Artificial Cognition & Human–AI Interaction
Experimental design, psychometrics, AI behavioral evaluation, human-factors research, and evidence synthesis for artificial cognition and human–AI interaction
Use this for
- Testing how AI systems behave, reason, adapt, and perform across different situations.
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Evaluating agents, copilots, AI coaches, assessment tools, and other AI-powered products.
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Studying how people understand, trust, use, or rely on AI systems.
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Turning ideas such as reasoning, memory, empathy, personalization, or autonomy into things that can be measured and tested.
What you walk away with
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Evaluation Plan — what to test, who or what to test it with, the scenarios to use, and how success will be judged.
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AI Test Battery — prompts, tasks, and situations designed to show where the system works well and where it fails.
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Human–AI Study Plan — how people understand, trust, use, question, or rely on the system.
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Measurement Framework — ways to score behavior, performance, reliability, and other important outcomes.
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Findings and Risk Map — the system’s strengths, weaknesses, failure patterns, and the issues that matter most.
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Evidence Pack — study protocol, analysis, findings, and recommendations for what to improve or test next.
Patterns we reach for
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Test what the system actually does — turn claims about AI behavior into things that can be observed and measured.
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Separate AI performance from human response — a system can work well technically but confuse, mislead, or frustrate users.
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Compare fairly — use matched tasks, useful baselines, and consistent scoring wherever possible.
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Test difficult situations — include ambiguity, missing information, conflicting goals, unusual inputs, and real-world complexity.
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Check that results hold up — repeat tests across prompts, users, tasks, runs, and model versions before drawing conclusions.
Quality gates
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Specific definitions — be clear about what ideas such as reasoning, empathy, memory, adaptation, or autonomy actually mean.
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Reliable measurement — use scoring and evaluation methods that produce consistent, interpretable results.
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Real-world coverage — test difficult, unusual, and realistic situations, not only ideal examples.
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Human impact — understand how people interpret, trust, use, question, or respond to the system.
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Failure awareness — identify important errors, how often they happen, how serious they are, and whether the system can recover.
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Decision readiness — end with evidence about what is working, what needs improvement, and what should be tested next.
Rapid · 2–3 weeks
Focused evaluation sprint
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Review the AI system, its goals, claims, and how people interact with it.
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Build and run a focused set of AI-behavior and human–AI tests.
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Deliver the main findings, failure patterns, priorities, and next-step recommendations.
Build · 4–6 weeks
Reusable evaluation framework
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Define what to measure, build the test scenarios, and create a clear scoring framework.
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Design the human–AI study and any model or product comparisons.
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Deliver the analysis plan, evidence pack, and reusable materials for future testing.
Validation · By arrangement
Decision-grade study
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Finalize the study design, sample, test conditions, and success criteria before testing begins.
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Run the comparative or user study and analyze the results statistically.
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Deliver a final evidence report covering the findings, limitations, remaining risks, and recommendations.
Example runs
Compare AI systems on consistency, adaptation, reasoning, uncertainty, and how well they recover when things go wrong
Test how people respond when an AI is confident, uncertain, incorrect, or decides to hand the task to a human
Build a structured benchmark for abilities such as planning, memory, reasoning, self-monitoring, or adaptation
Evaluate whether professionals know when to trust, question, ignore, or override AI recommendations
Boundaries
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We do not infer consciousness, intention, emotion, or other human-like inner states from surface behavior alone
Why Work with Us
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Cognitive science + measurement — We bring experimental design, psychometrics, and more into AI questions.
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Verifiable track record — Project experience that can be discussed and evidenced where appropriate.
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Free consultation and progress tracking — We can talk by phone and/or video call at the start and during the project.
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Clear fees — Pricing is based on project scope and task complexity, with hourly or fixed-fee options, milestone structures, and a pre-agreed maximum number of hours per task.
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NDA agreements on request — Confidentiality can be formalized if needed.
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No prepayments — Invoices are sent only after the agreed task is submitted and approved.
BUILT ON THE DUAL NEURONS FRAMEWORK
Expert judgment. Proprietary workflows. AI-accelerated results.
We combine specialist scientific expertise with proprietary AI-enabled workflows to accelerate research, analysis, quality control, and evidence synthesis.
Turn ideas into results that travel.
Book a free 15-minute consultation or ask for a sample.
FAQ
Do you need access to our model weights or source code?
Not for most behavioral or human–AI interaction projects. We can often work through a live product, API, sandbox, logs, or exported outputs. If a question depends on internal activations or model components, we scope that separately based on the access and technical setup available.
Can you work with the evaluations we already have?
Yes. We can start with your existing benchmarks, prompt sets, scorecards, or internal review process, keep what is useful, and identify gaps rather than rebuilding everything from scratch.
Can production logs or customer-support data be part of the study?
Yes, where privacy and data-access rules allow it. Real-world logs and support themes can help identify recurring problems, which can then be turned into controlled test cases.
Need Some Help?
Feel free to contact us for any inquiry or book a free consultation.
