Cognitive Science and the Social

A Primer

Stephen P. Turner

• Editorial: Routledge
• Año: 2018
• ISBN:
  9781351180504
• Edición: 1
• Categoría: Psychologie
• Categoría: Kognitive Neurowissenschaften & Neuropsychologie

The rise of cognitive neuroscience is the most important scientific and intellectual development of the last thirty years. Findings pour forth, and major initiatives for brain research continue. The social sciences have responded to this development slowly--for good reasons. The implications of particular controversial findings, such as the discovery of mirror neurons, have been ambiguous, controversial within neuroscience itself, and difficult to integrate with conventional social science. Yet many of these findings, such as those of experimental neuro-economics, pose very direct challenges to standard social science. At the same time, however, the known facts of social science, for example about linguistic and moral diversity, pose a significant challenge to standard neuroscience approaches, which tend to focus on "universal" aspects of human and animal cognition.

A serious encounter between cognitive neuroscience and social science is likely to be challenging, and transformative, for both parties. Although a literature has developed on proposals to integrate neuroscience and social science, these proposals go in divergent directions. None of them has a developed conception of social life. This book surveys these issues, introduces the basic alternative conceptions both of the mental world and the social world, and show how, with sufficient modification, they can be fit together in plausible ways.

The book is not a "new theory " of anything, but rather an exploration of the critical issues that relate to the social aspects of cognition which expands the topic from the social neuroscience of immediate interpersonal interaction to the whole range of places where social variation interacts with the cognitive. The focus is on the conceptual problems produced by any attempt to take these issues seriously, and also on the new resources and considerations relevant to doing so. But it is also on the need for a revision of social theoretical concepts in order to utilize these resources. The book points to some conclusions, especially about how the process of what was known as socialization needs to be understood in cognitive science friendly terms. But there is no attempt to resolve the underlying issues within cognitive science, which will doubtless persist.

1

Perspectives on the Brain and Cognition

The Problem Domain

If one begins with the navigational powers of an ant, and infers from them the features that an ant’s brain must have in order to do the navigating that can be observed in ant experiments, one arrives, as Roger Gallistel (1989) does, with a picture like this: the brain is made up of “functional” components that perform specific tasks in sequences or in relation to one another. It is a reasonable assumption that these components are not the product of learning, but are innate, inherited, fixed, and the product of extremely long evolution. This gets us a striking picture: that the brain is like a transistor radio with separate functional units that match up to physical processes and spaces, and which can be treated as something like transistors which are the same from person to person and “produced by evolution” to be standardized units. Brain regions do have different significance for the capacity to act, speak, perceive, and so forth. But because there are few cases in which these hypothetical structures match physical localized patterns in the brain, one must allow for a good deal of looseness in the assumed relation between these “functional” components and their physical realization.

If one begins with the problems of computation, recognizing that all models of the cognitive processes in the brain are computational, the issues look like this: what is the structure, or architecture, of the computational processes that allow the brain to do the big things that it unquestionably does – remember, often from the distant past and often with difficulty and error; consciously perform logical reasoning and computation; make intuitive distinctions, decisions, and the like that differ from conscious reasoning; receive perceptual inputs and process them; learn and change responses because of learning, and so forth. The constraints on computational models turn out to be substantial: models that suppose the brain to be a more or less fixed computer turn out to be bad at accounting for learning, for example. Constructing models that fit with the apparent malleability or “plasticity” of the brain is difficult to reconcile with the kinds of fast, routine processing that even infants are capable of. But there are ways of modeling those processes too. And, according to computationalists, there is no alternative: all models of cognitive processes are computational models. So the constraints on the models tell us what the constraints on the computational brain are.

If one begins with concepts and reasoning with concepts, the picture looks like this: the brain processes representations through rules embedded in the brain to produce new representations. Indeed, if the brain is ever engaged in manipulating representations according to rules, it is difficult to see how there could be any “thinking” that does not work in this way, simply because any supposed “other” kind of thinking would need to link, through rules and representations, to every other kind of thinking. There is a social angle to this: it is at least tempting to say that the rules are shared, that communication and mutual understanding are possible because and only because of these shared rules. In order to learn a language understood as a whole set of concepts together with their relations one needs to already be able to think about them; to learn about the rules governing the object of thought “apples” one must already be able to identify the object, and think about it, which amounts to having a concept with logical connections to other concepts – that is, to already have a “Language of Thought” (Fodor 1975).

Another linguistic place to start thinking about the brain involves grammar. And here is a puzzle that any account of the brain’s capacities must solve: grammarians who have attempted to construct complete grammars for given languages have failed; but children quickly acquire the ability to speak grammatically. This seems to imply that they already had this ability in some form, such as a universal set of rules of language stored in the brain. If one begins with this problem, one wants a model of the brain as “language ready.” But why stop there? Why think that only grammatical rules are innate? One can expand this notion to the idea of the “culture-ready” brain, one that is poised and equipped to acquire a culture. The picture here is this: cultures and languages consist of rules, which follow a template but which vary in content, to a limited extent; the values and parameters need to be plugged into the template, at which point the culture or language can be rapidly acquired, mutual understanding is possible, and social life can proceed.

If one starts with development from infancy or the womb to competent adults, one gets thoughts like this: “Senses, reflexes and learning mechanisms – this is what we start with, and it is quite a lot, when you think about it. If we lacked any of these capabilities at birth, we would probably have trouble surviving” (Epstein 2016: n.p.). But is this enough, or must there be something more? The developmental process provides many clues. We get stages in which capacities appear, usually in order and in particular time frames. These indicate some sort of dependence of one capacity on another. But reconstructing how any of this works is difficult. Capacities seem to come and go – such as the ability to pick up a language, or to imitate faces, or reflexes. And the relation between learning, senses, and reflexes is difficult to disentangle. There is a lot of social learning, or learning through interaction, which may mask more fundamental human capacities that do not depend on learning, but are simply activated at certain developmental stages. An example of this is the appearance of the capacity to reason about the erroneous thinking of other people and to infer what they would do based on their erroneous beliefs. This reasoning requires – or is claimed to require – a “theory of mind,” which can’t be derived merely empirically, from experience, but also represents a stage of social development.

If one begins with issues of human rationality, one focuses on the mind as a system, but one which is not quite as rational as the formal theory of rationality would have it – does not obey rules like transitivity of preferences, for example – and does strange things with discounting future benefits, probabilities, and so forth. Moreover, it seems that the brain runs at two speeds with two distinct systems: the slow conscious system and the fast unconscious one. The fast one makes systematic errors. But if our ancestors had waited for the slow one to tell them how to act, they would have been eaten by predators. And we ourselves could not function normally if we were to reason our decisions and reactions out explicitly or in the “slow” mode.

If one starts with the biological and chemical processes of the brain, one gets a different picture, and a different set of problems. The brain is regulated and mental processes affected by all sorts of complex chemical interactions and processes. These do not look particularly computer-like. This can be put even more starkly from the point of view of basic brain anatomy and biology:

here is what we are not born with: information, data, rules, software, knowledge, lexicons, representations, algorithms, programs, models, memories, images, processors, subroutines, encoders, decoders, symbols, or buffers – design elements that allow digital computers to behave somewhat intelligently. Not only are we not born with such things, we also don’t develop them – ever.

We don’t store words or the rules that tell us how to manipulate them. We don’t create representations of visual stimuli, store them in a short-term memory buffer, and then transfer the representation into a long-term memory device. We don’t retrieve information or images or words from memory registers. Computers do all of these things, but organisms do not.

(Epstein 2016: n.p.; italics in the original)¹

With small exceptions, the brain is made up of the same kind of stuff – neurons that connect to other neurons, connect to some and not others, and strengthen their connections as a result of “firing” together so that “neurons that fire together wire together.” The physical brain is constantly changing as a result of this, and highly individualized so that each brain’s set of connections produces a distinct “signature.” Although there is spatial differentiation that reflects difference in functions of parts of the brain – as evidenced by the effects of brain injuries or lesions and, more recently, by mapping techniques that have distinguished a large number of areas of the brain by function, or more precisely by type of neural activity – it is also the case that the brain is malleable, that functions can sometimes be recovered, and that, astonishingly, one can function without much brain at all.

If one assumes that brains and cognitive processes are subject to the same degree of biological variability as physical features of the body, one is forced to ask how this variability relates to variations in cognitive processes. Is human sexual dimorphism deeply consequential, both for embodiment and its cognitive consequences and for cognition itself? Or does the social construction of gender and bodies overwhelm any effects of embodiment and differentials in embodied experience? If embodiment is important, how can differences in embodiment not also be important, and not only for cognition but also for social organization? One need not be a believer in the strong effects on social life of evolved capacities to wonder whether recurrent features of social life, such as hierarchy, are associated with biological variation. But talking about the embodied mind and bodies in general opens the door to and many other questions, such as this: how is normal biological variability consistent with theories that assume elaborate, common, rule-like structures in the brain?

If we start with evolution, we get a new set of considerations. Evolved structures don’t just happen: they happen over tremendous time scales, and the more complex, the more time it takes to evolve them. If the brain consists of complex systems made up of small, high-speed, transistor-like elements, each doing a specific job, understanding the brain will depend on understanding the constraints and implications of this basic evolutionary fact. As Tooby and Cosmides write in their classic assault on the conventional social science model:

Each of the neural automata … is the carefully crafted product of thousands or millions of generations of natural selection, and each makes its own distinctive contribution to the cognitive model of the world that we individually experience as reality. Because these devices are present in all human minds, much of what they construct is the same for all people, from whatever culture; the representations produced by these universal mechanisms thereby constitute the foundation of our shared reality and our ability to communicate. Yet, because these evolved inference engines operate so automatically, we remain unaware of them and their ceaseless, silent, invisible operations. Oblivious to their existence, we mistake the representations they construct (the color of a leaf, the irony in a tone of voice, the approval of our friends, and so on) for the world itself – a world that reveals itself, unproblematically, through our senses.

(Tooby and Cosmides 1995: xii)

This tells us how to think about the ant’s dead reckoning capacity, but it also gives a specific picture of the brain’s capacities in general: they are already formed, inherited, and sophisticated; and, because of their place in the complex system that produces such things as images of a leaf, or irony detection, which involve lots of these capacities working together, they are fixed and rigid, but also largely universal. If “culture,” the core idea of the standard social science model, matters, it is only a superficial overlay on these universal mechanisms evolved in the distant past, rather than the handful of generations who have had anything like civilized life.

Moreover, it seems that basic brain structure is shared by almost all animals, with the apparent exception of the octopus, which seems terrifically intelligent but has a radically different, decentralized nervous system. Indeed, as Gallistel (2011: 254) argues, thinking itself takes a noun-verb form. As studies of animal intelligence advance, more and more of what was considered uniquely human turns out to appear in other species, including species whose evolutionary trajectory separated from that of humans very early.

But there is a twist here as well. “Evolution” is not a big hand that produces “crafted products” and inserts them into our brains. It operates through genetic transmission. And genetic transmission operates through DNA coding. But the codes do not correspond to crafted products of this kind. And thinking about the genetics of the brain leads to some complicated considerations. Although genes work through “codes” which are chemical sequences in DNA molecules and in other molecules that are involved in the process of development (and this is what is genetically transmitted), there is no simple relationship between genetic content, genotype, and what is actually expressed as the characteristics of the person, the phenotype. Genes do not ordinarily have a one-to-one relation with the overt characteristics of interest to neuroscience.

So evolution provides us with a paradox: much of the human genome is shared with the rest of the animal kingdom; and virtually all of it is shared with the higher primates. Most of what we are, as beings with brains, is also shared. And there is a constant barrage of research on animal cognition that shows that animals, even birds, that are far from us on the evolutionary tree, have remarkable reasoning skills, and that the things that were thought to be unique to human cognition are not unique at all. This line of reasoning suggests that we are heavily determined by our genes in our “psychology,” which in evolutionary psychology is taken to include rationality, morality, sexual preferences, and much else.

The relation of genes to their expression points in another direction: there is not only no direct one-to-one relation of genes to properties of interest; there are also intervening processes that govern and provide a great deal of variability in the expression of genes. This is the domain of “epigenetics.” Trauma, simple variation in biological conditions, and many other factors have effects on how genes are expressed. The effects of trauma indicate something potentially important: the effects are likely to be socially variable. So we can add some sort of social determination to the process of the expression of genes in the individual developmental process.

If one starts instead with psychological experiments, one gets this: a long list of anomalous findings that beg to be integrated, together with a body of ideas that is not far removed from common sense and the common language of the mental. How do false memories happen? Why do the notions that are central to such conventional pieces of psychology – such as Icek Ajzen’s model of intentional action (1991, 2002; Ajzen et al. 2009) – seem to fall apart when they are applied in terms of the notion of consciousness, where it seems that the “intentional” part of decisions become conscious only after the relevant traces of activity in the brain have occurred (Libet et al. 1983; Libet 1985; Wegner 2002; Haggard 2012). There is, in short, a gap between the plethora of psychological experiments and the phenomena they establish, described in language that is not far removed from common sense, and the physical processes they should be connected to. Worse, there is a chronic problem about the relation between “cognition in the wild” and what goes on in experiments that are highly contrived and do not incorporate the same contextual elements as ordinary cognition (Hutchins 1995).

There is another starting point: we already have a language of the mental, and the theory of the mind behind it is sometimes derisively called “folk psychology.” It has functioned for millennia, or so it would seem. It would be odd if it were “wrong”: that there was nothing corresponding to such terms as intention and belief, for example. So it is fair to take it as a first approximation of the true scientific theory of mind, and to see how the concepts of folk psychology are realized in the physical brain, and to leave the details of the answer to this question to the empirical future. There is another reason: we have a brain-based capacity, perhaps based on or utilizing mirror neurons, for the empathic understanding of others. Folk psychology is, in some sense, the language we use to express our understanding of others. Indeed, without this ability to understand others we would be hard pressed to do neuroscience at all: much of it, notably lesion studies, depends on our ability to ask patients questions and to engage in interaction with them; similarly for experiments that require instructions to the subjects, or intelligible responses.

The dependence on folk psychology, or something like it, is pervasive – Daniel Dennett (1987) uses the term “intentional stance,” while in this book I will be using Verstehen. It can be largely ignored in contexts where the issue is not social i...