According to the neurobiological study by Immornio-Yang and Damasino (2007), there is an extensive overlap between emotion and cognition known as “Emotional thought,” which is the mental platform for learning, memory, decision-making, social functioning, and creativity. "Lello" was designed in response to my current research interests, which include establishing the overlap between affective and cognitive components of learning and the influence of learners' emotional metacognition on learning processes. The following literature review will discuss current perspectives on the relationship between emotion and cognition, which will clarify the reasoning behind Lello's design.

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The majority of seminal articles on emotion and learning adopted a cognitivist approach, with an emphasis on information processing. While earlier research tended to regard emotion as a subordinate component to cognition and focused on its role in a cognitive system, more recent research has emphasized the reciprocal nature of the interaction between emotion and cognition. A recurring theme throughout the articles was the view that emotion and cognition are not mutually exclusive, but rather their functional relationship is bidirectional (Lazarus, 1991). Emotions have also been viewed as a form of information exchanged between individuals (Feidakis et al., 2013), implying the social nature of emotions. This is closely tied to the human decision-making process, which will be discussed later.

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It is critical for teachers to assist students in developing emotional awareness and emotional self-regulation, as emotions have the potential to facilitate or obstruct access to a variety of cognitive processing processes. To begin, an individual’s affective state has an influence on how information is stored and retrieved. When storing a memory, the emotional reaction to the experience may serve as an index for the mind to utilize in retrieving a comparable emotional memory in the future. When retrieving, the activation of a certain emotion boosts the activation of associatively relevant content in long-term memory (Bower, 1981). State-dependent learning and state-congruent retrieval are terms used to describe this process. Being able to elucidate learners’ emotional state and build self-awareness about their mood state will assist in later locating a mood-state-dependent memory. In addition, it is important to note that enhancing and/or interfering with information storage and retrieval underpins the formation of academic task motivation (Pekrun, 1992).

 

Learners’ working memory is another area where emotions impact cognition. It’s generally accepted that depression or anxiety impairs cognitive performance, as a learner’s attentional capacity is severely limited. A significant negative emotional reaction to a distressing experience engages or fills their working memory with ruminations about the event, thereby causing them to rehearse it repeatedly. To explain this phenomenon, Ellis and Ashbrook (1989) proposed the resource allocation theory model, claiming that emotional states regulate the amount of capacity available for learning tasks. Moran (2016)'s meta-analysis also found a consensus among scholars that there is a negative correlation between stress and cognitive functioning, given that stress and anxiety impair working memory performance. Pekrun (1992)’s argument was in the same vein; he stated that a situation free of urgent action allows mental room for learners to cognitively play around to generate new thoughts. Therefore, a positive mood that does not accompany cognitive load facilitated holistic and creative thinking, by improving both the quantity and quality of divergent associations for creative thinking. Additionally, positive emotions are also found to be necessary for both the cognitive and motivational aspects of self-regulated learning. In particular, positive emotions may be critical for the formation of intrinsic and sustained motivation (Pekrun, 1992).

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Affective aspects pose significant implications not only for cognitivism but also for the constructivist view of learning as well. Constructivist theories posit that learning occurs when an individual actively interprets his or her experiences, knowledge, and context (Richey et al., 2010). Throughout the learning process, the learner’s emotion functions as a mode of perceiving the world, serving as interpretive filters for reality (Lazarus, 1991), since emotion suggests momentary preferences for certain types of (re)actions over others (Pekrun, 1992). In this sense, when learners engage with their environment, emotions operate as a "commentator," reacting to the existing situations and evaluating plans and their outcomes. Furthermore, academic emotions, which refer to the student’s emotion in the context of learning settings like test anxiety and frustration, are linked with Piaget’s adaptation theory. Lazarus asserted that emotions communicate to the cognitive system the discrepancies between actual and predicted outcomes that are to be resolved through learning. The discrepancy between previous schema and new information results in an interruption (discrepant outcome), which encourages learners to direct their attention on the circumstance they were unable to explain using their prior knowledge. This shifts the way we see negative emotion, in that it is not always detrimental to the learning process given that it can serve as a cue for attention. Therefore, it is critical to identify general emotion from academic emotion when conducting research due to their distinct effects on cognitive activities.

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Last but not least, emotions, decision-making, ethical development, and transfer are intertwined. Emotion is considered a basic form of decision-making, a collection of actions and behaviors that enables people to respond appropriately in a variety of situations. In this view, ethical behavior results from the emotional decision-making process that involves appropriately labeling situations as positive or negative from an affective point of view, leading to the selection of advantageous or profitable solutions (Immordino-Yang & Damasio, 2007). This emotional appraisal for the situation (Lazarus, 1991) is also important in the learners’ decision on when and how to apply the gained knowledge outside of the classroom, referred to as transfer.

 

Taken together, emotion is a critical component of students' psychological well-being and may have a profound effect on their cognitive strategies for learning, meaning-making processes, motivation, transfer, and subsequent achievement. Given the reciprocal causation between emotion, learning, and achievement, it is worthwhile to investigate how addressing learners' affective aspects can facilitate learning.

 

 

 

References

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• Bower, G. H. (1981). Mood and memory. American psychologist, 36(2), 129.

• Ellis, H. C., & Ashbrook, P. W. (1989). The" state" of mood and memory research: A selective review. Journal of Social Behavior and Personality, 4(2), 1.

• Feidakis, M., Daradoumis, T., Caballé, S., & Conesa, J. (2013, July). Measuring the Impact of Emotion Awareness on e-learning Situations. In 2013 Seventh international conference on complex, intelligent, and software intensive systems (pp. 391-396). IEEE.

• Immordino‐Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, brain, and education, 1(1), 3-10.

• Lazarus, R. (1991). Cognition and motivation in emotion. The American Psychologist, 10.1037/0003-066x.46.4.352

• Lukasik, K. M., Waris, O., Soveri, A., Lehtonen, M., & Laine, M. (2019). The relationship of anxiety and stress with working memory performance in a large non-depressed sample. Frontiers in psychology, 10, 4.

• Moran T. P. (2016). Anxiety and working memory capacity: a meta-analysis and narrative review. Psychol. Bull. 142 831–864. 10.1037/bul0000051

• Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied psychology, 41(4), 359-376.

• Schank, R., Osgood, R., & Jona, K. (1990). A content theory of memory indexing. Technical Report 2, Northwestern University Institute for the Learning Sciences.

• Richey, R. C., Klein, J. D., & Tracey, M. W. (2010). The instructional design knowledge base: Theory, research, and practice. Routledge.