Thermoceptive predictions and prediction errors in the anterior insula
Toussaint, B.; Heinzle, J.; Friedli, N.; Zahnd, N. J.; Federici, E.; Koechli, L.; Harrison, O. K.; Iglesias, S.; Stephan, K. E.
Show abstract
Contemporary theories of interoception propose that the brain constructs a model of the body for predicting the states and allostatic needs of all organs, including the skin, and updates this model using prediction error signals. However, empirical tests of this proposal are scarce in humans. This computational neuroimaging study investigated the presence and location of thermoceptive predictions and prediction errors in the brain using probabilistic manipulations of skin temperature in a novel interoceptive learning paradigm. Using functional MRI in healthy volunteers, we found that a Bayesian model provided a better account of participants skin temperature predictions than a non-Bayesian model. Further, activity in a network including the anterior insula was associated with trial-wise predictions and precision-weighted prediction errors. Our findings provide further evidence that the anterior insula plays a key role in implementing the brains model of the body, and raise important questions about the structure of this model.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation 96%
- Boosting Brain Signal Variability Underlies Liberal Shifts in Decision Bias 95%
- Task-related hemodynamic responses in human early visual cortex are modulated by task difficulty and behavioral performance 95%
Similar papers in this journal
- Asymmetric representation of aversive prediction errors in Pavlovian threat conditioning 95%
- Arbitration between insula and temporoparietal junction subserves framing-induced boosts in generosity during social discounting 95%
- Discovering the shared biology of cognitive traits determined by genetic overlap 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.