| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Examines internal mechanisms by which the mind perceives, encodes, stores, retrieves, transforms, and manipulates information. Includes perception, memory systems, attention, reasoning, language comprehension, decision-making, problem-solving, mental imagery, and representational structures. Excludes social, emotional, or developmental phenomena unless emergent from cognitive operations. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at individual-level cognitive timescales (milliseconds to minutes) and representational levels (features → objects → schemas → concepts → executive control), not neural-micro or social-interactive scales unless linked through cognitive mechanisms. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Cognitive representations, mental schemas, working-memory buffers, attention systems, perceptual modules, long-term memory stores, decision rules, processing pathways, cognitive architectures (symbolic, connectionist, hybrid). |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Capacity limits, processing speed, representational fidelity, attentional focus, memory strength, activation levels, cognitive load, decision thresholds, error rates, pattern-recognition accuracy. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Perceptual processes, attentional systems, memory subsystems (working, episodic, semantic, procedural), reasoning and inference systems, linguistic processors, executive-control structures, representational formats (symbolic vs. distributed). |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Activation levels, working-memory load, attentional allocation, retrieval strength, processing time, decision thresholds, accuracy rates, confidence levels, representational stability/instability, interference magnitude. |
| | Parameterization | How variables encode and represent the system’s state. | Encoded through reaction times, accuracy scores, memory-load manipulations, attentional-cueing designs, computational representations, model parameters (connection weights, production rules, utility values). |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Assuming modularity; idealizing noise-free processing; treating representations as stable; assuming rational processing; ignoring emotional/motivational influences; reducing cognition to discrete stages; assuming homogeneous cognitive capacity across individuals. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Breakdown occurs in high-stress contexts, emotional interference, pathological cognitive states, cross-cultural meaning variation, multitasking environments, or when representational assumptions fail (e.g., ambiguous stimuli). |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes mental processes operate on representations; cognition follows systematic rules; processing is measurable; cognitive systems have functional architecture; behavior can be decomposed into information-processing components. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes internal representations exist and are structured; cognition is lawful and regular; mental operations can be experimentally isolated; underlying architecture is consistent across tasks; observable behavior reflects internal processes. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Representational assumptions must align with processing models; memory, perception, and decision models must not contradict each other; attentional and executive-control frameworks must integrate. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Requires integration across perception, memory, attention, language, and reasoning systems; computational models must match behavioral results; representational formats must support processing demands. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Reaction times, error rates, gaze patterns, fixations, attention shifts, memory recall accuracy, recognition curves, categorization choices, reasoning steps, neural activation patterns (as indirect evidence), decision-response distributions. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Internal representations inaccessible directly; covert thought unobservable; fast cognitive events below measurement precision; noise in reaction times; ambiguity in linking neural signals to specific cognitive processes; difficulty isolating processes without interference. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Milliseconds (RT), accuracy scores, memory strength indices, attentional-allocation percentages, confidence ratings, decision thresholds, effect sizes, error magnitudes, eye-movement metrics. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Reaction-time software, eye-trackers, EEG/ERP, fMRI, behavioral tasks, computer-based cognitive batteries, psychometric tools, verbal-protocol coding systems, computational model-fitting tools. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Definitions of attention, working memory load, retrieval strength, cognitive load, schema activation, decision threshold, recognition sensitivity (d′), representational fidelity, processing stage. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Administering behavioral tasks; varying memory load; cueing attention; manipulating perceptual complexity; recording gaze; collecting reaction times; fitting decision models; coding reasoning sequences; running recall/recognition paradigms. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Controlled lab experiments; repeated trials; randomized task orders; counterbalancing; within-subject and between-subject designs; longitudinal cognitive tracking; neurocognitive recording sessions. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Sampling participants across age, cognitive ability, or expertise; sampling trials across conditions; sampling stimuli types; sampling task difficulty levels; sampling repeated measures over time. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Reaction-time datasets; accuracy matrices; gaze-tracking logs; EEG/ERP waveforms; fMRI activation maps; response curves; confidence distributions; coded reasoning transcripts; computational model parameters. |
| | Resolution | The granularity or precision with which data is captured. | Determined by temporal precision of instruments (e.g., EEG vs fMRI), granularity of behavioral sampling, quality of stimulus control, computational-model specificity, and resolution of measurement noise. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibrating reaction-time systems; validating eye-tracking precision; synchronizing EEG timestamps; adjusting baseline activation levels; verifying task counterbalancing; checking inter-rater reliability for coded data. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Measurement noise, attentional lapses, fatigue effects, instrumentation drift, miscalibration of thresholds, signal-to-noise issues in neural data, misunderstanding of task instructions, model-misfit errors. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Capacity limits in working memory; speed–accuracy tradeoffs; serial vs. parallel processing laws; attentional bottlenecks; forgetting curves; recognition memory regularities; decision-curve signatures (drift-diffusion patterns). |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Persistence of cognitive load constraints; stable attentional biases; constant pattern-recognition thresholds; stable schema-driven interpretation patterns; consistent activation/decay dynamics in memory representations. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Encoding mechanisms; retrieval mechanisms; attentional-selection mechanisms; categorization and schema-activation mechanisms; mental-model construction; executive-control gating; inference and reasoning pathways. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Perceptual → attentional → representational → decision pathways; encoding → storage → retrieval sequences; cue → activation → recall pathways; stimulus → categorization → inference chains. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Working memory, attention, schema, mental model, representation, activation level, retrieval cue, cognitive load, executive control, reasoning strategy, perceptual filter, processing stage, decision threshold. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Types of memory (episodic, semantic, procedural); attention systems (top-down vs. bottom-up); reasoning types (deductive, inductive, heuristic); representational formats (symbolic, distributed, hybrid); processing modes (automatic vs. controlled). |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Drift-diffusion decision equations; signal-detection formulas (d′, β); memory-decay functions; activation–decay differential equations; Bayesian inference models; connectionist activation-update rules; production-system rules. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Multi-store memory models; working-memory models (Baddeley & Hitch); drift-diffusion models; Bayesian cognitive models; neural-network/connectionist architectures; ACT-R; SOAR; parallel-distributed processing models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Discrete-stage models; noise-free processing; idealized capacity limits; simplified task environments; feature-only representations; purely rational-agent models; schematic executive-control architectures. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Failures under emotional load, multitasking, fatigue, pathology, ambiguous stimuli, high-noise environments, cross-cultural meaning divergence, or when representational assumptions break down. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Information-processing theory; computational cognitive architectures; dual-process theories; Bayesian cognition; predictive-processing frameworks; working-memory/executive-control integration theories. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Links to neuroscience (neural correlates of cognition), AI/ML (representation and inference models), linguistics (comprehension & parsing), economics (decision theory), philosophy of mind (representation & intentionality). |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating memory load, perceptual complexity, attentional cues, stimulus ambiguity, decision thresholds, or representational demands to test cognitive processing performance, speed, accuracy, and strategies. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Observing natural thinking patterns, eye movements during reading, spontaneous reasoning behaviors, incidental learning, and cognitive performance in real-world or minimally controlled environments. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Testing predictions of cognitive models; verifying working-memory capacity constraints; validating attention-shift predictions; testing recognition/recall models; evaluating reasoning strategies; confirming decision-threshold predictions. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating behavioral experiments across participants; re-running tasks with alternate stimuli; replicating computational-model fits; validating neural correlates with multiple imaging sessions; reproducing reaction-time and accuracy results across labs. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Analyzing reaction-time distributions; comparing accuracy rates; computing signal-detection indices; modeling decision curves; estimating memory-decay functions; fitting Bayesian or connectionist models; evaluating cognitive-load effects. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing drift-diffusion vs. signal-detection vs. Bayesian models; contrasting symbolic vs. connectionist architectures; evaluating fit to behavioral and neural data; comparing representational format assumptions. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying outlier responses; detecting lapses in attention; measuring instrument noise; correcting for reaction-time drift; accounting for practice or fatigue effects; identifying model-misfit patterns; evaluating coding inaccuracies in verbal protocols. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Counterbalancing task order; randomizing stimuli; blinding coders; controlling for expectancy effects; matching participants by demographic factors; standardizing instructions; minimizing experimenter influence. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Cross-lab evaluation of results; reanalysis of datasets; critique of preprocessing, model fitting, and assumptions; replication reports; independent testing of theoretical predictions; evaluation of measure reliability. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Adjusting representational assumptions; refining cognitive-architecture models; updating memory/attention theories; revising decision-process descriptions; modifying processing-stage models; integrating new behavioral/neural findings. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of task parameters, sampling methods, preprocessing steps, model assumptions, instrument settings, analytic pipelines, and exclusion criteria. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Protecting participant welfare; informed consent; data confidentiality; accurate reporting of limitations; avoiding overstated claims; ensuring reproducibility; avoiding manipulative or deceptive task designs unless ethically approved. |