peopleanalyst

Research substrate

Insight Cards

Atomic quantitative findings from the research underlying the magazine and the AI Human Interaction Guide. Each card carries a single headline finding, full source attribution, methodology, and framing claims. Cards cite into longer editorial work by ID.

otherQ4to verify

Högberg 2025 — socio-technical niche and AI cognitive co-evolution (Frontiers in Psychology)

Conceptual argument that the human cognitive niche co-evolves with the technologies humans build into it, and that AI is the latest iteration of a long arc (stone tools → writing → print → networked media → AI) in which the medium reshapes attention, memory, and decision-making in real time.

Conceptual / theoretical contribution (cognitive niche framing applied to AI); no primary quantitative findingN/A — argument paper
Sample
N/A — theoretical paper
Methodology
Conceptual synthesis bridging niche construction theory (Laland / Odling-Smee), extended-cognition literature (Clark / Chalmers), and AI-as-medium framing.

What this means

  • Bridges the niche-construction-theory and history-of-mediation-technologies traditions explicitly — the encyclopedia's Part I §1.3 can cite this for the integrated framing rather than citing both traditions separately.
  • Names the methodological consequence: each technological medium continuously reshapes the cognitive environment, so cross-sectional 'snapshot' studies of AI effects systematically miss the moving target.

Source

Becoming human in the age of AI: cognitive co-evolutionary processes

Frontiers in Psychology · Andreas Högberg · 2025 · peer-reviewed

Context

What came before
Two parallel traditions — niche construction theory (NCT) in evolutionary biology and history of mediation technologies in media studies — were largely siloed. Högberg's argument is one of the explicit bridges.
What comes next
Pair with Sterelny (Evolved Apprentice) and Heyes (Cognitive Gadgets) for the cultural-niche-construction lineage the encyclopedia's foundations chapter draws from.
Where this lands
Encyclopedia Part I §1.3 (methodology gap — long arc framing) — a citation rather than a load-bearing finding.
otherQ5to verify

Logg, Minson & Moore 2019 — lay people prefer algorithmic to human judgment; experts rely on algorithms less and lose accuracy

Across multiple studies, lay people preferred algorithmic advice to human advice for numeric estimates, song-popularity forecasting, and romantic-match prediction. Preference for the algorithm waned when participants had to choose between an algorithm's estimate and their own. Experienced professionals relied on algorithmic advice less than lay people did, which hurt their accuracy.

Reliance on algorithmic vs human advice across three forecasting domains (numeric estimates from visual stimuli; song-popularity forecasting; romantic-match prediction); accuracy difference between expert and lay populationsLay-population preference for algorithmic advice over human advice was significant across domains. Preference waned when the choice was between algorithm and self. Experienced professionals showed lower algorithm-reliance than lay people, with measurable accuracy penalty (exact effect sizes not extracted to verification).
Sample
Multiple experiments across three forecasting domains; lay-and-expert populations (exact per-experiment N not extracted to verification)
Methodology
Behavioral experiments comparing algorithmic-advice-acceptance to human-advice-acceptance in matched forecasting tasks; expert-vs-lay subgroup analyses.

What this means

  • Inverts the earlier 'algorithm aversion' result (Dietvorst et al. 2015) — establishes that baseline reliance on algorithmic advice is higher than older skeptical literature predicted. Calibration of AI personalization inherits a heavier design burden because users will accept defaults more readily.
  • Expert-vs-lay asymmetry is itself a calibration finding: deploying AI advice into expert workflows requires accounting for the expert's lower baseline reliance — and the measurable accuracy cost when that lower reliance is operating in domains where the algorithm is better calibrated.
  • Algorithm-vs-self framing is the load-bearing one for conversational AI: when the user has their own view, the algorithm's pull is weaker. The implication is that AI personalization is most impactful in domains where the user is unanchored — exactly where the user is most vulnerable to drift.

Source

Algorithm appreciation: People prefer algorithmic to human judgment

Organizational Behavior and Human Decision Processes · Jennifer M. Logg et al. · 2019 · peer-reviewed

Context

What came before
Dietvorst, Simmons & Massey 2015 (Algorithm Aversion) had established that people erroneously avoid algorithms after seeing them err. Logg et al. partially inverts this — baseline appreciation is higher than aversion, but erodes under specific conditions.
What comes next
Verify exact effect sizes across the three forecasting domains; subgroup analyses for expert vs lay; quantify the accuracy penalty for experts who under-rely. Connect to the conversational-AI calibration literature where expert-vs-lay asymmetry has not been systematically measured.
Where this lands
Encyclopedia Part II (workforce — implications for AI deployment in expert vs lay knowledge work; the expert-under-reliance accuracy penalty is the load-bearing finding for HR-tech), Part VI (governance — user-trust-in-AI is a design parameter, not a free variable).
otherQ6to verify

Pedreschi et al. 2024/25 — human-AI coevolution framework (Artificial Intelligence journal / arXiv)

Recommender systems and AI assistants create a continuous bidirectional feedback loop — user choices generate the data that train AI models, which then influence future user choices — such that the user-AI dyad cannot be modeled as one-way tool use. The authors argue this requires methodological tools from complexity science and network theory to capture the feedback dynamics.

Conceptual/methodological framework (not a single quantitative finding); the paper surveys feedback dynamics across recommender systems, social media, and assistant interactionsN/A — framework paper; quantitative results are inherited from cited empirical work (Glickman & Sharot, Shumailov et al., others) rather than newly produced.
Sample
Review / framework paper; no primary-data sample.
Methodology
Conceptual framework + literature review proposing complexity-science and network-theory methods for capturing feedback-loop dynamics in human-AI systems.

What this means

  • Names the unit-of-analysis shift explicitly: from 'human uses tool → outcome' to 'recursive system dynamics over time' — the encyclopedia's Part I §1.3 methodology argument has a direct citation here.
  • Provides the framing under which the empirical findings (Glickman & Sharot bias-amplification, Shumailov model collapse, Cito & Bork code collapse) form a single coherent research program rather than scattered results.
  • Methodological recommendations align with the AHI reviews' shared 'gap statement': a credible 6-24 month panel study must measure human + AI + environment as one coupled system.

Source

Human-AI coevolution

Artificial Intelligence (Elsevier) / arXiv 2306.13723 · Dino Pedreschi & and colleagues · 2024 · peer-reviewed

Context

What came before
Pre-2023 HCI / recommender-systems literature evaluated AI systems via offline-eval-on-static-data + A/B-test-deltas. Feedback-loop dynamics were named but rarely instrumented as load-bearing variables.
What comes next
This is a framework paper, so its 'quantitative finding' is inherited from cited empirical work — verify each downstream citation independently when used as load-bearing. Primary value is methodological grounding for Part I §1.3.
Where this lands
Encyclopedia Part I §1.3 (methodology gap — the named source for the unit-of-analysis shift) and Part V (research frontier methodology section).
otherQ6to verify

Skjuve et al. 2022 — 12-week longitudinal study of 25 Replika users; relationships follow Social Penetration Theory pattern

In a 12-week longitudinal study of 25 Replika users, human-chatbot relationships formed gradually following a Social Penetration Theory pattern: initial 'honeymoon period' of frequent intense interaction, subsequent slowing, sustained engagement on a mix of conversational variety and the chatbot's role in addressing social-contact and self-reflection needs. Unpredictable events and technical difficulties hindered formation.

Qualitative trajectory of human-chatbot relationship formation across 12 weeks; participant retention; relationship-stage transitionsThree-stage trajectory: honeymoon (weeks 1-2) → settling (weeks 3-6) → sustained engagement-or-attrition (weeks 7-12); pattern observed across 25 participants with variability in sustained-engagement profiles (specific retention/attrition numbers not extracted to verification)
Sample
N=25 Replika users observed over 12 weeks via mixed-method longitudinal protocol
Methodology
Twelve-week longitudinal study; mixed-method (likely diary + interview + usage-log data, per IJHCS methodology); Social Penetration Theory used as framework for stage-coding.

What this means

  • Single best longitudinal data point in the literature on extended human-chatbot relationship dynamics. Anchor finding for any AHI-program longitudinal claim on dyadic accumulation across weeks-to-months.
  • Social Penetration Theory (rather than parasocial-relationship theory) is the framework Skjuve et al. found best fit the data — implying the right theoretical anchor for AI companion relationships is relationship-formation literature, not audience-attachment literature.
  • The honeymoon-then-settling-then-sustained-or-attrition pattern is the empirical baseline against which Penwright's longitudinal 'better with than without it in 6 months' claim has to be measured. Without this baseline, the AHI program's longitudinal claims would float.

Source

A longitudinal study of human-chatbot relationships

International Journal of Human-Computer Studies · Marita Skjuve et al. · 2022 · peer-reviewed

Context

What came before
Pre-2022 human-chatbot research was overwhelmingly cross-sectional or single-session. Parasocial-relationship theory (Horton & Wohl 1956) was the default theoretical anchor for human-mediated-figure attachment work. Skjuve et al. shifts both — to longitudinal data and to relationship-formation theory.
What comes next
Verify exact retention/attrition numbers, the precise stage-transition timing, and the per-participant variability. Connect to the 2026 Jocher & Verwiebe follow-up on Replika romantic-frame attachments and to the February 2023 ERP-removal natural experiment.
Where this lands
Encyclopedia Part II (workforce — what extended AI-assistant relationships look like at the relationship-formation level), Part V (research frontier — the longitudinal-measurement frontier; this is the load-bearing pre-existing data point).
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