Milk production at a dairy farm was low, so the farmer wrote to the local university, asking for help. A multidisciplinary team of professors was assembled, headed by a theoretical physicist, and two weeks of intensive on-site investigation took place. The scholars then returned to the university, where the task of writing the report was left to the team leader. Shortly thereafter, the physicist returned to the farm, saying to the farmer, "I have the solution, but it works only in the case of spherical cows in a vacuum". (https://en.wikipedia.org/wiki/Spherical_cow)
Whilst an obvious joke, the scenario highlights how physicists often make use of wildly unrealistic—idealized—assumptions with the aim of simplifying a problem to make it easier to solve. Physics is full of idealizations such as frictionless planes, massless pulleys, uniform densities, point masses, zero gravitational effects, the list goes on. Indeed, all model-based sciences—physics, chemistry, biology, neuroscience, economics, social science, etc— make widespread use of idealization techniques to build models of parts of reality. Standard examples include: the ideal gas law, frictionless planes, infinite populations, neural networks trained on unrealistic learning algorithms, infinitesimal limits, isolated populations, perfectly rational agents or philosophical zombies (a physical replica of you, yet without phenomenal consciousness). In model-based science one starts by introducing an imaginary system (e.g., a neural network using back-propagation to reduce error and optimize learning), which is investigated to understand learning in the brain. Or one might use an idealized gas, where all molecules are treated without mass and dimension, to approximate the behavior of natural gases.The first core message of The Idealized Mind is that models of the mind and brain are idealized in precisely this way: they make use of idealized terms that have no counterpart in reality. Its second message is: despite being both simplified and idealized, models in cognitive science are still consistent with a version of scientific realism—the view that one actual and reasonable aim of science is to provide true (or, approximately true) descriptions of reality. In the book, I argue that these two messages have foundational implications for our understanding of the mind and brain based on scientific models. In cognitive science, ‘mental representation’ and ‘computation’ are fundamental theoretical concepts. Most neuroscientists and philosophers work with the assumption that the mind functions as an information-processing system, akin to a computer. Hence, the standard view in the field is that cognitive processes (e.g., perceiving or remembering or navigating) are realised by the occurrence, transformation and storage of information-bearing structures (mental representations) of one kind or another. All models of mental representation and neural representation are idealized models with no counterpart in reality. Ideal gases do not exist. Similarly, the brain does not literally compute; neurons do not send messages; nor does the brain in any literal sense process information. Constructs such as representation, computation, communication, information-processing might (and only might) be useful for modeling the mind and brain. Yet, they are nothing but abstract explanatory devices that exist mainly in the imagination of the scientific communities that use them to describe, explain, and predict aspects of the natural world.
