Predicting First-Person and Counterfactual Experiences of Selfhood: Insights from Anosognosia
Bibliographic record
Abstract
In this chapter, we build upon empirical findings on the neurological syndrome of anosognosia for hemiplegia (lack of awareness into one’s paralysis; AHP) and recent neuroscientific theories of self-awareness to propose that the experience of ones’ self entails at least two normally integrated levels of inference. Namely, inferences about the here-and-now of experience in the first-person and counterfactual inferences about the self beyond first-person experience. The relation between the two is understood as dynamic, in the sense that the contextual salience of different signals determines the degree to which prediction errors in first-person experiences will be explained away by more objectified predictions about the self, or will allow the updating of the latter. To unpack and support these claims, we present and combine clinical experiences of anosognosia with empirical findings from our own and other labs. Our main thesis is that anosognosia is best explained as a disconnection between how one expects the body to feel in a first-person perspective (emotionally mine and under my control) and how one perceives the body counterfactually. This difficulty in integrating current sensations and emotions from the body with their beliefs about the body corresponds to difficulties in the most abstract, metacognitive (allocentric and prospective) aspects of body awareness. Thus, self-awareness in anosognosia is subject to the influence of non-updated premorbid beliefs and emotions about the self. The dissociation observed in anosognosia allows us a rare glimpse of the normally unconscious processes of integration and inference that underlie self-experience in everyday life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".