Section 138 of 440
FAMILY 15: REASONING, JUDGEMENT & STRATEGY
Superdomain: Cognition & Learning. This family contains eight stable Power records.
PWR-113 · Deductive reasoning
Evidence route: Evidence dossier with a non-prescriptive guide frame. Use the linked canonical tutorial for current teaching, accessibility, safety and editorial-review status; this evidence dossier does not enlarge that tutorial’s authority.
Summary. Deductive reasoning can be practiced, but one logic app or test cannot certify a generally rational mind.
Definition. Capability to develop or express deductive reasoning in a declared context without inheriting broader claims.
What the current evidence supports. Deductive reasoning on specified problems may improve with structured practice, but the active-control advantage and durable transfer remain uncertain.
Measurement boundary. Declare formalism, language, item novelty, active control, response accuracy, explanation, delay and transfer domain; one task is not general rationality.
Myth. Become perfectly logical
Metric. Accuracy and explanation on predeclared novel deduction problems after delay
Boundary. A logic-task gain does not prove global intelligence or better high-stakes decisions.
Negative and limiting findings.
The training app did not outperform its active control.
Abstract logic principles alone had little effect unless mapping to concrete examples was made available.
Neither source tested durable retention or consequential real-world transfer.
Claims this evidence cannot support.
become perfectly logical
logic app raises intelligence
never make a reasoning error
deductive score proves rationality
Bounded non-prescriptive guide frame
Purpose. Explain deductive performance on declared problems without presenting a logic task as general rationality or high-stakes judgment.
This frame supports conceptual observation and reflection only. It collects no answers, stores nothing, produces no score, and does not become a training protocol.
What this frame may discuss.
Evidence and measurement boundaries for deductive reasoning
Task specificity, access configurations and negative findings
Limits on transfer, efficacy and authority
Conceptual observations.
Conceptually separate applying a formal rule from mapping that rule onto a concrete example.
Distinguish accuracy from the quality and verifiability of an explanation.
Reflection prompts.
Which assumptions and formalism make the conclusion follow?
What real-world facts would remain outside the deduction?
Accessibility alternatives.
Plain language, symbols, audio or diagrams may represent the same benign problem.
Declared tools and extra processing time are valid configurations.
No timed problem set or intelligence comparison is requested.
Stop the frame if.
Stop if the frame becomes an intelligence test, timed drill or consequential decision rule.
Do not apply it to legal, clinical, financial, employment or security judgments.
Escalation boundary.
- Consequential decisions require qualified domain review, current evidence and accountable human judgment.
Prohibited uses.
Logic drills, timed targets, scoring, ranking or progression.
General-intelligence, rationality or decision-authority claims.
Operational high-stakes decision rules.
What it cannot establish.
A logic-task gain does not prove global intelligence or better high-stakes decisions.
It cannot establish: become perfectly logical.
It cannot establish: logic app raises intelligence.
It cannot establish: never make a reasoning error.
It cannot establish: deductive score proves rationality.
Evidence references. [2] primary empirical support; limiting or contrary evidence; [15] primary empirical support; limiting or contrary evidence.
Next gate. No external confirmation is required for the current bounded explainer and non-prescriptive guide-frame permissions; any stronger efficacy, generalisation, protocol, promotion or independent-validation claim requires a new adjudication.
PWR-114 · Inductive inference
Evidence route: Evidence dossier with a non-prescriptive guide frame. Use the linked canonical tutorial for current teaching, accessibility, safety and editorial-review status; this evidence dossier does not enlarge that tutorial’s authority.
Summary. Inductive reasoning can remain improved for years after targeted training, but everyday transfer is limited and uneven.
Definition. Capability to develop or express inductive inference in a declared context without inheriting broader claims.
What the current evidence supports. Targeted inductive-reasoning training produced durable task gains in older adults, with limited evidence of everyday functional benefit.
Measurement boundary. Declare inference domain, item novelty, baseline cognition, sensory/language access, delay and functional tasks; trained test performance is not diagnosis or prevention.
Myth. Reverse cognitive ageing with pattern drills
Metric. Accuracy on novel inductive problems and objective chosen functions after delay
Boundary. Durable task gains do not prove dementia prevention or general intelligence.
Negative and limiting findings.
Objective functional transfer in ACTIVE was not uniform.
A large independent online trial found trained-task improvement without transfer to untrained tasks.
Neither study establishes dementia prevention or general reasoning enhancement.
Claims this evidence cannot support.
reverse cognitive ageing
raise general intelligence
prevent dementia
pattern training transfers everywhere
Bounded non-prescriptive guide frame
Purpose. Explain inductive task gains while keeping pattern inference separate from dementia prevention or general intelligence.
This frame supports conceptual observation and reflection only. It collects no answers, stores nothing, produces no score, and does not become a training protocol.
What this frame may discuss.
Evidence and measurement boundaries for inductive inference
Task specificity, access configurations and negative findings
Limits on transfer, efficacy and authority
Conceptual observations.
Conceptually distinguish a pattern inferred from examples from a conclusion guaranteed by premises.
Compare trained-task change with a separately measured everyday function.
Reflection prompts.
How novel are the examples relative to the evidence used to learn the pattern?
Which alternative pattern could fit the same observations?
Accessibility alternatives.
Examples can be verbal, visual, tactile or tool-assisted.
Extra time, sensory access and no-attempt are valid configurations.
No cognitive-age or diagnostic comparison is implied.
Stop the frame if.
Stop if the frame becomes a cognitive screen, dementia-prevention claim, pattern drill or intelligence score.
Do not use it to infer another person’s capacity or fitness.
Escalation boundary.
- Concerning cognitive change or capacity questions require appropriately qualified, rights-respecting professional assessment.
Prohibited uses.
Pattern-training dose, target, score, rank or progression.
Dementia-prevention, diagnosis or cognitive-age claims.
Capacity, fitness, general-intelligence or other high-stakes decision conclusions.
What it cannot establish.
Durable task gains do not prove dementia prevention or general intelligence.
It cannot establish: reverse cognitive ageing.
It cannot establish: raise general intelligence.
It cannot establish: prevent dementia.
It cannot establish: pattern training transfers everywhere.
Evidence references. [1] primary empirical support; limiting or contrary evidence; [5] primary empirical support; limiting or contrary evidence.
Next gate. No external confirmation is required for the current bounded explainer and non-prescriptive guide-frame permissions; any stronger efficacy, generalisation, protocol, promotion or independent-validation claim requires a new adjudication.
PWR-115 · Probabilistic reasoning
Evidence route: Evidence dossier with a non-prescriptive guide frame. Use the linked canonical tutorial for current teaching, accessibility, safety and editorial-review status; this evidence dossier does not enlarge that tutorial’s authority.
Summary. Probability judgment can improve—but only when the questions, outcomes, scoring and support system are made explicit.
Definition. Capability to develop or express probabilistic reasoning in a declared context without inheriting broader claims.
What the current evidence supports. Probabilistic reasoning can improve through representation training and configured forecasting practice, with gains bound to problem form and system design.
Measurement boundary. Declare event set, base rates, elicitation format, calibration, resolution, scoring rule, aggregation, missing outcomes and decision consequences.
Myth. Predict the future with certainty
Metric. Calibration, resolution and proper score on a predeclared event set
Boundary. Good forecasts in one domain do not establish foresight, certainty or safe high-stakes authority.
Negative and limiting findings.
Frequency format did not add the expected benefit after nested-set training.
Tournament performance intertwined training, teams, aggregation, selection and attrition.
Claims this evidence cannot support.
predict the future
become a human probability engine
Bayesian training eliminates uncertainty
forecast score proves wisdom
Bounded non-prescriptive guide frame
Purpose. Explain calibration and resolution in declared forecasting problems without implying certainty or high-stakes foresight.
This frame supports conceptual observation and reflection only. It collects no answers, stores nothing, produces no score, and does not become a training protocol.
What this frame may discuss.
Evidence and measurement boundaries for probabilistic reasoning
Task specificity, access configurations and negative findings
Limits on transfer, efficacy and authority
Conceptual observations.
Conceptually separate confidence calibration from the ability to distinguish outcomes.
Treat event definitions, base rates, resolution and missing outcomes as part of the forecast system.
Reflection prompts.
What event set and resolution rule would make a probability auditable?
Could good aggregate calibration hide important errors or consequences?
Accessibility alternatives.
Fictional, benign events are sufficient; personal or consequential predictions are excluded.
Plain language, frequencies and visual formats are alternative representations.
No personal forecast submission or scoring is requested.
Stop the frame if.
Stop if the frame becomes betting, financial advice, medical prognosis, political persuasion or a scoring competition.
Stop if prediction tracking becomes compulsive or distressing.
Escalation boundary.
- Consequential forecasting requires domain expertise, accountable governance and explicit decision safeguards.
Prohibited uses.
Forecast tournament, score, rank, target, repeated submission or progression.
Financial, clinical, legal, political or security decision authority.
Claims of certainty, universal foresight or safe transfer.
What it cannot establish.
Good forecasts in one domain do not establish foresight, certainty or safe high-stakes authority.
It cannot establish: predict the future.
It cannot establish: become a human probability engine.
It cannot establish: Bayesian training eliminates uncertainty.
It cannot establish: forecast score proves wisdom.
Evidence references. [4] primary empirical support; limiting or contrary evidence; [6] primary empirical support; limiting or contrary evidence.
Next gate. No external confirmation is required for the current bounded explainer and non-prescriptive guide-frame permissions; any stronger efficacy, generalisation, protocol, promotion or independent-validation claim requires a new adjudication.
PWR-116 · Causal reasoning
Evidence route: Evidence dossier with a non-prescriptive guide frame. Use the linked canonical tutorial for current teaching, accessibility, safety and editorial-review status; this evidence dossier does not enlarge that tutorial’s authority.
Summary. Causal reasoning can be improved for selected errors, but no short course reveals the true cause of every complex event.
Definition. Capability to develop or express causal reasoning in a declared context without inheriting broader claims.
What the current evidence supports. Instruction can reduce selected causal-reasoning errors in science-report interpretation, with short delayed retention.
Measurement boundary. Declare causal question, design, alternatives, confounding, mechanism, uncertainty, data limits and decision context; diagram quality is not causal truth.
Myth. See the true cause of anything
Metric. Accurate causal classification and alternative-cause generation on novel declared cases
Boundary. A training-task gain cannot certify causality in a complex real system.
Negative and limiting findings.
Evidence remains bounded to science-report and control-of-variables tasks.
Probe questions without direct instruction did not improve children’s unconfounded experimental reasoning.
No real policy, clinical or organizational outcome was tested.
Claims this evidence cannot support.
see the true cause of anything
causal training proves expertise
correlation never matters
one diagram explains a social system
Bounded non-prescriptive guide frame
Purpose. Explain causal classification and alternative causes without treating a diagram or training task as proof about a complex system.
This frame supports conceptual observation and reflection only. It collects no answers, stores nothing, produces no score, and does not become a training protocol.
What this frame may discuss.
Evidence and measurement boundaries for causal reasoning
Task specificity, access configurations and negative findings
Limits on transfer, efficacy and authority
Conceptual observations.
Conceptually distinguish association, intervention, confounding and plausible mechanism.
Consider how alternative causes and missing variables constrain a causal claim.
Reflection prompts.
What design would separate the proposed cause from a confounder?
Which alternative explanation remains compatible with the observations?
Accessibility alternatives.
A benign fictional case can be represented in text, audio or diagrams.
Declared tools and extra processing time are valid configurations.
No personal, clinical or political case is needed.
Stop the frame if.
Stop if the frame is used to accuse a person, diagnose a condition or justify policy, legal, financial or employment action.
Do not generate realistic misinformation or persuasion material as an example.
Escalation boundary.
- Consequential causal claims require domain experts, appropriate study design, affected-person input and accountable review.
Prohibited uses.
Causal-reasoning drill, score, target, rank or progression.
Diagnosis, blame, accusation or consequential decision rule.
Claims that a diagram or checklist establishes causal truth.
What it cannot establish.
A training-task gain cannot certify causality in a complex real system.
It cannot establish: see the true cause of anything.
It cannot establish: causal training proves expertise.
It cannot establish: correlation never matters.
It cannot establish: one diagram explains a social system.
Evidence references. [8] primary empirical support; limiting or contrary evidence; [13] primary empirical support; limiting or contrary evidence.
Next gate. No external confirmation is required for the current bounded explainer and non-prescriptive guide-frame permissions; any stronger efficacy, generalisation, protocol, promotion or independent-validation claim requires a new adjudication.
PWR-117 · Systems thinking
Evidence route: Externally gated evidence dossier. Use the linked canonical tutorial for current teaching, accessibility, safety and editorial-review status; this evidence dossier does not enlarge that tutorial’s authority.
Summary. Systems maps can make relationships visible—but they are models with boundaries, not the whole world.
Definition. Capability to develop or express systems thinking in a declared context without inheriting broader claims.
What the current evidence supports. Systems-thinking tools can help people and communities represent relationships, but training and transfer evidence is mixed.
Measurement boundary. Declare map boundary, participants, missing voices, evidence for links, uncertainty, simulation assumptions, decision and observed outcomes; complexity is not validity.
Myth. See and control the whole system
Metric. Model accuracy, stakeholder coverage, decision trace and observed outcome under declared assumptions
Boundary. A complex map or simulation cannot prove causal completeness or guarantee outcomes.
Negative and limiting findings.
Explicit systems-thinking material did not show the expected main effect in one experiment.
Combining simulation and instruction could increase cognitive load and reduce performance.
Claims this evidence cannot support.
see the whole system
solve complexity
map proves causation
digital twin predicts society
Evidence references. [7] primary empirical support; limiting or contrary evidence; [16] primary empirical support; limiting or contrary evidence.
Next gate. External confirmation is required before higher-authority use because: a clinical, high-risk or regulated configuration; cultural, community-rights or affected-person authority.
PWR-118 · Expert judgement
Evidence route: Externally gated evidence dossier. Use the linked canonical tutorial for current teaching, accessibility, safety and editorial-review status; this evidence dossier does not enlarge that tutorial’s authority.
Summary. Expert judgment is real but conditional: accuracy, calibration, domain, incentives and tools all matter.
Definition. Capability to develop or express expert judgement in a declared context without inheriting broader claims.
What the current evidence supports. Expert judgment can be assessed and sometimes improved within a defined domain, feedback environment and accountable decision system.
Measurement boundary. Declare domain, case mix, outcome delay, base rates, calibration, discrimination, conflicts, AI/version, overrides and appeals; credentials and confidence are not outcome accuracy.
Myth. Expert intuition never fails
Metric. Calibration, discrimination and consequential outcomes on a declared case set
Boundary. Credentials, eloquence or one domain score cannot certify universal judgment.
Negative and limiting findings.
Confidence and eloquence can increase persuasion independently of forecast accuracy.
Incorrect AI advice reduced human accuracy, and interface protections were incomplete and configuration-specific.
Claims this evidence cannot support.
expert intuition is always right
debias the expert
AI plus expert cannot fail
one score identifies the best decision-maker
Evidence references. [4] primary empirical support; limiting or contrary evidence; [9] primary empirical support; limiting or contrary evidence; [10] limiting or contrary evidence; [11] primary empirical support; limiting or contrary evidence; [14] primary empirical support; limiting or contrary evidence; [17] limiting or contrary evidence; official boundary context.
Next gate. External confirmation is required before higher-authority use because: a clinical, high-risk or regulated configuration; a restricted safety or legitimacy boundary.
PWR-119 · Decision under uncertainty
Evidence route: Externally gated evidence dossier. Use the linked canonical tutorial for current teaching, accessibility, safety and editorial-review status; this evidence dossier does not enlarge that tutorial’s authority.
Summary. Good decisions under uncertainty are built by a configured system—not by confidence, a single formula or an AI answer.
Definition. Capability to develop or express decision under uncertainty in a declared context without inheriting broader claims.
What the current evidence supports. Selected components of decision-making under uncertainty can improve, but outcomes depend on the person, information, values, interface, institution and stakes.
Measurement boundary. Declare options, values, probabilities, data, AI/version, timing, incentives, affected groups, process, outcome and appeal; accuracy is not the only legitimate value.
Myth. Always choose the optimal answer
Metric. Decision process, calibration, value alignment, harms and outcomes under declared uncertainty
Boundary. No laboratory task or model output can determine universally correct high-stakes choices.
Negative and limiting findings.
Incorrect AI advice reduced accuracy even in human-in-the-loop designs.
A human-first interface reduced but did not eliminate AI influence, and one forcing design reduced usability.
Claims this evidence cannot support.
make the optimal decision every time
remove uncertainty
trust the algorithm
decision score overrides lived experience
Evidence references. [3] primary empirical support; limiting or contrary evidence; [4] primary empirical support; limiting or contrary evidence; [9] primary empirical support; limiting or contrary evidence; [11] primary empirical support; limiting or contrary evidence; [14] primary empirical support; limiting or contrary evidence; [17] limiting or contrary evidence; official boundary context.
Next gate. External confirmation is required before higher-authority use because: a clinical, high-risk or regulated configuration; a restricted safety or legitimacy boundary.
PWR-120 · Strategic planning
Evidence route: Externally gated evidence dossier. Use the linked canonical tutorial for current teaching, accessibility, safety and editorial-review status; this evidence dossier does not enlarge that tutorial’s authority.
Summary. A plan is not a capability until people can execute, adapt and learn from it under real constraints.
Definition. Capability to develop or express strategic planning in a declared context without inheriting broader claims.
What the current evidence supports. Teams and leaders can improve selected planning processes, while durable strategic capability and causal impact remain context-specific.
Measurement boundary. Declare goal legitimacy, assumptions, dependencies, resources, contingencies, execution trace, adaptation, outcomes, harms and affected groups; plan quality is not success.
Myth. Write the winning master plan
Metric. Goal attainment, adaptation quality, harms and resource use under declared constraints
Boundary. A persuasive plan or simulation cannot guarantee execution, legitimacy or outcomes.
Negative and limiting findings.
Only 58.8% of local simulation goals were achieved.
Goal achievement was not associated with knowledge, self-efficacy or teamwork scores.
Claims this evidence cannot support.
master strategy
guarantee execution
outthink any opponent
AI writes the winning plan
Evidence references. [3] primary empirical support; limiting or contrary evidence; [12] primary empirical support; limiting or contrary evidence; [17] limiting or contrary evidence; official boundary context.
Next gate. External confirmation is required before higher-authority use because: a clinical, high-risk or regulated configuration; cultural, community-rights or affected-person authority; a restricted safety or legitimacy boundary.
Family source register
Numbers in each dossier refer to this family register. A source can support one bounded proposition while simultaneously limiting transfer, certainty, generalisation or safety.
[1] Sherry L. Willis; Sharon L. Tennstedt; Michael Marsiske; Karlene Ball; Jeffrey Elias; Kathy Mann Koepke; John N. Morris; George W. Rebok; Frederick W. Unverzagt; Anne M. Stoddard; Elizabeth Wright; ACTIVE Study Group (2006). Long-term Effects of Cognitive Training on Everyday Functional Outcomes in Older Adults. Primary research. https://pubmed.ncbi.nlm.nih.gov/17179457/
[2] Patricia W. Cheng; Keith J. Holyoak; Richard E. Nisbett; Lindsay M. Oliver (1986). Pragmatic versus syntactic approaches to training deductive reasoning. Primary research. https://pubmed.ncbi.nlm.nih.gov/3742999/
[3] Berra Yilmaz Kusakli; Betül Sönmez (2024). The effect of problem-solving and decision-making education on problem-solving and decision-making skills of nurse managers: A randomized controlled trial. Primary research. https://pubmed.ncbi.nlm.nih.gov/39038405/
[4] Barbara Mellers; Eric Stone; Pavel Atanasov; Nick Rohrbaugh; S. Emlen Metz; Lyle Ungar; Michael M. Bishop; Michael Horowitz; Ed Merkle; Philip Tetlock (2015). The psychology of intelligence analysis: Drivers of prediction accuracy in world politics. Primary research. https://pubmed.ncbi.nlm.nih.gov/25581088/
[5] Adrian M Owen; Adam Hampshire; Jessica A Grahn; Robert Stenton; Said Dajani; Alistair S Burns; Robert J Howard; Clive G Ballard (2010). Putting brain training to the test. Primary research. https://pubmed.ncbi.nlm.nih.gov/20407435/
[6] Miroslav Sirota; Lenka Kostovičová; Frédéric Vallée-Tourangeau (2015). How to train your Bayesian: A problem-representation transfer rather than a format-representation shift explains training effects. Primary research. https://pubmed.ncbi.nlm.nih.gov/25283723/
[7] Laura K. Brennan; Nasim S. Sabounchi; Allison L. Kemner; Peter Hovmand (2015). Systems Thinking in 49 Communities Related to Healthy Eating, Active Living, and Childhood Obesity. Primary research. https://pubmed.ncbi.nlm.nih.gov/25828223/
[8] Zhe Chen; David Klahr (1999). All Other Things Being Equal: Acquisition and Transfer of the Control of Variables Strategy. Primary research. https://srcd.onlinelibrary.wiley.com/doi/10.1111/1467-8624.00081
[9] Zana Buçinca; Maja Barbara Malaya; Krzysztof Z. Gajos (2021). To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making. Primary research. https://doi.org/10.1145/3449287
[10] Yhonatan S. Shemesh; Avi Gamoran; David Leiser; Michael Gilead (2025). Do People Listen to Cassandra? Persuasion and Accuracy in Geopolitical Forecasts. Primary research. https://pubmed.ncbi.nlm.nih.gov/41105022/
[11] Carey K. Morewedge; Haewon Yoon; Irene Scopelliti; Carl W. Symborski; James H. Korris; Karim S. Kassam (2015). Debiasing Decisions. Primary research. https://doi.org/10.1177/2372732215600886
[12] Dilys Walker; Susanna Cohen; Jimena Fritz; Marisela Olvera; Hector Lamadrid-Figueroa; Jessica Greenberg Cowan; Dolores Gonzalez Hernandez; Julia C. Dettinger; Jenifer O. Fahey (2014). Team training in obstetric and neonatal emergencies using highly realistic simulation in Mexico: impact on process indicators. Primary research. https://pubmed.ncbi.nlm.nih.gov/25409895/
[13] Colleen M. Seifert; Michael Harrington; Audrey L. Michal; Priti Shah (2022). Causal theory error in college students’ understanding of science studies. Primary research. https://pubmed.ncbi.nlm.nih.gov/35022946/
[14] Ujué Agudo; Karlos G. Liberal; Miren Arrese; Helena Matute (2024). The impact of AI errors in a human-in-the-loop process. Primary research. https://doi.org/10.1186/s41235-023-00529-3
[15] Robert A. Cortes; Adam B. Weinberger; Adam E. Green (2023). The Mental Models Training App: Enhancing verbal reasoning through a cognitive training mobile application. Primary research. https://pubmed.ncbi.nlm.nih.gov/36968736/
[16] Caroline Green; Owen Molloy; Jim Duggan (2021). An Empirical Study of the Impact of Systems Thinking and Simulation on Sustainability Education. Primary research. https://doi.org/10.3390/su14010394
[17] National Institute of Standards and Technology (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). Official authority. https://www.nist.gov/itl/ai-risk-management-framework