Attention

Attention has become the most precious resource in the information age. With an endless torrent of companies using content to vie for our limited time and focus, capturing attention is the holy grail for creators, marketers and platforms alike. Emerging artificial intelligence technologies stand to transform the attention economy radically.

Attention has always been a limited resource as we are only awake for 16 hours a day on average, but scarcity has drastically increased in the digital era. Our primitive brains evolved in small tribes where stimuli were relatively sparse. Today, we face an endless feed of notifications, alerts, posts and recommendations tailored to grab our senses. Fragmenting our focus into smaller and smaller micro-moments of consumption.

Platforms like Facebook, TikTok and YouTube have perfected an addictive dopamine rush, serving users content likely to elicit clicks, likes and shares. The resulting business models sell our fleeting attention to the highest bidder. Advertising has become the dominant funding source for our information ecosystem. This quest for attention distorts incentives around incubating future wisdom. Thoughtful discourse gives way to clickbaiting headlines, and complexity is sacrificed for viral trends. Outrage and extremism reliably grab attention, so platforms optimise for engagement over truth or nuance.

If you only ever watch and engage with cat videos on YouTube, all you will see are cat videos, cat food, cat toys, and cat clothes. Suppose we don’t change our relationship with the pursuit of attention. In that case, algorithmic recommendations will only get better at predicting which stimulus-seeking items users are most likely to click, watch or react to, further feeding us into an unconscious consumption state.

Emerging AI capabilities around generative content and synthetic media stand to accelerate the race for attention. Platforms already use AIs to churn out formulaic clickbait content, and the ability to effortlessly manipulate or completely fabricate photos and video raises the stakes on misinformation and manipulation. Responsibly capturing attention requires a deeper understanding of human cognition - our biases, quirks and vulnerabilities. Behavioural research must inform the design to avoid pushing psychological buttons unethically.

The questions remain daunting in scale and complexity. How do we design platforms to fulfil wants without manipulating needs? How does AI identify and amplify ideas that meaningfully matter to people?

To build technology augmenting human potential, we must understand the cognitive machinery behind attention in the brain. Insights from neuroscience help explain our craving for stimuli and vulnerabilities that platforms exploit. This information can guide designs supporting healthy engagement.

Primitive brain regions like the limbic system are wired for novelty-seeking and social reward. Sites like Facebook and YouTube hijack this system through predictive algorithms that elicit anticipation; personalised feeds release dopamine. So, by hijacking this primitive part of the brain, we compulsively return despite diminishing satisfaction. The prefrontal cortex handles higher cognition like focus, planning and self-control. But these advanced functions fatigue quickly while cravings persist. Willpower erodes when bombarded with content tuned to circumvent logic. Persuasive technology can override user intentions about time spent or content consumed. Memory and learning also suffer when attention scatters, causing fragmented focus, which impedes constructing knowledge.

Responsible technologists must reconsider these perverse incentives baked into prevailing platforms and business models. Metrics like watch time and engagement optimise for base cravings, not enlightenment. Imagine platforms rewarding time spent thoughtfully, not reactively. Content that elicits contemplation or spurs real-world actions deserves amplification over empty calories.

Shaping the attention economy requires aligning incentives, supporting cognitive health, and pursuing collective uplift. But ultimately, technology simply operationalises values that we encode.

This is where DKR comes in.

Dynamic Knowledge Rendering (DKR) can present complex ideas clearly and memorably through hyper-personalisation, but a key aspect of that personalisation is understanding an individual’s scope of attention. In the same way that DKR can understand an individual’s learning style, DKR can also understand an individual’s attention scope. Attention scope consists of the amount of time you have someone’s optimal attention and the key markers that maintain this.

Both examples underscore the importance of tailoring educational content to match individual learning preferences and attention capacities. By acknowledging and adapting to these unique characteristics, educators and content creators can significantly improve the effectiveness of their delivery methods, ensuring that learners like Qamir and Bea not only engage with the material but also derive maximum benefit from their educational endeavours.

	Modality	Duration	Markers
Qamir	Video	2 minutes	Concise, factual information High visual captions
Bea	Text	45 minutes	First person language

Educational tools can optimise teaching complex subjects using attention scope and markers. By continuously modelling a student’s comprehension from passive conversation or outcome indicators, the modality and material can adapt to maintain attention.

A potential indicator of success might include Qamir discontinuing videos precisely at the two-minute mark or his tendency to engage more deeply with videos that feature dynamic, visual captions, leading to subsequent actions on his part. Such passive insights into user preferences can be gleaned from interactions with products powered by DKR, whether through an AI assistant or a specific DKR tool. For instance, if Bea frequently requests that content be updated into a first-person narrative, it indicates her preference for this style. This kind of feedback allows us to understand and cater to individual learning styles more effectively, demonstrating the adaptability and personalised approach of DKR.

The idea proposed here doesn’t diverge radically from the attention-capturing dynamics of today’s viral cat videos on YouTube. The core of the proposition is to channel the captivating appeal of such entertaining content towards educational purposes while ensuring that control remains in the hands of the user.

Yet, the introduction of preferences (a concept to be further explored in the following chapter) alongside the notion of attention span is where the true innovation lies. With natural language increasingly becoming the primary means of interaction with technology, the potential to gather and leverage insights to tailor preferences and attention spans is vast. The depth of understanding that preferences provide about an individual underscores the critical importance of managing this information through personal oversight and development rather than allowing it to be under the control of centralised entities. It’s time to pivot towards systems that empower individuals, moving away from the paradigm where big tech companies treat user attention as a commodity to be monetised. This shift is essential for fostering an environment where educational content is engaging and respects and enhances the learner’s autonomy.