In this paper, top-down and bottom-up processing were studied regarding their effect on speech-in-noise. Three cognitive functions were also studied (divided attention, executive functioning, and semantic comprehension), and the effect they have on the speech processing and on each other. The research questions asked were if a difference in speech-in noise perception can be observed regarding the different levels of top-down and bottom-up support, if speech-in-noise is related to any of the researched cognitive abilities, and if there exists any correlation between these abilities. The method is a within-subject experimental design, consisting of four different tests: PASAT, to measure attention, LIT, to measure semantic comprehension, TMT, to measure executive functioning and SIN, to measure speech-in noise. The results showed a significant difference between top-down and bottom-up processing, a significant difference between top-down processing in decreasing and increasing conditions could also be seen. A negative correlation between the benefit of top-down support and the semantic comprehension task was found. Regarding the cognitive abilities a few correlations were found; the semantic comprehension task had a positive correlation to both the central executive task and the attentional task, the attentional task had a negative correlation to the central executive task, and both of the central executive subtasks had a positive correlation to each other. Most of the findings were expected, built on earlier cognitive hearing theories and studies.

This essay critically examines the limitations of artificial intelligence (AI) in achieving human-like understanding and intelligence. Despite significant advancements in AI, such as the development of sophisticated machine learning algorithms and neural networks, current systems fall short in comprehending the cognitive depth and flexibility inherent in human intelligence. Through an exploration of historical and contemporary arguments, including Searle's Chinese Room thought experiment and Dennett's Frame Problem, this essay highlights the inherent differences between human cognition and AI. Central to this analysis is the role of metaphorical thinking and embodied cognition, as articulated by Lakoff and Johnson, which are fundamental to human understanding but absent in AI. Proponents of AGI, like Kurzweil and Bostrom, argue for the potential of AI to surpass human intelligence through recursive self-improvement and technological integration. However, this essay contends that these approaches do not address the core issues of experiential knowledge and contextual awareness. By integrating insights from contemporary scholars like Bender, Koller, Buckner, Thorstad, and Hoffmann, the essay ultimately concludes that AI, while a powerful computational framework, is fundamentally incapaple of replicating the true intelligence and understanding unique to humans.