Computer Analogy in Information Processing Software
Cognitive processes play a central role in how we learn, make decisions, and interact with the world around us. One key framework for understanding these processes is information processing theory (IPT). By exploring the stages of memory and retention, IPT sheds light on how we process complex tasks and make sense of the world.
IPT is central to advancing in fields such as education, technology, healthcare, and marketing as it addresses how we understand and apply cognitive processes. Pursuing higher education is crucial to fully understanding IPT and leveraging it in the workplace. Soft skills, such as communication and creativity, are vital for those operating in instructional design or UX research.
Information Processing Theory Defined
IPT is a cognitive framework that explains how humans store, encode, and retrieve information. Unlike other cognitive theories, such as behaviorism, which focuses on observable actions, IPT delves into the internal mental processes that occur when information is received and acted upon. The origins of IPT date back to the 1950s, when it emerged alongside the rise of computer science, a field that offered a useful analogy for understanding human cognition. The theory identifies three key stages: sensory memory, working memory, and long-term memory. Each stage plays an imperative role in how information is absorbed, processed, and stored.
Information Processing Model Diagram
Structure-Mapping Theory and Analogy
Using cognitive science theories, Forbus and his collaborators have developed a model that could give computers the ability to reason more like humans and even make moral decisions. “In terms of thinking like humans, analogies are where it’s at,” said Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science in Northwestern’s McCormick School of Engineering.
The theory underlying the model is psychologist Dedre Gentner’s structure-mapping theory of analogy and similarity, which has been used to explain and predict many psychology phenomena. Analogies can be complex (electricity flows like water) or simple (his new cell phone is very similar to his old phone). “Relational ability is the key to higher-order cognition,” said Gentner, Alice Gabrielle Twight Professor in Northwestern’s Weinberg College of Arts and Sciences.
Previous models of analogy, including prior versions of SME, have not been able to scale to the size of representations that people tend to use.
SME and its applications
SME has also been used to learn to solve physics problems from the Advanced Placement test, with a program being trained and tested by the Educational Testing Service. “SME is already being used in educational software, providing feedback to students by comparing their work with a teacher’s solution,” Forbus said. But there is a vast untapped potential for building software tutors that use analogy to help students learn.”
The range of tasks successfully tackled by SME-based systems suggests that analogy might lead to a new technology for artificial intelligence systems as well as a deeper understanding of human cognition. For example, using analogy to build models by refining stories from multiple cultures that encode their moral beliefs could provide new tools for social science.
Many artificial intelligence systems - like Google’s AlphaGo - rely on deep learning, a process in which a computer learns examining massive amounts of data. By contrast, people - and SME-based systems - often learn successfully from far fewer examples.
Supported by the Office of Naval Research, Defense Advanced Research Projects Agency, and Air Force Office of Scientific Research, Forbus and Gentner’s research is described in the June 20 issue of the journal Cognitive Science.