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Optimizing the precision parameters corresponds to optimizing the gain of prediction errors (c.f., Kalman gain). In neuronally plausible implementations of predictive coding,<ref name="Mumford" /> this corresponds to optimizing the excitability of superficial pyramidal cells and has been interpreted in terms of attentional gain.<ref name="Feldman">Feldman, H., & Friston, K. J. (2010). [http://www.fil.ion.ucl.ac.uk/~karl/Attention%20uncertainty%20and%20free-energy.pdf Attention, uncertainty, and free-energy]. Frontiers in Human Neuroscience, 4, 215.</ref>

Optimizing the precision parameters corresponds to optimizing the gain of prediction errors (c.f., Kalman gain). In neuronally plausible implementations of predictive coding,<ref name="Mumford" /> this corresponds to optimizing the excitability of superficial pyramidal cells and has been interpreted in terms of attentional gain.<ref name="Feldman">Feldman, H., & Friston, K. J. (2010). [http://www.fil.ion.ucl.ac.uk/~karl/Attention%20uncertainty%20and%20free-energy.pdf Attention, uncertainty, and free-energy]. Frontiers in Human Neuroscience, 4, 215.</ref>

[[File:PESAIM.jpg|thumb|Simulation of the results achieved from a selective attention task carried out by the Bayesian reformulation of the SAIM entitled PE-SAIM in multiple objects environment. The graphs show the time course of the activation for the FOA and the two template units in the Knowledge Network.]]

[[File:PESAIM.jpg|thumb|Simulation of the results achieved from a selective attention task carried out by the Bayesian reformulation of the SAIM entitled PE-SAIM in multiple objects environment. The graphs show the time course of the activation for the FOA and the two template units in the Knowledge Network.]]

Optimal decision problems (usually formulated as partially observable Markov decision processes) are treated within active inference by absorbing  utility functions into prior beliefs. In this setting, states that have a high utility (low cost) are states an agent expects to occupy. By equipping the generative model with hidden states that model control, policies (control sequences) that minimise variational free energy lead to high utility states.

Optimal decision problems (usually formulated as partially observable Markov decision processes) are treated within active inference by absorbing  utility functions into prior beliefs. In this setting, states that have a high utility (low cost) are states an agent expects to occupy. By equipping the generative model with hidden states that model control, policies (control sequences) that minimise variational free energy lead to high utility states.

Concerning the top-down vs bottom-up controversy that has been addressed as a major open problem of attention, a computational model has succeeded in illustrating the circulatory nature of reciprocation between top-down and bottom-up mechanisms. Using an established emergent model of attention, namely, SAIM, the authors suggested a model called PE-SAIM that in contrast to the standard version approaches the selective attention from a top-down stance. The model takes into account the forwarding prediction errors sent to the same level or a level above to minimize the energy function indicating the difference between data and its cause or in other words between the generative model and posterior. To enhance validity, they also incorporated the neural competition between the stimuli in their model. A notable feature of this model is the reformulation of the free energy function only in terms of prediction errors during the task performance.

Concerning the top-down vs bottom-up controversy that has been addressed as a major open problem of attention, a computational model has succeeded in illustrating the circulatory nature of reciprocation between top-down and bottom-up mechanisms. Using an established emergent model of attention, namely, SAIM, the authors suggested a model called PE-SAIM that in contrast to the standard version approaches the selective attention from a top-down stance. The model takes into account the forwarding prediction errors sent to the same level or a level above to minimize the energy function indicating the difference between data and its cause or in other words between the generative model and posterior. To enhance validity, they also incorporated the neural competition between the stimuli in their model. A notable feature of this model is the reformulation of the free energy function only in terms of prediction errors during the task performance.

Neurobiologically, neuromodulators like dopamine are considered to report the precision of prediction errors by modulating the gain of principal cells encoding prediction error. This is closely related to – but formally distinct from – the role of dopamine in reporting prediction errors per se and related computational accounts.

Neurobiologically, neuromodulators like dopamine are considered to report the precision of prediction errors by modulating the gain of principal cells encoding prediction error. This is closely related to – but formally distinct from – the role of dopamine in reporting prediction errors per se and related computational accounts.

   

   

where, <math>E^{total}</math> is the total [[energy function]] of the neural networks entail, and <math>\varepsilon^{KN}_{knm}</math> is the prediction error between the generative model (prior) and posterior changing over time.<ref name="Abadi">Abadi K.A., Yahya K., Amini M., Heinke D. & Friston, K. J. (2019). [https://royalsocietypublishing.org/doi/full/10.1098/rsif.2018.0344 Excitatory versus inhibitory feedback in Bayesian formulations of scene construction]. 16 R. Soc. Interface</ref>)

where, <math>E^{total}</math> is the total [[energy function]] of the neural networks entail, and <math>\varepsilon^{KN}_{knm}</math> is the prediction error between the generative model (prior) and posterior changing over time.<ref name="Abadi">Abadi K.A., Yahya K., Amini M., Heinke D. & Friston, K. J. (2019). [https://royalsocietypublishing.org/doi/full/10.1098/rsif.2018.0344 Excitatory versus inhibitory feedback in Bayesian formulations of scene construction]. 16 R. Soc. Interface</ref>)

Active inference has been used to address a range of issues in cognitive neuroscience, brain function and neuropsychiatry, including: action observation, mirror neurons, saccades and visual search, eye movements, sleep, illusions, attention, hysteria and psychosis. Explanations of action in active inference often depend on the idea that the brain has 'stubborn predictions' which it cannot update, leading to actions that cause these predictions to come true.

Active inference has been used to address a range of issues in cognitive neuroscience, brain function and neuropsychiatry, including: action observation, mirror neurons, saccades and visual search, eye movements, sleep, illusions, attention, hysteria and psychosis. Explanations of action in active inference often depend on the idea that the brain has 'stubborn predictions' which it cannot update, leading to actions that cause these predictions to come true.

Comparing the two models reveals a notable similarity between their results while pointing out to a remarkable discrepancy, in that, in the standard version of the SAIM, the model's focus is mainly upon the excitatory connections whereas in the PE-SAIM the inhibitory connections will be leveraged to make an inference. The model has also proved to be fit to predict the EEG and fMRI data drawn from human experiments with a high precision.

Comparing the two models reveals a notable similarity between their results while pointing out to a remarkable discrepancy, in that, in the standard version of the SAIM, the model's focus is mainly upon the excitatory connections whereas in the PE-SAIM the inhibitory connections will be leveraged to make an inference. The model has also proved to be fit to predict the EEG and fMRI data drawn from human experiments with a high precision.

 

== Active inference 主动推理==

== Active inference 主动推理==