<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cognitive AI on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/cognitive-ai/</link><description>Recent content in Cognitive AI on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/cognitive-ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Thinking</title><link>https://terms-en.ai-term-hub.com/en/terms/thinking/</link><pubDate>Sat, 18 Jul 2026 10:18:23 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/thinking/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>While humans think biologically, AI &amp;rsquo;thinking&amp;rsquo; involves computational operations that mimic cognitive functions. It encompasses logical deduction, pattern recognition, and inference. Modern large language models simulate thinking through complex neural network activations, allowing them to process natural language, understand context, and generate coherent responses. This concept bridges the gap between raw data processing and high-level intelligence, enabling systems to perform tasks that traditionally required human mental effort.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>In AI, thinking refers to the cognitive processes of reasoning, problem-solving, and decision-making simulated by algorithms.&lt;/p></description></item><item><title>Spreading activation</title><link>https://terms-en.ai-term-hub.com/en/terms/spreading_activation/</link><pubDate>Sat, 18 Jul 2026 10:16:41 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/spreading_activation/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Spreading activation is a concept originally from cognitive psychology, adapted in neural networks to describe how signal propagation occurs through interconnected units. When a specific node is activated, it sends signals to its neighbors, potentially activating them based on connection weights. In deep learning contexts, this can refer to attention mechanisms or specific regularization techniques where the activation state of one layer influences others, mimicking associative memory processes. It helps in understanding how information flows and reinforces patterns across complex network architectures.&lt;/p></description></item></channel></rss>