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# Best of Cognitive Science — 2024

1. 1  
[](/posts/human-like-ai-cognitive-llm-agents-d408d7niq "Human-like AI: Cognitive LLM Agents")  
Article  
![Avatar of gopenai](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/f34dfd0c312c4a59b897eb64ad28d895)GoPenAI · 2y  
Human-like AI: Cognitive LLM Agents  
Cognitive LLM Agents leverage large language models (LLMs) for complex and probabilistic symbol transformations. These agents emulate human cognitive functions through a modular architecture, integrating elements like decision-making, memory, and learning. The CoALA framework demonstrates how to structure cognitive language agents utilizing LLMs as a core component, offering insights into modular agent development and structured reasoning techniques.  
18  
1
2. 2  
[](/posts/ai-agent-capabilities-engineering-mlmpdmdig "AI Agent Capabilities Engineering")  
Article  
![Avatar of tds](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds)Towards Data Science · 2y  
AI Agent Capabilities Engineering  
An AI Agent Capabilities Engineering framework is proposed to structure the design of AI agents by leveraging cognitive and behavioral sciences. The framework categorizes capabilities into Perceiving, Thinking, Doing, and Adapting to enable AI agents to perform complex tasks. Practical applications are illustrated through examples like customer support AI agents, emphasizing the integration of sophisticated capabilities and the reliance on technologies and techniques beyond LLMs for achieving human-like proficiency.  
16
3. 3  
[](/posts/the-centrality-of-stupidity-in-mathematics-sw3quucbb "The centrality of stupidity in mathematics")  
Article  
![Avatar of hn](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/hn)Hacker News · 2y  
The centrality of stupidity in mathematics  
Feeling stupid is a natural and essential part of learning mathematics. The essay underscores that periods of confusion and difficulty indicate deep learning. Overcoming these mental hurdles often results in sudden, profound comprehension. Teachers should recognize and validate these feelings while assuring students that they are a normal part of the learning process in mathematics.  
12  
2
4. 4  
[](/posts/cognitive-prompting-in-llms-dedirvj7d "Cognitive Prompting in LLMs")  
Article  
![Avatar of medium_js](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium)Medium · 2y  
Cognitive Prompting in LLMs  
Cognitive prompting helps large language models (LLMs) think more like humans by guiding them through structured cognitive operations such as goal clarification, decomposition, filtering, pattern recognition, and integration. Unlike traditional strategies like Chain-of-thought (CoT), this approach allows models to solve problems step-by-step, improving their performance in complex tasks. Research indicates that models using self-adaptive cognitive prompting achieve better results compared to those following a fixed sequence. This innovation has potential applications beyond math, extending to areas requiring logical reasoning and creativity.  
11  
1

[See all Cognitive Science archives](/tags/cognitive-science/best-of)

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