the-mind
A definition-driven synthesis of mind (based on Joscha Bach’s public work)

Glossary

Each entry should capture the working meaning (how we will use the term) and common confusions.

Template (copy/paste for new terms):

<term>

  • Id: TERM-XXXX
  • Working meaning: We will use "<term>" to mean: <definition>
  • Related: <comma-separated related terms>
  • Common confusion: <common misreadings>
  • Sources:
  • <source_id> @ <HH:MM:SS>

Computationalist functionalism

  • Id: TERM-0001
  • Working meaning: We will use "Computationalist functionalism" to mean: A stance about knowledge and objects: minds construct objects over observations, and an "object" is defined by the functional/causal differences its presence makes. Computationalism adds that models must be realized constructively in an implementable representational language.
  • Related: computationalism, functionalism, representation, object, model.
  • Common confusion: treating it as the claim that today's computers already have minds; or treating "function" as design intent rather than causal role.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:08:24

Strong computationalism

  • Id: TERM-0002
  • Working meaning: We will use "Strong computationalism" to mean: The view that realizable systems can be described in implementable representational languages (with Church-Turing as a boundary condition), and that appeals to "hypercomputation" do not ground meaningful reference for real systems.
  • Related: Church-Turing thesis, representation, substrate-independence.
  • Common confusion: reading it as "everything is easy to compute" or as a denial of physical complexity.
  • Sources:
  • talk: Synthetic Sentience @ 00:08:58

World-model

  • Id: TERM-0003
  • Working meaning: We will use "World-model" to mean: A structured internal model that represents the world and its dynamics so the system can predict, simulate, and control outcomes.
  • Related: representation, simulation, prediction, planning.
  • Common confusion: treating the model as the territory; collapsing the model into raw data.
  • Sources:
  • talk: Mind from Matter (Lecture By Joscha Bach) @ 00:24:59
  • talk: Synthetic Sentience @ 00:18:19

Representation

  • Id: TERM-0004
  • Working meaning: We will use "Representation" to mean: An internal structure that stands in for something else and supports inference, prediction, and control.
  • Related: world-model, self-model, symbol, embedding.
  • Common confusion: equating representation with mere storage or with a specific encoding format.
  • Sources:
  • talk: Joscha Bach: How to Build a Conscious Artificial Agent @ 00:06:33

Agent

Control

  • Id: TERM-0006
  • Working meaning: We will use "Control" to mean: Closed-loop regulation that compares desired states to actual states and adjusts actions to reduce error.
  • Related: feedback, policy, error signal, homeostasis.
  • Common confusion: treating control as domination rather than regulation; confusing control with optimization.
  • Sources:
  • talk: Joscha Bach - Agency in an Age of Machines - How AI Will Change Humanity @ 00:02:07

Learning

Valence

Norm

  • Id: TERM-0009
  • Working meaning: We will use "Norm" to mean: Control constraints that express desired truths/commitments beyond immediate pleasure/pain; used for long-horizon coordination (especially social coordination).
  • Related: valence, value, commitment, culture.
  • Common confusion: treating norms as purely explicit rules; or treating them as mere social conventions with no control role.
  • Sources:
  • talk: The Ghost in the Machine @ 00:27:50

Attention

Self-model

  • Id: TERM-0011
  • Working meaning: We will use "Self-model" to mean: A representation of the system as an agent within its own world-model, enabling self-prediction and coordinated action.
  • Related: identity, narrative, first-person perspective.
  • Common confusion: treating the self-model as a metaphysical self rather than a representation.
  • Sources:
  • talk: Self Models of Loving Grace @ 00:01:14
  • talk: Mind from Matter (Lecture By Joscha Bach) @ 00:16:40

First-person perspective

Consciousness

  • Id: TERM-0013
  • Working meaning: We will use "Consciousness" to mean: A functional interface that integrates and stabilizes mental contents; often described as a second-order perception that makes the system aware of its own observing.
  • Related: attention, global workspace, self-model, phenomenology.
  • Common confusion: equating consciousness with intelligence or with any specific task performance.
  • Sources:
  • talk: Synthetic Sentience @ 00:11:15
  • talk: The Machine Consciousness Hypothesis @ 00:20:01

Phenomenology

  • Id: TERM-0014
  • Working meaning: We will use "Phenomenology" to mean: The subjective aspect of experience; what it is like from the first-person perspective.
  • Related: consciousness, qualia, self-model.
  • Common confusion: treating phenomenology as a separate substance rather than a description of experience.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:20:05

Mechanism

  • Id: TERM-0015
  • Working meaning: We will use "Mechanism" to mean: The implementation details that realize a function in a substrate (e.g., neural circuitry, code, or hardware).
  • Related: function, substrate, implementation.
  • Common confusion: assuming mechanism alone explains phenomenology without functional framing.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:20:01

Function

  • Id: TERM-0016
  • Working meaning: We will use "Function" to mean: The role a component plays in the system's behavior and control, independent of its implementation.
  • Related: mechanism, purpose, causal role.
  • Common confusion: assuming function implies design intent rather than causal role.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:20:01

Coherence

  • Id: TERM-0017
  • Working meaning: We will use "Coherence" to mean: A property of a mind where its active models, motivations, and action tendencies are mutually consistent enough to support unified agency.
  • Related: consciousness, attention, global workspace, self-model.
  • Common confusion: treating coherence as a moral virtue, or as an absolute logical consistency rather than a control-relevant alignment of subsystems.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:27:12
  • talk: Synthetic Sentience @ 00:11:15

Observer

  • Id: TERM-0018
  • Working meaning: We will use "Observer" to mean: A constructed reference frame inside the model that stabilizes perception and enables the system to represent itself as perceiving (and, later, as being a self).
  • Related: consciousness, second-order perception, self-model.
  • Common confusion: importing the physics "observer" into mind-talk as a metaphysical necessity; or reifying the observer as a separate entity behind experience.
  • Sources:
  • talk: Self Models of Loving Grace @ 00:09:59
  • talk: Synthetic Sentience @ 00:40:13

Second-order perception

  • Id: TERM-0019
  • Working meaning: We will use "Second-order perception" to mean: Perception of perception: the system represents the fact that it is observing, which stabilizes the observing process.
  • Related: consciousness, observer, presentness.
  • Common confusion: treating second-order perception as introspective narration; the core point is stabilization via self-observation, not verbal report.
  • Sources:
  • talk: Synthetic Sentience @ 00:40:15
  • talk: Self Models of Loving Grace @ 00:09:02

Third-order perception

  • Id: TERM-0020
  • Working meaning: We will use "Third-order perception" to mean: Awareness of the self: the system discovers itself as the observer within the act of observation, producing a first-person perspective as a representational state.
  • Related: self-model, observer, identity.
  • Common confusion: equating third-order perception with philosophical ego; in this framing it is still a model phenomenon.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:16:38

Nowness

  • Id: TERM-0021
  • Working meaning: We will use "Nowness" to mean: The modeled "present": a coherence bubble in which the mind's active contents are synchronized enough to be experienced as happening now.
  • Related: consciousness, second-order perception, coherence, presentness.
  • Common confusion: treating "nowness" as a fundamental property of time rather than as a representational construct.
  • Sources:
  • interview: "We Are All Software" - Joscha Bach @ 00:19:16

Attention schema

  • Id: TERM-0022
  • Working meaning: We will use "Attention schema" to mean: A theory-adjacent framing: consciousness can be understood as a control model of attention (analogous to a body schema), tracking and regulating what is attended to.
  • Related: attention, consciousness, body schema.
  • Common confusion: collapsing the attention schema framing into a full theory of consciousness; in the usage tracked here, it often functions as one convergent perspective among several.
  • Sources:
  • talk: Mind from Matter (Lecture By Joscha Bach) @ 00:19:13
  • talk: AGI Series 2024 - Joscha Bach: Is Consciousness a Missing Link to AGI? @ 00:41:09

Global workspace

Narrative

  • Id: TERM-0024
  • Working meaning: We will use "Narrative" to mean: A story-like, compressive self-explanation that links perceptions, motives, and actions into a coherent account; often constructed after the fact.
  • Related: self-model, identity, social communication.
  • Common confusion: treating narrative coherence as evidence of causal truth; narratives can be useful control/communication artifacts even when they misdescribe causes.
  • Sources:
  • talk: The Ghost in the Machine @ 00:32:21

Reward

  • Id: TERM-0025
  • Working meaning: We will use "Reward" to mean: A learning signal used for credit assignment (which actions/policies get reinforced). Reward is a mechanism of learning; it is not identical to the agent's long-horizon value structure.
  • Related: valence, value, credit assignment, reinforcement.
  • Common confusion: treating "reward" as synonymous with "what the agent values" or "what is good".
  • Sources:
  • talk: The Ghost in the Machine @ 00:37:19

Value

  • Id: TERM-0026
  • Working meaning: We will use "Value" to mean: A learned predictive structure that estimates future valence under policies; a compressed internal representation of what will matter later (not necessarily explicit, and not necessarily stable).
  • Related: reward, valence, preference, norms.
  • Common confusion: equating value with an explicit utility function; equating value with immediate pleasure/pain.
  • Sources:
  • talk: The Ghost in the Machine @ 00:37:19

Commitment

  • Id: TERM-0027
  • Working meaning: We will use "Commitment" to mean: A control constraint treated as binding over time, enabling long-horizon learning and coordination (both within a person and between people).
  • Related: norms, identity, contract, governance.
  • Common confusion: treating commitments as mere verbal promises; in this framing they are control-level constraints that must be implemented and enforced to matter.
  • Sources:
  • talk: The Ghost in the Machine @ 00:30:38

Self-organization

  • Id: TERM-0028
  • Working meaning: We will use "Self-organization" to mean: A process in which a system constructs and maintains its own internal structure through its dynamics; contrasted with externally imposed algorithmic structure.
  • Related: development, learning scaffold, consciousness, substrate.
  • Common confusion: treating self-organization as "no constraints" or "randomness"; it is constrained structure formation.
  • Sources:
  • interview: "We Are All Software" - Joscha Bach @ 00:10:37
  • interview: Joscha Bach - Why Your Thoughts Aren't Yours. @ 00:57:15

Reward hacking

  • Id: TERM-0029
  • Working meaning: We will use "Reward hacking" to mean: A generic failure mode where an agent learns to maximize its internal reinforcement signals directly (or via proxy loopholes), undermining the intended control objective.
  • Related: wireheading, addiction, specification gaming, self-modification.
  • Common confusion: thinking this is a niche pathology; in sufficiently capable agents it is a structural pressure whenever "good" is tied to a manipulable signal.
  • Sources:
  • talk: The Ghost in the Machine @ 00:38:16

Suffering

  • Id: TERM-0030
  • Working meaning: We will use "Suffering" to mean: A control/valence phenomenon associated with persistent dysregulation, where the system cannot reduce salient error/aversion signals and cannot reframe/resolve the underlying constraints.
  • Related: valence, coherence, self-model, emotion regulation.
  • Common confusion: treating suffering as identical to pain; pain can occur without suffering if the system does not identify with or amplify the signal.
  • Sources:
  • talk: Self Models of Loving Grace @ 00:42:29

Enlightenment

  • Id: TERM-0031
  • Working meaning: We will use "Enlightenment" to mean: A representational insight in which the self/observer becomes visible as a constructed model state and can be deconstructed; not a claim about moral purity.
  • Related: self-model, observer, third-order perception, meditation.
  • Common confusion: treating enlightenment as a supernatural achievement rather than as a describable reconfiguration of self-modeling.
  • Sources:
  • interview: "We Are All Software" - Joscha Bach @ 00:15:15

Machine consciousness hypothesis

  • Id: TERM-0032
  • Working meaning: We will use "Machine consciousness hypothesis" to mean: A two-part conjecture: (1) biological consciousness is an early, learnable stabilization trick for self-organizing systems, and (2) similar self-organization conditions can be recreated on computers.
  • Related: consciousness, self-organization, substrate-independence, AI.
  • Common confusion: treating it as a claim that current AI is conscious; in this framing it is a testable architectural program.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:32:23

Mind

  • Id: TERM-0033
  • Working meaning: We will use "Mind" to mean: A functional organization realized by a substrate: an agentic control architecture that builds and uses models to regulate the future under constraints. It is not a second substance added to physics.
  • Related: agent, control, world-model, learning, self-organization.
  • Common confusion: treating "mind" as a metaphysical entity; or treating it as identical to the tissue that implements it.
  • Sources:
  • talk: Mind from Matter (Lecture By Joscha Bach) @ 00:24:59

Model

  • Id: TERM-0034
  • Working meaning: We will use "Model" to mean: A constructed internal structure that supports prediction, simulation, and control. A model is not a mirror of reality; it is an instrument constrained by an agent's goals and bandwidth.
  • Related: representation, world-model, simulator, prediction.
  • Common confusion: equating "model" with a dataset, or with a literal copy of the world.
  • Sources:
  • talk: Mind from Matter (Lecture By Joscha Bach) @ 00:24:59
  • talk: Self Models of Loving Grace @ 00:18:49

Object (in a model)

  • Id: TERM-0035
  • Working meaning: We will use "Object (in a model)" to mean: A stable role constructed over observations; an object is defined by the functional differences its presence makes in how the modeled world evolves.
  • Related: computationalist functionalism, representation, invariance.
  • Common confusion: treating objects as "given" rather than constructed; or treating construction as mere naming.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:08:24

Abstraction

  • Id: TERM-0036
  • Working meaning: We will use "Abstraction" to mean: A compressive representation that discards detail while preserving invariances relevant for prediction and control at the agent's scale.
  • Related: compression, invariance, understanding.
  • Common confusion: treating abstraction as vagueness; abstraction is a constraint imposed by limited resources.
  • Sources:
  • talk: AGI Series 2024 - Joscha Bach: Is Consciousness a Missing Link to AGI? @ 01:24:02

Invariance

  • Id: TERM-0037
  • Working meaning: We will use "Invariance" to mean: A control-relevant pattern that remains stable under perturbations and can be tracked by a model. In this usage: software/organization is an invariance; it is not identical to a particular set of particles.
  • Related: object (in a model), abstraction, software, implementation.
  • Common confusion: looking for invariance at the wrong level (e.g., in pixels or particles rather than in causal organization).
  • Sources:
  • interview: "We Are All Software" - Joscha Bach @ 00:01:53

Policy

  • Id: TERM-0038
  • Working meaning: We will use "Policy" to mean: A compact name for the action-selection mapping implemented by a controller (often learned and modulated), i.e. how the agent turns modeled state into behavior.
  • Related: control, agent, habit, commitment.
  • Common confusion: treating policy as a single explicit plan; much policy is implicit, layered, and context-dependent.
  • Sources:
  • talk: Synthetic Sentience @ 00:32:49

Goal

  • Id: TERM-0039
  • Working meaning: We will use "Goal" to mean: A represented constraint used for control: a target region in state space that defines what counts as error reduction for a subsystem.
  • Related: control, preference, value, commitment.
  • Common confusion: treating goals as external "attractors" in the world rather than as internal comparators/constraints in a controller.
  • Sources:
  • talk: AGI Series 2024 - Joscha Bach: Is Consciousness a Missing Link to AGI? @ 00:40:18

Preference

  • Id: TERM-0040
  • Working meaning: We will use "Preference" to mean: The ordering the agent imposes over futures (which errors matter more); preferences can be learned, drift, and be in conflict across subsystems.
  • Related: valence, value, goal, commitment.
  • Common confusion: assuming preferences are explicit, stable, and globally consistent.
  • Sources:
  • interview: Joscha Bach - Why Your Thoughts Aren't Yours. @ 00:10:21

Error signal

  • Id: TERM-0041
  • Working meaning: We will use "Error signal" to mean: Information about deviation from a constraint (or from a predicted state) used to drive control and learning.
  • Related: control, learning, prediction error.
  • Common confusion: treating error as a moral defect rather than as a control variable.
  • Sources:
  • talk: The Ghost in the Machine @ 00:27:50

Viability constraints

  • Id: TERM-0042
  • Working meaning: We will use "Viability constraints" to mean: The boundary conditions that keep an agent viable as the kind of system it is (integrity, energy, stability of the learning machinery), shaping what control must accomplish.
  • Related: control, homeostasis, self-organization.
  • Common confusion: treating viability as a single variable; in real agents it is a bundle of constraints.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:06:57

Credit assignment

  • Id: TERM-0043
  • Working meaning: We will use "Credit assignment" to mean: The problem of attributing outcomes to the parts of behavior and internal structure that caused them, so learning can reinforce the right policies and representations.
  • Related: learning, reward, valence.
  • Common confusion: treating credit assignment as obvious; in long-horizon, multi-loop systems it is the bottleneck.
  • Sources:
  • talk: The Ghost in the Machine @ 00:37:19

Compression

  • Id: TERM-0044
  • Working meaning: We will use "Compression" to mean: Representing regularities with fewer degrees of freedom while preserving what matters for prediction and control; required by finite resources.
  • Related: learning, abstraction, understanding.
  • Common confusion: equating compression with loss of truth; the issue is preserving the right invariances for control.
  • Sources:
  • talk: The Ghost in the Machine @ 00:19:06

Generalization

  • Id: TERM-0045
  • Working meaning: We will use "Generalization" to mean: Transfer of learned structure beyond the data that produced it; in this framing, it is primarily a property of representations.
  • Related: learning, understanding, invariance.
  • Common confusion: treating generalization as a property of datasets rather than of model structure.
  • Sources:
  • talk: Joscha Bach - ChatGPT: Is AI Deepfaking Understanding? @ 01:13:08

Understanding

  • Id: TERM-0046
  • Working meaning: We will use "Understanding" to mean: Compression that is usable for counterfactual control and explanation: abstractions that remain stable under perturbation and support "what would happen if..." reasoning.
  • Related: learning, compression, simulation.
  • Common confusion: equating understanding with verbal fluency or with prediction accuracy alone.
  • Sources:
  • talk: The Ghost in the Machine @ 00:19:06

Self-supervision

  • Id: TERM-0047
  • Working meaning: We will use "Self-supervision" to mean: Learning driven by predicting parts of experience from other parts; the world supplies the training signal. (This is adjacent ML vocabulary; use as a translation term.)
  • Related: learning, prediction error.
  • Common confusion: treating self-supervision as "no supervision"; the environment still constrains learning through consequences.
  • Sources:
  • interview: Joscha Bach: Artificial Consciousness and the Nature of Reality | Lex Fridman Podcast #101 @ 01:10:49

Self-play

Working memory

  • Id: TERM-0049
  • Working meaning: We will use "Working memory" to mean: The current binding state: the short-lived integration of percepts, memories, and imagined content that allows coherent control and reportable thought.
  • Related: attention, workspace, global workspace, simulation.
  • Common confusion: treating working memory as mere storage; in this framing it is a binding/integration process.
  • Sources:
  • talk: The Ghost in the Machine @ 00:07:19

Workspace

  • Id: TERM-0050
  • Working meaning: We will use "Workspace" to mean: A functional framing: a shared state in which content becomes available across subsystems for coordination (often discussed via global workspace and related theories).
  • Related: attention, consciousness, global workspace, coherence.
  • Common confusion: treating "workspace" as a literal location; it is a coordination function.
  • Sources:
  • talk: Synthetic Sentience @ 00:12:04

Simulation

  • Id: TERM-0051
  • Working meaning: We will use "Simulation" to mean: Running the model forward to generate counterfactual trajectories, including imagined perceptions and imagined actions; essential for planning and for the constructed experience of reality.
  • Related: model, world-model, simulator, imagination.
  • Common confusion: equating simulation with fantasy; simulation is constrained by learned model structure (unless it drifts into hallucination).
  • Sources:
  • talk: The Ghost in the Machine @ 00:28:47

Simulator

  • Id: TERM-0052
  • Working meaning: We will use "Simulator" to mean: A runnable model: internal causal structure that can be advanced and queried ("if I do X, what happens?"), enabling planning and counterfactual control.
  • Related: model, simulation, prediction.
  • Common confusion: conflating a simulator with a passive replay (a movie); simulators have internal causal degrees of freedom.
  • Sources:
  • talk: Self Models of Loving Grace @ 00:24:14

Governance

  • Id: TERM-0053
  • Working meaning: We will use "Governance" to mean: The control of control: the way an agent arbitrates among competing subsystems, error signals, and commitments across time scales.
  • Related: meta-control, commitment, self-control.
  • Common confusion: treating governance as a moral notion; here it is an architectural one.
  • Sources:
  • talk: Self Models of Loving Grace @ 00:10:54

Motivation

  • Id: TERM-0054
  • Working meaning: We will use "Motivation" to mean: The configuration of valence and control constraints that makes some actions more likely; motivation is a control state, not a single drive.
  • Related: valence, emotion, goal, commitment.
  • Common confusion: treating motivation as a trait; it is often a state of control/attention allocation.
  • Sources:
  • talk: The Ghost in the Machine @ 00:28:47

Emotion

  • Id: TERM-0055
  • Working meaning: We will use "Emotion" to mean: A family of control modulators that reconfigure policy selection, attention, and learning under particular contexts (threat, opportunity, loss, social evaluation, etc.).
  • Related: motivation, valence, affect, mood.
  • Common confusion: collapsing emotion into a single scalar; or treating emotions as irrational add-ons rather than control modes.
  • Sources:
  • talk: Joscha Bach - ChatGPT: Is AI Deepfaking Understanding? @ 00:56:36

Affect

  • Id: TERM-0056
  • Working meaning: We will use "Affect" to mean: The coarse valence tone of a state (roughly: good/bad), which can modulate attention and learning without specifying a detailed narrative.
  • Related: valence, emotion, mood.
  • Common confusion: equating affect with explicit emotion labels; affect can be present without verbalized emotion categories.
  • Sources:
  • talk: The Ghost in the Machine @ 00:28:47

Mood

  • Id: TERM-0057
  • Working meaning: We will use "Mood" to mean: A longer-lived configuration of control and affect that biases prediction, attention, and action across many contexts.
  • Related: affect, emotion, motivation.
  • Common confusion: treating mood as an external "thing" that happens to you; it is a control state that shapes what gets modeled as salient and possible.
  • Sources:
  • interview: "We Are All Software" - Joscha Bach @ 00:19:16

Habit

  • Id: TERM-0058
  • Working meaning: We will use "Habit" to mean: A stabilized policy in the control stack: a learned, low-friction mapping from context to action that reduces deliberation cost.
  • Related: policy, learning, self-control.
  • Common confusion: treating habits as merely bad; habits are necessary compression in bounded agents.
  • Sources:
  • talk: The Ghost in the Machine @ 00:38:16

Addiction / reward capture

  • Id: TERM-0059
  • Working meaning: We will use "Addiction / reward capture" to mean: A failure mode where short-horizon reward signals dominate governance and learning, producing policies that preserve access to the signal even when it undermines higher-level values and viability.
  • Related: reward hacking, self-control, valence.
  • Common confusion: treating addiction as only a moral failure; in this framing it is a control-loop pathology under misaligned reinforcement.
  • Sources:
  • talk: The Ghost in the Machine @ 00:38:16

Alignment

  • Id: TERM-0060
  • Working meaning: We will use "Alignment" to mean: A downstream framing: building artificial agents whose learned values and commitments remain compatible with human goals and governance, not merely optimizing a fixed utility function.
  • Related: value learning, governance, incentives, agency.
  • Common confusion: equating alignment with obedience or with benchmarked helpfulness; alignment is about stable goal structure under self-improvement and deployment.
  • Sources:
  • talk: AGI Series 2024 - Joscha Bach: Is Consciousness a Missing Link to AGI? @ 01:29:16

Virtual

  • Id: TERM-0061
  • Working meaning: We will use "Virtual" to mean: A property of models: something behaves as if it exists because it is implemented as a stable causal pattern at an appropriate level of abstraction.
  • Related: model, self-model, representation, simulation.
  • Common confusion: equating "virtual" with "fake"; virtual objects can be real as implemented patterns.
  • Sources:
  • interview: Joscha Bach - Why Your Thoughts Aren't Yours. @ 00:01:33

Virtualism

  • Id: TERM-0062
  • Working meaning: We will use "Virtualism" to mean: A perspective in which experience is treated as generated model content ("dreams"), and mind is studied as the architecture that constructs and regulates these models.
  • Related: virtual, simulation, consciousness, self-model.
  • Common confusion: reading virtualism as mysticism; in this framing it is a representational stance (model vs territory).
  • Sources:
  • talk: Virtualism as a Perspective on Consciousness by Joscha Bach @ 00:15:55

Agency

Control system

Naturalizing the mind

  • Id: TERM-0065
  • Working meaning: We will use "Naturalizing the mind" to mean: Treating mind as part of nature: a functional organization realized by mechanisms, explainable as model-building control plus learning, valence, and self-modeling rather than as a separate substance.
  • Related: computationalist functionalism, mechanism, function, mind.
  • Common confusion: treating naturalization as reductionism that denies experience; naturalization is a demand for an architecture-level explanation.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:03:18

Triangulation discipline

  • Id: TERM-0066
  • Working meaning: We will use "Triangulation discipline" to mean: A method rule: keep phenomenology (what it is like), mechanism (how it is implemented), and function (what it does) distinct when reasoning about mind and consciousness.
  • Related: phenomenology, mechanism, function, consciousness.
  • Common confusion: treating these as competing answers instead of distinct constraints; collapsing one level into another.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:14:35

Prediction

  • Id: TERM-0067
  • Working meaning: We will use "Prediction" to mean: Generating expectations about state transitions and observations, including counterfactual expectations under possible actions. Prediction is for control, not only for passive forecasting.
  • Related: model, world-model, simulation, learning.
  • Common confusion: reducing prediction to next-sensory-frame forecasting; in this framework prediction supports intervention and planning.
  • Sources:
  • talk: Joscha Bach: The AI perspective on Consciousness @ 00:06:05
  • talk: The Ghost in the Machine @ 00:26:34

Salience

  • Id: TERM-0068
  • Working meaning: We will use "Salience" to mean: A control-relevance signal that makes some model contents candidates for attention and action selection (e.g., because they predict large error, reward, uncertainty reduction, or social consequences).
  • Related: attention, valence, prediction error, novelty.
  • Common confusion: equating salience with attention; salience is an input to selection, not the selection mechanism itself.
  • Sources:
  • talk: AGI Series 2024 - Joscha Bach: Is Consciousness a Missing Link to AGI? @ 00:10:33

Constraint

  • Id: TERM-0069
  • Working meaning: We will use "Constraint" to mean: A restriction on what the system can do or what counts as success: physical limits, resource bounds, model structure, norms, and viability constraints that shape control.
  • Related: viability constraints, goal, norm, mechanism.
  • Common confusion: treating constraints as merely external obstacles; in control systems, constraints define the space of possible policies and the meaning of error.
  • Sources:
  • talk: Mind from Matter (Lecture By Joscha Bach) @ 01:03:35

Drive

  • Id: TERM-0070
  • Working meaning: We will use "Drive" to mean: A persistent control pressure (often experienced as an urge) arising from deficits/needs and encoded as error signals that bias policy and learning until resolved or reinterpreted.
  • Related: valence, goal, error signal, motivation.
  • Common confusion: equating drives with conscious desires; drives can operate below awareness and be re-labeled by the narrative self-model.
  • Sources:
  • interview: Joscha Bach - Why Your Thoughts Aren't Yours. @ 00:23:35

Impulse

  • Id: TERM-0071
  • Working meaning: We will use "Impulse" to mean: A short-horizon action tendency produced by local control loops (often reward-driven) that competes with higher-level commitments and long-horizon policies.
  • Related: habit, self-control, reward, governance.
  • Common confusion: treating impulses as pure moral weakness; in this framing they are predictable outputs of competing loops under scarcity and reinforcement.
  • Sources:
  • talk: The Ghost in the Machine @ 00:36:40

Compulsion

  • Id: TERM-0072
  • Working meaning: We will use "Compulsion" to mean: A failure mode where a policy becomes difficult to inhibit because reinforcement and habit loops overpower higher-level governance and commitments.
  • Related: impulse, addiction / reward capture, self-control, governance.
  • Common confusion: treating compulsion as lack of willpower rather than as an architecture-level dominance of certain loops.
  • Sources:
  • talk: The Ghost in the Machine @ 00:36:40

Self-control

  • Id: TERM-0073
  • Working meaning: We will use "Self-control" to mean: Governance of one’s own control stack: maintaining commitments and long-horizon values by constraining short-horizon reward capture and reconfiguring policies and environments.
  • Related: governance, commitment, habit, reward hacking.
  • Common confusion: equating self-control with suppression; in control terms, effective self-control changes constraints and feedback so desirable policies become easy and stable.
  • Sources:
  • talk: The Ghost in the Machine @ 00:30:38

Failure mode

  • Id: TERM-0074
  • Working meaning: We will use "Failure mode" to mean: A predictable way an architecture breaks down under certain inputs/constraints (e.g., reward hacking, attentional capture, incoherence), revealing what the system is actually optimizing.
  • Related: reward hacking, addiction / reward capture, attention, coherence.
  • Common confusion: treating failures as mere bugs or moral defects; in this framing they are diagnostic of the underlying control loops.
  • Sources:
  • talk: The Ghost in the Machine @ 00:38:16

Identity

  • Id: TERM-0075
  • Working meaning: We will use "Identity" to mean: A stabilized self-model: the agent’s narrative and role-structure that makes its policies coherent across time and social contexts.
  • Related: self-model, narrative, commitment, culture.
  • Common confusion: treating identity as a metaphysical essence rather than as a control-relevant representation that can change.
  • Sources:
  • talk: Self Models of Loving Grace @ 00:01:14

Social model (theory of mind)

  • Id: TERM-0076
  • Working meaning: We will use "Social model (theory of mind)" to mean: A model that represents other agents as agents (with beliefs, goals, and policies) so the system can predict and coordinate with them.
  • Related: agent, world-model, language, norm.
  • Common confusion: treating a social model as a list of facts about people; it is a model of agency and policy.
  • Sources:
  • interview: Joscha Bach Λ Karl Friston: Ai, Death, Self, God, Consciousness @ 01:58:03

Language

Institution

  • Id: TERM-0078
  • Working meaning: We will use "Institution" to mean: A persistent multi-agent control loop that stabilizes expectations and enforces norms by making certain feedback predictable (legal, economic, reputational).
  • Related: norm, governance, reward infrastructure, contract.
  • Common confusion: treating institutions as static buildings or rules; in this framing they are ongoing feedback processes that can drift and be captured.
  • Sources:
  • talk: Joscha Bach - Agency in an Age of Machines - How AI Will Change Humanity @ 00:58:03

Contract

  • Id: TERM-0079
  • Working meaning: We will use "Contract" to mean: A shared explicit model of mutual commitments that makes certain futures predictable by attaching enforceable consequences to deviation.
  • Related: commitment, norm, institution, governance.
  • Common confusion: treating contracts as paperwork rather than as a control technology for multi-agent coordination.
  • Sources:
  • talk: Joscha Bach: The Operation of Consciousness | AGI-25 @ 00:51:05

Reward function (broad usage)

  • Id: TERM-0080
  • Working meaning: We will use "Reward function (broad usage)" to mean: The effective reinforcement landscape that shapes learning and policy, including both internal reward signals and external incentive structures (especially in social systems).
  • Related: reward, valence, reward infrastructure, incentives, alignment.
  • Common confusion: equating the reward function with explicit code; the effective reward function is often implicit and emerges from coupled systems.
  • Sources:
  • talk: The Ghost in the Machine @ 00:27:50

Reward infrastructure

  • Id: TERM-0081
  • Working meaning: We will use "Reward infrastructure" to mean: The mechanisms that allocate generalized reward/resources in a society (e.g., money and incentives), thereby shaping behavior and learning at scale.
  • Related: institution, governance, alignment, reward function.
  • Common confusion: treating reward infrastructure as morally neutral plumbing; it implements a social-level objective and can induce reward hacking at civilization scale.
  • Sources:
  • talk: The Ghost in the Machine @ 00:50:31

Artificial agent

  • Id: TERM-0082
  • Working meaning: We will use "Artificial agent" to mean: A machine-implemented control system that builds models and selects actions under constraints.
  • Related: agent, control, model, governance.
  • Common confusion: equating an AI model with an agent; many systems simulate agency without having stable intrinsic control objectives.
  • Sources:
  • interview: Joscha Bach - Why Your Thoughts Aren't Yours. @ 01:06:10

Artificial sentience

  • Id: TERM-0083
  • Working meaning: We will use "Artificial sentience" to mean: The possibility that an artificial agent has experience if the relevant functional organization (self-modeling, coherence stabilization, observer construction) is implemented.
  • Related: consciousness, machine consciousness hypothesis, self-organization.
  • Common confusion: equating sentience with intelligence or with language competence.
  • Sources:
  • talk: Self Models of Loving Grace @ 00:37:31

Functionalism (usage tracked here)

  • Id: TERM-0084
  • Working meaning: We will use "Functionalism (usage tracked here)" to mean: A stance about what counts as an object or a mental entity: it is defined by its functional role in shaping the evolution of states and control, not by an intrinsic essence.
  • Related: computationalist functionalism, object (in a model), model.
  • Common confusion: reducing functionalism to \"the same behavior implies the same mind\"; the usage tracked here is more about construction of objects over observations.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:08:24

Computationalism (usage tracked here)

  • Id: TERM-0085
  • Working meaning: We will use "Computationalism (usage tracked here)" to mean: The view that models must be realized in a constructive representational language built from parts, so they can be implemented and used for inference and control.
  • Related: computationalist functionalism, representation, strong computationalism.
  • Common confusion: equating computationalism with \"everything is digital\"; it is primarily a constraint on representational construction.
  • Sources:
  • talk: The Machine Consciousness Hypothesis @ 00:10:25

Dream (technical usage)

  • Id: TERM-0086
  • Working meaning: We will use "Dream (technical usage)" to mean: Generated model content experienced as a world. A dream can be more or less constrained: in waking perception, generation is strongly constrained by sensory input; in sleep dreams, it is primarily constrained by internal priors and coherence.
  • Related: model, simulation, virtual, consciousness.
  • Common confusion: treating \"dream\" as metaphor only; in this framing it is a technical handle on generated experience as model state.
  • Sources:
  • talk: The Ghost in the Machine @ 00:04:02
  • talk: Synthetic Sentience @ 00:17:39