TriadicFrameworks
Panoramica

substrate_mind_science

README — Conscious Transfer Substrate Map v1

docs/substrate_mind_science/ct_substrate/README.md#

1. Purpose of This Directory#

This directory contains the Conscious Transfer Substrate Map v1, a formal, RTT‑compliant schema that defines the minimal functional structure of mind that can be:

  • Repeatably measured
  • Traceably mapped to behavior and physiology
  • Transfer‑addressable across substrates

It is the first layer of the Resonance Atlas designed specifically for Conscious Transfer research.

This schema is not a psychological or psychiatric model.
It is a substrate model — a representation of the functional invariants that survive across tasks, individuals, and implementations.

2. Relationship to the Resonance Atlas#

The Resonance Atlas has three major layers:

  1. Minimal Empirical Mind Substrate v1

    • The distilled scientific core extracted from psychology + psychiatry
    • Only green‑zone constructs (empirical, measurable, reproducible)

  2. Legacy‑Narratives Quarantine

    • All yellow/red constructs (interpretive, institutional, cultural, mythic)
    • Preserved for context, but never allowed in substrate models

  3. Conscious Transfer Substrate Map (this directory)

    • The RTT‑filtered subset of the Minimal Substrate
    • Formalized into a machine‑readable schema
    • Ready for vST overlays and implementation work

The Conscious Transfer Substrate Map is therefore the intersection of:

  • empirical mind science
  • RTT constraints
  • transfer‑ready formalization

3. What This Schema Represents#

The schema defines four substrate layers:

A. Behavioral Layer#

The lowest‑level functional invariants:

  • conditioning laws
  • reinforcement structures
  • reaction‑time distributions
  • accuracy/error patterns
  • speed–accuracy tradeoff parameters

These are the observable invariants that any conscious substrate must reproduce.

B. Cognitive Layer#

The representational/computational invariants:

  • working‑memory parameters
  • attention control parameters
  • drift‑diffusion decision models
  • signal‑detection parameters
  • reinforcement‑learning parameters

These are the task‑bound cognitive operators that survive RTT filtering.

C. Measurement Layer#

The interfaces that validate the substrate:

  • psychometric reliability/validity
  • neuropsychological task batteries
  • normative z‑scores

These are not the mind — they are the measurement operators that define equivalence across substrates.

D. Anchoring Constraints#

The biological correlates that meet RTT criteria:

  • lesion‑to‑function mappings
  • task‑locked neurophysiological signatures
  • stable pharmacological modulation profiles

These are constraints, not requirements for biological tissue.

4. What This Schema Explicitly Excludes#

This schema cannot contain:

  • DSM/ICD categories
  • personality disorders
  • psychoanalytic constructs
  • typologies (MBTI, Enneagram, etc.)
  • humanistic/existential frameworks
  • narrative‑based therapeutic models
  • cultural/institutional psychiatric categories
  • unfalsifiable or non‑substrate constructs

All such material is stored in the Legacy‑Narratives Quarantine.

This ensures the substrate remains scientifically clean and transfer‑ready.

5. How RTT Interacts With This Schema#

RTT imposes three hard constraints:

  • Repeatability:
    Every field in the schema corresponds to a measurable pattern that replicates across trials.

  • Traceability:
    Every field has a clear mapping to behavior, physiology, or computational structure.

  • Transfer‑addressability:
    Every field can be instantiated in a non‑biological substrate without loss of functional identity.

If a construct fails any RTT condition, it is excluded.

6. How vST Will Overlay This Schema#

vST (Vectorized Substrate Topology) will attach to this schema by:

  • defining state‑spaces for each cognitive/behavioral operator
  • defining transition operators for learning and decision models
  • defining mapping functions for anchoring constraints
  • defining equivalence tests for substrate‑to‑substrate transfer

The Conscious Transfer Substrate Map is therefore the foundation on which vST builds the actual transfer mechanics.

7. File Structure#

ct_substrate/
  ├── ct_substrate.schema.json     # The formal schema (RTT-filtered)
  ├── README.md                    # This file
  └── examples/                    # Optional: future example substrate instances

8. Contribution Rules#

To maintain substrate integrity:

  • No yellow/red constructs may be added to this schema
  • All additions must satisfy RTT conditions
  • All fields must be operationalizable (task‑bound, measurable)
  • All mappings must be explicit, not narrative
  • All biological references must be functional, not anatomical storytelling

Pull requests that introduce narrative, interpretive, or diagnostic constructs will be redirected to the Legacy‑Narratives Quarantine. ```json

{ "$schema": "https://json-schema.org/draft/2020-12/schema", "$id": "https://triadicframeworks.org/schemas/ct_substrate.schema.json", "title": "Conscious Transfer Substrate Map v1", "type": "object", "required": [ "behavioral_layer", "cognitive_layer", "measurement_layer", "anchoring_constraints" ], "properties": { "behavioral_layer": { "$ref": "#/$defs/BehavioralLayer" }, "cognitive_layer": { "$ref": "#/$defs/CognitiveLayer" }, "measurement_layer": { "$ref": "#/$defs/MeasurementLayer" }, "anchoring_constraints": { "$ref": "#/$defs/AnchoringConstraints" } },

"$defs": { "BehavioralLayer": { "type": "object", "required": ["conditioning_models", "performance_metrics"], "properties": { "conditioning_models": { "$ref": "#/$defs/ConditioningModels" }, "performance_metrics": { "$ref": "#/$defs/PerformanceMetrics" } } },

"ConditioningModels": {
  "type": "object",
  "properties": {
    "classical": {
      "type": "object",
      "properties": {
        "cs_us_mapping": { "type": "array", "items": { "type": "string" } },
        "acquisition_rate": { "type": "number" },
        "extinction_rate": { "type": "number" }
      }
    },
    "operant": {
      "type": "object",
      "properties": {
        "reinforcement_schedule": { "type": "string" },
        "learning_rate": { "type": "number" },
        "discount_factor": { "type": "number" }
      }
    }
  }
},

"PerformanceMetrics": {
  "type": "object",
  "properties": {
    "reaction_time_distribution": {
      "type": "array",
      "items": { "type": "number" }
    },
    "accuracy": { "type": "number" },
    "error_types": {
      "type": "array",
      "items": { "type": "string" }
    },
    "speed_accuracy_tradeoff": {
      "type": "object",
      "properties": {
        "slope": { "type": "number" },
        "intercept": { "type": "number" }
      }
    }
  }
},

"CognitiveLayer": {
  "type": "object",
  "required": [
    "working_memory",
    "attention",
    "decision_models",
    "learning_models"
  ],
  "properties": {
    "working_memory": { "$ref": "#/$defs/WorkingMemory" },
    "attention": { "$ref": "#/$defs/Attention" },
    "decision_models": { "$ref": "#/$defs/DecisionModels" },
    "learning_models": { "$ref": "#/$defs/LearningModels" }
  }
},

"WorkingMemory": {
  "type": "object",
  "properties": {
    "capacity": { "type": "integer" },
    "decay_rate": { "type": "number" },
    "refresh_rate": { "type": "number" }
  }
},

"Attention": {
  "type": "object",
  "properties": {
    "selective_filter_strength": { "type": "number" },
    "sustained_attention_stability": { "type": "number" },
    "switching_cost": { "type": "number" }
  }
},

"DecisionModels": {
  "type": "object",
  "properties": {
    "drift_diffusion": {
      "type": "object",
      "properties": {
        "drift_rate": { "type": "number" },
        "boundary_separation": { "type": "number" },
        "non_decision_time": { "type": "number" }
      }
    },
    "signal_detection": {
      "type": "object",
      "properties": {
        "sensitivity_d_prime": { "type": "number" },
        "criterion_c": { "type": "number" }
      }
    }
  }
},

"LearningModels": {
  "type": "object",
  "properties": {
    "reinforcement_learning": {
      "type": "object",
      "properties": {
        "learning_rate": { "type": "number" },
        "exploration_rate": { "type": "number" },
        "reward_sensitivity": { "type": "number" }
      }
    }
  }
},

"MeasurementLayer": {
  "type": "object",
  "required": ["psychometrics", "neuropsych_tests"],
  "properties": {
    "psychometrics": { "$ref": "#/$defs/Psychometrics" },
    "neuropsych_tests": { "$ref": "#/$defs/NeuropsychTests" }
  }
},

"Psychometrics": {
  "type": "object",
  "properties": {
    "reliability_alpha": { "type": "number" },
    "test_retest_stability": { "type": "number" },
    "factor_structure": {
      "type": "array",
      "items": { "type": "string" }
    }
  }
},

"NeuropsychTests": {
  "type": "object",
  "properties": {
    "task_battery": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "task_name": { "type": "string" },
          "score": { "type": "number" },
          "normative_z": { "type": "number" }
        }
      }
    }
  }
},

"AnchoringConstraints": {
  "type": "object",
  "properties": {
    "circuit_mappings": { "$ref": "#/$defs/CircuitMappings" },
    "neurophys_signatures": { "$ref": "#/$defs/NeurophysSignatures" },
    "pharmacological_profiles": { "$ref": "#/$defs/PharmacologicalProfiles" }
  }
},

"CircuitMappings": {
  "type": "array",
  "items": {
    "type": "object",
    "properties": {
      "region": { "type": "string" },
      "associated_function": { "type": "string" },
      "lesion_effect": { "type": "string" }
    }
  }
},

"NeurophysSignatures": {
  "type": "array",
  "items": {
    "type": "object",
    "properties": {
      "task": { "type": "string" },
      "frequency_band": { "type": "string" },
      "amplitude": { "type": "number" },
      "latency": { "type": "number" }
    }
  }
},

"PharmacologicalProfiles": {
  "type": "array",
  "items": {
    "type": "object",
    "properties": {
      "agent": { "type": "string" },
      "receptor_target": { "type": "string" },
      "effect_profile": { "type": "string" }
    }
  }
}

} }

```json

{
  "behavioral_layer": {
    "conditioning_models": {
      "classical": {
        "cs_us_mapping": ["tone → airpuff", "light → mild startle"],
        "acquisition_rate": 0.42,
        "extinction_rate": 0.18
      },
      "operant": {
        "reinforcement_schedule": "VI-30",
        "learning_rate": 0.21,
        "discount_factor": 0.87
      }
    },
    "performance_metrics": {
      "reaction_time_distribution": [412, 398, 405, 421, 389, 402],
      "accuracy": 0.93,
      "error_types": ["omission", "commission"],
      "speed_accuracy_tradeoff": {
        "slope": -0.0041,
        "intercept": 0.97
      }
    }
  },

  "cognitive_layer": {
    "working_memory": {
      "capacity": 4,
      "decay_rate": 0.12,
      "refresh_rate": 0.31
    },
    "attention": {
      "selective_filter_strength": 0.74,
      "sustained_attention_stability": 0.81,
      "switching_cost": 142
    },
    "decision_models": {
      "drift_diffusion": {
        "drift_rate": 0.28,
        "boundary_separation": 1.12,
        "non_decision_time": 0.29
      },
      "signal_detection": {
        "sensitivity_d_prime": 1.84,
        "criterion_c": -0.12
      }
    },
    "learning_models": {
      "reinforcement_learning": {
        "learning_rate": 0.19,
        "exploration_rate": 0.11,
        "reward_sensitivity": 0.63
      }
    }
  },

  "measurement_layer": {
    "psychometrics": {
      "reliability_alpha": 0.91,
      "test_retest_stability": 0.88,
      "factor_structure": ["WM", "ATTN", "EXEC"]
    },
    "neuropsych_tests": {
      "task_battery": [
        {
          "task_name": "Digit Span",
          "score": 12,
          "normative_z": 0.45
        },
        {
          "task_name": "Stroop Interference",
          "score": 37,
          "normative_z": -0.22
        },
        {
          "task_name": "Trail Making B",
          "score": 74,
          "normative_z": 0.10
        }
      ]
    }
  },

  "anchoring_constraints": {
    "circuit_mappings": [
      {
        "region": "DLPFC",
        "associated_function": "working memory manipulation",
        "lesion_effect": "reduced WM capacity and increased distractibility"
      },
      {
        "region": "ACC",
        "associated_function": "conflict monitoring",
        "lesion_effect": "elevated error rates under high-conflict trials"
      }
    ],
    "neurophys_signatures": [
      {
        "task": "Go/No-Go",
        "frequency_band": "theta",
        "amplitude": 4.7,
        "latency": 310
      },
      {
        "task": "Oddball",
        "frequency_band": "P300",
        "amplitude": 11.2,
        "latency": 342
      }
    ],
    "pharmacological_profiles": [
      {
        "agent": "methylphenidate",
        "receptor_target": "DAT/NET blockade",
        "effect_profile": "increased drift rate and reduced RT variability"
      },
      {
        "agent": "lorazepam",
        "receptor_target": "GABA-A modulation",
        "effect_profile": "increased non-decision time and reduced d'"
      }
    ]
  }
}

# ct_example_annotations.md

Human‑readable explanation of every field in ct_example.json#

This document explains the meaning of each field in the Conscious Transfer Substrate Map v1 example instance.
It is written for clarity, not for formal specification — the schema remains the authoritative source.

1. Behavioral Layer#

The behavioral layer captures observable, repeatable patterns in how an agent learns and performs tasks.

1.1 Conditioning Models#

classical.cs_us_mapping#

Pairs of stimuli showing which conditioned stimulus (CS) predicts which unconditioned stimulus (US).
Example: “tone → airpuff” means the tone reliably precedes an airpuff.

classical.acquisition_rate#

How quickly the agent learns the CS–US association.

classical.extinction_rate#

How quickly the association fades when the CS is no longer followed by the US.

operant.reinforcement_schedule#

The rule governing when rewards are delivered (e.g., variable interval 30 seconds).

operant.learning_rate#

How strongly new outcomes update behavior.

operant.discount_factor#

How much future rewards matter relative to immediate ones.

1.2 Performance Metrics#

reaction_time_distribution#

A sample of reaction times (in milliseconds) from a standard task.

accuracy#

Proportion of correct responses.

error_types#

Categories of mistakes the agent makes (e.g., omissions, commissions).

speed_accuracy_tradeoff.slope / intercept#

How the agent balances speed vs. accuracy — a stable behavioral signature.

2. Cognitive Layer#

The cognitive layer captures task‑bound computational operators that survive RTT filtering.

2.1 Working Memory#

capacity#

How many items can be actively maintained at once.

decay_rate#

How quickly information fades without rehearsal.

refresh_rate#

How quickly the agent can refresh or cycle items to keep them active.

2.2 Attention#

selective_filter_strength#

How effectively irrelevant information is filtered out.

sustained_attention_stability#

How consistently attention is maintained over time.

switching_cost#

Extra time required to switch between tasks or rules.

2.3 Decision Models#

drift_diffusion.drift_rate#

How quickly evidence accumulates toward a decision.

drift_diffusion.boundary_separation#

How cautious or bold the decision threshold is.

drift_diffusion.non_decision_time#

Time spent on perception and motor execution, not decision‑making.

signal_detection.sensitivity_d_prime#

How well the agent distinguishes signal from noise.

signal_detection.criterion_c#

Bias toward saying “yes” or “no” when uncertain.

2.4 Learning Models#

reinforcement_learning.learning_rate#

How strongly new outcomes update value estimates.

reinforcement_learning.exploration_rate#

How often the agent tries new actions instead of exploiting known ones.

reinforcement_learning.reward_sensitivity#

How strongly rewards influence behavior.

3. Measurement Layer#

The measurement layer defines how the substrate is validated, not the substrate itself.

3.1 Psychometrics#

reliability_alpha#

Internal consistency of measurement instruments.

test_retest_stability#

How stable scores are across repeated testing.

factor_structure#

Latent dimensions extracted from performance (e.g., working memory, attention).

3.2 Neuropsych Tests#

task_battery.task_name#

Name of a standardized cognitive test.

task_battery.score#

Raw performance score.

task_battery.normative_z#

How the score compares to population norms (in standard deviations).

4. Anchoring Constraints#

These are biological correlates that meet RTT criteria — stable, traceable, and functionally meaningful.

4.1 Circuit Mappings#

region#

Brain region with a known functional role.

associated_function#

What cognitive/behavioral process the region supports.

lesion_effect#

What happens when the region is damaged — a functional anchor.

4.2 Neurophysiological Signatures#

task#

The task during which the signature appears.

frequency_band#

EEG/MEG frequency or ERP component.

amplitude#

Strength of the signal.

latency#

Time delay between stimulus and neural response.

4.3 Pharmacological Profiles#

agent#

Drug or compound with a known effect profile.

receptor_target#

Primary biological target (e.g., dopamine transporter).

effect_profile#

How the agent modulates behavior or cognitive parameters.

How to Use This File#

This annotation file is meant to:

  • help researchers understand the meaning of each field
  • support onboarding into the Conscious Transfer substrate
  • clarify how the example instance relates to the schema
  • maintain conceptual cleanliness without narrative drift

It is not a substitute for the schema — it is a guide to interpreting instances. ```json

{ "behavioral_layer": { "conditioning_models": { "classical": { "cs_us_mapping": [ "tone → airpuff", "light → mild startle", "vibration → reward cue" ], "acquisition_rate": 0.47, "extinction_rate": 0.16 }, "operant": { "reinforcement_schedule": "VR-12", "learning_rate": 0.24, "discount_factor": 0.91 } }, "performance_metrics": { "reaction_time_distribution": [ 412, 398, 405, 421, 389, 402, 395, 417, 409, 393 ], "accuracy": 0.94, "error_types": [ "omission", "commission", "intrusion" ], "speed_accuracy_tradeoff": { "slope": -0.0038, "intercept": 0.98 } } },

"cognitive_layer": { "working_memory": { "capacity": 5, "decay_rate": 0.11, "refresh_rate": 0.34 }, "attention": { "selective_filter_strength": 0.78, "sustained_attention_stability": 0.83, "switching_cost": 128 }, "decision_models": { "drift_diffusion": { "drift_rate": 0.31, "boundary_separation": 1.18, "non_decision_time": 0.27 }, "signal_detection": { "sensitivity_d_prime": 1.92, "criterion_c": -0.08 } }, "learning_models": { "reinforcement_learning": { "learning_rate": 0.22, "exploration_rate": 0.14, "reward_sensitivity": 0.67 } } },

"measurement_layer": { "psychometrics": { "reliability_alpha": 0.93, "test_retest_stability": 0.89, "factor_structure": [ "WM", "ATTN", "EXEC", "RL" ] }, "neuropsych_tests": { "task_battery": [ { "task_name": "Digit Span", "score": 13, "normative_z": 0.62 }, { "task_name": "Stroop Interference", "score": 35, "normative_z": -0.10 }, { "task_name": "Trail Making A", "score": 29, "normative_z": 0.55 }, { "task_name": "Trail Making B", "score": 71, "normative_z": 0.18 }, { "task_name": "N‑Back (2‑back)", "score": 87, "normative_z": 0.41 } ] } },

"anchoring_constraints": { "circuit_mappings": [ { "region": "DLPFC", "associated_function": "working memory manipulation", "lesion_effect": "reduced WM capacity and increased distractibility" }, { "region": "ACC", "associated_function": "conflict monitoring", "lesion_effect": "elevated error rates under high-conflict trials" }, { "region": "Ventral Striatum", "associated_function": "reward prediction", "lesion_effect": "flattened reward sensitivity and impaired RL updating" } ], "neurophys_signatures": [ { "task": "Go/No-Go", "frequency_band": "theta", "amplitude": 4.9, "latency": 308 }, { "task": "Oddball", "frequency_band": "P300", "amplitude": 11.7, "latency": 339 }, { "task": "Flanker", "frequency_band": "ERN", "amplitude": -5.1, "latency": 92 } ], "pharmacological_profiles": [ { "agent": "methylphenidate", "receptor_target": "DAT/NET blockade", "effect_profile": "increased drift rate, reduced RT variability, improved WM stability" }, { "agent": "lorazepam", "receptor_target": "GABA-A modulation", "effect_profile": "increased non-decision time, reduced d', reduced selective filter strength" }, { "agent": "modafinil", "receptor_target": "DAT inhibition + orexin modulation", "effect_profile": "enhanced sustained attention stability and reduced switching cost" } ] } }

```json

{
  "behavioral_layer": {
    "conditioning_models": {
      "classical": {
        "cs_us_mapping": [],
        "acquisition_rate": 0.0,
        "extinction_rate": 0.0
      },
      "operant": {
        "reinforcement_schedule": "FR-1",
        "learning_rate": 0.0,
        "discount_factor": 0.0
      }
    },
    "performance_metrics": {
      "reaction_time_distribution": [],
      "accuracy": 0.0,
      "error_types": [],
      "speed_accuracy_tradeoff": {
        "slope": 0.0,
        "intercept": 0.0
      }
    }
  },

  "cognitive_layer": {
    "working_memory": {
      "capacity": 0,
      "decay_rate": 0.0,
      "refresh_rate": 0.0
    },
    "attention": {
      "selective_filter_strength": 0.0,
      "sustained_attention_stability": 0.0,
      "switching_cost": 0
    },
    "decision_models": {
      "drift_diffusion": {
        "drift_rate": 0.0,
        "boundary_separation": 0.0,
        "non_decision_time": 0.0
      },
      "signal_detection": {
        "sensitivity_d_prime": 0.0,
        "criterion_c": 0.0
      }
    },
    "learning_models": {
      "reinforcement_learning": {
        "learning_rate": 0.0,
        "exploration_rate": 0.0,
        "reward_sensitivity": 0.0
      }
    }
  },

  "measurement_layer": {
    "psychometrics": {
      "reliability_alpha": 0.0,
      "test_retest_stability": 0.0,
      "factor_structure": []
    },
    "neuropsych_tests": {
      "task_battery": []
    }
  },

  "anchoring_constraints": {
    "circuit_mappings": [],
    "neurophys_signatures": [],
    "pharmacological_profiles": []
  }
}



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