (shrink)

Physicalists typically believe that neurophysiology has refuted the thesis that non-physical minds can interact with the brain. In this paper, I argue that it is precisely a closer look at the neurophysiology of volitional actions that suggests otherwise. I start with a clarification of how the present inquiry relates to the main argument for physicalism, and how the most common alternative views relate to the findings of my study. I then give a brief overview of the neurophysiological research about volitional (...) actions, finding that there is no research specifically directed at the pertinent question. I proceed by pointing out what it would take for a complete physical explanation of volitional actions to be true: namely a complete physical explanation of the increase in the firing rate of the neurons with which the sequence leading up to volitional actions starts. Since no dedicated research about this question is available, I offer a study of the known mechanisms of neuronal excitation as a substitute, finding that there is no plausible biochemical or physical mechanism that could explain the causal initiation of volitional actions – at least none that upholds energy conservation. But non-conservation is precisely what interactive dualism, in its most plausible version, predicts. Thus, rather than buttressing physicalism, our empirical knowledge of volitional actions points toward interactive dualism. (shrink)

Despite the scientific revolutions of the twentieth century, mechanistic explanations show a striking methodological continuity from early modern science to current scientific practice. They are rooted in the traditional method of analysis and synthesis, which was the background of Galileo’s resolutive-compositive method and Newton’s method of deduction from the phenomena. In early modern science as well as in current scientific practice, analysis aims at tracking back from the phenomena to the principles, i.e., from wholes to parts, and from effects to (...) causes. Vice versa, synthesis aims at explaining the phenomena from the parts and their interactions. Today, mechanistic explanations are atomistic in a generalized sense. They have in common to explain higher-level phenomena in terms of lower-level components and their causal actions or activities. In quantum physics, the lower-level components are subatomic particles, and the causes are their quantum interactions. After the quantum revolution, the approach continues to work in terms of the sum rules which hold for conserved properties of the parts and the whole. My paper focuses on the successes and limitations of this approach, with a side glance at the recent generalization of mechanistic explanations in cognitive neuroscience. (shrink)

Neurophenomenology is a research program that seeks to integrate the fields of neuroscience and phenomenology for the purpose of investigating the nature of human experience. Contemporary neurocognitive models pertaining to self-regulation and execution suggests that individuals interpret objects they perceive and approach as definite in their experiential encounter. Yet, a comprehensive analysis on the phenomenology of awareness and behavior reveals that during the process of detecting or interacting with objects, we experience them in a convoluted manner that underlies an adversarial (...) association of default and executive control networks. In this regard, numerous studies have invested in specific tasks involving creative-thinking that engage large-scale networks during artistic performance to understand the intricate cognitive processes of goal-oriented, self-generated thinking when subjects interact with objects and the world around them. This perspective provides a cognitive neuroscience lens on first-person narrative and third-person neural data co-development through the use of neurofeedback, aiming to enhance our understanding of the dynamic interplay between large-scale neural networks and acknowledges the challenges associated with the concurrent acquisition of both phenomenological and neuroscientific data. By doing so, research gaps and explanations for apparent discrepancies are elaborated, supporting executive function with a more in-depth phenomenological understanding of ourselves. (shrink)

In recent decades, there has been a significant increase in the interest taken by experimental researchers in issues that have traditionally belonged to the sphere of interest of the philosophy of mind, the philosophy of cognition, and even the theology of spirituality. The availability of new research methods, as well as the increase in the knowledge of the world, has opened another door to the reality in which we live. By studying the functioning of the human brain in specific laboratory (...) conditions, scientists are looking for the answers to questions that have been troubling us for centuries: what consciousness is, how do we recognize intentions, and what are spiritual experiences? This article deals with the topic of prayer as a specific religious experience in the context of neurocognitive research. In the 20th and 21st centuries, the frequency of such studies has increased significantly; for example, through the experiments of Nina Azari, Mario Beauregard, and Andrew B. Newberg. Selected experiments in the field of the neuroscience of spirituality and their results are discussed. Important philosophical questions about how we define prayer and other spiritual experiences in the context of the above research, the interdisciplinary nature of the research, as well as emerging problems and implications, e.g., the risk of reducing these phenomena to their biological base, the explanations of religious practices and experiences in cognitive science of religion have also been raised. (shrink)

This paper considers the question of whether the influential distinction between levels of explanation introduced by David Marr can be used as a general framework for contemplating levels of explanation in cognitive sciences. Marr introduced three levels at which we can explain cognitive processes: the computational, algorithmic, and implementational levels. Some argue that Marr’s levels of explanation can only be applied to modular cognitive systems. However, since many psychological processes are non-modular, it seems that Marr’s levels of explanation cannot explain (...) such psychological processes. To show that the latter claim is not convincing, the paper draws upon an influential paradigm from cognitive sciences that is based on the principle of free energy. Based on this paradigm, the paper argues that even non-modular psychological processes can be computationally analyzed and algorithmically implemented. The conclusion of the paper is that, at least under the assumption that the func- tion of the mind is to minimize free energy, Marr’s levels of explanation can be successfully used as a general framework for understanding psychological processes at different levels of description. (shrink)

Trauma and traumatization have arguably always been part of the human experience yet have in the last few decades come to occupy a prominent place in various popular and academic contexts. This chapter offers an interdisciplinary and comparative investigation of trauma and traumatization in different historical contexts. More specifically, my aim is to discuss whether the rich bodies of research in trauma and traumatization in Holocaust survivors and their descendants yield relevant insights for post-slavery contexts. It has been shown that (...) children of Holocaust survivors suffer from stress and other symptoms related to their parents’ traumatization which influence the interactions with their environments. Such results made me wonder whether the traumatic impact of chattel slavery—which has been abolished some 160 years ago—might have a similar impact, yet now across several generations. Issues of the transmission and current persistence of trauma are inherently linked to questions of social justice, recognition and reparations. This chapter is meant, however, as an exploration of interdisciplinary connections that should be studied in concert to account for the traumatic impact of historical and present day experiences. It starts by discussing the concepts of trauma and post-traumatic stress disorder. Continuing by exploring the phenomenon of the inter- and transgenerational transmission of trauma, it relies in part on the important body of research conducted on families of Holocaust survivors. I then turn to the much less researched ‘post-traumatic slave syndrome’ (DeGruy) and discuss two factors that might contribute to the transgenerational transmission of trauma in the families of former enslaved: epigenetics and the continuation of traumatization even after the abolition of slavery as articulated in Historical Trauma theory. Drawing upon these insights, I conclude that it is plausible that a continuing transgenerational transmission of trauma might occur in some families of slavery survivors, the knowledge of which might help to break the chains of such traumatization across generations. (shrink)

In this book, Matej Kohar demonstrates how the new mechanistic account of explanation can be used to support a non-representationalist view of explanations in cognitive neuroscience, and therefore can bring new conceptual tools to the non-representationalist arsenal. Kohar focuses on the explanatory relevance of representational content in constitutive mechanistic explanations typical in cognitive neuroscience. The work significantly contributes to two areas of literature: 1) the debate between representationalism and non-representationalism, and 2) the literature on mechanistic explanation. Kohar begins with an (...) introduction to the mechanistic theory of explanation, focusing on the analysis of mechanistic constitution as the basis of explanatory relevance in constitutive mechanistic explanation. He argues that any viable analysis of representational contents implies that content is not constitutively relevant to cognitive phenomena. The author also addresses objections against his argument and concludes with an examination of the consequences of his account for both traditional cognitive neuroscience and non-representationalist alternatives. This book is of interest to readers in philosophy of mind, cognitive science and neuroscience. (shrink)

The philosophical literature on scientific explanation in neuroscience has been dominated by the idea of mechanisms. The mechanist philosophers often claim that neuroscience is in the business of finding mechanisms. This view has been challenged in numerous ways by showing that there are other successful and widespread explanatory strategies in neuroscience. However, the empirical evidence for all these claims was hitherto lacking. Empirical evidence about the pervasiveness and uses of various explanatory strategies in neuroscience is particularly needed because examples and (...) case studies that are used to illustrate philosophical claims so far tend to be hand-picked. The risk of confirmation bias is therefore considerable: when looking for white swans, all one finds is that swans are white. The more systematic quantitative and qualitative bibliometric study of a large body of relevant literature that we present in this paper can put such claims into perspective. Using bibliometric tools, we identify the typical linguistic patterns used in the alleged mechanistic, dynamical, and topological explanations in the literature, their preponderance and how they change over time. Our findings show abundant use of mechanistic language, but also the presence of a significant neuroscience literature using topological and dynamical explanatory language, which grows over time and increasingly differentiates from each other and from mechanistic explanations. (shrink)

We provide three innovations to recent debates about whether topological or “network” explanations are a species of mechanistic explanation. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. (...) We conclude from this that topological explanations are autonomous from their mechanistic counterparts. (shrink)

The relationship between topological explanation and mechanistic explanation is unclear. Most philosophers agree that at least some topological explanations are mechanistic explanations. The crucial question is how to make sense of this claim. Zednik (Philos Psychol 32(1):23–51, 2019) argues that topological explanations are mechanistic if they (i) describe mechanism sketches that (ii) pick out organizational properties of mechanisms. While we agree with Zednik’s conclusion, we critically discuss Zednik’s account and show that it fails as a general account of how and (...) when topological explanations are mechanistic. First, if topological explanations were just mechanism sketches, this implies that they could be enriched by replacing topological terms with mechanistic detail. This, however, conflicts how topological explanations are used in scientific practice. Second, Zednik’s account fails to show how topological properties can be organizational properties of mechanisms that have a place in mechanistic explanation. The core issue is that Zednik’s account ignores that topological properties often are _global_ properties while mechanistic explanantia refer to _local_ properties. We demonstrate how these problems can be solved by a recent account of mechanistic completeness (Craver and Kaplan in Br J Philos Sci 71(1):287–319, 2020; Kohár and Krickel in Calzavarini and Viola (eds) Neural mechanisms—new challenges in the philosophy of neuroscience, Springer, New York, 2021) and use a multilayer network model of Alzheimer’s Disease to illustrate this. (shrink)

The demise of the scholastic worldview and the rise of the mechanistic one may give the impression of a parallel demise of the scholastic explanatory framework. In this paper, I argue that this impression is wrong. To this end, I first outline Descartes’ representative and particularly sharp mechanistic criticism of the scholastic notion of explanation. Deploying conceptual machinery from contemporary philosophy of science, I then suggest a reconstruction of the scholastic notion that is immune to Descartes’ criticism. Based on this (...) reconstruction, I reinterpret the dispute between Descartes and the scholastics as one that concerns the extent of two legitimate conceptions of explanation. Finally, I outline a contemporary dispute within cognitive neuroscience that reflects the Cartesian-scholastic one as thus reinterpreted, thereby showing that aspects of the world may well require a scholastic-like approach for their explanation. The aim of this paper, then, is to shed light on a most important philosophical-cum-scientific historical controversy from a modern perspective, but also to highlight the deep historical roots of a related contemporary dispute. Based on this, the paper also seeks to draw a substantial philosophical conclusion concerning the issue under dispute in both controversies. (shrink)

Levenstein et al. aptly highlight some of the foundational issues in theoretical neuroscience, such as the role of abstraction and idealization in providing scientific explanations and understanding, and distinguishing under which conditions neuroscientific models provide genuine explanations, or mere descriptions, predictions, or control. -/- The authors rightly emphasize that philosophers of science can also gain valuable insights from the vast body of neuroscience literature, by employing methods of digital humanities, such as text mining, in line with the cognitive metascience approach (...) (Miłkowski, 2023). -/- Conversely, neuroscientists can benefit from the latest research in philosophy of science, particularly on the epistemic norms of diverse kinds of explanations in neuroscience, e.g., topological (Kostić, 2020), computational (Miłkowski, 2013; Chirimuuta, 2018), dynamical (Favela, 2020), and functional (Egan, 2017). Understanding the diverse goals of theories is conducive to fruitful exploratory and explanatory scientific practice. Some forms of scientific understanding are not always grounded in explanations, but also in diverse theoretical representations, e.g., diagrams, taxonomies, and diagnostic manuals (Miłkowski, 2023). Philosophical theories of understanding can also integrate different kinds of explanations in a single framework (Khalifa et al., 2022). -/- The need for these interdisciplinary connections is particularly vivid in the case of network models that are increasingly being used in neuroscience (Kostić et al., 2020), and especially when it comes to evaluating their explanatory power (Kostić, 2020; Kostić and Khalifa, 2023). -/- In conclusion, we strongly support Levenstein et al.'s call for greater collaboration between philosophers of science and neuroscientists on the foundational issues in neuroscience. Embracing this interdisciplinary approach is essential for advancing our knowledge of the brain and its function. (shrink)

Changes in synaptic strength are described as a unifying hypothesis for memory formation and storage, leading philosophers to consider the ‘synaptic efficacy hypothesis’ as a paradigmatic explanation in neuroscience. Craver’s mosaic view has been influential in understanding synaptic efficacy by presenting long-term potentiation as a multi-level mechanism nested within a multi-level structure. This paper argues that the mosaic view fails to fully capture the explanatory power of the synaptic efficacy hypothesis due to assumptions about multi-level mechanisms. I present an alternative (...) approach that emphasizes the explanatory function of unification, accounting for the widespread consensus in neuroscience regarding synaptic efficacy by highlighting the stability of synaptic causal variables across different multi-level mechanisms. (shrink)

According to recent discussion, cross-explanatory integration in cognitive science might proceed by constraints on mechanistic and dynamic-mechanistic models provided by different research fields. However, not much attention has been given to constraints that could be provided by the study of first-person experience, which in the case of multifaceted mental phenomena are of key importance. In this paper, we fill this gap and consider the question whether information about first-person experience can constrain dynamic-mechanistic models and what the character of this relation (...) is. We discuss two cases of such explanatory models in neuroscience, namely that of migraine and of epilepsy. We argue that, in these cases, first-person insights about the target phenomena significantly contributed to explanatory models by shaping explanatory hypotheses and by indicating the dynamical properties that the explanatory models of these phenomena should account for, and thus directly constraining the space of possible explanations. (shrink)