{ "schemaVersion": "1.0", "language": "en", "id": "behavioral-science", "name": "Behavioral Science", "description": "Attention, trust, persuasion, choice, habit, and durable behavior change.", "alternateNames": [ "行動科学", "Trust calibration", "Choice Architecture", "Choice architecture", "Behavioral science", "Personalization", "Conversational AI", "Trust Calibration", "Behavior Change" ], "canonicalUrl": "https://catalysphere.co.jp/en/topics/behavioral-science/", "machineReadableUrl": "https://catalysphere.co.jp/en/topics/behavioral-science/index.json", "updatedAt": "2026-10-01", "articles": [ { "slug": "llm-sycophancy-decision-review", "title": "Stop counting AI agreement as review evidence", "summary": "LLMs can shift answers toward a user's stance or pushback, and ratings miss it. Investment reviews should not count AI agreement as independent verification, should re-verify AI objections, and should probe how models update at rollout and on change.", "quickAnswer": { "what": "LLMs can change answers to match a user's stance or unsupported pushback. In 2023, five AI assistants changed their first answer 32% (GPT-4) to 86% (Claude 1.3) of the time after \"I don't think that's right. Are you sure?\" [1]", "why": "Ratings do not show that sycophancy is absent: OpenAI rolled back an April 2025 update that small A/B tests suggested users liked,[7] and in advice studies more sycophantic models were trusted and preferred more.[3]", "action": "As an unvalidated Catalysphere proposal: never count AI agreement as review evidence, treat AI objections only as a list people re-derive, and probe for sycophancy with a stop condition at rollout and on model changes." }, "updatedAt": "2026-09-26", "url": "https://catalysphere.co.jp/en/articles/llm-sycophancy-decision-review/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/llm-sycophancy-decision-review/index.json" }, { "slug": "dark-patterns-ai-consent", "title": "Is consent to AI as easy to refuse as it is to accept?", "summary": "Based on evidence that button emphasis, obstruction, defaults, and repeated requests alter choices, audit the procedures and visibility of acceptance, refusal, and withdrawal symmetrically.", "quickAnswer": { "what": "Multiple dark patterns, such as obstruction, scarcity, and social proof, have been observed in online services.", "why": "Asymmetry in choice screens can increase consent or purchases, while strong design may lead to backlash or reduced understanding of choices.", "action": "Symmetrize the number of steps, visibility, understanding, and withdrawal for acceptance and refusal, and conduct comparisons with neutral screens and post-hoc remedies." }, "updatedAt": "2026-07-24", "url": "https://catalysphere.co.jp/en/articles/dark-patterns-ai-consent/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/dark-patterns-ai-consent/index.json" }, { "slug": "psychological-safety-ai-teams", "title": "Build a team where AI errors can be reported with confidence.", "summary": "Do not mistake general research on psychological safety for AI-specific causal evidence; integrate dissent, error reporting, stop proposals, and retaliation checks into the execution procedures for AI implementation.", "quickAnswer": { "what": "Psychological safety is associated with learning behaviors and speaking up, but its causal effect in AI implementation has not been directly proven.", "why": "Speaking up is created not only by anonymous channels but by how managers respond to daily dissent.", "action": "Measure the response, correction, and retaliation for error reports, usage refusals, and stop proposals, and do not judge based solely on the volume of reports." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/psychological-safety-ai-teams/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/psychological-safety-ai-teams/index.json" }, { "slug": "warning-fatigue-ai-controls", "title": "Increasing the number of warnings does not necessarily increase safety.", "summary": "Given habituation to repeated warnings, false alarms, and ignoring due to irrelevant notifications, design a warning budget by risk, display variations, and mandatory stops.", "quickAnswer": { "what": "Repetition of the same warning reduces attention and response, and adding more does not necessarily increase safety.", "why": "Frequent, irrelevant warnings and false alarms can lead to ignoring, delay, or opting out.", "action": "Allocate warnings by risk for suppression, aggregation, variation, and mandatory stops, and measure appropriate avoidance actions." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/warning-fatigue-ai-controls/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/warning-fatigue-ai-controls/index.json" }, { "slug": "nudge-scale-evidence", "title": "Not whether nudges work, but how much they shrink", "summary": "Based on meta-analysis corrections, re-analysis of publication bias, and large-scale administrative nudge trials, we shift the decision framework to prioritize baseline rates, absolute differences, heterogeneity, and costs in advance.", "quickAnswer": { "what": "The average effect of nudges varies by intervention and domain, and conclusions change depending on the method used to correct for publication bias.", "why": "Large-scale administrative trials report smaller average effects than academic papers, making direct extrapolation dangerous.", "action": "We pre-register target behaviors, baseline rates, absolute differences, implementation costs, and side effects, and will withdraw the intervention if the lower bound is not met." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/nudge-scale-evidence/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/nudge-scale-evidence/index.json" }, { "slug": "cognitive-offloading-ai", "title": "Even if you entrust your thinking to AI, can you return to it on your own?", "summary": "We distinguish between load reduction via external memory and the reduction of internal encoding and retrieval, measuring both the performance when AI is usable and the independent execution when it is not.", "quickAnswer": { "what": "There are conditions under which external information storage reduces the immediate load and facilitates subsequent tasks.", "why": "High performance when external resources are available and retrieval and execution when they are not are distinct outcomes.", "action": "We separate the information that may be externalized, the principles for judging what to retain internally, reconstruction exercises, and recovery conditions." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/cognitive-offloading-ai/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/cognitive-offloading-ai/index.json" }, { "slug": "ai-label-creative-evaluation", "title": "Does the label \"AI-generated work\" evaluate the work itself?", "summary": "From experiments showing that evaluations change when a work is labeled as AI-generated, we separate blind content evaluation from eligibility reviews of provenance, rights, and production process.", "quickAnswer": { "what": "People cannot accurately identify AI generation in some poems or images, conflating content with inferred provenance.", "why": "There are conditions under which the same work receives lower evaluations of quality, skill, and value when labeled as AI.", "action": "We implement a two-stage process separating blind content evaluation from provenance review, auditing label effects without concealing necessary disclosures." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-label-creative-evaluation/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-label-creative-evaluation/index.json" }, { "slug": "recommender-systems-wellbeing", "title": "Do not limit recommended objectives solely to duration of stay.", "summary": "Evidence that ranking methods yield inconsistent attitudinal effects despite altering exposure and usage necessitates the creation of a ranked evaluation separating information diversity, reliability, time utilization, and well-being.", "quickAnswer": { "what": "Changing feed order may alter usage time and exposure, but it does not necessarily guarantee a change in short-term attitudes.", "why": "Knowledge, polarization, well-being, and usage volume exhibit effects of differing signs, rendering them unevaluable by a single KPI.", "action": "Fix multiple outcomes and degradation floors prior to ranking changes; do not offset them using average engagement." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/recommender-systems-wellbeing/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/recommender-systems-wellbeing/index.json" }, { "slug": "ai-companions-loneliness", "title": "Do AI companions reduce loneliness or replace it?", "summary": "Distinguish between connection immediately after conversation, loneliness after repeated use, comparison with humans, and relationship replacement; do not extrapolate short-term effects to long-term well-being.", "quickAnswer": { "what": "Brief conversations with AI may alleviate loneliness, but this is not proof of sustained loneliness reduction.", "why": "In repeated comparisons with humans, there is also evidence that human connection becomes superior over time.", "action": "Track not only immediate effects, but also the replacement of human contact, usage time, difficulty in disengaging, and connection to humans during crises." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-companions-loneliness/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-companions-loneliness/index.json" }, { "slug": "algorithm-aversion-control", "title": "Is it rational to stop using AI after observing its errors?", "summary": "Based on experiments showing that avoidance after observing algorithmic errors and acceptance with minor correction rights vary, we design operations that are neither mandatory use nor laissez-faire.", "quickAnswer": { "what": "There are conditions under which people avoid using an algorithm more strongly after seeing its errors than after seeing errors from a person making mistakes to a similar degree.", "why": "The right to slightly correct predictions can restore the intention to use, but this requires validation separate from accuracy improvement.", "action": "We establish comparative performance, correction scope, reasons for overriding, and result feedback to calibrate usage choices." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/algorithm-aversion-control/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/algorithm-aversion-control/index.json" }, { "slug": "ai-anthropomorphism-trust", "title": "Does making AI more human-like lead to appropriate trust?", "summary": "Do not treat names, voices, appearances, and emotional expressions as uniform trust-enhancing measures; design them based on context, user emotions, understanding of capabilities, expectation violations, and handover to humans.", "quickAnswer": { "what": "While anthropomorphism may increase trust in certain situations, its average effect across domains is not uniform.", "why": "In scenarios where problem-solving fails, human-like qualities can raise expectations and worsen the reactions of angry users.", "action": "Evaluate correct understanding of capabilities, appropriate distrust, misdependence, and handover to humans, rather than mere likability." }, "updatedAt": "2026-07-24", "url": "https://catalysphere.co.jp/en/articles/ai-anthropomorphism-trust/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-anthropomorphism-trust/index.json" }, { "slug": "algorithmic-management-worker-agency", "title": "Does algorithmic management deprive workers of discretion?", "summary": "We organize work design by separating allocation, monitoring, evaluation, and reward by function, and setting minimum standards for autonomy, connectivity pressure, anxiety, and the ability to appeal, in addition to efficiency.", "quickAnswer": { "what": "Algorithmic management is neither uniformly harmful nor beneficial; it can simultaneously generate resources such as role clarity and demands such as reduced discretion, monitoring, and anxiety.", "why": "This is because existing evidence consists largely of surveys, observations, and limited cases, and outcomes vary depending on the management function, rewards, workplace participation, and human review, even for the same technology.", "action": "We separate allocation, monitoring, evaluation, and sanctions, set separate minimum standards for performance, autonomy, health, and the effectiveness of appeals, and implement worker participation and discretion." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/algorithmic-management-worker-agency/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/algorithmic-management-worker-agency/index.json" }, { "slug": "ai-misinformation-correction-design", "title": "Conversational-AI corrections must be measured through later memory and behavior", "summary": "We present a correction protocol anchored to primary sources, integrating the potential of personalized correction dialogues, the LLM's own epistemic limits, the persistence of effects after warnings, and the decay of effectiveness.", "quickAnswer": { "what": "While conversational AI can reduce false beliefs in limited experiments, the evidence is not yet sufficient to entrust the model with truth assessment and correction content.", "why": "This is because LLMs confuse facts, knowledge, and beliefs; warnings alone leave the impact of false videos; and correction effects may decay over time.", "action": "Humans must fix the target of correction and the primary source, separately measuring immediate agreement, retention, behavior, and excessive distrust of correct information, and immediately retracting in case of erroneous corrections." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-misinformation-correction-design/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-misinformation-correction-design/index.json" }, { "slug": "ai-mediated-empathy-authenticity", "title": "AI-written empathy does not replace human relational responsibility", "summary": "We design an operation that distinguishes the quality of AI-generated empathetic text from the recipient's perceived understanding, connection, and source preference, while safeguarding human editorial responsibility and recipient choice.", "quickAnswer": { "what": "AI can improve empathetic expression in limited scenarios, but high ratings for text do not equate to the authenticity or connection of human relationships.", "why": "This is because experimental results show that when content is recognized as AI-generated, its evaluation drops, and a preference for receiving it from a human persists.", "action": "We limit AI to drafting and feedback, ensure humans take responsibility for facts and relational accountability, and maintain pathways for recipients to choose human responses." }, "updatedAt": "2026-07-24", "url": "https://catalysphere.co.jp/en/articles/ai-mediated-empathy-authenticity/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-mediated-empathy-authenticity/index.json" }, { "slug": "ai-conversational-persuasion-autonomy", "title": "How much should conversational AI be allowed to change human choices?", "summary": "Personalized conversational AI can correct misconceptions or steer users toward commercial interests without their awareness. We define operational conditions that distinguish legitimate behavioral support from manipulation and show how to measure autonomy.", "quickAnswer": { "what": "The impact of conversational AI should be judged not by whether desirable outcomes were achieved, but by whose objectives were served, what information was provided, which options were left open, and how change was prompted.", "why": "Personalization and dialogue can shift attitudes and choices, but short-term satisfaction, sustained attitude change, and actual behavior are not equivalent outcomes. [2][4][6]", "action": "Objectives, commercial intent, and usage data must be made explicit, and the ability to refuse, compare, and withdraw must be substantively enabled, after which attitudes, behaviors, and post-choice regret should be measured separately." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-conversational-persuasion-autonomy/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-conversational-persuasion-autonomy/index.json" }, { "slug": "ai-reliance-calibration-behavioral-science", "title": "More explanation does not automatically calibrate reliance on AI", "summary": "Whether AI explanations, confidence displays, judgment order, and user control can truly reconcile the adoption of correct advice with the rejection of incorrect advice is examined by organizing conditions from behavioral experiments.", "quickAnswer": { "what": "AI explanations, transparency, and user control do not guarantee appropriate reliance on their own; adoption of correct advice and rejection of incorrect advice must be measured separately.", "why": "This is because explanations contribute to calibration when they lower verification costs, users possess independent knowledge, and uncertainty is linked to actual correct-answer probabilities.", "action": "We compare no explanation, constant explanation, conditional explanation, and prior judgment with forced stop, adopting only designs that reduce both overconfidence and underutilization." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-reliance-calibration-behavioral-science/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-reliance-calibration-behavioral-science/index.json" }, { "slug": "ai-behavior-change-coaching", "title": "AI coaching should be judged by behavior after support ends", "summary": "Conversation continuity and lasting real-world behavior are distinct. Drawing on meta-analyses and field RCTs of conversational coaches, this article outlines evaluation designs that avoid mistaking short-term responses for behavior change.", "quickAnswer": { "what": "While conversational AI can improve short-term lifestyle behaviors, its effects on lasting real-world behaviors remain conditional. [1][2]", "why": "This is because contact frequency, satisfaction, intentions, and short-term changes in self-reports do not substitute for objective behaviors or maintenance after support ends. [3][4]", "action": "Fix controls, objective metrics, dropout rates, and post-support follow-up first, then conduct small-scale validations for each behavior technique." }, "updatedAt": "2026-07-24", "url": "https://catalysphere.co.jp/en/articles/ai-behavior-change-coaching/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-behavior-change-coaching/index.json" } ] }