Testing hypotheses about the harm that capitalism causes to the mind and brain: a theoretical framework for neuroscience research

This article reviews and discusses the part of neuroscience relevant to mental health within the contemporary capitalist context, and suggests ways in which the effects of this context on the nervous system can be reconceptualised and researched in the future. Firstly, the principal components of neoliberal capitalism are presented together with how it has historically influenced neuroscience. We then argue in favour of a neurodiversity perspective, as opposed to the dominant model of conceptualising neural (mal-)functioning, brain plasticity and potential for change and adaptation. We review the available empirical research indicating that the socio-economic environment is harmful to minds and brains. Lastly, we set out a theoretical framework that can generate neuroscientific hypotheses with regards to the effects of the capitalist context on brains and minds, as well as a frame for post-capitalist research.

Elements of Neuroanthropology

Neuroanthropology is the integration of neuroscience into anthropology and aims to understand “brains in the wild.” This interdisciplinary field examines patterns of human variation in field settings and provides empirical research that complements work done in clinical and laboratory settings. Neuroanthropology often uses ethnography in combination with theories and methods from cognitive science as a way to capture how culture, mind, and brain interact. This article describes nine elements that outline how to do neuroanthropology research: (1) integrating biology and culture through neuroscience and biocultural anthropology; (2) extending focus of anthropology on what people say and do to include what people process; (3) sizing culture appropriately, from broad patterns of culture to culture in small-scale settings; (4) understanding patterns of cultural variation, in particular how culture produces patterns of shared variation; (5) considering individuals in interaction with culture, with levels of analysis that can go from biology to social structures; (6) focusing on interactive elements that bring together biological and cultural processes; (7) conceptual triangulation, which draws on anthropology, psychology, and neuroscience in conjunction with field, clinic, and laboratory; (8) critical complementarity as a way to integrate the strengths of critical scholarship with interdisciplinary work; and (9) using methodological triangulation as a way to advance interdisciplinary research. These elements are illustrated through three case studies: research on US combat veterans and how they use Brazilian Jiu Jitsu as a way to manage the transition to becoming civilians, work on human-raptor interactions to understand how and why these interactions can prove beneficial for human handlers, and adapting cue reactivity research on addiction to a field-based approach to understand how people interact with cues in naturalistic settings.

On logical inference over brains, behaviour, and artificial neural networks

In the cognitive, computational, and neuro- sciences, we often reason about what models (viz., formal and/or computational) represent, learn, or "know", as well as what algorithm they instantiate. The putative goal of such reasoning is to generalize claims about the model in question to claims about the mind and brain. This reasoning process typically presents as inference about the representations, processes, or algorithms the human mind and brain instantiate. Such inference is often based on a model's performance on a task, and whether that performance approximates human behaviour or brain activity. The model in question is often an artificial neural network (ANN) model, though the problems we discuss are generalizable to all reasoning over models. Arguments typically take the form "the brain does what the ANN does because the ANN reproduced the pattern seen in brain activity" or "cognition works this way because the ANN learned to approximate task performance." Then, the argument concludes that models achieve this outcome by doing what people do or having the capacities people have. At first blush, this might appear as a form of modus ponens, a valid deductive logical inference rule. However, as we explain in this article, this is not the case, and thus, this form of argument eventually results in affirming the consequent – a logical or inferential fallacy. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the logic, the metatheoretical calculus, our fields deploy in high-level scientific inference.

You can't always get what you want

The predictive processing framework has gained significant popularity across disciplines investigating the mind and brain. In this article we critically examine two of the recently made claims about the kind of headway that the framework can make in the neuroscientific and philosophical investigation of consciousness. Firstly, we argue that predictive processing is unlikely to yield significant breakthroughs in the search for the neural correlates of consciousness as it is still too vague to individuate neural mechanisms at a fine enough scale. Despite its unifying ambitions, the framework harbors a diverse family of competing computational models which rely on different assumptions and are under-constrained by neurological data. Secondly, we argue that the framework is also ill suited to provide a unifying theory of consciousness. Here, we focus on the tension between the claim that predictive processing is compatible with all of the leading neuroscientific models of consciousness with the fact that most attempts explaining consciousness within the framework rely heavily on external assumptions.

How a Brain Makes a Mind

A historical overview is given of interdisciplinary work in physics and psychology by some of the greatest nineteenth-century scientists, and why the fields split, leading to a century of ferment before the current scientific revolution in mind-brain sciences began to understand how we autonomously adapt to a changing world. New nonlinear, nonlocal, and nonstationary intuitions and laws are needed to understand how brains make minds. Work of Helmholtz on vision illustrates why he left psychology. His concept of unconscious inference presaged modern ideas about learning, expectation, and matching that this book scientifically explains. The fact that brains are designed to control behavioral success has profound implications for the methods and models that can unify mind and brain. Backward learning in time, and serial learning, illustrate why neural networks are a natural language for explaining brain dynamics, including the correct functional stimuli and laws for short-term memory (STM), medium-term memory (MTM), and long-term memory (LTM) traces. In particular, brains process spatial patterns of STM and LTM, not just individual traces. A thought experiment leads to universal laws for how neurons, and more generally all cellular tissues, process distributed STM patterns in cooperative-competitive networks without experiencing contamination by noise or pattern saturation. The chapter illustrates how thinking this way leads to unified and principled explanations of huge databases. A brief history of the advantages and disadvantages of the binary, linear, and continuous-nonlinear sources of neural models is described, and how models like Deep Learning and the author’s contributions fit into it.

Final Reflections and Future Trends

In this final chapter some big-picture topics are described and discussed. These include the concept of hierarchies in memory theory and an assessment of their validity in the levels-of-processing (LOP) framework (e.g., does the construct of “LOP” connote a continuum of depth or a series of qualitative stages?). A further topic is the hypothesized organization of representations from episodic-specific to abstract-general. Other issues in encoding, retrieval, and their interactions are considered, including some recent findings on the effects of divided attention (DA) at the time of retrieval. Hintzman’s ideas on reminding and recurrence, and also Jacoby’s concept of source-constrained retrieval, are discussed and evaluated. The author’s perspective on working memory is described, including the view that there is no need to invoke discrete stores or memory buffers. Some further issues in cognitive aging are discussed, including a proximal-distal hypothesis of when deficits are found. The similarities and differences between perception and memory are assessed, and the author’s perspective on the concept of memory systems is described and discussed. The final conclusion is that remembering should be viewed as an activity of mind and brain.

Remembering

The book sets out Fergus Craik’s view of human memory as a dynamic activity of mind and brain. In this account, remembering is understood as a system of active cognitive processes, similar to the processes underlying attending, perceiving, and thinking. The book therefore extends and elaborates the concept of “levels of processing” proposed by Craik and Lockhart (1972). Thus, encoding processes are essentially the mental activities involved in perceiving and understanding, and retrieval is described as the partial reactivation of these same processes. It is further suggested that “memory traces” are represented by a hierarchically organized system of analyzers, modified, sharpened, and differentiated by encounters with successive events. This account proposes that episodic and semantic memory should be thought of as levels in a continuum of specificity rather than as separate systems of memory. The book also covers Craik’s views on working memory and on changes in memory as a function of aging. In the latter case the losses are attributed largely to a difficulty with the self-initiation of appropriate encoding and retrieval operations, compensated by support from the external environment. There is a short chapter on the cognitive neuroscience of human memory, and a final chapter bringing the ideas together. The book covers the development of these ideas, illustrated substantially by experiments from Craik’s own laboratory, and also by empirical and theoretical contributions from other researchers. The final product is a broad account of current ideas and findings in contemporary memory research but viewed from Craik’s personal theoretical standpoint.