Overcoming information overload with circular attention economies - Cosmik Labs

Rethinking the cognitive foundations of the attention economy

The attention economy is the economic system in which human attention is the scarce resource. This literature takes a particular passage in Herbert Simon’s seminal Chapter Designing Organization fo...

How to reclaim your attention from the distraction economy | Psyche Videos

Could you watch this video on full screen, 1x speed, with no distractions? Here are five steps to rebuild your attention span

The engineering manager's attention budget

Every Engineering Manager has 5 jobs - most distribute their 100 'attention points' across only 2-3 of them

Deficient executive control in transformer attention

Abstract Although transformers in large language models (LLMs) effectively implement a self-attention mechanism that has revolutionized natural language processing, they lack an explicit architecture for the executive control of attention found in humans, which is essential for resolving conflicts and selecting relevant information in the presence of competing computations and is critical for adaptive behavior. To investigate the impact of this limitation in LLMs, we employed the classic color Stroop task, widely regarded as the gold standard, to test the executive control of attention in these models. Our results revealed a typical conflict effect of underperformance in terms of accuracy in the incongruent condition (e.g. naming the color of the word RED in blue) compared with the congruent condition (e.g. naming the color of the word RED in red), in short word lists, similar to human performance. However, as the length of the word lists increased, performance on the incongruent condition degraded toward near-total performance collapse, even as accuracy in the congruent condition remained excellent, and word reading (e.g. reading the word RED [in red] or RED [in blue], ignoring the color) was near-perfect. These findings demonstrate that transformer attention mechanisms are fundamentally limited in their capacity for conflict resolution across extended contexts, and a failure to up-regulate control adaptively under rising interference. We suggest that incorporating executive control mechanisms akin to those in biological attention is crucial for achieving artificial general intelligence.

An attention economic perspective on the future of the information age

In this paper, we apply an attention economic perspective to explain current and predict future trends in our society. We first describe the rise of the attention economy, and we highlight one important mechanism of this economy: a spiral of attention scarcity. Second, we show that an attention economic perspective provides glimpses of a potential future. In particular, we predict an information environment that increasingly targets citizens with attention-grabbing content, form, and technology; a continuing trend toward excessive media consumption levels; and a continuing trend toward inattentive uses of information. We also predict increasing problems of public misinformation and misconceptions; an increased prevalence of certain mental and physical health issues; and an increased reliance on technology to perform mental tasks. At the end of this paper, we show why and how despite these predictions, alternative futures are conceivable. These alternatives largely depend on the behavior of various social actors. We discuss resistance to the attention economy by consumers and producers, a laissez-faire policy toward the attention economy, a policy of taxing attention-seeking efforts, and the promotion of public values in regulatory policies toward the internet.

Deficient executive control in transformer attention

Abstract. Although transformers in large language models (LLMs) effectively implement a self-attention mechanism that has revolutionized natural language p

If You are Asking for Human Attention, Demonstrate Human Effort | Tom Bedor's Blog

An ever-increasing volume of debug investigations, document writing, and code is written by robots. This has created a new etiquette question when working with a team - when is it OK to forward the output of an AI to another human to read?

Domain-Driven Agents

Slow thoughts on fast tech.

Almost anything you give sustained attention to will begin to loop on itself and bloom

When people talk about the value of paying attention and slowing down, they often make it sound prudish and monk-like. But we shouldn’t forget how interesting and overpoweringly pleasurable sustained attention can be.

Attention is all we have

A conjectural theory of cognitive inequality

Titans + MIRAS: Helping AI have long-term memory

Ali Behrouz, Student Researcher, Meisam Razaviyayn, Staff Researcher, and Vahab Mirrokni, VP and Google Fellow, Google Research

Comprehension Debt - the hidden cost of AI generated code.

Comprehension debt is the hidden cost to human intelligence and memory resulting from excessive reliance on AI and automation. For engineers, it applies most to agentic engineering.