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:

Minimal Empirical Mind Substrate v1
- The distilled scientific core extracted from psychology + psychiatry
- Only green‑zone constructs (empirical, measurable, reproducible)
Legacy‑Narratives Quarantine
- All yellow/red constructs (interpretive, institutional, cultural, mythic)
- Preserved for context, but never allowed in substrate models
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": []
  }
}

Updated Jul 17, 2026