Priors in perception: Top-down modulation, Bayesian perceptual learning rate, and prediction error minimization

2017 ◽  
Vol 47 ◽  
pp. 75-85 ◽  
Author(s):  
Jakob Hohwy
Keyword(s):  
Perceptual Learning ◽  
Prediction Error ◽  
Learning Rate ◽  
Error Minimization ◽  
Top Down ◽  
Prediction Error Minimization

scholarly journals Why should any body have a self?

2017 ◽  
Author(s):  
Jakob Hohwy ◽  
John Michael
Keyword(s):  
Brain Function ◽  
Prediction Error ◽  
The Body ◽  
The Self ◽  
Error Minimization ◽  
Computational Framework ◽  
Key Characteristics ◽  
Prediction Error Minimization ◽  
Hierarchical Bayesian Inference

We use a general computational framework for brain function to develop a theory of the self. The theory is that the self is an inferred model of endogenous, deeply hidden causes of behavior. The general framework for brain function on which we base this theory is that the brain is fundamentally an organ for prediction error minimization.There are three related parts to this project. In the first part (Sections 2-3), we explain how prediction error minimization must lead to the inference of a network of deeply hidden endogenous causes. The key concept here is that prediction error minimization in the long term approximates hierarchical Bayesian inference, where the hierarchy is critical to understand the place of the self, and the body, in the world.In the second part (Sections 4-5), we discuss why such a set of hidden endogenous causes should qualify as a self. We show how a comprehensive prediction error minimization account can accommodate key characteristics of the self. It turns out that, though the modelled endogenous causes are just some among other inferred causes of sensory input, the model is special in being, in a special sense, a model of itself.The third part (Sections 6-7) identifies a threat from such self-modelling: how can a self-model be accurate if it represents itself? We propose that we learn to be who we are through a positive feedback loop: from infancy onward, humans apply agent-models to understand what other agents are up to in their environment, and actively align themselves with those models. Accurate self-models arise and are sustained as a natural consequence of humans’ skill in modeling and interacting with each other. The concluding section situates this inferentialist yet realist theory of the self with respect to narrative conceptions of the self.

scholarly journals Boredom begets creativity: a solution to the exploitation-exploration trade-off in predictive coding

2017 ◽  
Author(s):  
Jaime Gomez-Ramirez ◽  
Tommaso Costa
Keyword(s):  
Mathematical Model ◽  
Prediction Error ◽  
Predictive Coding ◽  
Error Minimization ◽  
Trade Off ◽  
Black Scholes ◽  
Intelligent Behavior ◽  
Prediction Error Minimization ◽  
Key Drivers ◽  
Technical Systems

AbstractHere, we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non recurrent problems. We argue that there is a key ingredient that has been overlooked by researchers that needs to be incorporated to understand intelligent behavior in biological and technical systems. This ingredient is boredom. We propose a mathematical model based on the Black-Scholes-Merton equation which provides mechanistic insights into the interplay between boredom and prediction pleasure as the key drivers of behavior.

scholarly journals Blindness, Psychosis, and the Visual Construction of the World

2019 ◽  
Vol 46 (6) ◽  
pp. 1418-1425
Author(s):  
Thomas A Pollak ◽  
Philip R Corlett
Keyword(s):  
Prediction Error ◽  
Visual Loss ◽  
Later Life ◽  
Visual Deprivation ◽  
Bayesian Prediction ◽  
Psychotic Symptoms ◽  
Error Minimization ◽  
The World ◽  
Prediction Error Minimization ◽  
The Relationship

Abstract The relationship between visual loss and psychosis is complex: congenital visual loss appears to be protective against the development of a psychotic disorder, particularly schizophrenia. In later life, however, visual deprivation or visual loss can give rise to hallucinosis, disorders of visual insight such as blindsight or Anton syndrome, or, in the context of neurodegenerative disorders, more complex psychotic presentations. We draw on a computational psychiatric approach to consider the foundational role of vision in the construction of representations of the world and the effects of visual loss at different developmental stages. Using a Bayesian prediction error minimization model, we describe how congenital visual loss may be protective against the development of the kind of computational deficits postulated to underlie schizophrenia, by increasing the precision (and consequent stability) of higher-level (including supramodal) priors, focusing on visual loss-induced changes in NMDA receptor structure and function as a possible mechanistic substrate. In simple terms, we argue that when people cannot see from birth, they rely more heavily on the context they extract from the other senses, and the resulting model of the world is more impervious to the false inferences, made in the face of inevitably noisy perceptual input, that characterize schizophrenia. We show how a Bayesian prediction error minimization framework can also explain the relationship between later visual loss and other psychotic symptoms, as well as the effects of visual deprivation and hallucinogenic drugs, and outline experimentally testable hypotheses generated by this approach.

scholarly journals Skull-bound perception and precision optimization through culture

2013 ◽  
Vol 36 (3) ◽  
pp. 222-222 ◽  
Author(s):  
Bryan Paton ◽  
Josh Skewes ◽  
Chris Frith ◽  
Jakob Hohwy
Keyword(s):  
Social Interaction ◽  
Prediction Error ◽  
Situated Cognition ◽  
Error Minimization ◽  
Prediction Error Minimization

AbstractClark acknowledges but resists the indirect mind–world relation inherent in prediction error minimization (PEM). But directness should also be resisted. This creates a puzzle, which calls for reconceptualization of the relation. We suggest that a causal conception captures both aspects. With this conception, aspects of situated cognition, social interaction and culture can be understood as emerging through precision optimization.

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