Ambiguity is a result of the detachment between human processing and responses that lead to original thought.
“Psycholinguistics, which focuses on the psychological study of language, is concerned with comprehension, representation, speech production, and acquisition of language” (Goldstein, p. 324, 2018).
By integrating the knowledge of how important language is to the creation of the reality perceived by an individual, advancements in human-machine interfaces (HMI) would allow improvements in the usability of the functions now available.
As thought is the basis of language, true cognition cannot flourish without the existence of external communication with an environment. Due to the components discussed by Goldstein (2018), natural language is structurally different from that of computer languages operating in current devices that interact with humans on a mass scale. These vary from medical devices to vehicular assistance, yet collectively present the same issue of ambiguity in all levels of natural language that are not present thus far in computer syntax and structure.
Goldstein (2018) discusses lexical ambiguity as a situation where “words can often have more than one meaning” (p. 327). This creates a disparity between the human interaction with a machine-learning system based on algorithms and deep-learning modules that do not account for activities of the default mode network (DMN) as noted in current “available neuroimaging evidence, [which supports] the embodied semantics approach, although the complex organization of the human motor cortex imposes limits to the anatomical localization of complex actions” (Fernandino & Iacoboni, p. 53, 2010).
To construct a research project to examine how to demonstrate this aspect of cognition, the specific research question is:
“What features and absolute barriers for identifying limitations and boundary issues to prevent misunderstanding between human and machine system interactions using natural language could be implemented to address the potential human delay and knowledge interference challenges?”
Variables that influence semantic organization or retrieval of knowledge by the operators must be addressed, along with potential semantic knowledge deficits or categorization differences that appear across cultures creating disparate experiences on an individual level. These can include placement of visual displays, characters for conveying meaning, and colors used for warnings or other indications which can vary on a cultural and national level. For example, Hangul, the Korean alphabet, is read from left to right as in English but utilizes characters instead of letters. Some characters of the Korean language have similarities to English letters; however, the similarities cause confusion rather than clarity due to the prior knowledge associated with the shapes. On the other hand, the Persian and Chinese languages must be read from a different direction which can lead to operator confusion regarding the initiation of a sequence.
Interference due to prior knowledge is an issue regarding systems consolidation as both language and meaning can occur on a retrograde in the physicality of the written construct itself, as well as in the working memory. The dynamics in this function of cognition manipulates information as was researched by Baddeley (2012) which in turn lends to the formation of concepts (Goldstein, 2018). Prior knowledge is necessary to interact with the interface; however, the origin of the design is affected by the biases of the developer leading to situations such as the need for all pilots to speak English or substantial amounts of biased research due to the availability of subject matter (Boroditsky, 2018).
Understanding that humans assimilate added information more slowly and may experience interference with prior knowledge must be a consideration of human factors professionals. Variables such as language, culture, nation, and other colloquial idiosyncrasies influence the learning process in ways that are not yet understood. This leaves much to be considered in future research.
Learning is the basis for all that is present. As a result, the research community has a profound fascination with understanding how the brain processes information in semantic episodes to utilize the working memory for creating neuroplastic movement leading to retention in long-term memory. By utilizing motor skills along with visual, auditory, and vibrational cues, the information stored in the nervous system that changes through “integrated processes of neuroplasticity” (Boyd, 2015) may be more readily available for immediate use. In understanding the event-related potential (ERP) at an individual level to create an allocated time for a human response that can be tailored specifically to the user, advancements in the reaction time for operators could be improved.
To address biases as discussed by Boroditsky (2018), all major languages should be considered due to ambiguities of basic computer programming languages currently based on the English language as the initial neural network for a brain to learn may have been determined by another system of language. Basic computer psycholinguistics requires that users conform to human-machine interfacing (HMI) due to a lack of foresight in design where the “What is?” question may have been overlooked. In reviewing this area for improvements, the connections and dissociations can be reviewed for implementation as design enhancements.
Stimuli responsible for interference stem from visual and auditory stressors; it is possible that stressors are also present due to changes in pressure and vibrational tones. Due to the presence of known cycles such as the circadian rhythm create our sleep pattern to naturally re-energize the body as referenced by Guastello (2013), it is possible that other rhythms based on low-frequency vibrations and noise (LVFN) play into an audible rhythm that affects the discrimination index and sustained attention reflecting changes in the level of default mode network (DMN) activity, as well as the cognitive level of information being processed as low- or high-load. Internal sustained attention has been researched in the Buddhist monk community showing that acuity and perceptual discrimination can improve vigilance on low-load tasks (LaBrie, 2014). It is possible that these interferences lead to automation bias which could take effect due to a low task load in the working memory without real-time information manipulation requirements.
Guastello (2013) suggests that based on the work of Wolfram in the 1980s, “small differences in the initial values of rule parameters can produce some very different end results. Thus, the concept of sensitivity to initial conditions, which is a hallmark of chaos, can be observed in cellular automata,” which have “acquired numerous applications that go beyond biological phenomena” (p. 343).
By improving the semantic memory interactions as noted in Goldstein’s text (2018) by accounting that new learners may have prior knowledge based on parameters not considered in the design, the ability to remain at stasis while utilizing the working memory in conjunction with the long-term memory may lead to an increased functional result of the user’s division and interaction of the sensory and procedural memories leading to live-experience response based on healthy stasis of external and internal sustained attention.
In noting that research remains quite biased, many opportunities exist to implement design enhancements based on language learning (Boroditsky, 2018). Without more information on language structures beyond the English language, research faces limitations and could benefit from multi-cultural considerations that initiated specific systems based on phonetics, direction, etc. There are also opportunities for improvements in how humans understand the language of computers available in through education, yet a balance of technology and the natural environment to encourage contextual development would be the most ideal to ensure healthy neuroplasticity in support of organic stasis to achieve a human-centric design.
Baddeley, A. (2012). A Lecture in Psychology: Working Memory: Theories, Models, and Controversies. Annual Reviews. Retrieved November 27, 2022, from A Lecture in Psychology: Working Memory: Theories, Models, and Controversies.
Boroditsky, L. (2018). How language shapes the way we think. TED Talks. YouTube. Retrieved November 27, 2022, from https://www.youtube.com/watch?v=RKK7wGAYP6k&t=848s.
Boyd, L. (2015). After watching this, your brain will not be the same. TEDx Vancouver. (14:24/YouTube). Retrieved November 27, 2022, from After watching this, your brain will not be the same.
Goldstein, E. B. (2018). Cognitive psychology: connecting mind research and everyday experience (5th ed.). Wadsworth Cengage Learning.
Guastello, S. J. (2013). Human factors engineering and ergonomics: A systems approach, second edition. Taylor & Francis Group.
Fernandino, L. & Iacoboni, M. (2010). Are cortical motor maps based on body parts or coordinated actions? Implications for embodied semantics. (Vol. 112, Issue 1). 44-53. Brain and Language. Retrieved November 27, 2022, from https://doi.org/10.1016/j.bandl.2009.02.003.
LaBrie, R. (2014). The Cognitive Neuroscience of Sustained Attention and Classical Mindfulness: Volume 1. YouTube. Retrieved November 27, 2022, from
The Cognitive Neuroscience of Sustained Attention and Classical Mindfulness: Volume 1.