predictive processing
In plain wordsThe idea that the brain is a prediction machine: it constantly guesses what its senses will report next and learns mainly from its mistakes, so what we perceive is largely its best guess.
A framework in cognitive sciencecognitive science, n.: The scientific study of how minds work — how we reason, use language, remember and see — combining psychology, neuroscience, linguistics, philosophy, anthropology and artificial intelligence. on which the brain continually generates top-down predictions of its sensory input from a hierarchical generative model of the world and the body, and passes upward mainly prediction errors — mismatches between prediction and input — which are used to revise the model or, through action, to change the input. Perception becomes inference to the best hypothesis about the causes of sensation, with signals weighted by their expected precision (reliability) and attention treated as the optimization of precision. Predictive coding is the narrower neural and algorithmic scheme for exchanging predictions and errors between levels; the Bayesian brain hypothesis is the broader claim that the brain approximates Bayesian inferenceBayesian inference, n.: Reasoning that treats unknowns as uncertain beliefs and updates them with evidence using Bayes' theorem. The result is not one answer but a range of answers, each with a probability.. The framework parallels self-supervised learningself-supervised learning, n.: A way for AI to learn from huge amounts of unlabeled data by setting itself fill-in-the-blank puzzles, such as guessing a hidden word or a missing patch of an image. and next-token predictionnext-token prediction, n.: The simple task most AI chatbots were trained on: given some text, guess the next small piece of it. Repeating that guess, piece by piece, produces whole answers. in AI, and Anil Seth and others use it to describe perception as 'controlled hallucination'.
Origin
Rooted in Hermann von Helmholtz's account of perception as unconscious inference (1867). Rajesh Rao and Dana Ballard's predictive-coding model of visual cortex (1999) and Karl Friston's work from 2005 made it a theory of cortical function. Andy Clark's 'Whatever Next?' (Behavioral and Brain Sciences, 2013), Jakob Hohwy's The Predictive Mind (2013) and Clark's Surfing Uncertainty (2016) carried the framework into philosophy and cognitive science, where 'predictive processing' became the usual umbrella term; its earliest use is not established.
Usage note
Not to be confused with predictive analytics or with predictive coding in signal compression, its 1950s engineering ancestor. Whether the framework is a testable theory or a flexible modeling language is debated.
Related
- free energy principlefree energy principle, n.: A sweeping theory by the neuroscientist Karl Friston that living things persist by keeping themselves in expected states — in effect, by constantly minimizing surprise about what they sense.
- active inferenceactive inference, n.: A theory that living things, and machines built on the same idea, act in order to make the world match their predictions, choosing actions expected to keep them in preferred states and reduce their uncertainty.
- Bayesian inferenceBayesian inference, n.: Reasoning that treats unknowns as uncertain beliefs and updates them with evidence using Bayes' theorem. The result is not one answer but a range of answers, each with a probability.
- world modelworld model, n.: An AI's internal simulation of how its world works, which lets it predict what will happen next — especially in response to its own actions — and so plan, imagine or practice without acting for real.
- self-supervised learningself-supervised learning, n.: A way for AI to learn from huge amounts of unlabeled data by setting itself fill-in-the-blank puzzles, such as guessing a hidden word or a missing patch of an image.
- embodied cognitionembodied cognition, n.: The idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand.
- consciousnessconsciousness, n.: Having experiences: there being something it feels like to be you, as when you see red or feel pain. Whether any AI is or could become conscious is an open and fiercely debated question.
Sources
- Rao, R. P. N. & Ballard, D. H. (1999). Predictive Coding in the Visual Cortex: A Functional Interpretation of Some Extra-Classical Receptive-Field Effects. Nature Neuroscience 2(1), 79–87.
- Clark, A. (2013). Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science. Behavioral and Brain Sciences 36(3), 181–204.
Mentioned in
consciousnessconsciousness, n.: Having experiences: there being something it feels like to be you, as when you see red or feel pain. Whether any AI is or could become conscious is an open and fiercely debated question., world modelworld model, n.: An AI's internal simulation of how its world works, which lets it predict what will happen next — especially in response to its own actions — and so plan, imagine or practice without acting for real., cognitive sciencecognitive science, n.: The scientific study of how minds work — how we reason, use language, remember and see — combining psychology, neuroscience, linguistics, philosophy, anthropology and artificial intelligence., embodied cognitionembodied cognition, n.: The idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand., perceptionperception, n.: How an AI system or robot turns raw sensor signals, such as camera pixels, microphone sound or laser scans, into an understanding of what is around it: objects, people, words and distances., active inferenceactive inference, n.: A theory that living things, and machines built on the same idea, act in order to make the world match their predictions, choosing actions expected to keep them in preferred states and reduce their uncertainty., free energy principlefree energy principle, n.: A sweeping theory by the neuroscientist Karl Friston that living things persist by keeping themselves in expected states — in effect, by constantly minimizing surprise about what they sense., indicator properties of consciousnessindicator properties of consciousness, n.: A checklist of design features that leading scientific theories link to consciousness, used to judge how likely it is that a particular AI system could be conscious., Karl FristonKarl Friston, prop. n.: A British neuroscientist who proposed that brains, and perhaps all living systems, work by constantly predicting their surroundings and acting to reduce surprise, an idea some see as a blueprint for AI.