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Free energy minimisation provides a useful way to formulate normative (Bayes optimal) models of neuronal inference and learning under uncertainty<ref>Friston, K. (2010). [http://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20principle%20A%20unified%20brain%20theory.pdf The free-energy principle: a unified brain theory?] Nat Rev Neurosci. , 11 (2), 127–38.</ref> and therefore subscribes to the [[Bayesian brain]] hypothesis.<ref>Knill, D. C., & Pouget, A. (2004). [http://mrl.isr.uc.pt/pub/bscw.cgi/d27540/ReviewKnillPouget2.pdf The Bayesian brain: the role of uncertainty in neural coding and computation]. Trends Neurosci. , 27 (12), 712–9.</ref> The neuronal processes described by free energy minimisation depend on the nature of hidden states: <math> \Psi = X \times \Theta \times \Pi </math> that can comprise time-dependent variables, time-invariant parameters and the precision (inverse variance or temperature) of random fluctuations. Minimising variables, parameters, and precision correspond to inference, learning, and the encoding of uncertainty, respectively.

Free energy minimisation provides a useful way to formulate normative (Bayes optimal) models of neuronal inference and learning under uncertainty<ref>Friston, K. (2010). [http://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20principle%20A%20unified%20brain%20theory.pdf The free-energy principle: a unified brain theory?] Nat Rev Neurosci. , 11 (2), 127–38.</ref> and therefore subscribes to the [[Bayesian brain]] hypothesis.<ref>Knill, D. C., & Pouget, A. (2004). [http://mrl.isr.uc.pt/pub/bscw.cgi/d27540/ReviewKnillPouget2.pdf The Bayesian brain: the role of uncertainty in neural coding and computation]. Trends Neurosci. , 27 (12), 712–9.</ref> The neuronal processes described by free energy minimisation depend on the nature of hidden states: <math> \Psi = X \times \Theta \times \Pi </math> that can comprise time-dependent variables, time-invariant parameters and the precision (inverse variance or temperature) of random fluctuations. Minimising variables, parameters, and precision correspond to inference, learning, and the encoding of uncertainty, respectively.

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.

关于自上而下与自下而上的争论，已经被作为一个主要的开放性的注意问题，一个计算模型已经成功地说明了自上而下和自下而上机制之间的往复循环性质。利用已建立的注意涌现模型SAIM，作者提出了一个称为PE-SAIM的模型，与标准模型相比，该模型从自上而下的角度来处理选择性注意。该模型考虑了发送到同一级别或更高级别的转发预测误差，以最小化表示数据及其原因之间的差异的能量函数，换句话说，生成模型和后验模型之间的差异。为了提高有效性，他们还在模型中加入了刺激物之间的神经竞争。该模型的一个显著特点是仅根据任务执行过程中的预测误差来重新构造自由能函数。

=== Perceptual inference and categorisation 感性推理与分类===

=== Perceptual inference and categorisation 感性推理与分类===