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.
Thirty-two terms used across the BAX platform and the IAB attention measurement standard. Attention measurement, AI visibility, and behavioral biometrics — defined plainly.
Attention
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
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
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
The rate at which human cognitive engagement with a piece of content diminishes over time and position. Mathematically described by the BBE decay function phi(d) = alpha × e^(-lambda × d) + (1-alpha) × e^(-mu × d²), where lambda is the primary decay coefficient calibrated per content format. Attention decay predicts content ROI and AI citation probability more accurately than engagement metrics such as time on page or scroll depth.
Attention
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
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
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
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
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.
Attention
The primary parameter in the BBE decay function controlling the steepness of attention drop-off per unit of content depth or position. Lambda values are calibrated per content format: YouTube Shorts (0.31), web editorial (1.40), YouTube long-form (1.44), AI responses (1.56), TikTok (approximately 10–12). Lower lambda values indicate slower decay and higher sustained cognitive engagement. The near-identical lambda values for web editorial and YouTube long-form (1.40 vs 1.44) constitute the BBE cross-channel validity proof.
Attention
The current phase of digital information retrieval in which a significant proportion of search queries are resolved within AI interfaces without the user visiting any external website. Characterized by approximately 60% of AI-integrated searches ending without a click to source content. In the zero-click era, brand influence operates at the AI synthesis layer rather than the traffic layer, requiring attention-based measurement rather than session-based analytics.
AI Visibility
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
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
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
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
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
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
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
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.
AI Visibility
The practice of improving a brand's probability of appearing in AI-generated responses, at a favorable position, with accurate and positive framing. Distinct from SEO in that citation probability is determined by behavioral attention quality, source authority, and content depth rather than by keyword density, link volume, or domain authority scores. GEO works with probability distributions across query sets rather than discrete ranking positions.
AI Visibility
A publisher or platform classified by BAXindex as generating systematically low attention profiles: high Headline Skimmer and Distracted Browser session rates, steep attention decay curves, and poor retention. Appearing on toxic sources actively degrades a brand's AI citation probability by associating the brand with low-attention content environments in LLM corpus representations. A source can be brand-safe in the conventional sense and toxic in the attention sense simultaneously.
AI Visibility
BAXindex classification of publisher and platform inventory by behavioral attention profile, cross-referenced against AI citation frequency data. Sources are classified as Trusted (consistent Deep Reader and Active Explorer patterns, high citation frequency), Neutral (average attention profiles, standard decay curves), or Toxic (high Headline Skimmer and Distracted Browser rates, steep decay, low re-read signals). Source classification in the Trust Sphere directly influences a brand's AI citation probability.
Behavioral Biometrics
One of seven cognitive engagement segments identified by the Behavioral Biometrics Engine. Characterized by non-linear navigation, high interaction frequency, and investigative scroll patterns including forward movement, return to earlier sections, and internal link following. Typically associated with research or comparison phase of a decision process.
Behavioral Biometrics
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
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
One of seven cognitive engagement segments identified by the Behavioral Biometrics Engine. Characterized by low scroll velocity, frequent pause signals, re-read loops at high-density passages, and sustained attention retention through content depth. Deep Reader sessions generate the behavioral corpus signal most strongly associated with AI citation probability and brand recall. Research indicates Deep Reader engagement produces recall rates approximately twice those of passive content exposure.
Behavioral Biometrics
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
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
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
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
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
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.
No terms in this category.
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