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BAX Reference · Glossary

Glossary of Attention Measurement

Twenty-five terms used across the BAX platform and the IAB attention measurement standard. Attention measurement, AI visibility, and behavioral biometrics — defined plainly.

25 terms shown

Attention

aTime

Attentive Time. The portion of a session in which a user is actively engaged with content, measured in milliseconds. Distinct from viewable time, which only requires presence in the viewport.

Attention

Attentive Page View

A page view that exceeded the attention threshold defined by the IAB attention measurement standard. Counts only sessions where the reader demonstrably engaged with the content.

Attention

Viewable Time

Total milliseconds the content was visible in the viewport per MRC viewability criteria. A lower bar than attentive time, and a precondition for it.

Attention

Attention Decay Curve

The exponential curve describing how cognitive engagement drops with position in a piece of content. Steepest for AI chat responses, gradual for web editorial, and channel-calibrated everywhere in between.

Attention

Decay-weighted Reach

Reach metric weighted by the attention decay curve. A brand mentioned first reaches 77 percent of users; mentioned last, 18 percent. Decay-weighted Reach accounts for that gap. Reach without it does not.

Attention

Scroll Depth

Percentage of page depth reached by the user, broken into ten zones. Used together with per-zone dwell time to reconstruct the actual reading path, not just the maximum point reached.

Attention

EDP Score

Engagement Depth Profile. A composite intent signal combining text selections, share intents, video replays, and similar deliberate actions. Reads behavior more reliably than dwell time alone.

Attention

IAB Attention Measurement Standard

The normative framework for attention measurement published by IAB and MRC in November 2025. Defines layered requirements from bot filtration to attention intensity. BAX is auditor-aligned with it.

AI Visibility

BAX Index

Composite attention metric scaled 0–100, computed identically across AI, web, and video. Weights: 45 percent Decay-weighted Reach, 25 percent Sentiment, 20 percent Exposure Quality, 10 percent Accuracy. A BAX Index of 72 means the same quality of attention on any channel.

AI Visibility

BAX — Brand Attention Index

The AI module of the BAX platform. Tracks brand visibility, citation position, sentiment, share-of-voice, narrative accuracy and hallucinations across 300+ AI model variants on 6 major platforms including ChatGPT, Perplexity, and Gemini.

AI Visibility

Citation Position

The ordinal position of a brand mention inside an AI response. Earlier positions carry disproportionately more attention; the differential between position one and position four typically exceeds 50 percentage points.

AI Visibility

Brand Exposure Index (BEI)

A measure of the quality of context around a brand mention: clarity of surrounding text, authority of the source, relevance of the framing to the user's query. Powers the Exposure Quality component of the BAX Index.

AI Visibility

Hallucination Detection

Surfacing of inaccurate attributions in AI responses. A mention is scored negatively when the information AI models associate with a brand is factually wrong or contradicts the brand's documented positioning.

AI Visibility

Share-of-Attention

Attention-weighted alternative to Share-of-Voice. Counts not how often a brand appears in AI responses but how much cognitive engagement those appearances actually carry. The latter predicts sales outcomes; the former fills slides.

AI Visibility

Source Authority

The credibility of the websites AI models cite when mentioning a brand. Determines whether a brand's AI presence rests on durable ground or content that will be deprioritized in the next model update.

AI Visibility

Cognitive-weighted Sentiment

Sentiment scored not as binary positive or negative, but weighted by cognitive load. A neutral mention in a clean sentence outweighs a glowing endorsement buried in dense technical caveats.

Behavioral Biometrics

Behavioral Biometrics Engine® (BBE)

Proprietary engine calibrated on 5M+ URLs over 9 years of R&D. Measures attention through behavioral signals in standard browsers — scroll, dwell, cursor dynamics, reading depth — without panels or hardware.

Behavioral Biometrics

Cognitive Segment

One of seven reader classifications BBE assigns within 15 seconds: Deep Reader, Active Explorer, Targeted Scanner, Flow Scroller, Headline Skimmer, Distracted Browser, Content Binger. Every attention metric is weighted accordingly.

Behavioral Biometrics

Deep Reader

High-attention cognitive segment. Deep scroll progression, low cursor jitter, sustained dwell. A Deep Reader encountering a brand has roughly twice the recall probability of a Flow Scroller exposed to the same content.

Behavioral Biometrics

Flow Scroller

Fast, consistent vertical movement with low per-zone dwell. Reads pattern and structure but not content depth. Weighted accordingly in the attention math behind every BAX index.

Behavioral Biometrics

Headline Skimmer

Reader who engages with the top of the page and leaves. High first-zone dwell, near-zero progression beyond zone two or three. A common segment on social referrals and homepage entries.

Behavioral Biometrics

Cursor Dynamics

Mouse velocity, total distance, sample density and jitter score. Together they discriminate human reading patterns from scripted or automated traffic — and separate active explorers from distracted browsers.

Behavioral Biometrics

Scroll Velocity

Average scroll rate in pixels per second, paired with a velocity profile classifier (organic vs. burst) and a reversal count. Bursts and zero reversals are bot tells; organic profiles with reversals are reading.

Behavioral Biometrics

GIVT Signals

General Invalid Traffic indicators surfaced at beacon level: webdriver flag, absent mouse movement, zero-jitter cursor, burst-scroll cadence, headless-browser fingerprints. The first filter in the BAX measurement stack.

Behavioral Biometrics

Census-scale Measurement

Measurement covering every user and every session, as opposed to panel or sample methodologies. BAX operates at census scale by design — every visit produces a beacon, every beacon flows into the index.

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