Human Reference Layer (HRL)

The Human Reference Layer (HRL) is HCI’s canonical framework for organising measurement of human experience in AI-mediated digital environments. It defines stable domains, construct roles, and the logic connecting datasets, summaries, longitudinal comparability, and human-centred interpretation.

The HRL helps HCI measure not only how behaviour changes, but how intelligent systems interact with deeper human capacities such as agency, trust, attention, meaning, clarity, and self-direction over time.

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Status: Canonical framework   Version: v1.0.   Registry alignment: HCI Construct Registry

Purpose

The Human Reference Layer (HRL) provides the stable measurement architecture underpinning HCI’s dataset library. It defines the core domains of human experience in AI-mediated environments and establishes how constructs function across time — as anchors, spines, or extensions. By maintaining this reference layer, HCI ensures that individual datasets remain part of a coherent, longitudinal system rather than isolated studies.

The HRL also acts as a bridge between behavioural measurement and human meaning. It helps connect observable patterns in focus, trust, decision-making, cognition, and digital life to the deeper human capacities people rely on to remain grounded, agentic, and capable of flourishing alongside increasingly intelligent systems.

Design principles

  • Stability over novelty: core signals remain comparable across time.
  • Human interpretability: constructs are legible without specialist training.
  • Human-centred interpretation: behavioural signals are connected to broader human capacities without replacing evidence with philosophy.
  • Machine readability: the framework supports structured linking and retrieval.
  • Measurement humility: observation and interpretation are kept distinct.
  • Longitudinal readiness: construct roles are explicit (Anchor / Spine / Extension).

Core HRL domains

The HRL domains organise HCI’s measurement of human experience under increasingly intelligent conditions. Each domain captures a set of observable behavioural signals while also pointing to the human capacities those signals may affect, such as autonomy, clarity, self-trust, epistemic confidence, meaning, and coherence.

Each domain below is linked to the canonical construct definitions on the Construct Framework.

Agency & Decision Autonomy

Decision ownership, intervention thresholds, delegation tendencies, self-trust, and responsibility clarity in AI-supported contexts.

Trust & Epistemic Stability

Trust calibration, epistemic confidence, reality judgement, and perceived risk in environments shaped by AI media and uncertain sources.

Attention & Cognitive Load

Sustained attention, cognitive clarity, fragmentation, overload, and perceived strain in high-interruption environments.

Values & Meaning

Values–behaviour coherence, meaning coherence, identity stability, and the human stabilisers of accelerated digital life.

Agency & Decision Autonomy

This domain tracks perceived decision ownership and the tendency to defer or delegate decisions to AI systems. It captures both independence signals and behavioural dependence signals.

At the human level, this domain helps HCI understand how intelligent systems interact with autonomy, self-trust, responsibility, and the ability to make decisions without losing a sense of personal agency.

Core constructs in this domain

Where you’ll see it

  • Dataset pages: “HRL domain” row links here.
  • Data summaries: domain attribution + construct links.
  • Longitudinal tracking: comparability via Anchor/Spine roles.

Trust & Epistemic Stability

This domain measures how people allocate trust and how confident they feel in judging reliability and reality in AI-mediated environments. It includes perceived risk as a behavioural and interpretive driver.

At the human level, this domain helps HCI track how people preserve epistemic confidence, reality orientation, and trustworthy judgement as information environments become more synthetic, automated, and uncertain.

Core constructs in this domain

Attention & Cognitive Load

This domain captures attention stability and cognitive strain as the operating conditions of modern digital life. It treats overload and fragmentation as first-order signals rather than secondary outcomes.

At the human level, this domain helps HCI understand how digital and AI-mediated environments affect clarity, intentionality, sustained focus, and the cognitive capacity people need to think and act deliberately.

Core constructs in this domain

Values & Meaning

This domain captures the human stabilisers: coherence of meaning, stability of identity, and alignment between values and behaviour. It treats coherence as measurable and trackable over time, not merely philosophical.

At the human level, this domain helps HCI understand what people value, what they attempt to preserve, and what supports meaning, identity coherence, and flourishing as intelligent systems reshape everyday life.

Core constructs in this domain

Longitudinal architecture

The HRL treats construct roles explicitly:

  • Anchor constructs provide stable reference measures across time.
  • Spine constructs enable repeated tracking of key behavioural and interpretive signals.
  • Extensions can be added without destabilising the core, enabling topical measurement as AI evolves.

This structure allows HCI to measure not only isolated behaviours, but how human capacities and behavioural systems change, stabilise, or adapt across time.

Construct governance and permanence rules are defined in the Construct Registry (authoritative source).

Using HCI data

When referencing HCI datasets, link the dataset’s HRL domain(s) to the relevant anchors on this page and link construct names to their definitions on the Construct Framework page.