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a5:Ta9a1b,Habitus — Instrument HB-04 | Computational Sociology Laboratory

HabitusComputational Sociology Laboratory

Protocol HB-04 · Rev. 2.3SESSION —— · ——

Participant briefing · Read in full

An instrument for observing the distance between political reasoning and political perception

You are about to take part in a research session conducted by the Computational Sociology Laboratory. The session has two parts. In the first you will evaluate public policies. In the second you will evaluate portraits of public representatives. The two parts are recorded separately and are never shown to you at the same time.

No proposal is attributed to any person, party, movement or country. No portrait is accompanied by a name, an office or an affiliation. This omission is deliberate: the purpose of the study is to observe how judgement behaves when the usual identifying cues are removed.

There are no correct answers, no performance measure and no comparison against other participants. You may end the session at any point without giving a reason, and your responses will be discarded if you do.

Block A
01
Deliberative evaluation
You will read individual legislative proposals presented without author, party, chamber, date or jurisdiction. You will state whether you agree or disagree with each proposal and indicate how important the matter is to you.
Block B
02
Affective evaluation
You will view standardised portraits of individuals holding public office. No name, affiliation, office or tenure is disclosed. You will rate your immediate impression on three scales.
Debrief
03
Presentation of your own data
The relationship between your two sets of responses will be displayed back to you. The visualisation is descriptive. It does not evaluate you and it produces no score, ranking or outcome.

Estimated duration 9 minutes · Responses are not attributed to you

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It is a Computational Sociology research prototype whose purpose is to investigate the relationship between rational political decision-making and emotional political perception while minimizing every possible source of interface-induced bias. The interface itself is part of the scientific methodology. Users should feel they are participating in a cognitive experiment, not using a product. Think about how laboratories, research institutes and scientific instruments communicate credibility, neutrality and rigor. ## Design Philosophy Primary inspiration: - Scientific laboratories - MIT Media Lab - CERN - Nature journal - Stanford research interfaces - Academic software - Clinical psychology experiments - Human-computer interaction laboratories The interface should communicate: - scientific rigor - neutrality - trust - transparency - precision - calm - reflection NOT excitement. NOT persuasion. NOT entertainment. NOT marketing. NOT political identity. The interface should disappear, allowing the participant to focus entirely on the decision-making process. ## Visual Style Extremely minimal. Very generous white space. Large margins. Excellent typography. Inter font. Thin dividers. No visual noise. No shadows unless extremely subtle. No gradients. No glassmorphism. No neumorphism. No skeuomorphism. No illustrations. No mascots. No decorative graphics. No stock photography. No political imagery. No faces. No flags. No party colors. No patriotic symbols. No emojis. Use a restrained grayscale palette with one neutral accent color (desaturated blue). Everything should resemble a scientific instrument rather than an application. ## UX Principles The interface should reduce cognitive load. Every screen should contain only one primary task. Avoid unnecessary decisions. Hierarchy must be obvious. Buttons should feel functional, not promotional. Animations should be almost imperceptible. Every interaction should feel calm and deliberate. ## Create these screens 1. Welcome screen Explain that the participant will evaluate public policies and political representatives without revealing identities. The language should be neutral and institutional. 2. Rational Evaluation Screen Present one legislative proposal anonymously. No names. No parties. No dates. No political clues. The participant evaluates only the proposal itself. Include: - proposal title - proposal summary - Agree / Disagree - importance slider - Continue button 3. Emotional Evaluation Screen Present only one neutral portrait of a politician. No identifying information. Ask the participant to rate immediate impressions such as: - confidence - competence - closeness Use restrained visual design. 4. Results Screen Display comparisons between emotional affinity and rational agreement. Charts should be minimal. Avoid celebration. Avoid gamification. Avoid scores that imply success or failure. Instead communicate: "This visualization represents the relationship between your emotional evaluations and your policy preferences." The tone should be scientific. ## Deliverables Produce high-fidelity desktop UI mockups. Use realistic spacing and typography. Think like a senior UX designer working for a university research lab. The final result should feel closer to an experimental instrument than to a modern SaaS product. The user should immediately think: "I'm participating in serious scientific research."b1:T56d,I'm planning a single-page flow app with a light theme, Inter typography, and a grayscale palette with desaturated blue accents across four screens: welcome, rational, emotional, and results. There's a tension between the visual style guideline that says "no faces" and the emotional screen's need for a neutral portrait—I'm interpreting "no faces" as referring to decorative imagery, so I'll generate a grayscale anonymous portrait with a neutral expression and plain background for that screen. Now I'm structuring the component architecture with a main page orchestrating stage state, an instrument frame for the header with study ID and progress tracking, individual screen components, custom UI elements like a minimal slider and binary choice component, and lightweight SVG-based charts instead of external libraries. For typography, I'm using Inter as the primary font with IBM Plex Mono for numeric readouts to maintain the lab aesthetic while staying within the two-font limit. I'm setting up a light-only color scheme by configuring the viewport and root CSS variables with desaturated, paper-like neutrals—near-white background, near-black foreground, muted grays, and a desaturated blue accent—then overriding any dark mode styles in globals.css. I'll generate the three grayscale portrait images now since that process takes time and can run in parallel with the code.b2:T539,I'm laying out the shared UI components: an instrument frame with header and footer sections, a scale slider with accessible controls and tick marks, and a binary response component. For the frame itself, I'm designing a fixed top bar with a thin bottom border, positioning the "HABITUS" wordmark and lab identifier on the left side, and placing the session ID and phase indicator on the right in monospace. The footer will be a fixed bottom bar showing the phase progress and a thin segmented meter alongside an ethics note. The main content area uses a desktop-first approach with generous side margins and padding to keep the layout readable at around 68 characters per line. For the scale slider, I'm using a native HTML range input with custom styling for both accessibility and simplicity — a thin track line with a small square thumb, tick marks below, and a numeric readout in monospace. I'll add global CSS utilities to handle the webkit and Mozilla thumb styling, then layer component-level classes on top. The slider will span 0–100 continuously, with anchor labels for importance and affect items, and I'm showing the numeric value as "——" until the user first interacts with it, then displaying it in mono to avoid anchoring bias while maintaining transparency. I'll also include a keyboard hint for accessibility.b3:T584,I'm thinking through how to visualize the results with minimal custom SVG charts. The key challenge is comparing emotional responses to rational agreement for each stimulus—I could show paired distributions side by side, scatter plots of the affect components, and divergence bars to highlight where emotional and rational assessments diverge. I'm sketching out three minimal visualizations to present this data clearly. For the divergence plot, I'd use a slope chart with two marker rows: one showing the mean affective index per stimulus and another showing a deliberative agreement index computed from weighted policy positions. The connecting lines between them reveal where emotion and reason align or diverge. Below that, a component profile with horizontal bars for confidence, competence, and closeness across stimuli, plus a per-item table showing each proposal with its position and importance weighting. I'm also planning a stats row summarizing the key metrics: item count, affective index, deliberative index, divergence delta, and dispersion. For the deliberative index calculation, I'm defining an importance-weighted agreement score per item where agreeing with high importance yields a high score (around 100) and disagreeing with high importance yields a low score (around 0). This gives a defensible, transparent formula I can document in the UI. Building the slope chart visualization...b4:T17b4,[V0_FILE]typescriptreact:file="/components/results-figures.tsx" 'use client' const X0 = 56 const X1 = 704 const scale = (v: number) => X0 + (v / 100) * (X1 - X0) const TICKS = [0, 25, 50, 75, 100] type SeriesRow = { label: string sublabel: string observations: { code: string; value: number }[] mean: number } /** * Figure 1 — paired position of the two indices on a shared 0–100 scale. * Individual observations are drawn as hairline strokes; the index is drawn in * the accent tone. The connector states the arithmetic difference only. */ export function DivergenceFigure({ affective, deliberative, }: { affective: SeriesRow deliberative: SeriesRow }) { const rows = [ { ...affective, y: 58 }, { ...deliberative, y: 158 }, ] const delta = affective.mean - deliberative.mean return (

Figure 1 · Position of both indices on a common scale Hairline strokes: single observations · Accent stroke: index value

) } /** * Figure 2 — component profile of the affective block. */ export function ComponentFigure({ items, }: { items: { label: string; mean: number; observations: number[] }[] }) { return (

{items.map((item) => (

{item.label} {item.mean.toFixed(1)} / 100

{item.observations.map((o, i) => (

))}

Figure 2 · Component means of the affective block

) }b5:T17b4,[V0_FILE]typescriptreact:file="/components/results-figures.tsx" 'use client' const X0 = 56 const X1 = 704 const scale = (v: number) => X0 + (v / 100) * (X1 - X0) const TICKS = [0, 25, 50, 75, 100] type SeriesRow = { label: string sublabel: string observations: { code: string; value: number }[] mean: number } /** * Figure 1 — paired position of the two indices on a shared 0–100 scale. * Individual observations are drawn as hairline strokes; the index is drawn in * the accent tone. The connector states the arithmetic difference only. */ export function DivergenceFigure({ affective, deliberative, }: { affective: SeriesRow deliberative: SeriesRow }) { const rows = [ { ...affective, y: 58 }, { ...deliberative, y: 158 }, ] const delta = affective.mean - deliberative.mean return (

Figure 1 · Position of both indices on a common scale Hairline strokes: single observations · Accent stroke: index value

) } /** * Figure 2 — component profile of the affective block. */ export function ComponentFigure({ items, }: { items: { label: string; mean: number; observations: number[] }[] }) { return (

{items.map((item) => (

{item.label} {item.mean.toFixed(1)} / 100

{item.observations.map((o, i) => (

))}

Figure 2 · Component means of the affective block

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\n \n )\n}\n"},"id":"x5IWIBJo0VJzhO2C"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785115804089,"finishedAt":1785115804688,"lastPartSentAt":1785115804661,"taskNameActive":"Reading layout","taskNameComplete":"Read layout","toolCallId":"toolu_018MhxUmpUySPZVz1zcxp1mq","agentTurnId":"turn_FxVs0MPhyc8aKK34","filePath":null,"id":"0dJTXPHVlmjtRBk2","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/app/layout.tsx"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785115811598,"finishedAt":1785115826478,"lastPartSentAt":1785115826478,"taskNameActive":null,"id":"oO2dH2dx9nhUxgok","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":15,"thought":"$b1"}]}}],["p",{},["text",{},"I'll build this as a single-flow research instrument. First, generating the neutral grayscale portrait stimuli."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_xk8df4fulhg","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/public/stimuli/portrait-a.png","baseName":"portrait-a.png","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/public/stimuli/portrait-b.png","baseName":"portrait-b.png","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/public/stimuli/portrait-c.png","baseName":"portrait-c.png","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]png:file=\"/public/stimuli/portrait-a.png\" url=\"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/attachments/gen-images/vercel/share/v0-project/public/stimuli/portrait-a-owEInAkP8ZCWbGureLO3CRQ2SzkBmu.png\"\n\n[V0_FILE]png:file=\"/public/stimuli/portrait-b.png\" url=\"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/attachments/gen-images/vercel/share/v0-project/public/stimuli/portrait-b-kkLMMHPL2sGsYScdYtGtTo8cN6vmCD.png\"\n\n[V0_FILE]png:file=\"/public/stimuli/portrait-c.png\" url=\"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/attachments/gen-images/vercel/share/v0-project/public/stimuli/portrait-c-n9HTNFfRFfJ7V5S05Hn5vHlfBsP5uG.png\"\n","latestDeploymentInfo":"$ab:0:1","lastDeployedAt":"2026-07-27T01:38:56.032Z","sourceVersionKey":"v_1_z7n40_s3","needsBlockSource":true,"isAllImages":true,"blockId":"b_xk8df4fulhg","meta":{"taskNameActive":"Generating stimulus C","taskNameComplete":"Generated stimulus C","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Generating stimulus C","taskNameComplete":"Generated stimulus C"},"id":"block-b_xk8df4fulhg"}],["AssistantMessageContentPart",{"part":{"createdAt":1785115842390,"finishedAt":1785115842852,"lastPartSentAt":1785115842852,"taskNameActive":null,"id":"RRcbe4HKAPDr2Ecg","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to add CSS overrides to globals.css by appending a new `:root` block at the end of the file, which will take precedence over the existing theme variables through cascade order. Let me check what's currently at the tail of globals.css first."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785115842937,"finishedAt":1785115843145,"lastPartSentAt":1785115843125,"taskNameActive":"Reading css tail","taskNameComplete":"Read css tail","toolCallId":"toolu_01AVfkWJ9oWSagEyvXDjoQ44","agentTurnId":"turn_MaT11yp2RV9N9Vgn","filePath":null,"id":"OgdZwPrQgn5XIYE3","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/app/globals.css"},{"type":"reading-file","filePath":"/vercel/share/v0-project/app/globals.css","offset":114,"limit":50}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785115847089,"finishedAt":1785115847391,"lastPartSentAt":1785115847391,"taskNameActive":null,"id":"Z1dGr9NNlzLS6AeC","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to add the `light` class to the html element to override the dark media query, then rewrite globals.css from scratch with a light-only, minimal approach for cleaner styling."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_eKgk7cT7ZlY","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/app/globals.css","baseName":"globals.css","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]css:file=\"/app/globals.css\" \nGENERATING","latestDeploymentInfo":"$ab:1:1","lastDeployedAt":"2026-07-27T01:38:56.032Z","sourceVersionKey":"v_1_0sd3z_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_eKgk7cT7ZlY","meta":{"taskNameActive":"Writing design tokens","taskNameComplete":"Wrote design tokens","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing design tokens","taskNameComplete":"Wrote design tokens"},"id":"block-b_eKgk7cT7ZlY"}],["p",{},["text",{},"Now the layout with Inter and IBM Plex Mono."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_itiXFGCE5iH","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/app/layout.tsx","baseName":"layout.tsx","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]tsx:file=\"/app/layout.tsx\" \nGENERATING","latestDeploymentInfo":"$ab:2:1","lastDeployedAt":"2026-07-27T01:38:56.032Z","sourceVersionKey":"v_1_shye3_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_itiXFGCE5iH","meta":{"taskNameActive":"Writing root layout","taskNameComplete":"Wrote root layout","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing root layout","taskNameComplete":"Wrote root layout"},"id":"block-b_itiXFGCE5iH"}],["AssistantMessageContentPart",{"part":{"createdAt":1785115873607,"finishedAt":1785115874363,"lastPartSentAt":1785115874363,"taskNameActive":null,"id":"jvHg6UsH4OmAtJgw","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"Now I'm mapping out the file structure—the protocol layer will handle proposals, stimuli, and session management, while the shared components cover the UI shell, input controls, and visualization. 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An instrument for observing the distance between political reasoning and political perception

Deliberative evaluation

Affective evaluation

Presentation of your own data