How to collect data about cognitive processes and events, how to analyze CTA findings, and how to communicate them effectively: a handbook for managers, tr
(PDF) The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis
PDF | On Jan 1, 2005, P. Pirolli and others published The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis | Find, read and cite all the research you need on ResearchGate
Cognitive Load | Laws of UX
The amount of mental resources needed to understand and interact with an interface.
Hierarchical Task Analysis - an overview | ScienceDirect Topics
Cognitive engineering
Cognitive engineering is an interdisciplinary field that applies principles from cognitive psychology, cognitive neuroscience, and human factors to design and develop engineering systems that effectively support or enhance human cognitive processes.[1][2] The field emerged in the 1980s when Donald Norman and others recognized the need to better understand how humans interact with complex technological systems.[3]
Cognitive Bias Lab | Learn to Make Better Decisions
Explore cognitive biases with interactive tests, simulations, and real-world examples. Free platform to sharpen decision-making and critical thinking — no sign-up needed.
When Using AI Leads to “Brain Fry”
As firms increasingly incentivize employees to build and oversee complex teams of agents—for example, by measuring and rewarding token consumption as a proxy for performance—people are finding themselves pushed to their cognitive limits. Participants in a recent study described a “buzzing” feeling or a mental fog with difficulty focusing, slower decision-making, and headaches. The authors call this phenomenon “AI brain fry,” defined as mental fatigue from excessive use or oversight of AI tools beyond one’s cognitive capacity. This AI-associated mental strain carries significant costs in the form of increased employee errors, decision fatigue, and intention to quit. The findings also show how AI-driven workflows can be designed to diminish burnout and point toward specific manager, team, and organizational practices to avoid mental fatigue even as AI work intensifies.
The Cognitive Debt of Digging Through Preprints
Your Brain on MIT Media Lab
Reflector: Infrastructure for Meetings, by Greyhaven
Most organizations run on meetings. Strategy gets debated there. Decisions get made there. Context lives there. And yet...
Prompting Science Report 4: Playing Pretend: Expert Personas Don't Improve Factual Accuracy
<span> <p><span>This is the fourth in a series of short reports that help business, education, and policy leaders understand the technical details of working w
Prompting Science Report 4: Playing Pretend: Expert Personas Don't Improve Factual Accuracy
<span> <p><span>This is the fourth in a series of short reports that help business, education, and policy leaders understand the technical details of working w
Note-Taking and Personal Knowledge Management — Unattributed
Bridge heading into the smokey landscape. License: CC-0 I read Brennan Kenneth Brown's What have note-taking PKMs accomplished, really?...
I work, I think? - Annotated
How AI may quietly dismantle the feedback loop that turns inexperienced people into competent ones, and why my work matters to me.
Theory and Memory: Two Forces Shaping Software Team Knowledge
How insights from cognitive science and social psychology explain why software knowledge is so hard to preserve
Addressing the Precision-Breadth-Simplicity Impossible Trinity in Psychological Research: A Comprehensive Exploration Approach
Psychological research faces a fundamental challenge—the Precision-Breadth-Simplicity (PBS) impossible trinity. While experimental findings are often precise and simple, they tend to be narrow in scope. Conversely, broad-and-simple concepts frequently lack precision. Developing theories that are both precise and broad is scientifically valuable but inevitably introduces complexity, which conflicts with humans’ cognitive limitations in processing complexity. To address this impossible trinity, I propose a comprehensive exploration (CE) approach—a data-guided theory-building framework that involves: (1) designing experimental conditions in a stimulus-driven way, with minimal upfront theoretical specification; (2) conducting experiments with tens of millions of observations (e.g., 40 million responses in Huang, 2025a); (3) modeling the results through iterative improvements; and (4) producing the outcome: a moderately complex quantitative information-processing model to integrate diverse empirical findings. Inspired by similar strategies that drove breakthroughs in artificial intelligence (e.g., ImageNet’s role in advancing object recognition), the CE approach offers a promising path toward more integrative psychological theories. Initial implementations in visual working memory research demonstrate both its practicality and potential to transform how we study mental processes.
My productivity app is a never-ending .txt file
The biggest transition for me when I started college was learning to get organized. There was a point when I couldn't just remember everything in my head. And having to constantly keep track of things was distracting me from whatever task I was doing at the moment.