Clark is a philosopher who specializes in logic and metaphysics. However, this book combines neuroscience and embodied cognition to give a theory of the predictive brain. Clark has coined this process predictive processing and it relies heavily on Bayesian logic. “It is the kind of automatically deployed, deeply probabilistic, non-conscious guessing that occurs as part of the complex neural processing routines that underpin and unify perception and action…. Brains like ours…. are predictive engines, constantly trying to guess at the structure of the incoming sensory array. Such brains are incessantly pro-active, restlessly seeking to generate the sensory data for themselves using the incoming signal (in a surprising inversion of much traditional wisdom) mostly as a means of checking and correcting their best top-down guessing. Crucially, however, the shape and flow of all that inner guessing is flexibly modulated by changing estimations of the relative uncertainty of (hence our confidence in) different aspects of the incoming signal. The upshot is a dynamic, self-organizing system in which the inner (and outer) flow of information is constantly reconfigured according to the demands of the task and the changing details of the internal (interoceptively sensed) and external context.” Our brains are constantly in action, making top-down guesses about the sensory data to learn about our external world. The brain is creating models and then slowly modulating them, based on past experience, to improve future predictions. Crucially, the brain is not a passive system. Action plays a critical role in predictive processing. “Our massed recurrent neuronal ensembles are not just buzzing away constantly trying to predict the sensory stream. They are constantly bringing about the sensory stream by causing bodily movements that selectively harvest new sensory stimulations. Perception and action are thus locked in a kind of endless circular embrace…. [The brain is able to] use action upon the world to reduce the complexity of its own inner processing, selecting frugal, efficient routines that trade movement and environmental structure against costly computation.”
The brain is constantly learning as it is constantly predicting everything that will happen to the body in the next moment, in an ever-rolling cascade. Perception is very near-term top-down prediction modified by the senses. “Prediction error [is] a kind of proxy for any as-yet-unexplained sensory information. Prediction error here reports the ‘surprise’ induced by mismatch between the sensory signals encountered and those predicted…. Perception is indeed a process in which we (or rather, various parts of our brains) try to guess what is out there, using the incoming signal more as a means of tuning and nuancing the guessing rather than as a rich (and bandwidth-costly) encoding of the state of the world.” It is actually our expectations, to a large extent, that determine what we see, smell, and hear. A prediction does not create our sensory world, but it does focus our attention. “Brains like ours are constantly trying to use what they already know so as to predict the current sensory signal, using the incoming signal to select and constrain those predictions, and sometimes using prior knowledge to ‘trump’ certain aspects of the incoming sensory signal itself. Such trumping makes good adaptive sense, as the capacity to use what you know to outweigh some of what the incoming signal seems to be saying can be hugely beneficial when the sensory data is noisy, ambiguous, or incomplete.” Again, the non-passive nature of the brain is crucial. “Action is not so much a ‘response to an input’ as a neat and efficient way of selecting the next input, driving a rolling cycle. These hyperactive systems are constantly predicting their own upcoming states and actively moving about as to bring some of them into being.”
Each percept is constructed with the help of the brain’s priors. These priors influence future expectations in a probabilistic manner. The brain then combines the likelihood of these priors with raw sensory data. “Attention, thus construed, is a means of variably balancing the potent interactions between top-down and bottom-up influences by factoring in their so-called ‘precision’, where this is a measure of their estimated certainty or reliability.” When evaluating sensations, the brain is constantly separating the signal from the noise, using prior knowledge. This process happens through hierarchical Bayesian inference based on precision-weighted guesses at every level. These predictions then influence future action in a proactive fashion. Memory is also crucial in predicting our future. Fernyhough suggests, “if memory is fallible and prone to reconstructive errors, that may be because it is oriented towards the future at least as much as towards the past…. similar neural systems are involved in both autobiographical memory and future thinking, and both rely on a form of imagination.” In the end, perception is the brain’s best guess as to reality. “Perception (rich, world-revealing perception) occurs when the probabilistic residue of past experience meets the incoming sensory signal with matching prediction.”
Prediction allows our bodies to live in the present. As Franklin and Wolpert assert, “delays are present in all stages of sensorimotor system, from the delay in receiving afferent sensory information, to the delay in our muscles responding to efferent motor commands…. we effectively live in the past, with the control systems having access to out-of-date information about the world and our own bodies.” This is overcome by predictive processing. “Forward models provide a powerful and elegant solution to such problems, enabling us to live in the present and to control our bodies…. you treat the desired (goal) state as observed and perform Bayesian inference to find the actions that get you there…. Motor control is, in a certain sense, subjunctive. It involves predicting the non-actual proprioceptive trajectories that would ensue were we performing some desired action. Reducing prediction errors calculated against these non-actual states then serves…. to make them actual. We predict the proprioceptive consequences of our own action and this brings the action about….. ‘Active Inference’ then names the combined mechanism by which perceptual and motor systems conspire to reduce prediction error using the twin strategies of altering predictions to fit the world, and altering the world to fit the predictions.”
Frith makes the case that “our perceptions are fantasies that coincide with reality.” Hohwy suggests, “what we perceive is the brain’s best hypothesis, as embodied in a high-level generative model, about the causes in the outer world…. Conscious experience is like a fantasy or virtual reality constructed to keep the sensory input at bay.” Thus, the human brain is bounded by an external reality. “Prediction-driven learning delivers a grip upon affordances: the possibilities for action and intervention that the environment makes available to a given agent.” The brain is then constantly making its best guess as to what course will reduce prediction error. This might be by modifying its predictions or it might be through acting. This results in “‘affordance competition’ in which…. possible motor responses are being simultaneously prepared, and in which ‘the human brain does not wait for a decision to be completed before recruiting the motor system but instead passes partial information to prepare in a graded fashion for a probable action outcome’…. Such pro-active readiness, to be genuinely useful, must necessarily be multiple and graded. It must allow many possible responses to be simultaneously partially prepared, to degrees dependent upon the current balance of evidence.”
The world that humans perceive is “our world” in that it is the world best understood by humans. “What we perceive is (when all is going well) the structured external world itself. But this is not the world ‘as it is’, where that implies the problematic notion of a world represented independent of human concerns and human action repertoires. Rather, it is a world parsed according to our organism-specific needs and action repertoire.” Humans create and actively modify our own world. Through language “our own thoughts and ideas now become available, to ourselves and others, as potential objects for deliberate processes of attention…. Courtesy of all that material public vehicling in spoken words, written texts, diagrams, and pictures, our best predictive models of the world (unlike those of other creatures) have thus become stable, reinspectable objects apt for public critique and systemic, multi-agent, multi-generational test and refinement. Our best models of the world are thus able to serve as the basis for cumulative, communally distributed reasoning…. Our human-built worlds are not merely the arenas in which we live, work, and play. They also structure the life-long statistical immersions that build and rebuild the generative models that inform each agent’s repertoire for perception, action, and reason.”
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