Detecting absence: A dedicated prediction-error signal emerging in the auditory thalamus

Introduction to the show: ident: Genomics Radio. Generated commentary on the latest computational biology and genomics papers.

Ines: We're looking at the paper: "Detecting absence: A dedicated prediction-error signal emerging in the auditory thalamus".

Marcus: Here's the gist of the paper. The detection of sensory absence relies on a distinct form of prediction error, absence prediction error, dedicated to the update of beliefs on stimulus occurrence Hypothesis and Theoretical Framework The study proposes that the detection of absence relies on a distinct form of prediction error, absence prediction error, dedicated to the update of beliefs on stimulus occurrence.

Ines: This is hypothesized to be mediated by reductions on presence plausibility through what is called absence prediction error.

Marcus: The authors argue that feature prediction errors, which are related to updating beliefs about the features of a stimulus, are insufficient for updating internal beliefs on the sensory world because they only model Gaussian distributions centered in the predicted and perceived pitch.

Ines: They propose that absence prediction error is paramount to this process.

Paper summary: Ines: So, we're looking at this paper 'Detecting absence: A dedicated prediction-error signal emerging in the auditory thalamus'. The main idea here is that we can't just rely on sensory signals because they are messy and ambiguous; the brain needs prior beliefs to make sense of what it hears.

Marcus: Exactly. And current theories explain how we update beliefs about features, like pitch or motion velocity, using feature prediction errors, which is just the mismatch between what we expected and what actually happened. But this paper claims that updating beliefs about whether a stimulus is even there requires something separate from that feature error.

Yuki: So it's suggesting there's a specific way the brain handles the absence of something, not just a general feature update mechanism. That sounds like it could be important for understanding how we perceive missing information in real life.

Ines: Right. The authors hypothesize that this is mediated by what they call absence prediction error, which they say is about reductions on presence plausibility, and this is different from feature prediction error that already happens in the sensory pathway.

Marcus: They propose that feature prediction errors only model Gaussian distributions centered around the expected pitch or velocity, but absence prediction error seems to be paramount for updating beliefs about occurrence. They suggest this separation between positive and negative prediction errors might be a hallmark of the signals used to update these beliefs on stimulus occurrence.

Yuki: If it's a separate signal, that implies there are dedicated circuits handling the idea of presence versus absence, rather than just one general error mechanism for everything. That speaks to how specialized those systems might be in processing sensory input.

Ines: The experiment they set up involved measuring BOLD responses in the human auditory pathway using fMRI to see this absence prediction error in response to different levels of expectations about the omissions. They tested whether expectation level affected the response, which is a key part of their design.

Marcus: They showed that this absence prediction error was represented in both the auditory thalamus and cortex when comparing more and less expected responses to an absence. But they also found that feature prediction error, which we already know about, is encoded in the auditory midbrain, while this absence prediction error wasn't found there.

Yuki: So the finding that absence prediction error emerges along the ascending auditory pathway suggests a circuit-specific way of handling what's missing in sound rather than just a general processing hub. That links it to where those thalamic structures are located in relation to other brain areas.

Ines: The study found that responses to unexpected omissions were stronger than responses to repeated tones, specifically for position four which they called om4, across all auditory regions measured <ref:2511.21605#pg1>. They also saw significant absence prediction error in the medial geniculate body and the posterior auditory cortex, but not in the inferior colliculus.

Paper summary: Marcus: So they are pinpointing specific regions that handle this absence signal differently. They noted that when comparing contrasts normalized by BOLD sensitivity, the om4 greater than standard tone contrast and the absence prediction error contrast were significantly stronger in the medial geniculate body compared to the inferior colliculus.

Yuki: That suggests a clear pathway for this specific type of error signal to travel up from the thalamus toward higher processing areas like the cortex. It’s not just anywhere in the auditory system; it has a specific route.

Ines: The implication here is that we have identified a neural mechanism specifically for detecting sensory absence, and these mechanisms might be vulnerable in conditions where detection of absence is impaired, like psychosis. This provides a concrete neural correlate for something we observe clinically.

Marcus: And the authors suggest this dissociation between feature identification and presence/absence prediction error means that feature identification isn't necessarily affected by this specific mechanism, since evidence for a tone's pitch seems always direct.

Yuki: If feature identification is separate from absence detection, it suggests two distinct computational processes are happening in the auditory system when we hear something or when we don't. It’s like having a system that checks if the sound matches the pattern, and another one that checks if the sound is actually there at all.

Ines: That distinction is what they are really making here: feature prediction error handles pitch or velocity mismatch, while absence prediction error handles whether a stimulus occurs or not. This separation helps us understand how perception builds stable representations of our sensory world.

Marcus: The paper's findings suggest that the detection of absence isn't just a byproduct of feature processing; it relies on a dedicated type of prediction error computed by specific circuits in the auditory thalamus and cortex. That is what they call absence prediction error, and it’s distinct from feature prediction error.

Yuki: So, for someone who only listens to this show, it means that when we hear something missing—a tone that should be there but isn't—the brain doesn't just fail to process the feature; it fails at a different level of belief updating related to presence.

Ines: The authors suggest that this asymmetry isn't present in how features are identified, because evidence for a tone’s pitch is always direct, but the way the brain updates its beliefs about occurrence is handled by this distinct absence prediction error signal.

Marcus: This specific finding about the auditory thalamus and cortex encoding absence prediction error could explain why certain perceptual deficits, like those seen in psychosis, involve an impairment in detecting what is absent. It gives us a target for understanding how these systems can break down.

Yuki: It really shows how much more complex the system is than just one signal telling it everything about a sound; there's this additional layer of probabilistic belief updating that deals with things not being present. That adds depth to our models of perception.

Conclusion: Ines: It’s titled "Detecting absence: A dedicated prediction-error signal emerging in the auditory thalamus." The authors are looking at how our brains handle missing sound versus just processing what's there.

Marcus: Yeah, they show that this isn't one general error mechanism for everything; it's a separate thing called absence prediction error. It’s dedicated to updating beliefs about whether a stimulus is present or absent.

Yuki: From a population genetics angle, this suggests we need to consider how species evolved mechanisms for detecting absence, not just presence. What the sample they used can really tell us about the human auditory system's history.

Ines: They found that this absence prediction error shows up in both the thalamus and the cortex when participants have different expectations about what’s missing. That’s a big deal because it points to dedicated circuits for absence processing.

Marcus: And they separated it from feature prediction error, which is already known to happen in the midbrain, meaning absence uses a different circuit entirely. They measured BOLD responses using fMRI across the auditory pathway to see this difference.

Yuki: If those specific circuits are what’s handling absence, that opens up a whole new area for thinking about how perceptual systems manage uncertainty in the real world. It’s not just about identifying a tone; it's about confirming its existence.

Ines: And the implication is that these specific circuits might be disrupted in conditions like psychosis, where people often struggle to detect what's absent. This gives us a concrete neural target for understanding those perceptual deficits.

Marcus: So, the main point is that absence detection relies on a specific prediction error computed by dedicated circuits in the auditory thalamus and cortex, distinct from how we handle feature identification.

Yuki: That’s what it means for us to look at how these systems function across different species and conditions. We need to see if this mechanism is conserved or unique.

Ines: It makes you wonder where else in the brain we have these dedicated absence circuits, because if they exist, they could be involved in so many perceptual problems we don't yet understand. That leads us right into how these specific computational errors actually build up over time and experience.