Predictive coding
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In neuroscience, predictive coding (also known as predictive processing) is a theory of brain function which postulates that the brain is constantly generating and updating a "mental model" of the environment. According to the theory, such a mental model is used to predict input signals from the senses that are then compared with the actual input signals from those senses.[1] With the rising popularity of representation learning, the theory is being actively pursued and applied in machine learning and related fields.[2][3]
The phrase 'predictive coding' is also used in several other disciplines such as signal-processing technologies and law in loosely-related or unrelated senses.
Origins
Theoretical ancestors to predictive coding date back as early as 1860 with
In the late 1990s, the idea of top-down and bottom-up processing was translated into a computational model of vision by
In 2004,[7] Rick Grush proposed a model of neural perceptual processing according to which the brain constantly generates predictions based on a generative model (what Grush called an ‘emulator’), and compares that prediction to the actual sensory input. The difference, or ‘sensory residual’ would then be used to update the model so as to produce a more accurate estimate of the perceived domain. On Grush’s account, the top-down and bottom up signals would be combined in a way sensitive to the expected noise (aka uncertainty) in the bottom-up signal, so that in situations in which the sensory signal was known to be less trustworthy, the top-down prediction would be given greater weight, and vice-versa. The emulation framework was also shown to be hierarchical, with modality-specific emulators providing top-down expectations for sensory signals as well as higher-level emulators providing expectations of the distal causes of those signals. Grush applied the theory to visual perception, visual and motor imagery, language, and theory of mind phenomena.
Today, the fields of computer science and cognitive science incorporate these same concepts to create the multilayer generative models that underlie machine learning and neural nets.[8]
General framework
Most of the research literature in the field has been about
In general, it can be more easily stated that it minimizes the amount of surprise (the measure of difference). This is also the reason for what is nowadays called confirmation bias or what might historically be prejudice (although the latter has more negative connotations) since it better fits one's individual experience accumulated so far and supports consistency. Therefore, this turns out to be rather a disadvantage in today's world.[10]
If, instead, the model accurately predicts driving sensory signals, activity at higher levels cancels out activity at lower levels, and the posterior probability of the model is increased. Thus, predictive coding inverts the conventional view of perception as a mostly bottom-up process, suggesting that it is largely constrained by prior predictions, where signals from the external world only shape perception to the extent that they are propagated up the cortical hierarchy in the form of prediction error.
In predictive coding, errors are neither good nor bad, but simply signal the difference between the expected and actual input. The exception is in reward processing, where a better than expected reward produces a positive prediction error and a disappointing result produces a negative prediction error.[11]
Precision weighting
Expectations about the precision (or inverse variance) of incoming sensory input are crucial for effectively minimizing prediction error in that the expected precision of a given prediction error can inform confidence in that error, which influences the extent to which the error is weighted in updating predictions.
Active inference
The same principle of prediction error minimization has been used to provide an account of behavior in which motor actions are not commands but descending proprioceptive predictions. In this scheme of
Neural theory in predictive coding
Evaluating the empirical evidence that suggests a neurologically plausible basis for predictive coding is a broad and varied task. For one thing, and according to the model, predictive coding occurs at every iterative step in the perceptual and cognitive processes; accordingly, manifestations of predictive coding in the brain include genetics, specific cytoarchitecture of cells, systemic networks of neurons, and whole brain analyses. Due to this range of specificity, different methods of investigating the neural mechanisms of predictive coding have been applied, where available; more generally, however, and at least as it relates to humans, there are significant methodological limitations to investigating the potential evidence and much of the work is based on computational modeling of microcircuits in the brain. Notwithstanding, there has been substantial (theoretical) work that has been applied to understanding predictive coding mechanisms in the brain. This section will focus on specific evidence as it relates to the predictive coding phenomenon, rather than analogues, such as homeostasis (which are, nonetheless, integral to our overall understanding of Bayesian inference but already supported heavily; see Clark for a review[9]).
Much of the early work that applied a predictive coding framework to neural mechanisms came from sensory neurons, particularly in the visual cortex.[6][16]
More generally, however, what seems to be required by the theory are (at least) two types of neurons (at every level of the perceptual hierarchy): one set of neurons that encode incoming sensory input, so called feed-forward projections; one set of neurons that send down predictions, so called feed-backward projections. It is important to note that these neurons must also carry properties of error detection; which class of neurons has these properties is still up for debate.[17][18] These sort of neurons have found support in superficial and non-superficial pyramidal neurons.
At a more whole-brain level, there is evidence that different cortical layers (aka laminae) may facilitate the integration of feedforward and feed-backward projections across hierarchies. These cortical layers, divided into granular, agranular, and dysgranular, which house the subpopulations of neurons mentioned above, are divided into 6 main layers. The cytoarchitecture within these layers are the same, but they differ across layers. For example, layer 4 of the granular cortex contain granule cells which are excitatory and distribute thalamocortical inputs to the rest of the cortex. According to one model:
“...prediction neurons... in deep layers of agranular cortex drive active inference by sending sensory predictions via projections ...to supragranular layers of dysgranular and granular sensory cortices. Prediction-error neurons ….in the supragranular layers of granular cortex compute the difference between the predicted and received sensory signal, and send prediction-error signals via projections...back to the deep layers of agranular cortical regions. Precision cells … tune the gain on predictions and prediction error dynamically, thereby giving these signals reduced (or, in some cases, greater) weight depending on the relative confidence in the descending predictions or the reliability of incoming sensory signals.”[19]
The theory that the unit of prediction is the cortical column[20] is based on the remarkable correspondence between the microcircuitry of the cortical column and the connectivity implied by predictive coding.[21]
Applying predictive coding
Perception
The empirical evidence for predictive coding is most robust for perceptual processing. As early as 1999, Rao and Ballard proposed a hierarchical
Interoception
There have been several competing models for the role of predictive coding in interoception.
In 2013, Anil Seth proposed that our subjective feeling states, otherwise known as emotions, are generated by predictive models that are actively built out of causal interoceptive appraisals.
In 2015, Lisa Feldman Barrett and W. Kyle Simmons proposed the Embodied Predictive Interoception Coding model, a framework that unifies Bayesian active inference principles with a physiological framework of corticocortical connections.[19] Using this model, they posited that agranular visceromotor cortices are responsible for generating predictions about interoception, thus, defining the experience of interoception.
Contrary to the inductive notion that emotion categories are biologically distinct, Barrett proposed later the theory of constructed emotion, which is the account that a biological emotion category is constructed based on a conceptual category—the accumulation of instances sharing a goal.[24][25] In a predictive coding model, Barrett hypothesizes that, in interoception, our brains regulate our bodies by activating "embodied simulations" (full-bodied representations of sensory experience) to anticipate what our brains predict that the external world will throw at us sensorially and how we will respond to it with action. These simulations are either preserved if, based on our brain's predictions, they prepare us well for what actually subsequently occurs in the external world, or they, and our predictions, are adjusted to compensate for their error in comparison to what actually occurs in the external world and how well-prepared we were for it. Then, in a trial-error-adjust process, our bodies find similarities in goals among certain successful anticipatory simulations and group them together under conceptual categories. Every time a new experience arises, our brains use this past trial-error-adjust history to match the new experience to one of the categories of accumulated corrected simulations that it shares the most similarity with. Then, they apply the corrected simulation of that category to the new experience in the hopes of preparing our bodies for the rest of the experience. If it does not, the prediction, the simulation, and perhaps the boundaries of the conceptual category are revised in the hopes of higher accuracy next time, and the process continues. Barrett hypothesizes that, when prediction error for a certain category of simulations for x-like experiences is minimized, what results is a correction-informed simulation that the body will reenact for every x-like experience, resulting in a correction-informed full-bodied representation of sensory experience—an emotion. In this sense, Barrett proposes that we construct our emotions because the conceptual category framework our brains use to compare new experiences, and to pick the appropriate predictive sensory simulation to activate, is built on the go.
Challenges
As a mechanistic theory, predictive coding has not been mapped out physiologically on the neuronal level. One of the biggest challenges to the theory has been the imprecision of exactly how prediction error minimization works.
Future research could focus on clarifying the neurophysiological mechanism and computational model of predictive coding.[according to whom?]
See also
- Blue Brain Project
- Cognitive biology
- Cognitive linguistics
- Cognitive neuropsychology
- Cognitive neuroscience
- Cognitive science
- Conceptual blending
- Conceptual metaphor
- Cortical column
- Embodied bilingual language
- Embodied cognitive science
- Embodied Embedded Cognition
- Embodied music cognition
- Enactivism
- Extended cognition
- Extended mind thesis
- Externalism
- Heuristic
- Image schema
- Moravec's paradox
- Neuroconstructivism
- Neuropsychology
- Neurophenomenology
- Philosophy of mind
- Plant cognition
- Practopoiesis
- Situated cognition
- Where Mathematics Comes From
References
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- ^ Ransom M. & Fazelpour S (2015). Three Problems for the Predictive Coding Theory of Attention. http://mindsonline.philosophyofbrains.com/2015/session4/three-problems-for-the-predictive-coding-theory-of-attention/