This brief chapter considers what we mean by knowledge, explanation and understanding, aspects that have and remain areas of debate in the philosophy of science. Despite scientists referring to these aspects routinely in ways that suggest their meaning is clear, examples are given that suggest the terms can actually be used in various ways by different people. It is important to consider what is being claimed and why in a claimed explanation or a claim to understanding, because the terms carry different weights and subjectively mean different things. This can lead to confusion and errors of reasoning that can constrain a field.

This chapter looks at claims to understanding. It begins by looking at the system I have worked on, the lamprey spinal cord locomotor circuit, and claims that circuit function and behaviour can be understood in terms of the interactions of spinal cord nerve cells. I highlight that the claims to experimental confirmation actually reflect various assumptions and extrapolations and that the claimed understanding is lacking. I then look at the Nobel Prize winning work on the Aplysia gill withdrawal reflex, making the same conclusion as the lamprey, various assumptions and extrapolations are used to claim causal links, and in doing this commit various logical fallacies, including confusing correlation for causation and begging the question. I finish by looking at hippocampal long-term potentiation and claims it is the cellular basis of memory, again highlighting that the claimed links have not been made.

This introductory chapter starts by considering the distinction between doubt and denial, and why retaining doubt in science is needed to ensure claims are accurate. It then discusses neuroscience aims and claims, and how the insight obtained is directed at translations to practical use in artificial intelligence, neurology, psychiatry and wider translations to society; for example, education and cognitive enhancement. The chapter highlights the relevance of philosophy and history to science, aspects to which science students are seldom exposed. This includes discussion of science denial by popularist politicians and corporations who try and ignore or dismiss evidence that negates their views or products. These aspects are highlighted as being important to defend science and ensure that scientific claims are as accurate as possible, and that in an age of disinformation we all need to think critically, mirroring the workers’ educational movements of the late nineteenth century.

This chapter looks specifically at neural circuits, assemblies of neurons that influence sensory, motor and cognitive functions. I discuss the conventional criteria for understanding these circuits, which are reductionist in their approach, and highlight various caveats in experimental and conceptual approaches that are routinely followed. I also consider the use of motifs, arrangements of component parts of a circuit that serve specific functions like electronic components. I follow others in highlighting the utility of appealing to motifs, but again highlight caveats of these motifs that mean we cannot assume their presence or the function when we know they are present. I finish by discussing aspects that have been identified over the last few decades that may add to the aspects we need to study, including plasticity, glial cells, variability and ephaptic signals.

This chapter considers reductionism, a major aspect of neuroscience research. I consider reductionist claims that we can only understand nervous systems from knowledge of their component parts. I then consider reductionist approaches and what we have learnt by following them, highlighting that a complete reductionist account of any nervous system region hasn’t been and is probably impossible to achieve. I then discuss decomposable hierarchical and non-decomposable heterarchical systems, and how relational aspects suggest we cannot understand the latter systems from cataloguing their individual components. I then discuss two effects that have received little attention despite being known for decades – volume transmission and ephaptic signalling – that highlight the need to consider component parts in relation to the whole system. I finish by discussing non-reductionist views, equipotentiality, cybernetics, the holonomic brain and embodied cognition, highlighting, as many have in the past, that debating between reductionist and non-reductionist approaches is a false dichotomy.

This chapter looks at social influences on neuroscience. It outlines that science is a social system, and subject to various social pressures that can affect what we study, how we study it, and how we interpret the data we obtain. This includes financial conflicts of interest, claims to priority, scientific prizes, peer review, ‘scientmanship’ that attempts to promote or suppress certain scientific views and scientists, and the recent quantification of social pressures in science from surveys that suggest that social pressures and career structures introduce behaviours that make science a difficult career for those lower in the scientific hierarchy, including racial and sexual biases, and can see those higher up using their prominence to affect how science is done and the claims made. I highlight that awareness of these negative social influences is starting to lead to approaches that aim to address these issues.

This chapter focuses on aspects of the philosophy of science, in particular the twentieth century views of Karl Popper and Thomas Kuhn. It briefly covers earlier aspects, including Francis Bacon and William Whewell who highlighted the need for, and influence of, subjective factors in science. In discussing Popper, it considers inductive and deductive reasoning and his falsification approach, while discussion of Kuhn focuses on his view of scientific paradigms, normal science, anomalies and crises, and paradigm shifts and scientific revolutions. It highlights both Popper’s and Kuhn’s views using neuroscience examples, including chemical synaptic transmission, animal electricity and adult neurogenesis. The conclusion is that there is no formal scientific method, no formula for discovery: scientists use, and need to use, a diversity of approaches.

This chapter considers induction, deduction and abduction as methods of obtaining scientific knowledge. The introductory section again ends by highlighting that there is no single method, and refers to claims that scientific reasoning uses various heuristics or rules of thumb based on the specific approach and the background information we have, and that we should recognise that this can introduce various errors of reasoning: by being aware of the potential for making these errors, we are better able to guard against making them. The bulk of the chapter then looks at specific logical fallacies, using neuroscience examples to illustrate them. These include ad hoc reasoning; begging the question; confusing correlation for causation; confirmation and disconfirmation biases; false dichotomies; false metaphors; the appeal to authority, tradition and emotion; the mereological fallacy; the naturalistic fallacy; and straw man arguments.

Computational neuroimaging is defined broadly as the use of neuroimaging to investigate the localization and representation of parameters in formal mathematical models. We focus upon models of behavior and neural processing that have been adopted widely in behavioral sciences and cognitive neuroscience, including reinforcement learning, predictive coding, decision theory (drift diffusion and evidence accumulation), population receptive field models, and encoding models (including artificial neural networks). The aim is not to explain all the technical details of the various models, but illustrate and discuss the added value of combining such models with neuroimaging.

Chapter 11 introduces basic EEG and MEG data analysis methods. It begins with an explanation of the noise components in EEG and MEG signals and discusses various methods of noise reduction, including filtering and independent component analysis (ICA). Spectral analysis, event-related response (ERR) analysis, and steady-state evoked response (ssER) analysis are then introduced. Each method is explained in plain language, followed by more detailed explanations to meet the different needs of beginners and advanced readers. Relevant statistical methods and data presentation formats are also introduced, using various data analysis platforms.