My current mindset for creating a deep connection between technology and humans is based on applying strong theories from behavioral and educational sciences. I still deeply believe that scientific sources, focused research, and solid theories are the best tools available.
Since my field of study is educational psychology, and I am especially familiar with learning sciences, I write mostly about them. I believe combining research-based evidence is always more valuable and reliable than relying solely on personal ideas, even if they are logical.
In my writings and articles, I have repeatedly emphasized that sometimes we need to look back and integrate well-established scientific theories with modernity and artificial intelligence. I combine scientific evidence, including research articles and theoretical frameworks, with my own analyses, using them as a bridge to technology.
This approach and strategy prevent many potential risks. Instead of a preachy, rigid, or purely philosophical perspective, we adopt a systematic, scientific approach to derive practical solutions. One of the issues and concerns frequently discussed these days, which I have also mentioned in my recent articles, is the “consequences of excessive ease of performance through artificial intelligence.”In my latest article, I discussed the absence of “Fraction.”
In this article, I do not intend to discuss Fraction directly but rather focus on another challenge in the same area, which is not entirely unrelated to Fraction. This topic is the “level of anxiety and arousal resulting from facing performance.”
First, I will briefly explain this concept and then examine its connection to artificial intelligence systems.
Arousal Theory in Learning Psychology
One important theory in the neurophysiology of learning is Donald Hebb’s framework, which aligns with evolutionary approaches.
According to these perspectives, the human brain needs challenges to survive. The nervous system has evolved in challenging environments, and both anxiety and an optimal level of arousal have always been essential for survival. They increase alertness against potential risks and guide humans toward growth and the adaptation of necessary skills.
Donald Hebb, a neuroscientist, studied human learning, and one of his significant contributions was explaining the role of arousal in learning.
In Hebb’s framework, “arousal” is considered the fuel for the cerebral cortex to process information. Learning depends on neural plasticity, and this process occurs under an optimal level of arousal.
From this perspective, the brain is not simply trying to reduce tension but is seeking an optimal level of stimulation. If environmental stimuli are too low, the brain may create artificial stimuli or lose part of its natural efficiency.
As a result, neural firing and synaptic strengthening occur under the influence of arousal, and when arousal decreases significantly, the likelihood of forming or strengthening these connections decreases.
In addition to Hebb’s explanation, the classical “Yerkes-Dodson Law” also supports this necessity. According to this law, human performance improves with increasing physiological or mental arousal up to a certain point. When arousal is very low (a state toward which AI tools tend to push us), individuals experience reduced focus and cognitive motivation, and learning efficiency reaches its lowest point. In fact, a certain level of pressure or anxiety is not harmful; it is a prerequisite for achieving peak mental performance.
The “Arousal Gap” Challenge in Interaction with AI
As briefly explained in Hebb’s framework, the prerequisite for the neural interactions that lead to learning, perception, and cognitive actions is stimulation and arousal.
This moderate level of stimulation, which Hebb calls optimal arousal, is neither unpleasant nor at odds with the brain’s evolutionary nature in adaptation processes.
Now, imagine that a significant portion of our tasks is performed by an artificial partner and creates no direct cognitive responsibility for the individual. In such a scenario, what challenge will arise in human thinking?
These days, many articles and writings discuss the “excessive ease” challenge posed by AI tools. However, this article specifically focuses on reducing arousal levels and achieving optimal anxiety, according to Donald Hebb’s framework. Here, anxiety is considered one form of arousal, not equivalent to it entirely.
If most daily tasks are performed without prior stimulation or anxiety and without active cognitive engagement by AI, instead of the tools being under the consumer’s control, the consumer will be under their control.
From an evolutionary perspective, under such conditions, learning and cognitive adaptation processes will not align with the brain’s natural growth patterns, and the likelihood of effective knowledge adaptation will decrease.
The manifestations of this challenge will likely be observed in longitudinal studies as changes in the quality of cognitive performance and in neural circuit activity patterns.
References
Olson, M. H. & Hergenhahn, B. R. (2020). An Introduction to Theories of Learning (10th ed.). Routledge.
Schachtman, T. R. & Reilly, S. (Eds.). (2011). Associative Learning and Conditioning Theory: Human and Non‑Human Applications. Oxford University Press.
