A new academic paper published in the International Journal of Social Sciences asks a question that should concern anyone who cares about justice: What happens to judicial legitimacy when artificial intelligence enters the courtroom?

The article, “Reconfiguring Judicial Legitimacy: Artificial Intelligence, Cognitive Bias, and the Rise of Algorithmic Authority” by Assoc. Prof. Dr. Fikret Erkan, argues that we are witnessing a quiet but profound transformation in how judicial authority is constituted — and that without careful safeguards, the rule of law itself may be at risk.

The Hidden Bias of Human Judges

Before we can understand the risks of AI in courts, we need to acknowledge something uncomfortable: human judges are already biased.

Erkan’s paper draws extensively on cognitive psychology to catalog the types of bias that routinely affect judicial reasoning. These include:

Anchoring bias — where initial information (like a prosecutor’s sentencing request) disproportionately influences the final outcome. Confirmation bias — where judges unconsciously seek information that supports their initial impressions. Attribution bias — which affects how judges interpret the motivations of defendants, often in ways that disadvantage those from marginalized groups. In-group bias — where judges show more lenient treatment toward defendants who share their social or cultural background.

These are not fringe findings. Decades of behavioral research confirm that judicial decisions are influenced by factors that have nothing to do with the law: the time of day, a judge’s mood after lunch, the race and gender of the defendant, even the order in which cases are heard.

Erkan makes a crucial point: judicial impartiality was never a natural attribute of adjudication. It has always been a fragile institutional ideal that requires active protection. The problem is that AI doesn’t solve this problem — it transforms and potentially amplifies it.

The Algorithm Is Not Neutral

The paper’s most important contribution may be its analysis of algorithmic bias — and why calling AI “objective” is itself a dangerous myth.

Algorithmic systems used in courts are trained on historical data. That data reflects decades of human decisions — including all their biases. A risk assessment tool trained on past recidivism rates will inevitably encode the racial and socioeconomic disparities that existed in those past decisions. The algorithm doesn’t create bias from scratch; it inherits, systematizes, and scales it.

Erkan identifies three key sources of algorithmic bias in judicial contexts:

Training data bias — when historical patterns of discrimination are baked into the model’s foundations. Architectural bias — the design choices and optimization objectives of the model itself, which reflect the values of those who built it. Feedback loops — when biased outputs feed back into future training data, compounding errors over time.

Perhaps most troubling is the “black box” problem. Many AI systems cannot explain why they reached a particular conclusion. This directly conflicts with a foundational principle of constitutional law: the right to a reasoned decision. If a judge cannot explain — or does not know — why an algorithm flagged someone as high-risk, how can that decision be challenged on appeal?

The paper also addresses the automation bias phenomenon: the documented tendency of human decision-makers to defer to algorithmic outputs, especially under cognitive load or time pressure. The very presence of an AI recommendation can crowd out independent judgment.

The Legitimacy Crisis: When Statistics Replace Justice

At the heart of Erkan’s paper is a theoretical claim that goes beyond individual bias cases: the rise of AI in courts represents a transformation in the very source of judicial authority.

When a judge cites case law, constitutional principles, and legal reasoning, their decision derives legitimacy from what Max Weber called “rational-legal authority” — the idea that power is legitimate when it follows established rules and procedures. But when a judge defers to a statistical prediction, the basis of legitimacy shifts from normative reasoning to probabilistic accuracy.

This is more than a philosophical distinction. It has concrete constitutional consequences. Consider:

If an algorithm predicts someone is 73% likely to reoffend, and a judge uses that to justify detention, the defendant is essentially being punished for a future crime they haven’t committed. The algorithm is aggregating patterns from thousands of other people’s histories and applying them to an individual case. Statistical probability is not the same as individual culpability.

Erkan argues that this creates a fundamental conflict between the logic of algorithms and the logic of law. Algorithms optimize for accuracy across populations. Courts adjudicate the rights of individuals. These are not the same thing — and conflating them erodes the constitutional foundations of the justice system.

A Four-Layer Solution: The ECASM Model

So what is to be done? Erkan’s answer is the Erkan Constitutional Algorithmic Safeguard Model (ECASM) — a four-layer framework for integrating AI into judicial processes without sacrificing constitutional legitimacy.

Layer 1: Normative Compatibility Review. Before any AI system can be deployed in a judicial context, it must undergo rigorous review to ensure it is compatible with constitutional rights — particularly equality, due process, and non-discrimination. This means evaluating not just the algorithm’s technical performance but its normative assumptions.

Layer 2: Transparency and Explainability Requirements. AI systems used in courts must be explainable. Judges, defendants, and appellate courts must be able to understand how a recommendation was generated, what factors were weighted, and why. This is a constitutional requirement, not just a technical preference.

Layer 3: Institutional Accountability Safeguards. There must be clear chains of accountability. Who is responsible when an algorithm contributes to an unjust outcome? Courts, developers, government agencies? The ECASM requires institutional structures that assign responsibility and create mechanisms for redress.

Layer 4: Behavioral Integrity Controls. This layer focuses on human behavior — specifically, on preventing automation bias. Judges must be trained to use AI as a tool, not a crutch. Protocols should be designed to ensure that algorithmic recommendations are considered alongside, not instead of, independent legal reasoning.

What the Courts of the World Are Actually Doing

The paper situates its analysis in a comparative legal context. Across the United States, Europe, and beyond, courts are already deploying AI tools — with wildly varying degrees of oversight and accountability.

In the United States, tools like COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) have been used in sentencing decisions for years, despite being commercially proprietary and essentially opaque to defendants. The Wisconsin Supreme Court case State v. Loomis (2016) upheld the use of COMPAS risk scores while noting concerns about transparency.

In Europe, the EU AI Act (2024) represents the most comprehensive regulatory framework to date, classifying AI systems used in the administration of justice as “high-risk” and imposing strict requirements for transparency, human oversight, and technical documentation. However, implementation and enforcement remain ongoing challenges.

Erkan argues that existing regulatory frameworks, while encouraging, do not yet constitute the kind of constitutionally grounded multi-layer oversight that the ECASM proposes. Technical compliance is not the same as normative legitimacy.

The Bottom Line: Algorithms Can Assist Justice, But Cannot Embody It

Erkan’s paper closes with a formulation that deserves to be quoted and repeated: in the AI era, judicial impartiality cannot be reduced either to human virtue or to algorithmic accuracy. It must be reconstructed as a dynamic constitutional equilibrium between human normative responsibility and technologically mediated decision-making.

This is not technophobia. The paper does not call for banning AI from courts. It calls for something harder: subjecting AI to the normative framework of law, rather than allowing law to be reshaped by the logic of algorithms.

For those of us who care about plain language and accessibility in law, this paper raises some urgent questions:

When an algorithm affects someone’s liberty, do they have the right to understand how it reached its conclusion? When AI recommendations are embedded in court decisions, how do defendants — especially those without legal expertise — challenge them? Who bears responsibility when an opaque algorithm contributes to an unjust outcome?

These are not purely technical or legal questions. They are questions about power, accountability, and what kind of justice system we want to build. The answer cannot be left to engineers and lawyers alone.

In the age of artificial intelligence, the rule of law demands more than code. It demands constitutional courage — the willingness to subject technological power to the norms of human dignity and equal treatment.

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Source: Erkan, F. (2026). “Reconfiguring Judicial Legitimacy: Artificial Intelligence, Cognitive Bias, and The Rise of Algorithmic Authority.” International Journal of Social Sciences (IJSS), Vol. 10, Issue 42, pp. 159–220. DOI: 10.52096/usbd.10.42.09

Core learnings in one glance

• Reply to all negative comments first. Can’t answer everything? Answer every negative comment and not just a mix of positive and negative. Readers see this as more systematic, fair, and genuinely customer‑oriented than replying to a selective mix. This improves attitudes toward the business from stronger perceptions of customer care.

• Use accommodative responses, not defensive ones. Accommodative replies (apology, explanation, concrete fix or compensation) significantly increase complaint satisfaction, perceived helpfulness, credibility, and emotional adequacy. Defensive replies (denial, justification, blame‑shifting) reliably damage these.

• Humanize the account. Showing a named person with photo and contact details makes replies feel more tangible, authentic, and diagnostic. It partially softens the harm of defensive replies (though it never makes them as effective as accommodative ones).

• Perceived customer orientation is the main lever. Complainants’ attitudes shift primarily through two things:

(1) whether they see the pattern of replying as fair and consistent and

(2) whether each response feels useful, empathetic, and credible.

• Accessibility + diagnosticity = impact. A visible, easily understood response pattern is more accessible. Combine with high‑quality, information‑rich, empathetic content (called diagnosticity). This what drives better evaluations, not just “being present” in the thread.​

What to do (practical rules for communicators)

1. Decide who gets a reply

• If you must be selective, always cover every negative comment before touching positives. This creates a clear recognizable pattern of “we always show up when there’s a problem,” which users read as fair and systematic.

• Only after all negatives are handled should you spend remaining capacity on positive comments like thanks or praise.

2. Shape the response type

For each complaint, aim for an accommodative script:

• Acknowledge and validate the experience.

• Apologize explicitly (even if responsibility is partial).

• Give a concrete explanation or next step.

• Offer some form of remedy where appropriate (fix, refund, alternative, escalation).

Avoid defensive patterns as your default (denying responsibility, blaming the customer or external factors, minimizing the issue). Because they lower perceived helpfulness, emotional fit, and fairness. And they depress satisfaction and attitudes even when some explanation is “factually correct.”

If you are forced into a defensive stance (as with clear abuse of policy):

• Keep the defensive content as lean as possible.

• Wrap it in as much empathy, clarity, and personal presence as you can to reduce the damage.

3. Make the account human

• Where possible, use humanized accounts: name, photo, role, and some form of contact path (email, DM option, dedicated support link.)

• This boosts authenticity, perceived customer orientation, and the sense that there is a concrete, reachable person trying to help.

• It especially helps when the content carries bad news or some defensiveness, by making the message feel less cold and more accountable.​

4. Write for diagnosticity: “useful, emotional, credible”

Every reply should pass three tests complainants have articulated in focus groups

Useful: Does this give me actionable information or a clear path to resolution?

• Emotional: Does it recognize my feelings with visible empathy and an apology where warranted?

• Credible: Is it specific, transparent, and consistent with what I see the business doing for others?

When replies meet those criteria, users judge the business as more customer‑oriented and rate the complaint handling and the brand more positively.

Quick example pattern

For a hotel complaint about an invalid promo on Instagram, the research suggests the most effective pattern is:​

• Respond from a humanized customer care account to every negative comment.

• Use accommodative language: “I’m sorry this happened,” “Here’s what we can do now,” “Here’s how we’re fixing the underlying issue.”

• Leave a clear public trail that all negative experiences receive this level of attention, so others can quickly see the pattern and infer fairness and genuine concern.

Effective complaint management on social networks: the impact of response strategy, type and account on consumer reactions

https://www.emerald.com/jsm/article-media/1336683/pdfviewer/11194068

https://doi.org/10.1108/JSM-03-2025-0215

From static balance to moving target

In Monday’s piece, I used Trieste and Turchetti’s work on customization, brand consistency, and identity to argue that writers live in a three‑variable system: customization, consistency, and identity. Push one too hard—especially customization—and your writing can slide into chaos, where small context changes trigger big shifts in how you sound. That model gives us a snapshot of the system at a moment in time.

Now I want to add movement.

A new study of artists on global music platforms, “Beyond static optimality: a dynamic and strategically contingent framework for marketing positioning in cultural markets,” shows that the “right” balance between standing out and fitting in is not fixed across time. The best mix changes with your stage of development and with how you choose to put work into the world. That insight travels neatly from music to marketing and everyday business communication.

What music positioning teaches communicators

The study revisits Optimal Distinctiveness Theory in the music industry: how artists balance conformity (staying inside genre norms) and uniqueness (sounding different). Using a large, long‑running dataset from streaming platforms, the authors confirm the familiar inverted‑U: if you are too generic you disappear, and if you are too unusual you also lose listeners.

The interesting part is what changes over time. The shape and location of that inverted‑U move as an artist’s career unfolds. Early on, greater “genre heterogeneity” (trying more styles) helps performance. Later, a more focused, recognisable sound does better. Collaborations amplify both the upside and downside of being different, and releasing singles softens the penalty for trying something distinctive. Artist status, surprisingly, does not make much difference to these patterns.

Translate this into communication terms, and you get:

• Early in a product, brand, or thought‑leadership “career,” exploration and variety are especially valuable.

• As your position matures, focus and recognisability matter more.

• Partnerships and formats are levers that change how risky “being distinctive” really is.

Updating the three‑variable model for time

The three variables still hold:

• Customization: how much you tailor to audience or platform.

• Consistency: how much your work “sounds like you.”

• Identity: what you stand for.

What this study adds is a time axis.

• Early stage: you can, and probably should, allow more variation in customization, tone, and format to discover what works, if identity stays minimally stable.

• Growth stage: you start pruning variation, so your most effective patterns harden into a clearer, more reliable “sound.”

• Mature stage: you protect identity and consistency more deliberately, and you push distinctiveness in smaller, lower‑risk experiments at the edges.

In other words, the “safe zone” of your system widens early and narrows later. The goal is not to freeze yourself. The goal is to notice that the level of personalization that is healthy for an emerging voice can be destabilising for a mature one.

Practical levers: partnerships and formats

The study’s most transferable idea for business communicators is that you can change the risk profile of distinctiveness with specific levers.

Two matter most here.

First, partnerships as amplifiers. In music, collaborations magnify both the rewards of successful differentiation and the penalties of niche overreach. In your world, co‑authored articles, joint webinars, and alliance campaigns do the same. They can make a distinctive angle land much harder—or fail more visibly. Better to save your most radical experiments for lower‑stakes contexts and use partnerships to scale distinctiveness that is already proving itself.

Second, formats as safety valves. Singles reduce the penalty for experimenting with a vastly different sound. In communication terms, short emails, micro‑posts, small pilots, and “single‑track” content drops are where you can test unusual framing, tone, or narrative without betting the whole brand.

This meshes with my “guardrails, not cages” advice. Guardrails can be dynamic. Early‑stage teams might set wide lanes for experimentation in small formats. Mature brands might keep tight guardrails for core channels (home page, flagship campaigns) and looser ones for experimental formats.

How to use this as a business communicator

A dynamic version of this model suggests a simple sequence.

• Diagnose lifecycle first. Are you working with a new initiative that needs discovery, or an established brand that people already think they know?

• Set different customization “budgets” by stage. Allow more tonal and structural variation in early‑stage communication, but ask more often, “Is this recognisably us?” as the brand matures.

• Treat identity as a long‑term attractor. Define clearly what must not change—values, promises, taboos—and let everything else flex around that over time.

• Use partnerships when you are amplifying a distinct position, not when you are still guessing.

• Match format to risk. Put your boldest experiments into “singles,” not into the equivalent of a full concept album.

Trieste and Turchetti showed how pushing customization, consistency, or identity too far can tip a communication system into chaos.

This newer work on dynamic optimal distinctiveness adds a time dimension and a set of levers: the way you manage identity, consistency, and customization should change as the voice, the brand, and the relationship with the audience mature.

The communicators who thrive will be the ones who know when to widen the lane, when to narrow it, and which levers to pull so that “being different” stays a managed risk rather than a coin toss.

Three variables for any writer

• Customization (cu): How much you tailor your message to a specific reader, context, or platform (e.g., changing examples, level of detail, and tone).​

• Consistency (co): How much your work “sounds like you” across pieces—same general tone, structure habits, and quality standards.

• Identity (id): The deeper, relatively stable “what you stand for” as a writer: your themes, values, worldview, and aesthetic preferences.

The model says: push any one of these too hard (especially customization), and the system can become chaotic—small shifts in context can cause large, unpredictable shifts in how your writing is perceived.

Implications for everyday writing

1. Don’t over-customize at the expense of your voice
   If you try to sound completely different for every audience or client, your writing can become fragmented and unrecognizable—even to you. You’ll spend more effort “shape-shifting” than thinking clearly, and readers may not know what to expect from you.

2. Treat your identity as the attractor
   Like the paper’s “brand identity,” your core writerly identity should pull all your customized pieces back into a recognizable pattern over time. That means defining, for yourself, things like default tone, typical sentence length, what you refuse to say or endorse, and the kinds of questions you care about.

3. Use guardrails, not rigid templates
   Style guides and “voice rules” are useful, but they should act as guardrails, not cages. For writers, that might mean:

   • You keep a consistent core (voice, values, typical structure)

   • You allow bounded variation in examples, metaphors, and tone based on audience

4. Expect nonlinear returns from personalization
   The inverted-U idea from marketing also applies:

   • A bit of tailoring (right level of jargon, relevant examples) makes writing much more effective.

   • Too much tailoring (rewriting your entire personality for each reader) produces confusion, inconsistency, and burnout.

5. Beware “chaos writing” from chasing every signal
   If you constantly react to every comment, trend, or platform norm—changing style with each nudge—you can end up with “brand chaos” as a writer: no one knows what to expect from you, and neither do you. The model suggests it’s rational to ignore some feedback and platform pressure to preserve a stable identity.

Speculative, predictive take

• As tools enable ever finer personalization (AI-assisted tone shifts, auto-adapted content), writers who don’t consciously protect their core identity and consistency will feel more and more “algorithm-shaped” and less self-authored.

• The most effective communicators will likely:

  • Define a clear writerly identity (their attractor)

  • Use system-aware guardrails (their own style guide)

  • Allow controlled personalization while deliberately ignoring some audience and platform signals to avoid sliding into chaos.

Put differently: write to specific people, but always from a stable self. The model says trouble starts when you forget the second part.

This is inspired by a recent study: Managing customization, brand consistency, and identity dynamic trade-offs: opportunities and chaos, Leopoldo Trieste, Giuseppe Turchetti

In my next post, I will turn this into a short “personal writing ruleset” you could adopt for your own work.

The core move: narrate your thinking, not just your decision

Most professional writing states outcomes: “We decided X. We will do Y.” What’s usually missing is the thin layer of thinking in between. Cognition words let you narrate that layer without sounding academic or long-winded. I think of it as being similar to writing an in-text definition.

Two quick examples of the shift:

• From: “We’re changing the process next month.”
  To: “We’re changing the process next month because the current approach is slow, and the new tool now makes a faster option possible.”

• From: “Engagement has declined this quarter.”
  To: “Engagement has declined this quarter, which suggests that our current communication approach isn’t landing, so we need to rethink how we explain changes.”

In each case, you move from bare facts to a visible chain of thought using because, which suggests, and so.

Pattern 1: Email announcements and updates

A. Change announcement email

Bare version
“Starting April 1, we will use a new project intake form. Please submit all new requests using this form. Old forms will no longer be accepted.”

Cognition-rich version
“Starting April 1, we will use a new project intake form because our current process makes it hard to prioritize and track work across teams, and the new form will help us compare requests more fairly. From that date, please submit all new requests using the new form, so we can make faster, clearer decisions about what to take on. We will no longer accept the old forms after April 1.”

Cognition moves you made:

• Why-words: because, so we can.

• Know-words: help us compare, make faster, clearer decisions.

This explains the rationale and the intended effect, not just the rule.

B. Progress update email

Bare version
“The migration project is 60% complete. We have finished the data mapping work. The next phase will start next week.”

Cognition-rich version
“The migration project is 60% complete. We have finished the data mapping work, which means we now understand the key dependencies and risks more clearly. Next week, we will start the testing phase so we can validate our assumptions before we move any customer data.”

Cognition moves you made:

• Which means frames an inference.

• So we can validate our assumptions names the mental work of testing, not just the task.

Quick email checklist

When drafting or editing an important email, ask:

• Do I say why this is happening (because / so that)?

• Do I name any assumptions, risks, or reasoning (assume, suggests, learn, test, decide)?

• Do I show how confident I am where it matters (might, likely, clearly, we recommend, we will)?

If the email is all what and when, but almost no why or how we’re thinking, you’re underusing cognition words.

Pattern 2: Slide narration and speaker notes

Slides are often full of abstract labels: strategy, drivers, risks, next steps. Cognition words mainly live in the spoken layer—your narration and notes.

A. Strategy

• Pillar 1: Customer Centricity

• Pillar 2: Operational Excellence

Flat narration
“Our strategy has 2 pillars: customer centricity and operational excellence. These pillars will guide our work over the next 3 years.”

Cognition-rich narration
“Our strategy has 2 pillars. We chose to focus on customers because our growth depends on deepening relationships with existing clients, not just winning new ones. We chose improving day‑to‑day operations because it’s the main way we can fund that growth without expanding our cost base. Together, these pillars force us to think about both how we win business and how we run the business.”

B. Risk

• Risk: Adoption

• Risk: Capacity

Flat narration
“We see 2 main risks: adoption and capacity. We will monitor both.”

Cognition-rich narration
“We see 2 main risks. Adoption is a risk because teams might not understand why this change helps them, which could lead to slow or passive uptake. Capacity is a risk because we are asking the same people to maintain current operations and adopt new processes at the same time. We think we can manage these risks by over-communicating the benefits early and by sequencing the rollout so teams can focus on one change at a time.”

Adoption is a risk because… (you unpack an abstract label into a mental model).

We think we can manage these risks by… (you signal a judgment and a plan).

Pattern 3: One-page strategy or recommendation summaries

One-page docs are where abstraction loves to hide: vision, principles, objectives, key bets.

A. Recommendation paragraph

Bare version
“We recommend focusing on the mid-market segment. This will allow us to grow revenue. The enterprise segment remains important.”

Cognition-rich version
“We recommend focusing on the mid-market segment because it offers the best balance between deal size and sales cycle length. The enterprise segment remains important. But we believe it is better to treat it as a selective opportunity this year while we build capabilities and proof points in mid-market. This approach lets us learn quickly and avoid overcommitting resources too early.”

We believe and lets us learn quickly show your stance and learning logic.

B. Principles/values section

Bare version
“Our decisions will be guided by 3 principles: impact, fairness, and transparency.”

Cognition-rich version
“Our decisions will be guided by 3 principles.

1. We prioritize impact because we want to focus effort where it meaningfully changes outcomes for customers or colleagues.

2. We emphasize fairness so we can explain our choices in a way people experience as consistent and just, even when they disagree.

3. We commit to transparency because we want people to understand how and why decisions are made, not just see the final result.”

Each abstract word is followed by a because phrase that explains the mental rationale.

Understand how and why decisions are made makes the cognitive goal explicit.

Turning cognition words into micro-habits

You don’t need to overhaul your writing style overnight. You can build cognition into your writing with a few small habits.

Habit 1: Add because or so that to every important message

• Ask yourself: “If I had to add one ‘because’ clause, where would it go?”

• This forces you to surface at least one causal link or rationale.

Habit 2: Use one but, yet, or however to highlight the main tension

• Look for the place where you’re balancing 2 considerations; put a but there.

• This makes trade-offs visible instead of buried.

Habit 3: Name one mental act in each abstract-heavy paragraph

• Use verbs like think, believe, assume, question, interpret, reconcile, evaluate, prioritize, test, learn.

Example: “This pattern suggests we should question our current pricing assumptions before we expand.”

These small moves add up; they make your writing feel more transparent and trustworthy because readers can see the thinking behind your statements.

A quick practice exercise you can repeat

Take a paragraph from a recent email, slide note, or one-pager that feels vague or abstract. Then:

1. Highlight abstract nouns (like strategy, alignment, trust, engagement, value).

2. Underline any cognition words (because, so, but, however, we think, we recommend, might, clearly).

3. Revise the paragraph by:

· Adding at least one because or so that clause.

· Adding at least one but, yet, or however to mark a tension.

· Naming at least one mental act (we think, we learned, this suggests, we decided).

Compare the before and after versions. If the second version makes it easier to answer “Why?” and “How are they thinking about this?”, you’re using cognition words well.

In a recent line of research on how we process word meanings, scientists describe some words as higher in “cognitive content”: their meanings involve mental processes like thinking, understanding, learning, memory, problem-solving, and mental products like ideas or theories.

Recent work on semantic richness shows that words whose meanings carry more features—emotional, introspective, or contextual—are recognized and integrated more easily, especially for abstract concepts (Kousta et al., 2011; Pexman et al., 2013; Yap et al., 2011).

chart by Perplexity.ai

What are “thinking words”?

Thinking words (what I’ll call cognition words) are small words and phrases that make your mental moves explicit: how ideas connect, how sure you are, what changed your mind.

Examples: because, but, maybe, clearly, so I think, we realized, this suggests, we should.

Without cognition words:
“We’re changing the process. The current approach is slow. The new tool is available and we expect you to use it.”

With cognition words:
“We’re changing the process because the current approach is slow, and the new tool now makes a faster option possible.”

The 5 main families of thinking words

For practical purposes, you can think of cognition words in 5 families.

1. Why-words (causation)
Examples: because, so, therefore, so that, as a result.
Plain-language role: Tell the reader why something is happening.

2. But-words (contrast and difference)
Examples: but, however, although, yet, on the other hand.
Role: Signal tension, trade-offs, and important distinctions.

3. Maybe-words (tentativeness)
Examples: maybe, might, could, possibly, likely.
Role: Show that something is a possibility or early-stage idea.

4. Know-words (insight and realization)
Examples: we think, we believe, we found, we learned, we realized.
Role: Mark conclusions or lessons from data or experience.

5. Sure-words (certainty/commitment)
Examples: clearly, definitely, certainly, always, never, we will.
Role: Show where you’re taking a stand vs. staying open.

Why they matter for professionals

Researchers in cognitive science talk about a “semantic richness” effect: words with richer meanings—more associations, emotional or introspective content—tend to be processed more quickly and easily. For abstract words, features that point to mental activity (thinking, deciding, evaluating) seem to be especially important for how easily readers grasp them.

You can think of this as a cognition effect: when a word clearly evokes thinking and mental states, readers often recognize and integrate it more easily—especially when the ideas themselves are abstract.

The practical takeaway is simple: cognition words are not decoration; they provide structure. Why-words and but-words make your logic visible, maybe-words and sure-words make your stance visible, and together they lower the mental effort your reader needs to understand your point.

• They show your logic: Why-words and but-words reveal the skeleton of your argument, so readers don’t have to infer it.

• They signal your stance: Maybe-words and sure-words tell people how firm your conclusions are (useful in risk, change, or strategy contexts).

• They reduce misinterpretation: When you spell out causality and contrast, fewer readers walk away with the wrong story.

Micro-example (strategy note):

Vague:
“We will invest more in Product A. Product B will remain important.”

With thinking words:
“We will invest more in Product A because it’s growing faster, but Product B remains important for maintaining our core customer base.”

Quick diagnostic: how many cognition words do you use?

Here is a simple self-check for you to do:

1. Grab a recent important email or slide script.

2. Highlight every thinking word like because, so, therefore, but, yet, maybe, might, think, believe, clearly, definitely.

3. Ask:

Do I ever show why, or do I just state what?

Do I mark the key contrast (but/yet)?

Do I ever signal how sure I am?

This exercise is to raise your awareness, not make a judgment: many professionals underuse these words when writing, even though they use them naturally in speech.

But now you know that words and phrases that strongly evoke thinking and mental states can make it easier for readers to recognize, classify, and remember your ideas—especially when those ideas are abstract.

A simple model sentence

Here is a reusable pattern:

“We think X because Y, but Z, so we will do A.”

Business example:
“We think this market will grow quickly because demand for automation is rising, but the competitive landscape is still fragmented, so we will focus on one segment first.”

If you can fill this pattern for any recommendation, you’re already using cognition words to make your reasoning visible.

Coming next in Articles 2 and 3 in this series

Article 2: How these thinking words make complex and abstract ideas feel easier to read (with before/after passages).

Less cognitive:
“Revision improves writing quality.”

More cognitive:
“Revision forces you to reconsider your assumptions and test new possibilities in your draft.”

The second sentence builds the idea out of named mental acts (reconsider, assumptions, test), which readers process more fluently.

Article 3: Practical patterns and templates for using cognition words in emails, reports, and slide decks. Treat cognition‑rich vocabulary (argue, infer, question, interpret, hypothesize, anticipate, reconcile, evaluate) as a craft resource, not just academic jargon.

Endnotes

For the “cognition effect” / abstract concepts paragraph:

(Kousta et al., 2011) or (Kousta et al., 2011; Pexman et al., 2013)

• Kousta, S.-T., Vigliocco, G., Vinson, D. P., Andrews, M., & Del Campo, E. (2011). The representation of abstract words: Why emotion matters. Journal of Experimental Psychology: General, 140(1), 14–34.

• Pexman, P. M., Holyk, G. G., & Monfils, M.-H. (2013). The semantic richness of abstract concepts. Frontiers in Human Neuroscience, 6, Article 315.

Abstract concepts often rely more on introspective, emotional, and contextual features; semantic richness speeds lexical processing.

For the general “semantic richness effects” claim:

(Yap et al., 2011) or (Yap et al., 2011; Pexman et al., 2013)

• Yap, M. J., Pexman, P. M., Wellsby, M., Hargreaves, I. S., & Huff, M. (2011). An introduction to the research topic: Meaning in mind – Semantic richness effects in language processing. Frontiers in Human Neuroscience, 5, Article 4.

• Yap, M. J., Lim, G. Y., & colleagues (2016). Effects of semantic richness on lexical processing in monolinguals and bilinguals. Frontiers in Human Neuroscience, 10, Article 382.

Big-picture findings useful to writers

• Plain-language sentences are measurably different from conventional ones in features like length, structure, and vocabulary choices.

• A machine-learning model can reliably distinguish plain-language from non-plain-language sentences, which means these features are systematic, not just “style” in a vague sense.

• Adding more training examples that are guided by specific features (feature‑guided augmentation) improves the model’s ability to detect plain language, underscoring that careful control of features matters.

What this implies about plain style

For writers, the study implies that “plain” is not just shorter sentences but a cluster of traits that tend to co‑occur. These include simpler syntax, clearer clause boundaries, and more common, concrete words rather than rare or highly technical ones. In practice, if you consistently manage those elements, you will produce text that is recognizably plainer even to an automated system.

Relevance beyond Hungarian

Although the work is on Hungarian, the method and conclusions transfer to other languages: you can define, measure, and train toward plain language using sentence-level features.

This supports the idea that plain-language guidelines (limit complexity; prefer familiar words; avoid unnecessary embedding) describe patterns robust enough to be operationalized and checked with tools rather than just “good advice. This article gives you evidence that “plain language” is a stable, describable linguistic variety, not just a loose editorial ideal, and that it can be distinguished from other forms of simplification at the level of individual sentences.

Plain vs “simple” language are not the same

• The authors compare a legal‑administrative plain‑language corpus (PL) with a news‑based “simple language” corpus (SL). They find systematic differences between them.

• PL sentences tend to be shorter, less lexically diverse, syntactically shallower, and use connective words differently than SL sentences.

• The upshot for theory: “plain language” is not just “simpler language” in general; it is a specific solution to institutional genres and audiences, with its own distribution of features.

Sentence-level features matter conceptually

• The study builds a classifier that labels individual sentences as plain‑language or not and does so with good reliability.

• This only works because plain‑language sentences consistently cluster in feature space: surface length, morphosyntax (e.g., depth of embedding), and discourse signals (connectives, etc.).

• For theorists, this supports treating plain language as a register or style with sentence‑internal diagnostics, not just document‑level intentions or outcomes.

PL as a gradient property

• Using logistic regression on handcrafted features, the authors derive a “PL‑likeness” score: sentences can be more or less plain, not just plain vs non‑plain.

• This reinforces existing plain‑language theory that talks about “better, best, good enough” approaches and “plain language as a variable,” but shows you can model that variability quantitatively at the sentence level.

Genre, corpus, and resource constraints

The study is explicitly low‑resource: high‑quality PL corpora in Hungarian are small, so they look at whether SL data can safely expand training data. They show that indiscriminately mixing in SL sentences harms precision, while carefully selected, PL‑like SL sentences yield only modest gains.​ Theoretically, this underlines how genre and communicative purpose shape “plain” style; not all simplified text is functionally or linguistically equivalent to PL.

Implications for plain-language theory

• You can now point to empirical evidence that PL has:

  • identifiable structural and lexical patterns,

  • measurable distances from neighboring registers (like SL), and

• a gradient of “plainness” that can be scored.

  This dovetails with plain‑language process theory (audience analysis, cognitive load, inference‑minimization) by showing that those principles leave visible traces in sentence form—traces strong enough to drive successful automatic detection.

This study gives you a concrete way to connect plain language to cognitive load: the very features that distinguish plain‑language sentences are the same ones psycholinguistics treats as major contributors to processing effort.

How the study links to cognitive load

• The model’s most informative features are sentence length, syntactic depth (embedding, dependency structure), lexical diversity, and connective use.

• Each of these has independent support as a driver of working‑memory demand and integration difficulty: longer sentences and denser intervening structure increase memory load, while more complex dependency configurations correlate with higher processing cost.

• You can therefore treat the “PL‑likeness” score as a proxy for reduced processing load at the sentence level, at least for readers of the target genres.

From plain-language features to reader effort

• Shorter, syntactically shallower sentences with fewer intervening heads reduce the number of items that must be maintained and integrated in working memory.

• Lower lexical diversity and more frequent, familiar words reduce decoding and lexical access costs, freeing capacity for situation‑model construction and inference.

• More disciplined connective use supports prediction and coherence building, which cognitive models of reading link to smoother, less effortful comprehension.

A theoretical move you can make

You can argue that plain language is best understood as a design strategy that systematically reduces extraneous cognitive load (avoidable form‑driven effort) at the sentence level, while preserving essential intrinsic load (the complexity of the subject matter).

This study operationalizes that strategy: its sentence‑level features approximate cues to memory load and integration difficulty, and the success of the classifier shows those cues are consistent enough to be formalized.

Examples and a checklist

Most policy updates arrive as a long email that everyone archives. You can do better by designing for interest first, then clarity. Situational interest research shows that people engage when content is easy to grasp, specific, and includes a small, useful surprise.

Below is a simple pattern you can reuse for things like privacy or data‑retention updates.

Step 1: Start with a curiosity question

Begin with a question that makes people want the answer.

• Instead of: “Updated Data Retention Policy”

• Try: “Why we’re deleting more data – and when it actually protects you.”

This kind of framing creates a clear “puzzle” the piece will solve, which is known to boost situational interest and perceived relevance.

Step 2: Make the first view effortless

On the first screen or top of the email, show only three short blocks, each with a simple icon:

• “What’s changing”

• “Why this protects us”

• “What you do differently”

The goal is something people can scan in about 10 seconds. Research links perceived ease of comprehension and clear grouping to higher interest and willingness to continue.

Step 3: Tell one concrete story

Replace abstract rules with a single, visual example.

• Show “A customer file older than 3 years” as a timeline with a person icon, a file icon, and a clear decision point.

• Walk through what happens before and after the new rule at that point.

Concrete, visualized examples consistently make materials more engaging and easier to understand than purely verbal, abstract descriptions.

Step 4: Make relevance feel helpful, not scary

Help people see why this matters for them without raising their anxiety.

• Add a “For you” panel next to the example:

  • Fewer manual archiving steps

  • Lower audit risk

  • Clear cut‑off dates

Studies show that personal relevance increases interest, but if the topic feels threatening or confusing, it can backfire and push people away. Framing relevance as time saved and risk reduced keeps it supportive.

If the topic is sensitive (compliance, layoffs, health), also:

• Chunk information into small sections

• Use calm, reassuring micro copy

• Offer obvious escape routes (e.g., “Short version,” “Read later,” or a quick summary view) so people don’t feel trapped.

Step 5: Add one on‑topic surprise

Introduce a single, focused surprise that challenges a default assumption.

• For example, show that shorter retention windows can make investigations faster, contradicting the “keep everything forever” mindset.

• Visualize this with a simple side‑by‑side graphic.

Situational interest is strongly linked to novelty and unexpected outcomes, if they are tightly connected to the main idea.

Step 6: Use color and style as signals

Keep the design calm and purposeful:

• Neutral base colors

• One color for “policy” elements

• One color for “you/your work” elements

• One accent color only for actions (buttons, key dates, “do/avoid/ask”)

Research on emotional design and color cues shows that color can guide attention, improve motivation, and reduce perceived effort—especially when it highlights relevant information rather than decorating irrelevant details.

A quick checklist for any internal piece

When you review your next slide, page, or email, walk through this:

• Can a new viewer immediately tell what question this piece is answering? (Specificity.)

• Is there at least one concrete visual example or scenario, not just abstractions? (Concreteness.)

• Within a few seconds, can they see how this affects them or people like them? (Relevance, carefully framed.)

• Does the layout look easy to understand at first glance? (Ease of comprehension.)

• Is there a small, on‑topic surprise that challenges an assumption? (Novelty.)

• Do color and stylistic choices carry meaning, instead of just filling space? (Functional aesthetics.)

If you can answer “yes” down this list, you are no longer just decorating a policy. You are intentionally designing the moment of interest—and that’s what turns a mandatory update into something people read.

Sample visuals applying understandability unexpectedness and specificity

This curiosity‑gap infographic demonstrates how a headline can pose a specific, intriguing question and then visually walk through the answer. That’s remarkably close to framing a policy update as “Why we’re deleting more data – and when it protects you,” then supporting it with clear panels.

If you tell me whether you’re mostly designing slides, emails, or intranet pages, I can sketch one or two concrete wireframe layouts based on these patterns.

Here are concrete visual ideas that combine understandability, unexpectedness, and specificity in ways that align with the study’s high‑interest configurations.

1. “One decision, big impact” mini case

• Visual: A simple 3‑panel strip showing an extremely specific moment:
  Panel 1: “September 12: We change our onboarding form” with a single form field highlighted.
  Panel 2: A specific metric line jumps (e.g., “Trial-to-paid +18%”).
  Panel 3: A short takeaway sentence in plain language.

• Understandability: Only one change, one metric, one takeaway; clear cause–effect.

• Unexpectedness: The magnitude of impact (“One field changed our quarter”) is surprising versus the tiny, ordinary action.

• Specificity: Exact date, exact field, exact metric and percentage.

2. “Before / after” payslip for a policy change

• Visual: Two side‑by‑side payslips labelled “Before new tax rule” and “After new tax rule,” each with just 3–4 labelled rows.

• Understandability: Users can immediately scan differences by position and color-coded highlights; the layout is familiar.

• Unexpectedness: Highlight a non-obvious effect (e.g., “Net pay down slightly, but savings tax-free up significantly”).

• Specificity: Uses a concrete salary figure, a specific month, and a real percentage change, not “Employee X” or “some staff.”

3. “Your day with / without X” timeline

• Visual: A horizontal timeline of a workday split into two stacked tracks:
  Track A: “Without the new tool,” showing 4–5 blocks (e.g., “Manual report 45 min”).
  Track B: “With the new tool,” where one block is collapsed (“Report 5 min”) and a freed slot is relabelled (“Deep work 40 min”).

• Understandability: Time is a familiar axis; blocks are labeled with simple verbs and durations.

• Unexpectedness: The surprise is how much time is reclaimed in a single, specific day, not “X saves time in general.”

• Specificity: Anchors to one persona, one day (“Alex – Tuesday”), and one recurring task.

4. “We thought X, the data says Y” misconception chart

• Visual: A bar chart where each category has two bars: “What most of us guessed” vs “What actually happens,” collected from an internal quick poll or user research.

• Understandability: Simple paired bars, clear legend, only a handful of categories.

• Unexpectedness: The mismatch between guessed and actual values provides the surprise and triggers interest.

• Specificity: The question, sample, and time are explicit (“Survey of 142 support tickets in January”).

5. “One customer, three touchpoints” journey snapshot

• Visual: A short journey map that covers only three steps for a single named persona (e.g., “Maya, first-time buyer”): Search → Sign‑up → First purchase.

• Understandability: Each step shows one screenshot or icon plus a single emotion word (e.g., “Confused,” “Relieved”).

• Unexpectedness: Tag one step with a surprising friction point (“Drop‑off spikes here, not at checkout”) and highlight it visually.

• Specificity: Real quote snippet, specific device, and time stamp (“Mobile, Sunday 9:17 pm”).

These patterns keep the first glance easy, make a single, concrete situation do the explanatory work, and weave in a small, focused surprise so the piece feels worth exploring without becoming noisy or gimmicky.

Core findings for marketers

• IMC directly and indirectly increases brand preference, meaning well-coordinated campaigns do more than just “get noticed”; they measurably shift what people choose and recommend.

• Brand awareness and brand image are not side metrics but key mediators: IMC → awareness → image → preference is the dominant pathway.

• The model explains over half of the variance in brand preference, indicating strong practical impact rather than marginal effects.

Practical implications for campaign design

• Emphasize message consistency across all touchpoints (paid, owned, and in‑store) to strengthen encoding, recall, and recognition of the brand, especially in cluttered media environments.

• Design campaigns so that frequency and coherence build familiarity first (awareness), then layer in emotional, symbolic, and quality cues to shape a favourable image.

• Treat brand preference as the cumulative outcome of cognitive ease (easy to recall) plus positive meaning (what the brand stands for), not just short‑term persuasion.

Measurement and optimization implications

• Track IMC as a construct: consistency across platforms, unified narrative, and alignment between advertising and on‑site/in‑store experience, using multi‑item scales similar to those in the study.

• Monitor awareness (recognition, recall) and image (associations such as quality, modernity, reputation) as mediators rather than only end KPIs, and model their effects on preference where possible.

• Use structural equation modeling or path analysis, when feasible, to quantify direct and indirect effects of campaigns on preference rather than relying purely on last‑touch or single‑metric attribution.

Strategic relevance across categories and markets

• Although tested in Indonesian ceiling and interior materials, the mechanisms—information overload, risk reduction via familiarity, and image‑based differentiation—generalize well to other competitive, low‑differentiation categories.

• The findings support investing in long‑term, cross‑channel brand systems (visual identity, narrative, tone) because these systems are what enable IMC to build awareness and image over time.

• In B2B‑like or utilitarian categories, perceived quality, credibility, and modernity in brand image can be as decisive as functional specs, underscoring the value of robust brand positioning work.

Key takeaways for marketing professionals

• Prioritize integrated, coherent communication over fragmented tactics if the goal is sustained brand preference.

• Manage awareness and image as sequential levers: first win memory, then win meaning, then win.

• Build evaluation frameworks that explicitly link IMC quality to equity metrics and, ultimately, to preference/choice behaviour, to justify and optimize brand‑building spend.

  [PDF] The Pathway from Integrated Marketing Communication to Brand Preference

  G Gunawan, A Latief, SM Baharuddin - Global Review of Tourism and Social …, 2026

  … When awareness increases, consumers experience greater cognitive fluency,
  meaning that familiar brands are easier to process and evaluate, which in turn
  fosters more favourable attitudes. Studies show that heightened familiarity reduces …

This study looks at how pictures on eco-friendly packaging affect whether people want to buy “green” or environmentally friendly products. Companies often use natural-looking images to make their products seem pure, authentic, and good for the planet.

The researchers ran four online experiments to test two types of natural imagery on packaging:

• Iconic imagery — realistic images that closely resemble natural things (like leaves or trees).

• Symbolic imagery — more abstract or metaphorical visuals that suggest nature without directly showing it.

They found that iconic imagery works better because it fits what consumers expect, feels easier for the brain to process, and makes the product seem more natural. This, in turn, boosts people’s intention to buy eco-friendly products.

Symbolic imagery, by contrast, makes people think harder, which reduces how “natural” the product feels and lowers purchase intention. However, when symbolic images are paired with explicit text (clear, direct words about the product’s green qualities), people understand the message better and rate the product more positively.

The study combines ideas from semiotics (the study of signs and symbols) and marketing psychology to explain how visuals and words together influence consumers’ green choices. It also gives practical advice for designing packaging that effectively communicates sustainability.

Check out

Not All Imagery Evoking Nature is Perceived Equally: The Effect of Semiotic Packaging Design on Green Purchase Intention

ABSTRACT

With green, natural, and sustainable values becoming a core focus in marketing, natural packaging imagery has emerged as a key strategy for conveying authenticity, purity, and environmental responsibilities. Through four independent online experiments (N₁ = 149, N₂ = 234, N₃ = 110, N₄ = 240), this study examines how two types of natural packaging imagery (iconic vs. symbolic) influence consumers’ green purchase intentions. Drawing on semiotic theory, we investigate the mediating roles of schema congruity, processing fluency, and perceived product naturalness, as well as the moderating effect of verbal message type (explicit vs. implicit). The findings show that iconic natural imagery enhances schema congruity and processing fluency, thereby increasing perceived product naturalness and green purchase intention. Symbolic imagery, which requires greater cognitive effort, reduces fluency and purchase intention; however, pairing symbolic imagery with explicit verbal messages improves fluency and evaluation. By integrating semiotic theory with consumer information processing, this study’s results clarify how natural packaging imagery and verbal message types jointly shape green purchase intentions. It broadens the interdisciplinary literature connecting semiotics and marketing and provides practical insights into eco-friendly packaging design.

Find the study here: Pastel color does not modulate viewers’ emotions in audiovisual projects, HJ Leonard, C Andreu-Sánchez - Humanities and Social Sciences Communications, 2025

Key concepts

Color intensity is usually described in terms of saturation (how pure or vivid a color is) and brightness (how light or dark it appears). Highly saturated, bright colors are often linked to higher arousal (more activation or alertness), while darker, duller, or greyish colors tend to feel calmer, more subdued, or even sadder, depending on the hue and context.​

General emotional effects of intensity

Across many color–emotion studies, several broad patterns appear:

• More saturated, bright colors (especially reds, yellows, some greens and blues) are associated with stronger, more intense emotional reactions and higher arousal than less saturated or achromatic colors.

• Light, chromatic colors are more often linked to positive moods, while dark, greyish tones are more often linked to negative moods such as sadness, tension, or “heaviness.”

• Saturation can also bias how we perceive objects: high-saturation colors can make objects seem larger or more prominent, which can amplify their emotional impact.​

These patterns are not purely hard‑wired; learned associations (for example, red with danger or passion, green with nature or environmental friendliness) and culture shape how strong and what kind of emotion a given color intensity evokes.

When all emotions and all clips were analyzed together, there was no statistically significant overall effect of color condition (pastel vs saturated) on emotion ratings. The target emotion of each clip (e.g., disgust in the disgust clip) did not differ meaningfully between pastel and saturated versions.

In other words, within realistic audiovisual narratives, changing color intensity from saturated to pastel did not reliably weaken or strengthen the perceived emotional intensity of the scenes.

Role of context and other cinematic elements

The study’s discussion emphasizes that context (what is happening in the scene) and other sensory cues often dominate over color intensity in shaping emotional responses:

• Elements like script, acting, music, sound effects, editing rhythm, and shot composition have demonstrated, often larger, effects on emotional engagement than color alone.

• Prior work cited in the article shows that removing color from film clips (making them black‑and‑white) typically has only modest effects on reported emotion compared with, for example, removing sound.

This suggests that while intense colors can bias or reinforce an emotional tone, they rarely create that emotion by themselves in complex, real-world audiovisual content.

Practical implications

For communication or design work:

• Use higher saturation and brightness when you want to increase visual energy, urgency, or prominence; use lower saturation or softer pastels when aiming for gentler, “lighter” atmospheres—but expect these effects to be modest without strong supporting content.

• In film, advertising, or educational media, emotional impact depends more on narrative, sound, and pacing; color intensity fine‑tunes rather than drives the viewer’s feelings.​

What this implies for readers

• So how “on alert” a reader feels—stressed, drowsy, highly engaged, or physically restless—could bias early visual processing before conscious interpretation kicks in.​

• In practical terms, a highly aroused or task-focused reader might encode subtle visual differences (small contrasts, fine print, complex layouts) more sharply, while very high arousal or distraction could also cause the system to downplay some strong but irrelevant visual features.​

You will probably never know why some communications are misunderstood.

How the Prefrontal Cortex Tunes What We See

https://neurosciencenews.com/pfc-vision-neuroscience-29980/

Complex and aesthetically detailed eco-labels can unintentionally harm sustainable communication: they may trigger perceptions of organizational hypocrisy, which in turn undermine conservation behaviors.

Eco-labels are official symbols, marks, or labels placed on product packaging or displayed in catalogs to indicate that a product or service meets specific environmental standards or criteria.

Minimalist, clear design is more effective for resource-saving labels, especially among holistic thinkers. Aligning visual simplicity with the conservation message, matching design features to the audience’s cognitive style, and ensuring message consistency are all critical for effective sustainable advocacy.

Visual Design Complexity Undermines Credibility

• Complex eco-labels can suggest wastefulness and produce an ethical conflict in audiences, leading to reduced adoption of sustainable behaviors.

• The moral credibility of a conservation message is damaged when the visual form contradicts the value of restraint, and this design contradiction becomes a negative cue for observers.

Perceived Hypocrisy as a Key Mechanism

• When the label’s complexity appears at odds with its resource-saving claims, people are more likely to perceive organizational hypocrisy, diminishing the intended persuasive effect.

• This mechanism operates even in the absence of direct knowledge about a company’s actual behavior—visual signals alone prompt skepticism.

• The research introduces “moral fluency,” or the alignment between message and design, as a parallel to cognitive fluency in effective communication.

Audience Cognitive Style Moderates Effects

• Holistic thinkers are highly attentive to inconsistencies between design and message; for them, elaborate labels raise suspicion, making conservation messaging less effective.

• Analytical thinkers, who focus on explicit verbal content, are less susceptible to negative effects from design complexity.

• Cultural differences are relevant: holistic cognition is more common in East Asian populations, suggesting that elaborate “green” campaigns may be particularly problematic in these regions.

Practical Implications for Organizations

• Resource-saving advocacy should use visually simple and message-consistent design for maximal effect.

• Align label aesthetics—such as minimalism or low-impact production—with core conservation values to avoid triggering hypocrisy perceptions.

• Audience segmentation by cognitive style can increase communication effectiveness, making adaptive or targeted designs a promising strategy.

The study findings reinforce the broader principle that clear, purpose-driven, and contextually sensitive communication is essential. Communication strategies that avoid unnecessary complexity and maintain alignment between message and presentation are most likely to build trust and effect behavioral change.

See: Design dissonance in sustainability messaging: How visually complex conservation signs undermine resource conservation behavior

https://www.researchsquare.com/article/rs-7001637/v1

Why Confidence and Fact-Checking Don’t Beat Fake News—A New Study’s Surprising Findings

Fake news is everywhere—and it’s getting harder to spot. You might assume that if you’re unsure whether a news story is true or false, looking for more information will help you make a smarter, more accurate decision. Surprisingly, new research from Van Marcke and colleagues (2025) shows this isn’t always true—especially when it comes to fake news.

What Did the Study Test?

The researchers had over 300 Americans look at news headlines—some true, some fake—and decide which were real. Participants rated their confidence in their first guess, then could choose to see more information before making a final call and saying if they’d share the story on social media.

Here’s What They Found:

• Confidence Drives Fact-Checking: When people felt less confident about whether a headline was true or fake, they were more likely to ask for more information, just as research on easy, sensory decisions suggests.

• But Confidence is Unreliable for Fake News: For true news, higher confidence meant people were usually correct. But for fake news, the relationship was “U-shaped”—meaning people with medium confidence were actually less accurate than those with low or high confidence. This makes confidence a poor guide for whether you really need to fact-check a suspicious story.

• Information Seeking Doesn’t Help with Fake News: Looking for more information made people more accurate with true headlines. But for fake headlines, no matter how much extra information was provided, participants’ accuracy didn’t improve—it even got a little worse.

• Sharing Fake News is Hard to Prevent: Seeing more information didn’t make people less likely to share fake news. In fact, people were more likely to share news that fit their beliefs after fact-checking—even when that news was false.

• Confirmation Bias Remains Strong: If participants were confident in their first decision, they tended to stick with it, even after seeing more information. They were also more likely to believe and share stories that fit their political beliefs—a classic example of “confirmation bias,” where people seek information that supports what they already think.

What Does This Mean for Fighting Fake News?

This study shows that strategies based on encouraging people to “read more before you believe or share” may not work as well as hoped for combating fake news. When our own confidence isn’t a trustworthy guide, and more information can make us stick even harder to our first impressions or prior beliefs, interventions need to be more robust.

Why is Fake News So Sticky?

Unlike judging simple things (like whether a color is blue or green), figuring out if a complex, controversial story is true relies on prior beliefs and knowledge, which can be biased or incomplete. Extra details in fake news can actually make it feel more believable, not less.

Takeaway for Readers and Communicators:

• Don’t trust your gut, especially when you’re only a little uncertain about news online.

• Simply seeking more information—especially from the same source—may not protect you from being fooled by fake news.

• We’re all vulnerable to confirmation bias; double-checking with independent or contradicting sources is key.

• Innovative interventions, perhaps nudging attention to accuracy, may work better than just encouraging more reading.

In short: Confidence-driven fact-checking works for real news, but it doesn’t help us see through fake news—and sometimes, it can even backfire. Fighting misinformation will require smarter strategies than just “read before you share.”

“Confidence-driven information seeking is suboptimal in the context of fake news” by Van Marcke, Kunkle, & Desender (2025)

Matthew Bennett of Human Loop has reviewed the material from the flight data and reached new conclusions.

The Science Behind Cognitive Overload

At its core, cognitive overload occurs when the brain meets more information or challenges than it can manage at once. Our cognitive resources—like working memory, attention, and executive function—are limited. When too many tasks, alarms, or competing demands pile up, these mental “buckets” overflow, leading to confusion, mistakes, and even total breakdowns in decision-making.

Scientific findings reveal:

• Intense multitasking and rapid decision requirements (common in crisis situations or digital media use) consume attention and memory, leading to errors and loss of situational awareness.

• Emotional stress, surprise, or the “startle effect” amplifies overload by narrowing focus and pulling energy away from rational problem-solving.

A Real-World Illustration: The Air France 447 Case

The 2009 crash of Air France 447 highlights overload in the most vivid, tragic way. In just four minutes, three experienced pilots lost control of a functioning plane, despite thousands of hours in the air.

• Multiple alarms, rapid shifts from automated to manual flying, and disorienting instrument readings hit the crew all at once.

• As each pilot tried to interpret conflicting data, their communication became fragmented. The junior pilot’s abrupt actions went unexplained. The more experienced pilot was lost in reading alarms aloud but missed the bigger picture.

• Critically, the saturation of alarms and warnings prevented them from recognizing the true threat—a stalled plane. Their brainpower was depleted, leaving no capacity for prioritizing or coordinated action.

Why Does This Happen? A Human Perspective

Cognitive overload is not a flaw—it's a feature of how the human brain evolved. Our minds are wired for survival in simpler environments: to focus on one threat, solve one problem at a time, and use habits for efficiency. Modern experiences—from complex machinery to relentless digital alerts—can outstrip these evolutionary “settings,” overwhelming the systems that normally keep us safe and effective.

Other findings show:

• Overstimulation, such as doomscrolling or constant notifications, leads to emotional exhaustion, reduced memory, and difficulty keeping attention. This is sometimes referred to as “brain rot,” reflecting cognitive and emotional fatigue in a fast-paced world.

• Not just crisis workers are affected: adolescents, young adults, and anyone navigating complex digital environments can experience overload, resulting in increased anxiety, mistakes, and reduced resilience.

What Can Be Done?

Awareness is the first step. Recognizing that overload is a widespread, natural response allows individuals and organizations to act:

• Limit exposure to unnecessary noise—whether that’s digital alerts, multitasking, or information overload.

• Prioritize training that includes real-world scenarios, so responses in a crisis can become more automatic and less draining.

• Encourage mindful technology use—curating content, taking breaks, and engaging with the real world to recharge cognitive resources.

Conclusion

Cognitive overload is a universal human reaction when faced with more than the mind can juggle. By understanding its causes and consequences—from tragic accidents to everyday “brain fog”—we can design safer systems and healthier habits that respect the natural limits of the human brain.

1. https://pmc.ncbi.nlm.nih.gov/articles/PMC11939997/

2. https://arxiv.org/html/2504.19990v1

3. https://www.sciencedirect.com/science/article/pii/S2666956024000345

Human Loops

Human Loops Case Study I: Air France 447

Air France flight 447 (AF447) was an Airbus A330 aircraft en route from Rio de Janeiro (Brazil) to Paris (France) on the evening of May 31-June 1, 2009. It disappeared shortly after 2 a.m. over the Atlantic Ocean, from a cruise altitude of 35,000 feet. There were 228 souls on board, 216 passengers and 12 crew members (three pilots and nine cabin crew). Read the first full Human Loops case study right here…

Read more

8 months ago · 5 likes · Matthew Bennett

struggle with linguistic barriers, grammatical accuracy, and structural

coherence. With the advancement of artificial intelligence (AI), tools such as

ChatGPT have emerged as potential aids in overcoming these challenges. This

study explores the impact of using ChatGPT on academic writing among ESL

students, focusing on their perceptions, experiences, and challenges

encountered during its integration into their writing practices…

https://www.researchgate.net/profile/Mohan-Rathakrishnan/publication/390727694_THE_IMPACT_OF_USING_CHATGPT_ON_ACADEMIC_WRITING_AMONG_ENGLISH_AS_A_SECOND_LANGUAGE_ESL_STUDENTS/links/67fa5fb3ded4331557285805/THE-IMPACT-OF-USING-CHATGPT-ON-ACADEMIC-WRITING-AMONG-ENGLISH-AS-A-SECOND-LANGUAGE-ESL-STUDENTS.pdf

This study by Julia Mossbridge and Dean Radin explores precognition—defined as the ability to predict future events without inferential means—and its implications for understanding human cognition and time. The findings are particularly relevant to writers and communicators interested in storytelling, psychology, and the boundaries of human perception.

Significant Findings

1. Challenging Common Intuitions About Time

Everyday assumptions about the linearity of time (e.g., Event A must precede Event B) are questioned. The study posits that time may not unfold strictly in one direction, aligning with Einstein's concept of the "stubbornly persistent illusion" of time[1][2].

2. Empirical Evidence for Precognition

• Controlled experiments in domains like precognitive dreaming, forced-choice tasks, free-response tasks, and physiological measures (presentiment) suggest that humans may unconsciously access future information. For example:

• Precognitive Dreaming: Studies found statistically significant correlations between dream content and randomly selected future targets, though results remain inconclusive due to small sample sizes and methodological concerns[1][2].

• Presentiment: Physiological changes (e.g., heart rate, skin conductance) were observed seconds before random emotional stimuli were presented, indicating unconscious anticipation[1][2].

3. Meta-Analyses Support Small but Significant Effects

- Meta-analyses reveal consistent but small effect sizes across studies of precognition, suggesting the phenomenon is real but subtle. However, replication challenges and methodological skepticism persist[1][2].

4. Implications for Communication

- If precognition exists, it could influence how narratives are structured, particularly in speculative fiction or psychological storytelling. Writers could explore themes of retrocausality (future events influencing past decisions) or unconscious foresight as plot devices.

5. Psychological Mechanisms

Precognition appears to rely on nonconscious processes rather than deliberate thought. Fast-thinking paradigms (e.g., intuitive reactions) yielded stronger results than slow-thinking tasks requiring deliberation[1][2].

6. Recommendations for Future Research

- The study advocates for preregistered experiments with larger sample sizes and standardized methodologies to address biases like selective reporting or expectation effects[1][2].

Relevance to Writers and Communicators

• Creative Inspiration: The concept of precognition challenges conventional notions of time and causality, offering fertile ground for innovative storytelling.

• Psychological Depth: Exploring characters' unconscious awareness of future events can add layers of complexity to narratives.

• Public Engagement: Communicating these findings effectively requires simplifying scientific concepts while maintaining credibility.

In summary, this study opens intriguing possibilities for understanding human cognition and enriching creative endeavors through the exploration of precognition and its broader implications.

Citations:

[1] http://www.patriziotressoldi.it/cmssimpled/uploads/images/PrecognitionReview_Mossbridge18.pdf

[2] https://pmc.ncbi.nlm.nih.gov/articles/PMC7211853/

[3] https://www.research.ed.ac.uk/files/15020439/WattIJoDRAuthorVersion.pdf

[4] https://pmc.ncbi.nlm.nih.gov/articles/PMC6961415/

[5] https://www.psychologytoday.com/ca/blog/sense-time/201805/the-really-astonishing-hypothesis-looking-the-future

[6] https://pubmed.ncbi.nlm.nih.gov/32695495/

[7] https://scite.ai/reports/precognition-as-a-form-of-LeaZLZ5

[8] https://pmc.ncbi.nlm.nih.gov/articles/PMC9644185/

[9] https://www.cos.io/blog/the-decline-effect

[10] https://pmc.ncbi.nlm.nih.gov/articles/PMC10554559/

[11] https://koestlerunit.wordpress.com/wp-content/uploads/2015/06/watt-et-al-2015.pdf

[12] https://pmc.ncbi.nlm.nih.gov/articles/PMC10519358/

[13] https://pmc.ncbi.nlm.nih.gov/articles/PMC9641201/

[14] https://faculty.wharton.upenn.edu/wp-content/uploads/2003/01/78-JSA-Discovery-and-Communication-of-Important-Marketing-Finding-Evidence-and-Proposals.pdf

[15] https://psi-encyclopedia.spr.ac.uk/articles/feeling-future-precognition-experiments

[16] https://www.emerald.com/insight/content/doi/10.1108/978-1-78560-313-620151005/full/html

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Answer from Perplexity: pplx.ai/share

Stories to fuel your mind

Eight of Literature’s Most Powerful Inventions—and the Neuroscience Behind How They Work

These reoccuring story elements have proven effects on our imagination, our emotions and other parts of our psyche.

Smithsonian Magazine

• Angus Fletcher

https://getpocket.com/explore/item/eight-of-literature-s-most-powerful-inventions-and-the-neuroscience-behind-how-they-work

Longitudinal Abuse and Sentiment Analysis of Hollywood Movie Dialogues using LLMs

Rohitash Chandraa , Guoxiang Rena , Group-Ha, aTransitional Artificial Intelligence Research Group, School of Mathematics and Statistics, University of New South Wales, Kensington, Sydney, Australia

Abstract

Over the past decades, there has been an increasing concern about the prevalence of abusive and violent content in Hollywood movies.

This study uses Large Language Models (LLMs) to explore the longitudinal abuse and sentiment analysis of Hollywood Oscar and blockbuster movie dialogues from 1950 to 2024.

By employing fine-tuned LLMs, we analyze subtitles for over a thousand movies categorised into four genres to examine the trends and shifts in emotional and abusive content over the past seven decades. Our findings reveal significant temporal changes in movie dialogues, which reflect broader social and cultural influences. Overall, the emotional tendencies in the films are diverse, and the detection of abusive content also exhibits significant fluctuations.

The results show a gradual rise in abusive content in recent decades, reflecting social norms and regulatory policy changes. Genres such as thrillers still present a higher frequency of abusive content that emphasises the ongoing narrative role of violence and conflict. At the same time, underlying positive emotions such as humour and optimism remain prevalent in most of the movies.

Furthermore, the gradual increase of abusive content in movie dialogues has been significant over the last two decades, where Oscar-nominated movies overtook the top ten blockbusters.
https://arxiv.org/pdf/250

Talk about it in chat.

This research reveals important insights about how our brains process written words. When skilled readers see a word, their brains rapidly process both how it looks (orthography) and how it sounds (phonology) almost simultaneously, rather than in strict sequence.

Key Findings

Timing of Word Recognition
The brain can distinguish between real and made-up words incredibly quickly—within just 119-172 milliseconds after seeing them. This represents the initial visual recognition of word shapes.

Sound-Spelling Processing
Shortly after recognizing a word's shape (172-270 milliseconds), the brain automatically processes how the spelling patterns match with sounds. This effect continues up to 475 milliseconds, but only for real words.

Implications

This research challenges the idea that we process words in a strict sequence (first spelling, then sound, then meaning). Instead, our brains process multiple aspects of words almost simultaneously. When words have irregular spelling-sound patterns, it slows down both recognition and pronunciation. The findings demonstrate that even in tasks that don't explicitly require sounding out words, our brains automatically connect spelling patterns to their corresponding sounds. This automatic linking of visual and sound information appears to be a fundamental part of how skilled readers process written words.