Reward gain and punishment avoidance reversal learning
ds004295 · 20 high-confidence citations
- NSE Report: Topological Signatures of Reversal Learning EEG Dataset: ds004295 — Reward Gain and Punishment Avoidance Reversal Learning
- NSE Report: Topological Signatures of Reversal Learning EEG Dataset: ds004295 — Reward Gain and Punishment Avoidance Reversal Learning
- Negative Space Encoding: A Multi-Dataset Empirical Validation - Topological Signatures of Cognitive Constraints Across Four EEG Datasets
- Negative Space Encoding: A Multi-Dataset Empirical Validation - Topological Signatures of Cognitive Constraints Across Four EEG Datasets
- Refusal-Driven Dimensionality Reduction Theory: Empirical Validation and Theoretical Refinement
- Refusal-Driven Dimensionality Reduction Theory: Empirical Validation and Theoretical Refinement
- Effective Neural Power and the Phenomenal Vector: Dissipation, Differentiation, and the Locus of P_noncalc
- Effective Neural Power and the Phenomenal Vector: Dissipation, Differentiation, and the Locus of P_noncalc
- Neural Dissipation and the P_noncalc Boundary: The Dissipative Vector Δ as an Operational Criterion for Phenomenal Transition in EEG
- Neural Dissipation and the P_noncalc Boundary: The Dissipative Vector Δ as an Operational Criterion for Phenomenal Transition in EEG
- Computational Efficiency η and Dissipation Norm ||Δ|| as Universal Markers of Phenomenal Transition in EEG: Evidence from Three Independent Datasets
- Computational Efficiency η and Dissipation Norm ||Δ|| as Universal Markers of Phenomenal Transition in EEG: Evidence from Three Independent Datasets
- Computational Efficiency η and Neural Dissipation ||Δ|| as Universal Markers of Phenomenal State: Evidence from Five Independent EEG Datasets
- Computational Efficiency η and Neural Dissipation ||Δ|| as Universal Markers of Phenomenal State: Evidence from Five Independent EEG Datasets
- Two Attractors and Five Layers: A Phenomenal State Space Defined by Spatial Efficiency and Inter-Layer Tension of Neural Oscillations
- Two Attractors and Five Layers: A Phenomenal State Space Defined by Spatial Efficiency and Inter-Layer Tension of Neural Oscillations
- Phenomenal Transitions as Dissipative Events: Neural Entropy Production Rate Peaks at Moments of Conscious Reorganisation
- Phenomenal Transitions as Dissipative Events: Neural Entropy Production Rate Peaks at Moments of Conscious Reorganisation
- A Conservation Law for Phenomenal Transitions: Flow Symmetry between Spatial Efficiency and Hierarchical Coupling in Neural Oscillations
- A Conservation Law for Phenomenal Transitions: Flow Symmetry between Spatial Efficiency and Hierarchical Coupling in Neural Oscillations
No citations of that kind for this dataset.