But it’s not true.
A brain in a vat is arguably as complex as any organic matter floating in formaldehyde. To get a brain in a vat to do anything complex, it needs to be hooked up in the right ways to to all sorts of input and output channels. But as it turns out, these inputs and outputs (e.g, our senses and our muscles) are not mere “peripherals” for the brain. Many philosophers and scientists now see the ways in which the brain is embodied and environmentally embedded as constitutive of our brains’ computations. Indeed, cognitive computations are more properly seen as extending out into the world and enacting the mind. These 4 italicised e-words are critical features of the so-called 4E view of cognition. Accordingly, the mind is not the brain, and suggestions otherwise (by, for example, saying that the brain is the most complex entity in the known universe) are misplaced. Of course, the brain is important. But just as we should no longer think of the earth as the center of the physical universe, or humans as the top of the “tree of life”, so—on this view—should we no longer think of the brain as the center of our minds. Brains are critical links in the cognitive chain but not the entire chain itself.
And yet, still…it does seem like the brain must be more central than a mere link in the chain. Emily Dickinson creatively capture this idea:
The Brain — is wider than the Sky —
For — put them side by side —
The one the other will contain
With ease — and You — beside —
The Brain is deeper than the sea —
For — hold them — Blue to Blue —
The one the other will absorb —
As Sponges — Buckets — do —
The Brain is just the weight of God —
For — Heft them — Pound for Pound —
And they will differ — if they do —
As Syllable from Sound —
The Renaissance artist Michelangelo may also have suggested that the brain has god-like powers in his famous painting, The Creation of Adam, in the Sistine Chapel. Here, he seems to position the figure of God, who is bestowing life, inside a brain, complete with a number of neuroanatomical structures and landmarks:
Which of these perspectives on the brain strikes you as more likely? More useful? They certainly warrant further exploration!
In the end, what may matter more than which of these conceptions of the brain is “true” is how we can use an interdisciplinary and active-learning approach to neuroscience to frame and support our ongoing attempts to understand ourselves, make sense of our place in the world, and imagine how we can contribute to the world. This is why I have written this text.
I draw inspiration from educational philosophies like that of the Association of American Colleges and Universities, which advocates “an approach to undergraduate education that promotes integration of learning across the curriculum and cocurriculum, and between academic and experiential learning, in order to develop specific learning outcomes that are essential for work, citizenship, and life.” This philosophy is also echoed by The European Consortium of Liberal Arts and Colleges. Neuroscience is particularly well-suited to embody these aspirations as it is already an interdisciplinary field which employs a suite of analytical and technical skills to address fundamental issues about human nature. This text is also meant to strengthen education in neuroscience itself, as both integrative thinking and “the ability to articulate the interdisciplinary and interdependent nature of the neuroscientific enterprise” are considered key objectives of an undergraduate neuroscience education in the 21st century.
This text is accessible to anyone interested in the brain. It is specifically targeted to students and instructors of undergraduate neuroscience or biopsychology courses. It encourages an active exploration of the intersection of neuroscience with other disciplines through a large number of activities, demonstrations, reflection prompts, and critical thinking questions. It complements material presented in more conventional neuroscience or biopsychology textbooks. Connections to this material are also provided in this text. Because it is designed to increase the reach, appeal, and educational value of a course, the text is offered as an Open Educational Resource. It is freely available and easily reusable.
The activities, prompts, and questions in this text may be integrated into a course in many ways. Instructors can infuse material into their lectures to actively involve students in demonstrations, exercises, and discussions. Activities can be given as course assignments to be completed as homework. Critical thinking prompts can inspire online discussion boards. Instructors could even create laboratory modules drawing on the resources provided. Alternatively, the text is written at a level that would enable students to follow along on their own. The options are limited only by one’s imagination.
In the next section, I suggest pairings between chapters in this text and chapters often found in neuroscience and biopsychology texts. I have also included a syllabus at the back of the text. For more ideas, you can see an earlier syllabus of mine which used an open pedagogy project to stimulate student explorations like this. Finally, there is a password-protected solutions file at the back of the text. Instructors can email me for the password at c.j.may @ rug.nl. I’m also curious to hear from you and how you might be using the text!
Overview of Chapters
In the first chapter “Greater than the Sum its Parts” students are introduced to the concept of emergence. This is a seemingly magical phenomenon where the behavior of a collective exhibits surprisingly sophisticated organizational patterns and informational capabilities. Students can actively explore these by enacting and experimenting with cellular automata models. The chapter concludes by showing how a particular cellular model generates patterns also seen in two very different places: the striping patterns on a zebra’s coat and the spatial organization of ocular dominance columns in the striate cortex. This similarity suggests that similar fundamental dynamics may be at play in all three systems. Both this preface and Chapter 1 could accompany an introductory chapter.
In chapter 2, “Neural Networks“, the insights from chapter 1 are more directly linked to the functioning of the brain through neural network models. Here, students see how small circuits of idealized neurons can implement several logical functions. These functions can serve as the building blocks for constructing simple representations of visual objects. The chapter features a large number of exercises in which students map the functions of neural networks and manipulate parameters to understand that the whole computation is a function of the relationship between its computing parts. The chapter concludes with a demonstration of how these functions can even be implemented using dominos! This highlights what the truly critical ingredients are for emergent computation. This chapter would profitably complement an introduction to neurophysiology by demonstrating how the physiological details of neural processing can lead to sophisticated information processing.
Chapter 3, “Our Predictive Brains“, adds complexity and realism to the feedforward circuits introduced in chapter 2 by discussing the importance of feedback, or top-down, connections. These fundamentally alter the computations performed by the brain. Rather than constructing representations, as modeled in chapter 2, the brain is instead conceptualized as making predictions about incoming information and feeding forward error signals about the quality of those predictions. This has a number of very interesting implications, which students are encouraged to identify in their daily lives and critically deliberate. The chapter concludes by connecting the predictive proclivities of our brains with another prediction engine: chatGPT! Students explore the similarities and differences, as well as the relative strengths and weaknesses, of predictive systems like the brain and chatGPT. This chapter could complement an overview of neuroanatomy, focusing on hierarchies of information processing within and between sensory systems.
Chapter 4, “What’s the Difference?“, highlights the role of lateral inhibition in neural circuits for producing cells which are both sensitive to and exaggerate differences. This is illustrated by a number of demonstrations and illusions, which can be explained by the center-surround receptive fields of cells at multiple levels of the visual system. Such cells perform automatic contrast enhancement of features such as edges and colors, facilitating object recognition. Voluntary selective attention is also presented as a flexibly directed system for enhancing perceptual contrasts in a goal-directed manner. Selective attention can be thought of as imposing center-surround biasing fields on neural circuits. Our brains are not only sensitive to contrasts in a static spatial field, but also temporal contrasts over time. This is seen in attentional capture, which students are encourage to explore by considering the “attention economy”. More generally, we have a propensity to detect surprising or novel stimuli, pointing to a complementarity between the contrast enhancement focus of this chapter, and the discussion of predictive processing in the previous chapter. The chapter concludes with demonstrations of situations in which we may be perceptually blind to certain dynamics and changes in the environment. Students are given tools to consider those things in their own lives they may neglect, partially resulting from inhibitory processes in their brain. Chapter 4 serves as useful complement (either alone or in combination with Chapters 2 and 3) to an introduction to the visual system. The chapter can also extend presentations of attention.
Chapter 5, “Our Networked Selves“, begins with a brief interdisciplinary look at the nature of the “self”. Different quarters in philosophy, religious studies, and neuroscience argue for the view that who we are is better thought of in network terms than in essentialist terms. Indeed, we are “networks all the way down”. Moreover, many networks share common properties, such as a “small-world network” structure. This offers the possibility that understanding one type of network, such as a social network, can yield insight into analogues that may be operating in another type of network, such as a brain network. In this chapter, students explore the creation of small-world network structures and are also invited to map a social network. This opens the door to investigating key parallels between social and brain networks. Whereas Chapter 2-4 focused on bottom-up, top-down, and lateral processing, respectively, this chapter interrogates the advantages provided by a brain organization which has a low percentage of long-range connections (pairing far-removed brain regions) and a high percentage of short-range connections (creating local clustering). Since cortical connectivity patterns are a focus of this chapter, it (as well as Chapter 3) could complement presentations on neuroanatomy. Sections of the chapter could also extend an introduction to the somatosensory system.
Chapter 6, “Contemplating ‘Nervous’ Systems“, introduces contemplative practices as interdisciplinary tools for both studying the nervous system and cultivating well-being. The chapter opens by noting the origins of contemplative practices in spiritual traditions as well as the boom in recent decades of research on secularized practices. The chapter then draws analogies between divisions of the autonomic nervous system and elements in Taoism. The organization of the autonomic nervous system provides multiple pathways for alleviating stress. One is to activate the parasympathetic nervous system. This can be done using breathwork exercises such as those found in pranayama. Students are also introduced to focused attention meditation as another method. Experiences in this type of meditation expose certain attentional dynamics, which can be mapped onto distinct brain networks. Mind-wandering is linked to the default-mode network, which is anti-correlated with task-related attentional networks. Mind-wandering is shown to be both common and associated with unhappiness. This is directly addressed in Buddhist philosophy, which points to a prescription in a set of eudaimonic strategies improving well-being. Two of these are discussed with respect to empirically grounded models linking both mindfulness and positive emotions to well-being. In the final activity of the chapter, students are introduced to loving-kindness meditation, a practice linked to positive emotion. This chapter can be used as an adjunct to course modules on health, stress, or the reward-system of the brain. The first section of the chapter, which discuss the autonomic nervous system, might be paired with an overview of neuroanatomy. The second section of the chapter, contrasting the default-mode and task-related networks extends discussions on attention, also featured in Chapter 4.
The final chapter “Methods and Models” discusses and illustrates how all methods and models are partial. As it has been reiterated in various ways in the philosophy of science: all models are wrong, but some are useful. Students first reflect on the difference between an object and a representation of an object. This highlights how representations can be misunderstood. This is even more likely to occur in the case of complex representations, such as brain images. Through an activity in which students question a news report of neuroimaging results, it becomes clearer how brain images are not the straightforward pictures they appear to be. They are constructions, based on indirect measures of brain activity, which engender a particular tradeoff between spatial and temporal resolution. The second section of the chapter reviews all of the main models, each with their own tradeoffs, discussed in each of the previous chapters of this book. This section acts as a conclusion to the book. The book ends with two take-home messages. First, neuroscientific understanding would benefit from the inclusion of a greater variety of models. And second, all of our understanding—scientific and everyday—is both mediated by and expressed in models. Therefore, we should continue seeking out and exploring a wide range of models, in whichever discipline or arena of life they can be found. Chapter 7 would well complement a course module on neuroscience methods.