Scans show differences in the brains of those who love to put things offs compared with the "doers".
Why Procrastinators Procrastinate
The full story about why procrastinators routinely ruin their own lives.
How to Beat Procrastination
Part 2. Where does a procrastinator go wrong and how can you actually improve your procrastination habits?
The Akrasia Effect: Why We Don’t Follow Through on What We Set Out to Do and What to Do About It
Plus, 3 strategies to help you start beating procrastination now.
Why Your Brain Needs More Downtime
Research on naps, meditation, nature walks and the habits of exceptional artists and athletes reveals how mental breaks increase productivity, replenish attention, solidify memories and encourage creativity
Cognitive exponents and LLM leverage
I know a few people for whom LLMs have been a near-immediate multiplier of attention and effort. I know a lot for whom LLMs clearly make them worse at thinking and doing things. So: why?
Mindful Judgment and Decision Making
A full range of psychological processes has been put into play to explain judgment and choice phenomena. Complementing work on attention, information integration, and learning, decision research over the past 10 years has also examined the effects of goals, mental representation, and memory processes. In addition to deliberative processes, automatic processes have gotten closer attention, and the emotions revolution has put affective processes on a footing equal to cognitive ones. Psychological process models provide natural predictions about individual differences and lifespan changes and integrate across judgment and decision making (JDM) phenomena. “Mindful” JDM research leverages our knowledge about psychological processes into causal explanations for important judgment and choice regularities, emphasizing the adaptive use of an abundance of processing alternatives. Such explanations supplement and support existing mathematical descriptions of phenomena such as loss aversion or hyperbolic discounting. Unlike such descriptions, they also provide entry points for interventions designed to help people overcome judgments or choices considered undesirable.
Self-Control and Optimal Goals: A Theoretical Analysis
Consumers set goals to achieve a variety of objectives such as losing weight, saving for retirement, and achieving better health. A large body of literature in psychology and consumer behavior shows that goals can help consumers achieve these objectives. However, there is almost no research that examines how we should set optimal goals. The purpose of this paper is to develop a parsimonious framework that examines how goals can help performance and how we should set optimal goals. We use the literature on hyperbolic discounting to model these issues. Our results show that goals can often increase performance but can also sometimes encourage procrastination. We show that some goals are worse than having no goals, even when the goals are achieved and the consumer exerts more effort because of the goal. We also find that the presence of goals can lead to myopic consumers behaving as if they were hyperopic. Our results also show that the most difficult goals should be assigned to consumers with moderate levels of motivation and self-control problems. We also find that it is sometimes optimal to set goals that are never achieved.
It’s the Effort That Counts: The Effect of Self-Control on Goal Progress Perceptions
Does the amount of self-control consumers must exert to choose a goal-consistent action influence their perceptions of goal progress? For example, if you choose to go to the gym when one of your favorite TV shows is on (vs. when nothing interesting is on TV), do you perceive that you have made a differential amount of progress toward your goal, despite completing the exact same workout? In eight studies (N = 7,515), the authors demonstrate that consumers perceive that they have made more progress on their goals when more (vs. less) self-control is required to choose to complete an identical goal-consistent task. This is because when consumers exert more (vs. less) self-control to choose a goal-consistent task over the goal-inconsistent alternatives, they infer higher commitment to the goal. The higher inferred commitment, in turn, leads consumers to perceive that future goal pursuit will be easier. The authors demonstrate this effect across a variety of tasks and means of exerting self-control, as well as with both hypothetical scenarios and real-behavior studies.
The wandering mind: A gift we squander - Darius Foroux
Most people treat a wandering mind like a defect. They see it as a leak in their productivity bucket. We’ve been conditioned to believe that if we aren’t staring at a screen or checking off a to-do list, we are failing. But Daniel Goleman, the […]
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.
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.
Harnessing Frustration: Using LLMs to Overcome Activation Energy
One of my biggest weaknesses as a software engineer is procrastination when facing a new project. When the scope is unclear, I have a tendency to wait until I feel I’ve “felt out” the problem to start doing anything. I know I’ll feel better and work much faster when I get “stuck in” but I still struggle with that first step, overcoming the “activation energy” required to engage with the details. LLMs have been a game-changer for me in this respect: I can just throw a couple of sentences at them with the shape of the problem. This leads to one of two outcomes: The LLM comes up with a good solution, usually in a slightly different way than what I was thinking. I realize “oh wow the solution is much simpler than I thought”. Straight away I start thinking about the consequences of implementing and improving what the LLM suggested. The LLM comes up with a solution that I intuitively recognize as “wrong”. My immediate reaction is frustration (“How could it get it so wrong”) which leads me to go back and forth with the model, explaining to it why its solution could not possibly work. But in the process of arguing with the model, my brain is churning away and generating variations or different approaches that could work. After a while, even if the AI is still on the wrong track, the debate will trigger a moment of inspiration where suddenly the solution will come to me. I’ll excitedly start up a new conversation and start working through it with the model. The key is the emotional reaction I have immediately to the LLM’s response, either excitement or frustration. By harnessing this immediate feedback loop, I get my brain out of its passive, procrastination mode. It’s almost like a jolt: either I’m thrilled because it’s simpler than I thought, or I’m spurred to action by the urge to correct a perceived ‘wrong’ answer. This forces me to engage with the problem in a meaningful way.
The peril of laziness lost | The Observation Deck
In his classic Programming Perl — affectionately known to a generation of technologists as "the Camel Book" — Larry Wall famously wrote of the three virtues of a programmer as laziness, impatience, and hubris: If we’re going to talk about good software design, we have to talk about Laziness, Impatience, and Hubris, the basis of good software design. We’ve all fallen into the trap of using cut-and-paste when we should have defined a higher-level abstraction, if only just a loop or subroutine. To be sure, some folks have gone to the opposite extreme of defining ever-growing mounds of higher level abstractions when they should have used cut-and-paste. Generally, though, most of us need to think about using more abstraction rather than less.
be diligent
For individuals It's challenging to work a process when it moves slower than feels appropriate. In the context of computer conversations, the tactic of s...
