“Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems” (Brown, 2021).
Due to the misconceptions and limitations of current research, the brain and its functions are not fully understood. As a result, human-machine interaction (HMI) has been filled with complications due to the basic concept of computer programming. Since the Manhattan Project of the 1940s, the utilization of machines in the quest for human efficacy has driven research to develop concepts of integration and interaction to lessen the obstacles facing forward momentum. Guastello (2013) notes that a “disjoint between central control operations required to set the functions of the machine and the control operations required to operate the machine” exists leaving the interaction in the ability of the human operator to adapt which can lead to an overall decline in health and wellness (p. 313).
To provide an improved experience, machine learning could be matched with a changed perspective from the human operator’s expectation. Because it is possible to rely on a machine to learn in a set capacity, the choice to do so does not have to be made in that favor. There are currently four (4) proposals about how concepts are represented in the brain that could provide insight for more effectively matching machine-learning systems to human interaction. The problem with each of these approaches is that incomplete results are rendered yet expected to be applied in use for the general population.
Although sensory and function are important considerations, research has shown that more complex processes are operating leaving a simple distinction inconclusive. While each of the approaches regarding concept formation agrees “that information about concepts is distributed across many structures in the brain, with each approach emphasizing different types of information” (Goldstein, p. 291, 2018), the issues facing effective development may stem from the idea that learning is not supported by short-term memory (STM), but more-so long-term memory (LTM) resulting from integrated processes of neuroplasticity (Boyd, 2015).
To present fewer difficulties in a machine-learning environment, the functional outcome of the process must be considered. Various schemas are developed by diagnosing cognitive structures (Ifenthaler et. al., 2011). If the system is programmed to integrate image recognition, while artifacts are correctly identified, the next step in categorization presents complications due to phenomena such as crowding in the multiple-factor approach which inhibits distinction based on objects under scrutiny due to shared features. To combat this, the embodied approach’s foundational basis of interaction should be implemented to enhance the ability to activate and recall perceptual and motor areas associated with these concepts. Also, inserting the use of a category-specific approach is key to neuroplasticity and the connection of integrated networks of the brain. In the instance of speech recognition, a researcher may want to employ a higher degree of the semantic category approach because although it “focuses on areas of the brain that are specialized to respond to specific types of stimuli, it also emphasizes that the brain’s response to items from a particular category is distributed over a number of different cortical areas (Mahon et. al., 2007; Mahon & Carmazza, 2011)” (Goldstein, p. 289-290, 2018).
Due to the overwhelming lack of completeness, the choice of which current concept of formation to employ in a machine-learning system would be a combination of the four main approaches as “it is likely that as research on concepts in the brain continues, the final answer will contain elements of each of these approaches” (Goldstein, p. 291, 2018).
Boyd, L. (2015). After watching this, your brain will not be the same. TEDxVancouver (14:24/YouTube). Retrieved November 19, 2022, fromhttps://youtu.be/LNHBMFCzznELinks to an external site..
Brown, S. (2021, April 21). Machine Learning, explained. MIT Sloan. Retrieved November 19, 2022, from https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained#:~:text=Machine%20learning%20is%20a%20subfield%20of%20artificial%20intelligence%2C%20which%20is,to%20how%20humans%20solve%20problems Links to an external site..
Goldstein, E. B. (2018). Cognitive psychology: connecting mind research and everyday experience (5th ed.). 191-222; 263-296. Wadsworth Cengage Learning.
Guastello, S. J. (2013). Human factors engineering and ergonomics: A systems approach, second edition. 313-340. Taylor & Francis Group.
Ifenthaler, D., Masduki, I., & Seel, N. M. (2011). The mystery of cognitive structure and how we can detect it: tracking the development of cognitive structures over time. Instructional Science, 39(1), 41-61. https://doi.org/10.1007/s11251-009-9097-6 Links to an external site.