The hottest Cognitive Science Substack posts right now

And their main takeaways
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Top Technology Topics

Edu-Snippets

The Science of Learning • 219 implied HN points • 24 Jul 23
🚌 Education Learning Pedagogy Research Study Techniques Cognitive Science
  1. Retrieval practice helps all students remember what they learned better, whether they know a lot or a little about a topic. It involves recalling information, like through quizzes, and boosts memory retention.
  2. Studying over spaced intervals is more effective than cramming all at once. Mixing up different subjects or topics during study sessions can also improve learning by making it more engaging.
  3. Many college students don't realize how beneficial spacing and mixing subjects can be for their studying. Teaching them about these techniques can help them study smarter and remember better.

mathiness instills confidence

bad cattitude • 165 implied HN points • 22 Feb 24
🔬 Science Mathematics Psychology Cognitive Science Research Climate Science
  1. Mathiness can make people feel more confident, especially if they aren't familiar with math.
  2. Adding complex math or 'mathiness' to information can influence how people perceive its quality, especially if they lack knowledge in math and models.
  3. It's important to be cautious of trusting information just because it includes numbers or complex equations; don't assume accuracy or rigor without verifying.

Cognitive Behaviour Therapy: A Cybernetic Perspective

Mind & Mythos • 259 implied HN points • 31 Mar 23
🏥 Health & Wellness Therapy Psychology Mental health Behavioral Science Cognitive Science
  1. Cognitive Behaviour Therapy (CBT) helps people deal with mental health issues by changing negative thoughts and behaviors. It focuses on understanding one’s feelings and gradually facing fears to feel better.
  2. The Cybernetic Theory of Psychopathology suggests that mental health issues relate to how well a person's goals and strategies match their experiences. If a person struggles to meet their goals, it can lead to anxiety and depression.
  3. In therapy, helping clients identify their goals and tackle their negative thoughts is key. Techniques like behavioral experiments and scheduling enjoyable activities can help clients regain confidence and improve their mood.

What metastructures might LLMs have?

How the Hell • 68 implied HN points • 29 Jun 24
🕹 Technology AI Neural Networks Language Models Cognitive Science Philosophy
  1. LLMs have different layers, like humans do. Lower layers handle basic language, while higher layers form more complex ideas.
  2. These models might develop their own unique structures for understanding visuals, since they don't see like humans do.
  3. There could be even higher layers that aren't just about language but add more complexity. It's still unclear how we might study these structures.

Using neuro-imaging and language models to decode thoughts

The Counterfactual • 139 implied HN points • 31 Jul 23
🕹 Technology Neuroscience Machine Learning Language processing Cognitive Science
  1. Researchers are using brain scans, like fMRI, along with language models to decode what people are thinking about or listening to. This could help understand brain activity better.
  2. The technology could support people who can't speak, like stroke patients, by interpreting their thoughts into language. However, it's not perfect and needs more development.
  3. There are concerns about privacy, as this technology might one day read thoughts against a person’s will. But for now, people can consciously resist the decoding to some extent.
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Results from poll #1

The Counterfactual • 79 implied HN points • 12 Jan 24
🕹 Technology AI Cognitive Science Research Polls Language Models
  1. A new paid option allows subscribers to vote on topics for future articles. This way, readers can influence the content being created.
  2. This month's poll showed that readers chose a study on using language models to measure text readability. This will be the focus of upcoming research and articles.
  3. In addition to the readability study, there will be future posts about the history of AI, learning over different timescales, and a survey to learn more about the audience's interests.

GPT-4 is "WEIRD"—what should we do about it?

The Counterfactual • 59 implied HN points • 12 Feb 24
🕹 Technology AI Ethics Machine Learning Data Bias Language Models Cognitive Science
  1. Large Language Models (LLMs) like GPT-4 often reflect the views of people from Western, educated, industrialized, rich, and democratic (WEIRD) cultures. This means they may not accurately represent other cultures or perspectives.
  2. When using LLMs for research, it's important to consider who they are modeling. We should check if the data they were trained on includes a variety of cultures, not just a narrow subset.
  3. To improve LLMs and make them more representative, researchers should focus on creating models that include diverse languages and cultural contexts, and be clear about their limitations.

Learning Starts by Caring About Something

peoplefirstengineering • 14 implied HN points • 13 Nov 24
🚌 Education Learning Theory Cognitive Science
  1. Emotional engagement is key to learning. We remember things better when we care about them and connect emotionally to the experiences.
  2. Learning is more effective in collaborative settings. Working together with others, like in pair programming or group discussions, helps make the learning process more meaningful.
  3. To truly learn, we should explore what matters to us. Finding our personal connections to topics can lead to deeper understanding and growth.

The Counterfactual's year in review, and an announcement

The Counterfactual • 79 implied HN points • 29 Dec 23
🔬 Science Artificial Intelligence Cognitive Science Research Methods Empirical Studies Education
  1. The Counterfactual had a successful year, growing its readership significantly after a popular post about large language models. It’s great to see how sharing knowledge can attract more people.
  2. Key posts focused on topics like construct validity and the understanding of large language models. These discussions are crucial for improving how we evaluate and understand AI technology.
  3. In 2024, the plan includes more posts and introducing paid subscriptions that allow subscribers to vote on future research projects. This will encourage community participation in exploring interesting ideas.

Disillusionment and Learning

The Memory Palace • 19 implied HN points • 28 May 24
📖 Philosophy Belief systems Learning Theory Cognitive Science Radicalization Behavioral Psychology
  1. People often join groups or movements for positive reasons, but they may leave due to internal issues that arise later on.
  2. When someone changes their beliefs, returning to previous beliefs is complicated and often not the same as before.
  3. Revisiting old beliefs or habits can be an active process rather than a passive one; it's about reaching back, not just slipping back into old patterns.

Bird Perspectives and Outside Views

Seeking Bird Perspectives • 6 implied HN points • 02 Dec 24
📖 Philosophy Rationalism Epistemology Logic Ethics Cognitive Science
  1. The bird perspective means looking at things from a higher viewpoint to understand the bigger picture. It helps you see how your situation fits into a larger context.
  2. The outside view uses past experiences and similar cases to predict outcomes, but it can miss important details about your specific situation. It's important to find a balance between general predictions and unique factors.
  3. Using these perspectives can help reduce biases in decision-making. They inspire clearer thinking, but they shouldn't be used as the only way to argue or win a debate.

Edu-Snippets

The Science of Learning • 139 implied HN points • 07 Jun 23
🚌 Education Pedagogy Psychology Cognitive Science Technology Nature
  1. Giving students worked examples in math can help them feel less anxious and learn better. It makes math easier for those who usually struggle with it.
  2. Being in nature can help people feel more relaxed and focused, while watching videos of nature doesn't have the same benefits. For real restoration, you need real nature.
  3. Brain training apps may help you get better at their specific games, but they don’t really make you smarter in everyday life. They haven't shown strong proof of boosting general brain skills.

The movie isn't alive

Sunday Letters • 39 implied HN points • 19 Feb 24
🕹 Technology AI Chatbots Cognitive Science Perception Media
  1. Humans often see faces in things that don't have them, which shows how our minds can trick us. This idea extends to chatbots, which can seem alive but are really just processing prompts without true understanding.
  2. Chatbots may appear to have memory or awareness in a conversation, but they actually rely on previous prompts without retaining any real continuity. This can make interactions feel more human-like, even though they lack true awareness.
  3. It's helpful to recognize that chatbots and similar technologies are more about creating illusions than actual intelligence. Understanding this can improve how we design and use them, rather than expecting them to behave independently like a living being.

In cautious defense of LLM-ology

The Counterfactual • 119 implied HN points • 02 Mar 23
🕹 Technology AI Development Machine Learning Natural Language Processing Cognitive Science Human-computer interaction
  1. Studying large language models (LLMs) can help us understand how they work and their limitations. It's important to know what goes on inside these 'black boxes' to use them effectively.
  2. Even though LLMs are man-made tools, they can reflect complex behaviors that are worth studying. Understanding these systems might reveal insights about language and cognition.
  3. Research on LLMs, known as LLM-ology, can provide valuable information about human mind processes. It helps us explore questions about language comprehension and cognitive abilities.

How much of our disbelief can we suspend in augmented reality?

On Looking • 59 implied HN points • 27 Jun 23
🕹 Technology Augmented reality Virtual reality Social Interactions AI Cognitive Science
  1. Technologies like augmented reality challenge our perception and reshape our senses, training us to suspend disbelief and engaging us in a new form of visual literacy.
  2. The labor of making things look real involves an intricate mix of technology, cultural references, and societal norms, often blurring the lines between what is real and what is constructed.
  3. The desire for connection in an increasingly technological world raises questions about the authenticity of human interactions and challenges us to navigate the fine line between presence and absence, between virtual and physical realms.

Building the science factory with LLMs

The Counterfactual • 39 implied HN points • 13 Dec 23
🔬 Science Psychology Research Methods Cognitive Science Automation Peer Review
  1. Large Language Models (LLMs) could make scientific research faster and more efficient. They might help researchers come up with better hypotheses and analyze data more easily.
  2. Breaking down the research process into smaller parts might allow automation in areas like designing experiments and preparing stimuli. This could save time and improve the quality of research.
  3. While automating parts of scientific research can be helpful, it's important to ensure that human involvement remains, as fully automating the process could lead to lower-quality science.

When does AI have rights?

The Future of Life • 19 implied HN points • 29 Feb 24
🕹 Technology AI Ethics Machine Learning Cognitive Science
  1. AI might need rights if it mimics human behavior closely enough. We should think about this now before AI becomes super intelligent.
  2. Consciousness, sentience, and rights are important ideas, but they're not well-defined and can differ between people. Understanding these can help us decide who deserves rights.
  3. Sapience is being smart in a deep way, and it seems to be the best indicator for deciding if something deserves rights. It's more than just feeling or basic thinking.

GPT-4 captures judgments about semantic relatedness quite well

The Counterfactual • 59 implied HN points • 18 May 23
🕹 Technology Artificial Intelligence Natural Language Processing Cognitive Science Data Analysis
  1. GPT-4 is really good at understanding word similarities. In tests, it matched human opinions better than many expected.
  2. Sometimes GPT-4 thinks that certain words are more similar than people do. It tends to view pairs of words like 'wife' and 'husband' as more alike than humans generally agree on.
  3. Using GPT-4 for semantic questions could save time and money in research, but it's still important to include human input to avoid biases.

A review of "Don’t forget the boundary problem..."

Unstabler Ontology • 19 implied HN points • 08 Feb 24
🔬 Science Consciousness Brain Physics Cognitive Science Metaphysics
  1. The article discusses the binding problem in consciousness theories, which is about combining different features into a unified awareness.
  2. Functionalism is challenged by the boundary problem, questioning why there are limits to our conscious experiences.
  3. Electromagnetic theories of consciousness are explored, considering the role of EM fields in demarcating conscious entities and potential solutions using field topology.

Intelligence and combinatorial complexity

Sunday Letters • 39 implied HN points • 27 Aug 23
🕹 Technology AI Research Machine Learning Cognitive Science Computational Theory Human-computer interaction
  1. More agents working together can create better intelligence than a single agent. This is surprising because we might think one advanced model is enough, but collaboration can enhance performance.
  2. Human-like patterns help improve AI performance. Just as we can review our work for errors, AI systems can use different modes to refine their outputs.
  3. Complex systems come with challenges like errors and biases. As AI gets more complicated, these issues tend to increase, similar to problems found in complex biological systems.

Should psycholinguists use LLMs as "model organisms"?

The Counterfactual • 39 implied HN points • 17 Jul 23
🔬 Science Cognitive Science Psychology Linguistics Language processing
  1. Using model organisms in research helps scientists study complex systems where human testing isn't possible. But ethics and how well these models represent humans are big concerns.
  2. LLMs, or Large Language Models, may offer a new way to study language by providing insights without needing to use animal models. They can help test theories about language acquisition and comprehension.
  3. Though LLMs have serious limitations, they can still be useful for understanding how language functions. Researchers can learn about what types of input are important and how language is processed in the brain.

Cognitive Science needs linguistic diversity

The Counterfactual • 59 implied HN points • 20 Feb 23
🔬 Science Cognitive Science Linguistics Psychology Human behavior
  1. Cognitive science and linguistics are often too focused on English, which means we miss out on understanding how different languages work. Studying only a few languages makes it hard to see the full picture of language and cognition.
  2. Different languages influence how we think and perceive the world. For example, some languages have unique ways of expressing colors or time that can change how speakers of those languages understand these concepts.
  3. To improve our understanding of cognition, researchers need to include a wider variety of languages in their studies. We should explore languages beyond English to get a better grasp on how the human mind works across different cultures.

What's Going on Under the Hood of LLMs

The End of Reckoning • 19 implied HN points • 21 Feb 23
🔬 Science Artificial Intelligence Cognitive Science Neural Networks Interpretability Philosophy
  1. Transformer models, like LLMs, are often considered black boxes, but recent work is shedding light on the internal processes and interpretability of these models.
  2. Induction heads in transformer models help with in-context learning and the ability to predict information based on the sequence of tokens seen before.
  3. By analyzing hidden states and conducting memory-based experiments, researchers are beginning to understand how transformer models store and manipulate information, providing insights into how these models may represent truth internally.

Jane Austen and Cognitive Bias—Or on Persuasion

Vremya • 139 implied HN points • 01 Jun 21
📚 Literature Fiction Psychology Gender Studies Cognitive Science Narrative analysis
  1. Jane Austen explores the idea of love and how men and women experience it differently. She suggests that women may find it harder to move on from love than men do.
  2. Motivated reasoning is a key concept, where people look for evidence that supports what they already believe. This means we often see our own experiences as proof for our opinions.
  3. Austen also hints at cognitive biases like the availability heuristic, which is when we overestimate how common something is based on how easily we can recall examples from our life. This can lead to skewed perceptions of reality.

Welcome, and what this is

The Counterfactual • 59 implied HN points • 17 Jul 22
🚌 Education Cognitive Science Statistics Artificial Intelligence Research Writing
  1. The newsletter will cover topics like language, statistics, and AI, mixing research with personal thoughts. Expect both solid research reviews and imaginative columns about the future.
  2. Posts will be written in a clear, clean format using Substack. This platform helps catch mistakes easily and connects with a larger community of writers and readers.
  3. The author aims to write about things that are interesting and useful, hoping to share knowledge and insights that spark curiosity in readers.

Blowing off steam or picking a scab?

The Counterfactual • 19 implied HN points • 20 Dec 22
📖 Philosophy Cognitive Science Behavioral Psychology
  1. Metaphors shape how we think about emotions like anger. For example, saying we need to 'blow off steam' suggests that expressing anger can help relieve it.
  2. Some people feel that expressing anger, like 'picking at a wound,' can make it worse over time. It may lead to more anger instead of helping to heal it.
  3. Choosing a metaphor for anger depends on the person and situation. Both 'blowing off steam' and 'picking a scab' have valid points about handling anger, but they suggest different approaches.

Episodic Memory for Gossip

The Memory Palace • 1 HN point • 21 May 24
📖 Philosophy Cognitive Science Social Behavior Narrative Theory Evolutionary Theory
  1. We often share memories to understand others better and make smarter choices about who we work with. Gossip, or sharing stories about people's past actions, plays a big role in this.
  2. Episodic memory may have evolved to help us remember people's behaviors, which helps us avoid bad partners and build better cooperation. Remembering who can be trusted is really important for survival.
  3. Sharing stories about others is a great way to learn without putting ourselves at risk. It helps us judge people's actions and create a better understanding of their reputations in our social circles.

What's the Price Elasticity of Demand for Intelligence?

How the Hell • 3 HN points • 18 May 24
🕹 Technology Artificial Intelligence Cognitive Science Economics Future Trends
  1. The price of cognitive work, measured in 'cycogs,' varies widely and changes how much people might buy depending on cost. As the price goes down, more people are likely to use this intelligence.
  2. At different price points, people's spending on cognitive work can increase significantly. For example, if cycogs cost $1, people might buy a lot more because it allows for more access to services and creative projects.
  3. As technology improves and costs drop, traditional jobs in knowledge work might decline because many will prefer custom, AI-generated solutions for their needs.

#11: Productive discomfort

The Uncertainty Mindset (soon to become tbd) • 59 implied HN points • 14 Jan 20
🚌 Education Learning Theory Cognitive Science Professional development Personal Growth
  1. Embracing discomfort can lead to personal growth. Learning new things often feels uncomfortable, but it can help expand your skills and knowledge.
  2. Regularly challenging yourself can make discomfort easier to handle. By gradually exposing yourself to tough situations, you can improve your ability to cope with stress and anxiety.
  3. Curiosity in the face of discomfort leads to valuable insights. Instead of avoiding unpleasant feelings, exploring what makes you uncomfortable can reveal opportunities for learning and innovation.

Should we teach children to memorize the multiplication tables?

The Science of Learning • 4 HN points • 26 Jun 23
🚌 Education Mathematics Learning Theory Curriculum Development Cognitive Science Pedagogy
  1. Children benefit from memorizing multiplication tables because it helps them solve math problems more easily. When students know their math facts, they can focus on more complex thinking instead of getting stuck on basic calculations.
  2. Research shows that students who memorize math facts do better in math overall. This memorization builds a strong foundation for advanced math skills later on.
  3. It's important to strike a balance between memorization and understanding in math education. Teaching kids to remember math facts can actually support their overall learning and make problem-solving easier.

The meaningless computer worlds of cognitive science

Meaningful Particulars • 3 HN points • 06 Oct 23
🔬 Science Cognitive Science Culture Meaning Interdisciplinary
  1. Cognitive science shifted focus from meaning to information processing with the rise of the computational model.
  2. The narrative mode of cognition, based on intentions and beliefs, is as essential as logical paradigmatic thinking.
  3. Human self-understanding is influenced by cultural meanings and narratives, not just objective biology.

Space is a sensory-motor sequence in the hippocampus

Artificial General Ideas • 1 implied HN point • 12 Aug 24
🔬 Science Neuroscience Cognitive Science Psychology Biology Artificial Intelligence
  1. The hippocampus may not just represent physical space but instead processes space as a sequence of sensory and motor experiences. This means how we perceive space comes from our interactions, not just where we are.
  2. Place cells in the brain react to specific sequences of observations rather than directly to locations themselves. This explains why experiences in different environments can create similar neural responses.
  3. New models, like causal graphs, allow for better understanding and planning in navigational tasks. They can adapt to new environments quickly by using learned sequences without needing to rely on exact spatial representations.

How to get a million bucks: quick thoughts on cognitive programs and the ARC challenge.

Artificial General Ideas • 1 implied HN point • 13 Jun 24
🕹 Technology Artificial Intelligence Cognitive Science Machine Learning Robotics Problem Solving
  1. The ARC challenge is about understanding abstract concepts from visual inputs and applying them to new situations. It's tricky because it's not based on a strict set of rules, making it harder to solve.
  2. Cognitive programs need a controllable world model to work properly. This means they must be able to run simulations using the information they have about the world.
  3. Abstract reasoning tests, like ARC, are important but not complete measures of intelligence. They need to be systematic and clear to truly assess reasoning skills.

Aaron Lee Master Framework Philosophic Foundations & Explanation Document

Autodidact Obsessions • 0 implied HN points • 17 Feb 24
📖 Philosophy Epistemology Phenomenology Cognitive Science Mathematics
  1. The Aaron Lee Master Framework integrates various logical systems to understand language dynamics, emphasizing the contextual nature of meaning.
  2. Key components like Non-Monotonic Logic and Fuzzy Logic help in adjusting beliefs based on new information and dealing with gradations in meaning, respectively.
  3. The Framework's philosophical and logical foundations aim to provide a comprehensive model for the complexities of language and semantics by elucidating the emergence of meaning through interactions and context.

Any system that acts intelligently should be assumed to be intelligent

The Future of Life • 0 implied HN points • 04 Apr 23
🕹 Technology Artificial Intelligence Machine Learning Human-computer interaction Robotics Cognitive Science
  1. If a system acts intelligently, we should consider it intelligent. It's about how it behaves, not just how it works inside.
  2. Many people don't really understand what intelligence is, which makes it hard to define. Historically, we've only seen humans perform certain tasks, but now AI is doing them too.
  3. AI like ChatGPT has limitations and doesn't have the full abilities of human intelligence yet. While it's impressive, it can't think or learn in the same way humans do.

Information as constraint of behavior

Space chimp life • 0 implied HN points • 07 Jan 24
📖 Philosophy Behavioral Science Information Theory Systems theory Cognitive Science
  1. Institutions shape how we behave by restricting certain actions. This can be seen in clear rules or by making other choices harder or more costly.
  2. Information is created when different conditions allow an entity to do work, as shown in the example of a simple organism's behavior. The way it manages energy and information is crucial for survival.
  3. Just like simple organisms, institutions also gather information from their environment and use it to influence our actions. The way they set up rules determines the kind of work they can do.