Blog Post #1: Connections between Cognitive Science and Teaching

In this blog post, I explore Artificial Intelligence (AI), Mental Representations, Problem-solving, Learning Styles and Neural Processing and how they relate to my own educational context. How does the science of thinking (Cognitive Science) relate to teaching? Read more to find out!


Artificial Intelligence

The aspect of human thinking that I believe would be the most difficult for computers to perform or model is feeling/emotion. Computer models use logic, which only accounts for part of the thinking process. Lehrer (2007) reports that “it is impossible to understand how we think without understanding how we feel (para. 4). He gives examples of people affected in the feeling region of the brain, who make decisions with adverse reactions even thought the reasoning part of their brain is intact; this suggests “ that proper thinking requires feeling” (Lehrer, 2007, para. 18).

Patharkar (2011) gives analogous parts of a computer system and a brain, the computer system metaphor being a widely-accepted metaphor for the brain within cognitive science (Stanford Encyclopedia of Philosophy, 2010, sec. 3). Synapses, neurons, neural pathways, perception centers, memory and interpretation, thoughts (induction and inference) are all given corresponding parts/processing of a computer, except for creativity and individuality (including consciousness and being conscious of itself) (Patharkar, 2011, table 1). While computer systems are still not conscious, and therefore cannot be conscious of their own consciousness, computers systems have been successful in creating. Peter Bock (2011) guided a graduate student of his in creating a computer system that could, after analyzing paintings made by a particular artist, create its own interpretation of a picture based on varying degrees of realism, influence of the original artist and precision. This begs the question: “Are computers executing such algorithms merely simulating mental states and processes, or are they actually exhibiting them?” (Rapaport, 1996, p. 2).

Feeling/emotion would be the most difficult aspect for a computer system to have because little research has been done on the link between emotion and cognition until recently (Lehrer, 2007). Given that emotion/feeling is a requisite for proper thinking, with more time emotion/feeling will be able to be performed by a computer, because Artificial Intelligence (AI) is purported to be reality by 2036 (Bock, 2011). In terms of being different for a child and an adult, emotions/feelings weigh differently because of the development of the brain; brain development is thought to mature around age 25 (Bock, 2011). Chin et al. (2010) found that Teachable Agents (TA) improve student learning; integrating emotions/feelings into computer models/processes will only enhance learning with TA for learners of all ages. (For more about Artificial Intelligence, check out the content in this link.)


Mental Representations-Logic, Rules and Concepts

Formal logic rests on both deductive and inductive reasoning to produce inferences; rules take the form of hypotheses (If…Then…) and are subject to individual procedures for using said rules; concepts are sets of distinctive features which are used to match individual concepts with the outside world (Stanford Encyclopedia of Philosophy, n.d.). Goodman, Tenenbaum, Feldman, &Griffiths, (2008) give three accepted premises of concepts as “mental representations that are used to discriminate between objects, events, relationships, or other states of affairs…learned inductively from the sparse and noisy data of an uncertain world…formed by combining simpler concepts, and the meanings of complex concepts are derived in systematic ways from the meanings of their constituents” (pp. 108-109). Looking at logic, rules and concepts, I believe that concepts are the most complex mental representation because they presuppose a subjective framework. Pavel (2009) says that one must first be familiar with the context in order to define a concept and, taking Artificial Intelligence (AI) into account, he poses the possibility of AI’s learning from examples.


In my educational context, concepts are the most important mental representation. Forming a concept, or building off of a pre-existing concept, is important in language learning because it gives a context off of which the learner can associate prior and future knowledge in terms of vocabulary and phrases between Thai and English. Rules can be directly applied and defined as English grammar because they give premises (grammar rules) to work off of, as well as exceptions, resulting in the testing of hypotheses. Logic can be defined as the framework under which rules work, which could be deductive (given grammar rules) or inductive (realizing patterns in grammar first).



Barbey and Barsalou (2009) take a look at reasoning and problem solving by comparing the regions of the brain that are triggered with certain cognitive tasks. They find that generally, “reasoning tends to recruit broadly distributed and diverse neural systems”, which supports dual-process, dual-code, and embodied theories (p. 42). Piaget shows that humans develop cognitive processes in levels, and it stands to reason that problem-solving would be affected by cognitive development at a given time. For example, children are very ego-centric when younger and talk at you instead of to you, which develop into a more social-centric model of taking other people’s considerations and viewpoints into account and creating a dialogue (Piaget’s Developmental Theory: An overview, part 1, 1989). Problem-solving would still in theory occur across diverse neural systems, but the ‘data set’ of external stimuli with which to make sense of the world and would be helpful in problem-solving would be limited when compared with an adult.


AI systems can assist me in understanding logic, rules and concepts in regard to my own educational setting by continuing to try to mimic human mental representations. Rule-based concept learning, for example, has been found to closely relate to human concept learning, and more questions arise to refine said approach (Goodman, N.D., Tenenbaum, J.B., Feldman, J., &Griffiths, T.L., 2008). The closer humanity comes to creating AI, the closer humanity comes to understanding our own learning and representational processes in greater detail, specifically of value in my educational context are concept formation and learning. Hopefully, AI will be found to aid in concept formation, making it easier to acquire a second language.


My Learning Style! 

            After taking a ‘learning styles questionnaire’, the results show that I score a 5 as a reflective learner, a 9 as an intuitive learner, a 9 as a visual learner and a 1 as a sequential learner. Overall, I agree with the results and I feel as though they reflect my learning style. As a reflective learner, I like to ‘think’ more than ‘do’; I run scenarios in my head until I am confidant the task can be performed in real life instead of blindly trying things out. As an intuitive learner, I have a strong dislike of minute details and prefer to learn about ideas, concepts and other abstractions. I would characterize most of my free-time thinking as questioning abstract ideas/concepts and what implications certain definitions give rise to. As a visual learner, it is much easier for me to visualize information rather than to read or hear about it; a visual image can make sense out of pages of print and gives a framework to view information through. I often find that mapping my notes (using circles, boxes and making visual connections) helps me to understand the material better. As a sequential learner, I can make sense out of the smaller steps and use the information, but it really ‘clicks’ for me when I see the bigger picture. The numbers associated with each learning type also represents me well: I am highly visual and intuitive, have more of a preference for reflective learning and am flexible between sequential and global learning styles.

EDU510 brain-78440_640

Learning Styles and The Brain

            Neural synapses have the ability to connect, process and store information and, although there are conceivably more than one hundred trillion megabytes of information in the brain, it is not known how many synapses actually store information as opposed to processing it (Explainer: How many megabytes does your brain hold?, 2010). Information is found and processed using synapses, which move chemical signals between neurons; connections can occur in a variety of configurations (The Synapse, 2012). Because everyone has a unique learning style, as shown above, it reasons that one’s unique learning style is based on the neural chemistry and connections between different neurons in different parts of the brain. Perhaps my visual connections are stronger than my verbal connections in certain parts of my brain and that accounts for why I prefer learning visual. It may not even be the case that the visual connections are stronger; it could be that the processing neurons occur in specific region of the brain. This begs the question of if we can change the chemistry of how we think in order to strengthen a specific learning style. Andy James shows that, in processing MRI data, we can create a map of the neural connections in our brains, making it easier to see the connections between different parts of the brain as well as the connectivity between different areas of the brain (The cognitive connectome, 2011). This ongoing study shows brains at a resting rate, but if we could map the brain doing different mental activities (such as in forming mental representations), we could gain more knowledge about the ‘nuts-and-bolts’ of the brain’s processes. We could also map someone’s brain while learning and see the degree to which different configurations and connections take place.


            This information has helped me to see the importance of variety in my teaching style. Integrating various approaches/ways of explaining into a lesson helps learners grasp concepts. For example, in my Buddhism classes I find that if some students do not understand a verbal answer to a question, it helps to draw a visual that illustrates the concept. It also helps to know how an individual student learns; this is practical in classes of 8-15 students, but complicated in classes where I have 35-45 students. Overall, if information is presented, or prepared to be explained, according diverse learning styles, students will have a higher chance to learn the material.


Barbey, A.K. & Barsalou, L.W. (2009). Reasoning and problem solving: models. EmoryUniversity, ATL, GA: Elsevier Ltd.

Bock, P. (2011). Emergence of creativity in artificial intelligence. [Video] Retrieved from

Chin, D.B., Dohmen, I.M., Cheng, B.H., Oppezzo, M.A., Chase, C.C., & Schwartz, D.L. (2010). Preparing students for future learning with teachable agents. Education Tech Research Dev 58:649-669. DOI 10.1007/s11423-010-9154-5

Explainer: How many megabytes does your brain hold? (2010). [Video] Retrieved from

Goodman, N.D., Tenenbaum, J.B., Feldman, J., &Griffiths, T.L. (2008). A rational analysis of rule-based concept learning. Cognitive Science 32, 108-154.

Lehrer, J. (2007). Hearts & minds. The Boston Globe. Retrieved from

Patharkar, M. (2011). From data processing to mental organs: An interdisciplinary path to cognitive neuroscience. Brain, Mind and Consciousness: An International, Interdisciplinary Perspective. 9(1), p. 218-224.

Pavel, G. (2009). Concept learning-investigating the possibilities for a human-machine dialogue. Intelligent Support for Leaning Concepts from Examples. Retrieved from

Piaget’s developmental theory: An overview, part 1. (1989). Retrieved from

Rapaport, W. J. (1996). Cognitive science. Department of Computer Science, Department of Philosophy, and Center for Cognitive Science. Buffalo, NY: State University of New York at Buffalo.

Standford Encyclopedia of Philosophy. (2010). Cognitive Science. Retrieved from

The cognitive connectome. (2011). [Video] Retrieved from

The synapse. (2012). Neuroscience for kids. Retrieved from

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