This schematic illustrates how hidden states cause each other and sensory outcomes in the interoceptive and exteroceptive domain. The upper row describes the probability transitions among hidden states, while the lower row specifies the outcomes that would be generated by combinations of hidden states that are inferred on the basis of outcomes. The green panel specifies the model’s prior preferences; namely, the sorts of outcomes it expects to encounter. Please see main text for a full explanation. Although this figure portrays interoceptive and exteroceptive outcomes as separate modalities, they were in fact modelled as combinations–so that the prior preferences could be evaluated (this is necessary because the preferred physiological outc...
<p>This figure shows the form of the hierarchical dynamic model used to generate and subsequently re...
The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are ty...
Acknowledgements: We thank Valentin Wyart and Jean-Remi King for sharing their data. This work was s...
<p>The generative model is trying to predict the sensory states produced by the equations on the rig...
<p>The upper panel shows the likelihood matrices for the top left quadrant (location 2), under the v...
Left: We illustrate here the conditional dependence of state durations, states, reward probabilities...
The variables are shown in circles (with filled circles showing observable variables). An arrow from...
A: Feedforward mapping from an input to two neural units s1 and s2. The mapping is defined by two r...
To explore how interoceptive inference may shape metacognition, i.e., inference over inferred sensor...
<p><i>Top:</i> Outline of a trial. Participants clicked on a mouse button and a yellow dot was flash...
<p>The highest 3<sup>rd</sup> level hierarchy describes the dynamics of the three dimensional state ...
To infer the causes of its sensations, the brain must call on a generative (predictive) model. This ...
The concept of the brain as a prediction machine has enjoyed a resurgence in the context of the Baye...
<p>Both plots show illustrative snapshots of the two evolving decision states while in transit towar...
<p>Models differ along three dimensions: whether precision is equal or variable, the observer’s assu...
<p>This figure shows the form of the hierarchical dynamic model used to generate and subsequently re...
The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are ty...
Acknowledgements: We thank Valentin Wyart and Jean-Remi King for sharing their data. This work was s...
<p>The generative model is trying to predict the sensory states produced by the equations on the rig...
<p>The upper panel shows the likelihood matrices for the top left quadrant (location 2), under the v...
Left: We illustrate here the conditional dependence of state durations, states, reward probabilities...
The variables are shown in circles (with filled circles showing observable variables). An arrow from...
A: Feedforward mapping from an input to two neural units s1 and s2. The mapping is defined by two r...
To explore how interoceptive inference may shape metacognition, i.e., inference over inferred sensor...
<p><i>Top:</i> Outline of a trial. Participants clicked on a mouse button and a yellow dot was flash...
<p>The highest 3<sup>rd</sup> level hierarchy describes the dynamics of the three dimensional state ...
To infer the causes of its sensations, the brain must call on a generative (predictive) model. This ...
The concept of the brain as a prediction machine has enjoyed a resurgence in the context of the Baye...
<p>Both plots show illustrative snapshots of the two evolving decision states while in transit towar...
<p>Models differ along three dimensions: whether precision is equal or variable, the observer’s assu...
<p>This figure shows the form of the hierarchical dynamic model used to generate and subsequently re...
The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are ty...
Acknowledgements: We thank Valentin Wyart and Jean-Remi King for sharing their data. This work was s...