text: participants processed faces expressing fear (but not neutral 
  faces or faces expressing other emotions) when their heart rate 
  was high—hence congruent with the fearful expression (Pez- 
  zulo et al. 2018, Yu et al. 2021). The generative model shown 
  in Fig. 1 could support this kind of inference by using interocep- 
  tive information from the heart (i.e. high heart rate) as evidence 
  that “there might be something fearful out there” (Pezzulo 2013). 
  Another more complex example regards emotional awareness 
  and self-awareness—which significantly engage the brain regions 
  involved in interoception and the representation of physiologi- 
  cal processes (Garfinkel et al. 2013). The generative model shown 
  in Fig. 1 might support processes of emotional awareness in a 
  way that is neither purely bottom-up (i.e. as if interoceptive sig- 
  nals cause emotional awareness) nor top-down (i.e. as if emotional 
  awareness causes interoceptive signals), but rather through a 
  circular causality between central predictions about bodily state— 
  that engage autonomic reflexes—and interoceptive streams—that 
  update the predictions (Seth and Friston 2016). In this perspec- 
  tive, any representation that induces interoceptive predictions 
  could be associated with emotional or affective content; cru- 
  cially, this is also the case with some aspects of self-awareness 
  (e.g. recognizing one’s own face) that require integrating intero- 
  ceptive streams with concurrent exteroceptive (e.g. visual) and 
  proprioceptive cues. These examples illustrate that the genera- 
  tive model of Fig. 1 natively implements both the multisensory 
  integration required to unite (for example) interoceptive and exte- 
  roceptive streams and the active aspects that are supposed to 
  support emotional and self-processing—and the construction of 
  an “embodied self” (i.e. the circular causality between engag- 
  ing autonomic reflexes and capturing the ensuing interoceptive 
  signals). 