Cognitive Warfare in the Palm of your Hand

Have you ever been doom-scrolling your social media accounts, and stopped to wonder if some foreign intelligence agency might be exerting some kind of mind control over you? No? Me neither really: But, all indications currently are, that they actually could be. This AI-assisted article looks at the technology behind our addiction to social media and leaves it up to the reader to decide, who is in control?

AI bots are reportedly being trained to hijack the brain's attentional guidance system by using "clickbait" - personalising sales and marketing content, selling us our fantasies, and "othering" our demons. Everyday we are now consuming industrial size quantities of undiluted political propaganda. This dystopian vision may not be new, but given the overwhelming power and current direction of the US military - the sporadic anti-UK/EU rhetoric of Trump 2.0 - it deserves some serious consideration.

The cognitive science behind this technology connects the neuroscience of the brain with a digital record of our behaviour. This creates a feedback loop between person and machine - bypassing the normal social construction of reality. This means our private property could be sequestered, and used against us, in covert psychological operations, that we are not aware of, by any foreign power who holds the keys to this kingdom.

Powerful unelected and unaccountable interests will soon be able to condition what we fear; bias what our purchasing priorities are; manipulate who we trust; destabilise our emotional responses to a specific set of stimuli; reduce our impulse control; and determine how an entire demographic decides to vote at election time. That’s an extremely powerful proposition. 

Here's how it could work: The salience network is a brain system (mainly involving the anterior insula and anterior cingulate cortex) that filters out the noise to focus on what it believes, conceptually, is important - to the individual. Like an early warning system, it detects what is important, novel, or emotionally relevant; switches attention toward those stimuli; and helps decide “this is your priority — focus on it now”. It’s essentially your brain’s radar system.

“Clickbait” is content engineered to trigger that priority filter repeatedly and reliably. AI systems (especially those used by platforms like YouTube, Facebook, or TikTok) optimize content using huge datasets of user behaviour. Over time, they learn which stimuli most strongly activate salience detection and can target individual patterns of consumption in the following ways:

1. HYPER-OPTIMISED NOVELTY

Your salience network is highly sensitive to unexpected or new information. AI learns to trigger this by cuing you with: “You won’t believe…”; “This changes everything”; “Scientists hate this…” type scenarios. These create prediction errors—your brain didn’t expect this, causing attentional resources to spike. The result is a compulsive clicking driven by curiosity tension.

2. EMOTIONAL AMPLIFICATION

The salience network can process quite complex emotional interpersonal needs. This can be targetted, for example, by prioritising content that triggers: moral outrage (“this crime is disgusting”); existential fear (“you’re career is at risk”); or wish-fulfillment fantasies (“this will make you rich/ attractive/ successful”). This can be very distabilising, especially if the emotional schema being targetted was laid down during a difficult childhood. This makes it harder to switch off. Strong emotion = stronger salience tagging = harder to ignore.

3. PERSONALISED SALIENCE MAPPING

AI doesn’t just use general clickbait—it builds your specific salience profile over time. It learns: What you stop scrolling for;  What you hover over; What you click, rewatch, or share. So instead of generic hooks, it creates individually tuned triggers that are intentionally hooking you in to an addiction. The self-preserving instincts of your salience network are being “reverse-engineered” to serve the interests of a machine in real time.

4. INTERMITTENT REWARD (The Dopamine Loop)

Borrowing from behavioural psychology (similar to gambling mechanics) most content presented to you is selected for its mediocrity, but occasionally something is highly rewarding, tempting you to go further. This is called a variable reward schedule, known to strongly reinforce behaviour. You keep clicking because the current one maybe good, but the next one might be even better.

5. COGNITIVE "OPEN LOOPS" 

Clickbait often creates unresolved questions: “What happened next shocked everyone…”. This exploits the brain’s drive for closure whereby, unfinished information sets up a  persistent signal to finish the task. Too many unfinished tasks can overload the system with stress and anxiety. Apparently, this is known as the Zeigarnik effect -  but to me it sounds like the obsessional thoughts that drive compulsions in OCD. Your brain keeps allocating attention to the task, creating its own unmet needs, you cannot escape the anxiety, until the loop is closed.

6. THREAT PRIORITISATION BIAS

Humans have evolved to prioritise threats. This is intimately linked to our survival instincts and the fight or flight response. AI can bring to the surface, conflict, scandal, and danger signals. These are traditionally things that news feeds have used to keep us glued to an unfolding story. Even if low relevance, your salience network lights up: “This might matter for survival— you'd better pay attention.”

With these six strategies, AI is clearly being trained to mesmerise and exploit the consumer, at the same time as making access to you cheaper, by replacing an army of workers in the industry. It’s not just grabbing our attention—it is systematically overriding our political and personal priorities. This is no longer a science fiction. It is a global phenomena that is changing the way we perceive the world, and in the wrong hands, could be risking a Third World War.

Our goals (work, relationships) are slowly being replaced by artificially selected stimuli that are designed to: fragment attention; increase compulsive checking; bias our feelings; distort our perceptions of reality (e.g., thinking the world is more dangerous or extreme than it is); and therein, re-shape our interpersonal relationships.

AI can unintentionally (or potentially deliberately) amplify childhood trauma-linked salience triggers: if someone is sensitive to rejection, for instance, social comparisons may get prioritised; if they have been a victim of crime, neighbourhood threats may get amplified; if they have suffered a serious illness, stories about healthcare may dominate. 

Either way, the system has the intention of locking onto pre-existing neural sensitivities to strengthen them in its favour. This starts to resemble a mind altering substance that increases the risk of developing a mental illness. If this interacts with underlying neurodevelopmental traits it could predispose young people to an addiction to toxic social media channels, and a life of disengagement from mainstream education or employment.

Ultimately, cognitive science can be used for good or evil purposes. AI-driven clickbait works because it aligns almost perfectly with how the salience network evolved: It can be used to promote love, peace and interpersonal resilience, or force attention to focus on our trauma histories, and the causes of our interpersonal stress.

The Social Construction of Reality within Parliamentary Discourse

Principal Aim

This project seeks to develop, test, and ethically evaluate a novel computational methodology — perspectival sentiment mapping — which analyses how individuals and groups construct and contest social realities through pronoun-positioned sentiment toward shared concepts. Using UK Parliamentary Records (Hansard) as a grounded, publicly available testbed, the study will assess whether linguistic perspective structures correlate with observable political behaviour (e.g. voting records), and explore the implications for wider applications in primary care mental health, multi-agency working, and community-level social analysis.

 

Background and Rationale

Public institutions increasingly rely on sentiment analysis to understand public opinion, professional disagreement, and social conflict. However, conventional sentiment analysis treats opinion as an individual psychological attribute rather than a relational and perspectival phenomenon. This limits its usefulness in contexts where disagreement reflects differing constructions of reality rather than simple attitudinal polarity.

Linguistic research, social psychology, and clinical theory converge on the insight that pronoun usage encodes perspective, agency, and social alignment. In political, clinical, and community discourse, pronouns (“I”, “we”, “they”) structure who is positioned as agent, who is aligned, and who is othered. These structures are central to:

• contested political realities,
• professional disagreement in multi-agency systems,
• interpersonal conflict and relational distress in mental health contexts.

This project proposes an ethically constrained methodology that maps sentiment toward shared concepts as expressed from different pronoun positions, producing a structured representation of how realities are linguistically constructed and contested.

Hansard provides an ideal ethical test case: it is public, well-documented, attributed, temporally structured, and linked to independent behavioural records (votes). Demonstrating validity here provides grounding before any application to sensitive domains such as health or community data.

 

Research Questions

1. Can pronoun-positioned sentiment toward shared policy concepts be reliably extracted from parliamentary discourse?
2. Do these perspectival sentiment profiles cluster in ways that align with, but are not reducible to, party affiliation?
3. Do divergences in perspectival sentiment correlate with observable legislative behaviour (e.g. voting divergence, rebellion)?
4. What ethical insights emerge regarding the interpretation of contested realities, responsibility, and agency in institutional discourse?
5. Under what ethical constraints could this methodology be responsibly adapted for community and primary care mental health contexts?

 

Methodology

Data Sources

Primary dataset:

• UK Parliamentary Records (Hansard), accessed via official XML/API archives.

Supplementary dataset:

• Public voting records (e.g. UK Parliament API, Public Whip).

All data are publicly available, non-sensitive, and pertain to elected officials acting in their official capacity.

 

Sampling Strategy (Initial Phase)

The pilot phase will focus on:

• A defined parliamentary period (e.g. one parliamentary session or two-year window).
• 3–5 policy domains with high levels of contestation (e.g. housing, immigration, welfare, policing, climate/energy).
• All MPs contributing to debates within those domains during the sampling window.

This bounded sampling ensures analytical tractability while preserving institutional and ideological diversity.

 

Analytical Procedure

One Excel spreadsheet will be created for every concept identified, with rows populated by MPs and columns populated by the first/second and third person, singular or plural, pronouns they use in reference to the concept identified. A value will be assigned to each cell based on the frequency and intensity of positive or negative sentiments expressed by each MP towards the concept identified. These values will be used to conduct multivariate statistical analyses looking at how MPs positions towards the concepts cluster and change over time. 

1.    Text Processing: Tokenisation, part-of-speech tagging, and dependency parsing using established NLP libraries.

2.    Concept Identification: Policy concepts defined via curated keyword sets and validated through embedding-based expansion.

3.    Pronoun Positioning: Identification of pronoun usage (e.g. I, we, they, you, impersonal/passive forms); Contextual classification of collective pronouns (e.g. government, party, nation) using debate metadata and linguistic heuristics.

4.    Sentiment Attribution: Local sentiment analysis using scientifically approved coding methods, applied at sentence or clause level, linked specifically to concept mentions and pronoun positions.

5.    Perspectival Stance Vectors: For each MP, construct multidimensional vectors capturing sentiment toward each concept from each pronoun position.

6.    Mapping and Analysis: Dimensionality reduction and clustering to visualise perspectival similarity and divergence; Comparison with voting behaviour and party alignment; Temporal analysis to observe shifts preceding behavioural change.

Validation and Evaluation

Validity will be assessed through:

• Structural validity: recovery of known political alignments without supervised labels.
• Explanatory value: identification of meaningful intra-party variation and cross-party alignment.
• Temporal coherence: alignment between linguistic shifts and subsequent voting behaviour.

The project explicitly avoids claims of prediction or causal inference.

 

Ethical Considerations

Data Ethics

• All data are public and pertain to professional political activity.
• No private individuals are analysed.
• No personal or sensitive data are collected.

Interpretive Ethics

• The methodology does not label beliefs, motivations, or psychological traits.
• Findings are framed as structures of discourse, not properties of persons.
• Outputs are aggregated and contextualised to avoid reputational harm or misinterpretation.

Downstream Application Safeguards

This project functions as an ethical proving ground. Any future extension to community or health contexts would require:

• explicit informed consent,
• strict anonymisation and aggregation thresholds,
• separation of analytical insight from decision-making authority,
• prohibition of individual profiling, prediction, or enforcement use.

The methodology is designed to support institutional reflexivity and listening, not surveillance or control.

 

Upscaling and Future Phases

Following successful validation, the methodology could be ethically adapted to:

• Community discourse analysis (e.g. social media, consultations), using aggregate, anonymised data to understand relational sentiment toward public services.
• Multi-agency professional communication, supporting reflective practice and coordination without performance monitoring.
• Primary care mental health research, as a supplementary tool for understanding relational narratives and contested realities, never as a diagnostic instrument.

Each upscaling step would require a distinct ethics review and participatory governance model.

 

Wider Implications

This research contributes to:

• political science (discourse and institutional behaviour),
• computational social science,
• ethics of AI and public-sector analytics,
• mental health and systems-based care theory.

This research seeks to understand how interpersonal attachment relationships within a defined social network evolve over time. By reframing sentiment as perspectival and relational, the project offers a more humane and analytically precise way of understanding disagreement, conflict, and plural realities in democratic and care-based institutions. 

Conclusion

This proposal outlines a carefully bounded, ethically robust study that grounds a novel analytical methodology in a transparent, public, and high-stakes institutional setting. Demonstrating validity and limitations in this context provides a responsible foundation for any future application to more sensitive social and clinical domains.

The project prioritises interpretive caution, ethical governance, and public value throughout.

How are our Attachment Relationships being Exploited?

Three uncomfortable truths can be said of the work I do in the community as a mental health nurse. As a profession:

1. We exploit attachment relationships

2. We try to offer some protection to those who have had their attachment relationships exploited

3. We are vulnerable to the exploitation of our attachment relationships

These three truths suggest that the environments we work in will need to kept safe. From a psychological perspective this will involve close multi-agency co-operation among the safeguarding community. Where this breaks down, rapid responses are required. This AI-assisted article looks at how attachment relationships can be exploited. 

As a mental health nurse I work extremely hard to construct and maintain therapeutic relationships in fragmented and disorganised communities. These relationships can legitimately be said to exploit important elements of people's need for attachment. This can be triggering even at the best of times. But in the wrong hands, this science can become extremely toxic, even terrorising, the very anti-thesis of the safeguarding environment that we are trying to create. 

In essence, by tapping into some of the most powerful systems we have for safety, trust, and emotional regulation, our attachment relationships effectively bypass our normal defence mechanisms. So when our attachment needs are activated, however unique we are, they lower scepticism and increase dependence, making us more vulnerable to exploitation. 

Without the informed consent of both paties, without clear interpersonal boundaries, and without any transparency regarding motives, these relationships may violate the principle of doing no harm. For your own protection it is important that you know how this happens, so that you can spot any red flags in your attachment relationships, before they become pathological cycles:

1. Asymmetry of Power or Need

If one person needs the relationship more (emotionally, materially, socially), the other gains leverage. This shows up in abusive relationships, coercive caregiving, exploitative employment, or cult dynamics. The attachment figure becomes a gatekeeper to safety or belonging.

2. Intermittent Care and Unpredictability

Inconsistent warmth—alternating affection with withdrawal or punishment—strengthens attachment rather than weakening it. This “variable reinforcement” mirrors addiction mechanisms and can trap people in relationships that are harmful but emotionally gripping.

3. Moral Emotions as Control Tools

Guilt, shame, loyalty, gratitude, and obligation are powerful in attachment contexts. They can be weaponised (“after all I’ve done for you”, “good children/partners don’t…”), making resistance feel like moral failure rather than self-protection.

4. Identity Shaping and Reality Control

Attachment figures help define who we are and what is “normal.” Exploitation occurs when someone subtly reshapes another’s self-concept, values, or memory of events (gaslighting), so the person comes to doubt their own perceptions and relies more heavily on the attachment figure for truth.

5. Threats of Abandonment or Exclusion

Because attachment loss is experienced as existentially threatening—especially for those with early trauma—explicit or implicit threats of withdrawal (“I’ll leave”, “you’ll be alone”) are highly coercive, even without physical force.

6. Substitution for Secure Systems

Institutions, leaders, or movements can exploit attachment by presenting themselves as family, protector, or saviour—especially where social support is weak. This is common in gangs, extremist groups, and some therapeutic or spiritual settings.

7. Developmental Carry-Over

Early attachment patterns shape expectations. People with anxious or disorganised attachment may normalise exploitation, misread control as care, or feel undeserving of consistency—making them easier to exploit without overt manipulation.

Big Picture:

Attachment is meant to reduce cognitive load and threat vigilance. Exploitation happens when someone uses the reassurance that comes with these features of comfort and familiarity to extract compliance, labour, loyalty, or identity—often while maintaining the appearance of care. Charasmatic leadership traits, for instance, will often combine the paternal promise of protection with pathological demands for self-sacrifice. This will create conflicts of interest in their staff that they can leverage, by priviledging personal relationships over and above any legally-binding organisational policies and procedures. This is actually a very subtle and insipid form of corruption that I believe may be seeping into a lot of healthcare settings through the current wave of populist identity politics.

Here is how it might work in practice:

You put them under pressure for the way they exploited you, used you unfairly, discarded you. They broke the trust that existed between you. You want to return to trust. But this pressure triggers associations in them, loss of trust links them to the crimes they have commited. They split themselves off from their guilt, their hatred of themselves. They project their negative attributes on to you. They maintain an unrealistically positive opinion of themselves and demand that of others. You feel low mood, low self esteem, low self efficacy. You internalise their self-hatred. You are unable to resolve their self-hate, their splitting. You realise their splitting is belief-based coping. You start to realise you have to find a middle way. You do this through the construction of a narrative that restructures their polarised beliefs about themselves into more manageable, more neutral, emotions. You have to keep a focus on all the more healthy attachments you have had in the past. This takes a lot of hard work. You survive them. They survive the the threat you pose to them. They have exploited you, but you have escaped. If they still remain split, they will have to turn their focus elsewhere.