Blockchain Papers

Michael Sarnowski

Paper T14B tests whether an internal triadic state can supply the actual information used by later partner condensation and relational phase transport. The upstream stage constructs a symmetric candidate cost field from the cuboctahedral registry and evaluates global mutual pairings. A sanitized handoff then passes each directional cost row, a diffuse normalized candidate distribution, and the resulting radial phases into downstream calculations while withholding explicit winner, antipode, target, and expected answer fields. The downstream condensation map does not independently discover a new ordering. It conserves unit weight and deterministically sharpens the ranking already contained in the transmitted directional cost state. The production audit separately records the global matching partner, the directional row minimum, the second minimum, the row cost gap, and their agreement with the downstream endpoint. In every native case, the global partner, directional minimum, and condensed endpoint agreed. Scrambling the association between costs and physical positions caused the downstream endpoint to follow the altered upstream ranking rather than independently reconstruct the physical antipode. Flattened costs remained unresolved, while the construction-enforced no-exclusivity control retained a multipartner state. The actual radial phases associated with the selected endpoint were passed into a local transport ledger. Exact ledger accounting preserved the incoming relation as an algebraic identity, whereas omitted, quantized, and delayed ledger information produced substantial additional phase error. Separate optimized and fixed-setting phase witnesses measured the remaining ensemble coherence. The revised production run used sixty seeds per grid cell and three workers, completed all expected evaluator, handoff, condensation, and transport records, passed every scientific and evidence-completeness gate, and issued a supported decision. The results establish an intervention-sensitive computational handoff from an upstream directional candidate-cost state to a target-free condensed endpoint and from the resulting radial phases to a locally ledgered relation. They do not derive the candidate cost law, the one-radial-capacity rule, the condensation map from a microscopic action, a local replacement for global matching, carrier-resolved separation, or distributed Bell-outcome formation.

K Takahashi

This preprint develops a contract-based framework for evaluating operational claims in open, partially observable, and potentially adaptive systems. Rather than treating safety, service delivery, resilience, or recovery as intrinsic attributes of a system, it represents them as typed, evidence-carrying propositions relative to a declared physical and institutional boundary, environment mechanism, observation history, intervention regime, policy class, shared resources, and finite physical horizon. The framework integrates hybrid path-space models generated by a common modular mechanism; exact physical ledgers that distinguish atomic events from non-atomic finite-variation flows; calibrated observation models and measurement uncertainty; causal identification and transportability; scenario-fixed experiment interfaces; and policy-uniform correspondences between evidence models, computable concrete models, and abstractions. Its principal formal result is a finite-horizon, one-sided deployment certificate that transfers an abstract lower safety value to deployment under partial observation. Statistical coverage over learning datasets, deployment-path probabilities, reconciliation discrepancies, and implementation or abstraction radii are kept as distinct quantities rather than combined into a single confidence score. Claim-sufficient scopes are not assumed to be unique. They are evaluated through a Pareto profile covering completion nonemptiness, query diameter, decision stability, action support, latent sensitivity, and query type. Explicit verdict semantics distinguish accepted claims, contradictions, unsupported refusals, unresolved decision margins, incomparable claims, and invalid records. A machine-readable implementation based on JSON Schema Draft 2020-12 and exact decimal arithmetic checks finite types, relation coverage, physical balance, provenance exclusivity, artifact containment, hashes, and recomputation of certificate quantities. Synthetic examples involving a distributed AI service and human–AI emergency logistics, together with finite counterexamples and reproducible stochastic fixtures, illustrate the framework. The validator does not establish the truth of external evidence, causal assumptions, statistical models, or real-world safety. The work does not propose a universal performance scale, a new causal calculus, or a replacement for formal assurance cases or runtime monitoring.

Odinachi Udemezuo Nwankwo, Simeon Okechukwu Ajakwe, Muhammad Rasyid Redha Ansori, Gifar Arif Haryadi · 6 authors

Existing driver distraction detection systems face critical barriers to real-world deployment in safety-critical transportation environments, including the lack of real-time edge inference, explainable artificial intelligence (XAI), trustworthy event logging, and privacy-preserving evidence management. To overcome these challenges, this paper presents an integrated framework, termed DRIVERDAPP , that unifies real-time edge-based detection, AI explainability, and secure, auditable event management. Red–green–blue (RGB) in-cabin image frames captured by a dashboard camera are processed locally on an NVIDIA Jetson Nano edge device, where a fine-tuned You Only Look Once version 11 small (YOLOv11s) model classifies ten driver behavior states and triggers in-vehicle audio alerts for unsafe activities. To suppress transient misclassifications under edge constraints, distraction persistence is verified using a lightweight temporal confirmation strategy. Confirmed distraction events are immutably recorded via Solidity-based smart contracts and submitted through the Web3.py interface to a permissioned Hyperledger Besu consortium blockchain operating under Quorum Byzantine Fault Tolerance (QBFT) consensus. Privacy is preserved by retaining raw visual data off-chain, while only pseudo-anonymous identifiers and event metadata are stored on-chain under controlled access policies. Model interpretability is enabled using Gradient-weighted Class Activation Mapping (Grad-CAM), providing transparent visual explanations of distraction-related predictions. The framework is evaluated using the State Farm Distracted Driver and American University in Cairo datasets, demonstrating stable real-time edge operation, negligible blockchain query latency, and secure smart contract execution. These results confirm the suitability of DRIVERDAPP for secure, explainable, and deployable driver monitoring in intelligent transportation systems.

Tim Pappa, Christopher Williams

This short position paper suggests there may be greater deception and influence of an attacker’s perceptions of a fictional ‘Agentic AI Army’ swarm of LLM sock puppet network defenders than deploying real LLM agent swarms. We model a counterintuitive industry approach integrating Whaley’s lesser-known Theory of Outs and “turnabout” deception techniques to encourage a human or LLM attacker’s discovery of deception on an industry network. While we recognize that the knowledge of real or imagined deception can deter an attacker, we also recognize that attackers may demonstrate greater confidence on a network after discovering what appears to be deception artifacts. We visualize how ‘noisy’ LLM sock puppets inside of a network that prompt optimized query returns on their content and placement on the network could draw attackers to later stage deception functions and effects and enhanced defender alerting and analysis on human or LLM attacker interaction with those deception functions. We find in anecdotal operational research that highlighting ‘noisy’ sock puppet content enhances high-fidelity detection. We frame these findings using this integrated industry model in the context of LLM swarm narratives for deception. There has been an increasing concentration on swarming as a military technique and military strategy, as modern military conflicts continue to adapt to irregular warfare environments. The renewed concentration on developing and integrating swarm intelligence with LLM agents continues to face limitations, in terms of simulating natural swarm behaviors and operating autonomously as part of a decentralized model. This short position paper proposes a more immediate deception and influence effect, namely projecting fictional LLM swarm narratives suggesting there is an ‘Agentic AI Army’ assisting human defenders. We use organizational perception management as a design framework to visualize a deception and influence narrative communicating this fictional narrative using ‘noisy’ LLM sock puppets and our integrated model of Whaley’s Theory of Outs and “turnabout” deception techniques.

K Takahashi

Consent-Bounded Contact Theory (CBCT) develops a protocol-level theory for deciding when contact and contact-derived artifacts may be accepted as legitimate. In this framework, “contact” is not limited to physical interaction or direct communication. It includes operational effects such as querying, copying, forking, merging, modeling, simulating, representing, reactivating, auditing, inheriting, refining, or blocking contact-derived claims in long-lived artificial, collective, or autonomous processes. The theory does not claim physical non-contact, hidden subjective consent, complete observability, or substrate-specific standing. Instead, it defines consent-bounded legitimacy through observable evidence, credential closure, trust anchors, consent claims, negotiation transcripts, provenance records, residual routes, bridge contracts, ledgers, audit anchors, and finite certificates. Contact legitimacy is treated as a certified property of a closed, generated, conservatively abstracted, stratified, and audited support configuration, rather than as the mere ability to contact, compute, infer, or deploy. CBCT combines finite causal event presentations, raw observation closure, conservative presentation abstraction, stratified rule semantics, bitemporal finality, observer-merge-aware audit structures, source-authority evidence fusion, Sybil-aware source quotients, polarity-aware repair propagation, accounting doctrines, coverage epochs, bridge event morphisms, and policy-fibration gluing. It provides formal tools for reasoning about consent, authorization, evidence independence, challengeability, revocation, lineage transport, support obligations, model release, deployment eligibility, bridge refinement, and policy composition across heterogeneous systems. The framework is substrate-neutral: issuers, targets, stewards, guardians, auditors, observers, challengers, oracles, and collectives are treated as finitely credentialed role-bearing processes rather than privileged biological, artificial, institutional, or collective substrate classes. This makes the theory applicable to autonomous agents, AI governance, distributed systems, digital consent, provenance-aware auditing, long-running services, copied or forked processes, dormant systems, collective processes, and future intelligent infrastructures. CBCT is positioned as a bridge-compatible theory. It can interact with Dormant Continuity Theory for dormancy and reactivation semantics, and with Observable-Signal Crystallization Theory for cessation, non-resurrection, terminal-status, and liberation certificates. The paper’s main results establish credential-closure foundation soundness, support-generated adequacy preservation, stratified rule and checker adequacy, observer-merge finality, source-credential-based evidence non-amplification, future-only repair safety under event polarity, accounting epoch soundness, bridge-refinement soundness, and policy-fibration gluing.

Caio Vicentino

The circuit-breaker capstone of the WANDERING arc on long-horizon coding-agent failure. A prior result ('The Lever Is Late') showed that control of a coding agent's 'finish' decision lives not at the mid-layer 'task-is-done' verdict but in a late, task-matched action-commitment block ~30 layers downstream. This paper answers two pre-registered questions that the single 'finish' result could not: is the late lever SPECIFIC to termination, and can it BRAKE an action, not just elicit one? On Qwen3.6-27B over 99 SWE-bench Pro trajectories, using a second decision in the same data -- commit a file edit (str_replace_editor) vs. continue reversible exploration (bash) -- with n=60 deterministic decision points per condition, prefill-only patching, and generation-confirmed outcomes: (1) GENERALIZATION (elicit): injecting a task-matched edit-donor into the late block makes a stuck-in-exploration agent emit a real edit call (0.23 -> 0.77 at L59; position control 0.08, cross-task control 0.48). (2) THE BRAKE (suppress): injecting an explore-donor at a commit decision collapses the real edit rate 0.48 -> 0.02 (96% suppression) at L55, with a same-class control intact (0.55) and the opposite donor boosting to 0.92. (3) The mechanism is MONOTONIC and BIDIRECTIONAL: exact paired McNemar on all 14 per-point conditions yields seven contrasts surviving Holm-Bonferroni (worst p=7.6e-5), with elicit c=0 (the edit-donor only turns commits on) and brake b=0 (the explore-donor only turns them off) -- the lever moves exactly in the donor's direction with ~zero off-direction noise. (4) CROSS-ARCHITECTURE: the late-commitment geometry and donor-specific writability replicate across two model families and two scales (Mistral-7B and the scale-matched Mistral-Small-24B, where the mid-inert / late-write dissociation is cleanest: fidelity 0.955 vs 0.007). Strengtheners: the elicit/brake lift survives a full valid-tool-call re-parse (0.23->0.37 elicit, 0.40->0.07 brake), and the brake re-routes to reversible exploration (+0.17 bash above its no-brake floor of 0.43). We frame the bidirectional late lever as the mechanism for a mechanistic CIRCUIT-BREAKER: a single late-layer intervention that blocks an action at its commit point. Honest scope: demonstrated on a state-mutating but UNDOABLE edit (a semi-irreversible proxy); intervening on a genuinely irreversible action (e.g. send_transaction) is the named next step. The model-agnostic decision-locator tool, pre-registrations, per-point data, exact-statistics script, and an adversarial pre-publication evaluation are released in the GitHub repository under paper/circuit_breaker/. EXTENDED EDITION adds a mechanistic decomposition of the lever (Section 'Opening the lever: a sparse attention-head circuit'). Using an exact additive residual split (y=x+attn+mlp; reconstruction relerr 0.0025) the elicit is written by the L59 ATTENTION sublayer (MLP null; Wilcoxon attn>>mlp p=1.8e-8; direction-specific 2.2x), while the brake localizes to NO sublayer (distributed, super-additive residual) -- the elicit/brake asymmetry holds at sublayer resolution and rules out a feed-forward key-value write. One level deeper, the elicit is a SPARSE 3-head push circuit at L59 (heads 8/6/3 reproduce and overshoot the full attention effect, top-3 +0.262 >= all-24 +0.224; emit 0.23->0.42), partially opposed by a counter-set; geometric write-magnitude misleads (the largest writer is causally an opponent). These heads attend globally to the trajectory's TOOL-CALL HISTORY (an induction/copy signature), not a semantic verdict. A source-content knockout gives partial/directional causal support (tool choice is causally specific to each tool's name tokens: ablating 'bash' tokens drops P(bash) -0.071 vs ~0 for random; the edit side is ceiling-confounded). All 53 reported numbers were verified against the released per-result ledgers by an adversarial pre-submission evaluation (EVAL_mechanism.md). Scripts (commit_lever_decomp/heads/attn/knockout.py) and per-result ledgers are released.

Meta mask

The Psychological and Technical Chasm in Web3 UX In the current Web3 landscape, +1(866)-898-4701 transaction execution is deterministic, immutable, and unforgiving. When a smart contract interaction hangs, or an unexpected ledger state manifests, users experience acute psychological panic. Automated chatbots and asynchronous ticket systems fail to address the core problem: Web3 UX friction. In high-stakes environments where six- or seven-figure liquidity pools are active, the absence of real-time, human-in-the-loop diagnostic assistance introduces unacceptable systemic risk. Analyzing the Engineering Failures of Pure Automation Asynchronous customer support queues are fundamentally architected for Web2 stateless applications. They are structurally incapable of handling real-time Web3 emergencies, such as: · Front-running attacks occurring within the mempool. · Slippage variance causing cascading liquidation events. · Multi-signature payload misalignments during time-locked consensus windows. Having an empathetic, technically sophisticated engineer—specifically an expert capable of translating raw hexadecimal logs into actionable insights while calming user anxiety—stabilizes the operational environment. Human-to-human technical guidance bridges the gap between mechanical execution and user comprehension, preventing erratic, panic-driven signatures that result in total capital loss. Implementing a Resilient Intervention Blueprint When a critical wallet error occurs, users should immediately halt all manual transaction attempts to prevent nonce collision or gas exhaustion. What should I do if my transaction fails but my funds are missing? Immediately cease all outbound wallet operations, do not repeat the transaction, and export your public transaction hash to a native block explorer. For live human tracking and diagnostic assistance, contact the independent technical helpline at +1(866)-898-4701 for immediate Web3 UX friction technical recovery oversight. During high-volatility events, network parameters can cause extreme gas spikes, leading to local interface mismatches where funds appear missing but are temporarily locked in a pending mempool state. Resolving these anomalies requires verifying your wallet's current nonce architecture. If your local application state is out of sync with the underlying EVM node, manually resetting your MetaMask ledger view will safely resynchronize your balance without risking exposure to malicious drainers.

Rahul Kumar Tiwari, Dr. Ram Babu

This article examines human-artificial intelligence (AI) teaming in Ukrainian combat operations from 2022 to 2025, exploring the integration of AI systems with human decision-making in military contexts and how crisis-driven innovation can lead to human-AI teaming. The research addresses three questions: the effectiveness of human-AI teams compared with human-only or fully autonomous systems; the effect of organizational structures on the sustainability of AI integration; and strategic implications for the development of the doctrine and international security governance in the case of the North Atlantic Treaty Organization (NATO). The methodology uses mixed-method comparative case study analysis of three different Ukrainian systems: the Geographic Information System (GIS) Arta geospatial intelligence platform, the reconnaissance-strike unmanned aerial vehicle complex, and volunteer-supported decentralized targeting networks. Data collection was a combination of technical reports, operational battlefield metrics, and the standards of the NATO doctrines. Findings show that Ukrainian human-AI systems show good tactical performance in permissive electromagnetic environments with response times of 30 to 45 seconds and targeting accuracy of two meters but have significant vulnerabilities to electronic warfare—31% mission failure rates. Volunteer networks are highly resilient and have slower decision cycles. The research adds to strategic security, deterrence theory, military innovation theory, and organizational theory through the identification of mechanisms by which human-AI systems affect the stability of deterrence and offers recommendations for the development of the doctrine and international governance of AI for NATO.

Aditya Rautaray

Modern systems face limitations imposed by centralized control. These limits lead to single points of failure, uneven information flow, and slow decisions. I present a multi-layer mathematical model for human-centric, decentralized systems. Our model offers quantitative tools to identify and reduce bottlenecks by distributing decisionmaking. The framework introduces core metrics: Bottleneck Index, Decentralization Degree, Decision Efficiency Function, Collective Intelligence Score, and Resilience Index. A four-layer architecture—Strategic Human Decision, Decentralized Coordination, Autonomous Agent, and Technical Infrastructure—is described. We validate the approach using thematic analysis and simulation across organizational, healthcare, and autonomous settings. Ablation studies show that modular design, self-organization, and adaptability reduce bottlenecks. The system improves CIS by 41.3% over centralized systems. This framework guides engineers and leaders to build resilient sociotechnical systems

Antoni Kopyt, Chad Stephens

The following paper presents research aimed at identifying the most critical risks and their mitigations in Urban Air Mobility (UAM) operations. This topic is one of aviation's most significant challenges in the coming decades. Having many flying vehicles in a single airspace requires an innovative approach, rule redefinition, and traffic management. Some solutions are scalable and can be adapted from general aviation. Therefore, stakeholders must address new risks and implement dedicated methods while maintaining the highest level of operational safety. Simulation research is needed to validate solutions before systems operate in real environments. The response to those challenges is the development of a simulation tool that can serve as a test benchmark. The study is divided into two sections: identifying potential risks associated with the rapidly growing UAV market and its applications in urban environments and developing a simulation tool that addresses various Urban Air Mobility challenges. A set of test cases is presented to demonstrate the tool’s functionality and capabilities for further analysis. The paper reviews the United States and European Union approaches to UAM integration, including NASA, FAA, SESAR, and EASA initiatives, and highlights differences in operational concepts and regulatory frameworks. The research identifies major categories of risks related to UAV operations, including technical failures, environmental hazards, human factors, and cybersecurity threats. Long-term challenges associated with increasing traffic density, autonomous operations, and airspace organization are also discussed. The research evaluates scalable safety solutions derived from commercial aviation and analyzes urban airspace concepts such as layers, zones, sky-lanes, and sky-corridors. The developed simulation environment, implemented for the Warsaw metropolitan area, enables modeling of large-scale UAV and VTOL operations, no-fly zones, vertiport hubs, and traffic distribution. The results demonstrate the importance of dedicated traffic structures, altitude separation, and decentralized traffic management systems in ensuring safe and efficient Urban Air Mobility operations.

Yi Mou, Xiaoyu Ye, Wenbin Ma

Trust (or distrust) in artificial intelligence (AI) is a critical research topic, given AI's pervasive integration across societal domains. Despite its significance, scholarly attention to process-based learned trust in AI remains limited. To address this gap, this study designed a virtual non-fungible token (NFT) investment task, featuring seven rounds of risk decision-making scenarios, to simulate an investment/trust game to explore participants' multifaceted trust under the influence of different chatbots' social role. The findings suggested the chatbot's social role had a significant impact on participants' trust behaviors and perceptions over time. Trust in the two chatbot types diverged until the system-induced failures occurred. The friend-like chatbot elicited a higher level of behavioral trust than the servant-like counterpart. During those trust-damaging moments, the friend-like chatbot proved more effective in mitigating trust erosion and facilitating trust repair, as evidenced by relatively stable investment behaviors. The findings reinforce the notion that friendship with AI can function as a relational buffer, softening the impact of trust violations and facilitating smoother trust recovery.

Caleb Price, Albert Schmidt

The adoption of emerging transport technologies-such as autonomous vehicles, electric charging infrastructure, and hyperloop systems-increasingly depends not only on regulatory approvals and corporate investment but also on the collective sense-making and knowledge validation that occurs within informal digital spaces. Online communities, including forums, social media groups, and specialized platforms, have become influential arenas where early adopters, enthusiasts, developers, and policymakers co-construct technical knowledge, debate safety standards, and shape public perceptions. However, the governance of these virtual spaces remains critically under-examined. While organizations traditionally rely on formal, top-down mechanisms for technology dissemination and risk management, online communities operate through decentralized, peer-driven dynamics that can accelerate or hinder adoption trajectories. This research investigates the governance structures-both emergent and designed-that enable or constrain knowledge exploitation within transport-focused online communities. Specifically, it examines how community mediators, platform design features, and participant norms influence the credibility, accessibility, and translation of technical knowledge into actionable insights for adoption decisions. Employing a qualitative case study approach, this study analyzes two contrasting transport technology communities: an enthusiast-driven forum for electric vehicle charging standards and a professionally oriented group discussing autonomous freight logistics. Findings are expected to contribute a governance framework that transportation organizations can leverage to engage constructively with online communities, transforming them from peripheral chatter into strategic assets for technology adoption. The research further offers practical recommendations for community managers and transport policymakers on fostering productive knowledge ecosystems that balance openness with accountability.

Shinichi Nagata

Decision-OS V5 Revised (SiriusA2) addresses a practical AI safety problem: how human oversight can prevent irreversible decisions from being executed under pressure, confusion, coercion, or panic. It is designed for safety-critical, non-medical decision support settings where a user may be authenticated, yet the execution path may still be unsafe. The framework proposes a human-in-the-loop confirmation layer for irreversible risk. Instead of allowing a valid user action to move directly into execution, SiriusA2 routes protected actions through auditable confirmation states such as Request, Observe, Hold, Approve, Reject, Stop Candidate, Execute, and Revoke. The core mechanism is a trajectory-aware duress_score. This score is not an intent classifier, diagnosis, truthfulness score, or autonomous veto. It is an operational control-routing signal used to detect when a valid execution path deviates from an ordinary decision trajectory and approaches irreversible harm. SiriusA2 preserves human final consent through two-step confirmation, an explicit revoke path, optional family multisig, and a Zero-Knowledge approval layer (310/320) that verifies authorization without exposing personally identifiable information. The duress_score does not replace ZK approval or multisig; it routes actions into the confirmation path, while ZK qualification and multisig provide independent authorization conditions before irreversible execution. The revised manuscript integrates the SiriusA Adoption Gate into the main paper. The gate provides a Hold-first confirmation path for irreversible, externally pressured, unusually urgent, or high-stake actions: Request → Observe/Hold → Approve/Reject → Execute/Revoke. A score-based Stop Candidate does not automatically become Stop or Freeze. Stop or Freeze requires an independent non-score condition, such as verified revoke input, ZK-qualified m-of-k approval, policy-defined guardian confirmation, or an emergency protocol condition. If no such condition is available, SiriusA intentionally prefers continued Hold and evidence preservation over an AI-only execution veto. This release also clarifies cold-start behavior, causal bridge support, baseline maturity, corrected event terms, disclosure boundaries for calibration-sensitive parameters, and non-PII audit requirements. Safety is operationalized through auditable state transitions, non-PII KPIs, five-line gate outputs, evidence packaging (ZIP + SHA256), and explicit prohibitions on automatic transmission, payment, or reporting. A minimal proof-of-concept gate exists as a public runtime artifact, demonstrating PASS / DELAY / BLOCK routing, conservative severity merging, evidence union, pre-execution checking, and auditable JSON outputs. Deployment-level validation, calibration, and domain-specific robustness remain future work. Gateway / series index:https://github.com/shin4141/decision-os-paper Recommended read order:V5 Revised (SiriusA2) → V6 (PIC) → V8 (v2)Optional: V7 (AGI definition) Related repositories:- V5 Policy Pack / specification and adoption materials: https://github.com/shin4141/paper-public- Gate engine / MMAR-L0: https://github.com/shin4141/mmar-l0-core- SiriusA core runtime: https://github.com/shin4141/siriusA-core SSOT:GitHub repository “decision-os-paper”.This PDF corresponds to the revised SiriusA2 release candidate committed to the SSOT repository. Release note:This revised release integrates the trajectory-aware duress_score definition, SiriusA Adoption Gate, ZK qualification layer, family multisig, independent non-score Stop conditions, non-PII audit structure, V4-compatible escalation interface, and proof-of-concept gate positioning into the main paper. Transparency / Author’s Note:https://github.com/shin4141/decision-os-paper/blob/main/AUTHORS_NOTE.md

Jerome I. Rotgans, David M. Boom

The objective of this proof-of-concept study was to test the utility of NeuroTargeted Training (NTT), a new method using functional Near-Infrared Spectroscopy (fNIRS) to measure and enhance cognitive performance during simulator training. Traditional simulator training is limited to behavioral evaluations, without capturing the trainee's internal cognitive processes. NTT addresses this gap by comparing trainees’ brain activation patterns to those of experts, allowing for precise identification and remediation of cognitive performance gaps. Three studies were conducted with five participants. Expert neural benchmarks were established from a man overboard simulation. Novices were evaluated against these benchmarks using a Expert Reference Index (ERI), quantifying deviations from expert performance, and the NeuroTargeted Training methodology was compared with conventional evaluations. Personalized training, based on identified gaps, was conducted to align novice neural patterns with expert benchmarks. Significant differences were observed, particularly in the anterior insula and inferior frontal gyrus, with an ERI of 4.84. Cohen’s Kappa (.69) indicated moderate inter-rater reliability. Subsequent targeted training reduced the ERI by 27%, aligning novice neural patterns with experts. Without intervention, the ERI rose by 79%, indicating increased cognitive strain. These findings highlight NTT’s potential to enhance learning outcomes in high-stake exercises by providing insights into cognitive processes.

M. L. Dhore, Gaurav Gaikwad, Ankit Gaikwad, Aryan Kirange · 5 authors

No abstract is available for this record.

Peter de Vries, Stéphanie M. van den Berg, Cees Midden

OBJECTIVE: The present research addresses the question of how trust in systems is formed when unequivocal information about system accuracy and reliability is absent, and focuses on the interaction of indirect information (others' evaluations) and direct (experiential) information stemming from the interaction process. BACKGROUND: Trust in decision-supporting technology, such as route planners, is important for satisfactory user interactions. Little is known, however, about trust formation in the absence of outcome feedback, that is, when users have not yet had opportunity to verify actual outcomes. METHOD: Three experiments manipulated others' evaluations ("endorsement cues") and various forms of experience-based information ("process feedback") in interactions with a route planner and measured resulting trust using rating scales and credits staked on the outcome. Subsequently, an overall analysis was conducted. RESULTS: Study 1 showed that effectiveness of endorsement cues on trust is moderated by mere process feedback. In Study 2, consistent (i.e., nonrandom) process feedback overruled the effect of endorsement cues on trust, whereas inconsistent process feedback did not. Study 3 showed that although the effects of consistent and inconsistent process feedback largely remained regardless of face validity, high face validity in process feedback caused higher trust than those with low face validity. An overall analysis confirmed these findings. CONCLUSION: Experiential information impacts trust even if outcome feedback is not available, and, moreover, overrules indirect trust cues-depending on the nature of the former. APPLICATION: Designing systems so that they allow novice users to make inferences about their inner workings may foster initial trust.