Implicit Neurofeedback Training of Feature-Based Attention Promotes Biased Sensory Processing during Integrative Decision-Making
Bibliographic record
Abstract
Complex perceptual decisions, in which information must be integrated across multiple sources of evidence, are ubiquitous but are not well understood. Such decisions rely on sensory processing of each individual source of evidence, and are therefore vulnerable to bias if sensory processing resources are disproportionately allocated among visual inputs. To investigate this, we developed an implicit neurofeedback protocol embedded within a complex decision-making task to bias sensory processing in favor of one source of evidence over another. Human participants of both sexes (N = 30) were asked to report the average motion direction across two fields of oriented moving bars. Bars of different orientations flickered at different frequencies, thus inducing steady-state visual evoked potentials. Unbeknownst to participants, neurofeedback was implemented to implicitly reward attention to a specific “trained” orientation (rather than any particular motion direction). As attentional selectivity for this orientation increased, the motion coherence of both fields of bars increased, making the task easier without altering the relative reliability of the two sources of evidence. Critically, these neurofeedback trials were alternated with “test” trials in which motion coherence was not contingent on attentional selectivity, allowing us to assess the training efficacy. The protocol successfully biased sensory processing, resulting in earlier and stronger encoding of the trained evidence source. In turn, this evidence was weighted more heavily in behavioral and neural representations of the integrated average, although the two sources of evidence were always matched in reliability. These results demonstrate how biases in sensory processing can impact integrative decision-making processes. SIGNIFICANCE STATEMENT Many everyday decisions require active integration of different sources of sensory information, such as deciding when it is safe to cross a road, yet little is known about how the brain prioritizes sensory sources in the service of adaptive behavior, or whether such decisions can be altered through learning. Here we addressed these questions using a novel behavioral protocol that provided observers with real-time feedback of their own brain activity patterns in which sensory processing was implicitly biased toward a subset of the available information. We show that, while such biases are a normal and adaptive mechanism for humans to process complex visual information, they can also contribute to suboptimal decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".