Do current evidential standards in the science of consciousness help or hinder the discovery of signs of consciousness?
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Key points | Argues that conventional statistical significance thresholds in experimental consciousness research rest on a value judgment prioritizing avoidance of false positives over false negatives, which conflicts with a common normative intuition among consciousness researchers and may impede detection of signs of consciousness. Proposes that researchers shift to Bayes Factors instead. |
Abstract
One recent approach to determining whether comatose patients, non-human animals, or brain organoids are conscious is to examine whether they display features that scientific studies have found to be correlated with and indicative of consciousness. However, it is unclear to what extent scientific studies that search for such signs of consciousness rely on evidential standards that facilitate the detection of these features. Here, I argue that when it comes to standards of statistical significance, many of the studies at issue rest on a value judgment according to which false positive research conclusions are much more problematic than false negative ones. This value judgment contradicts a common normative intuition that many consciousness researchers have and may impede the discovery of signs of consciousness. Moreover, recent efforts to reduce replication failures of scientific studies by lowering the threshold for statistical significance may further increase the risk of consciousness researchers to miss evidence of consciousness in organic or artificial systems. I argue that these limitations provide reasons to shift from the conventional statistical significance thresholds in experimental consciousness research to Bayes Factors.