1. Conscious decision-making is required to prioritize competing needs, such as hunger versus fatigue, opening up a specialized space for, and, in many cases, requiring, careful, deliberative, and, at times, difficult, and, often, stressful, choices. Generally, addressing extreme fatigue first may be safer or more critical for cognitive function, while severe hunger might require immediate fuel to prevent metabolic issues, though both are often linked, as stress can cause hunger-like sensations when one is just tired.
Factors for Decision Making:
Priority: If you are extremely tired, your cognitive, emotional, and, sometimes, physical, functioning is likely impaired, making rest a higher priority for long-term health and, for, safety, as mentioned on Slower Hiking.
Physiological State: Hunger can be a result of stress rather than a true need for calories.
Decision-Making Space: Consciousness provides the opportunity to evaluate these, and, other, competing needs rather than relying on automatic, and, and, in some, cases, incorrect, actions.
2. This argument highlights the fundamental limitation of artificial intelligence in replicating the nuanced, high-stakes, and unpredictable nature of human social interaction. The assertion that "you can’t automate human social interaction" is supported by several key factors in the field of artificial intelligence and cognitive science:
The "Imagine My Way Into Your Head" (Theory of Mind) Problem: Humans possess a "theory of mind"—the ability to infer the internal states, emotions, and intentions of others, including those that differ from our own. AI currently lacks this capacity for genuine empathy, relying instead on programmed responses that cannot truly understand the "why" behind human actions.
The Problem of Too Many Elements (Complexity): Real-world social interactions are dynamic, multi-modal, and context-dependent. They involve simultaneously processing verbal cues, body language, facial expressions, and situational context. A 2025 study showed that over 350 AI models struggled to understand such dynamic scenes, often failing to interpret the "story" or emotional intent behind them.
Prediction and Unpredictability: Humans constantly predict what others will say or do next to navigate social worlds, especially in high-risk or complex scenarios (like negotiations or deep personal conversations). AI operates best in structured, rule-based environments, whereas human interactions are often messy, non-linear, and "irrational," making them difficult to model.
Structural Limitations in AI Development: Many AI neural networks are modeled after the brain's visual processing areas (which interpret static images) rather than the areas responsible for processing dynamic social, behavioral scenes. This creates a fundamental, structural "blind spot" in AI's ability to understand social dynamics.
The Importance of "Situated" Cognition: Human interaction is embodied and "situated" in a physical and social context. AI lacks this physical grounding and shared experience, meaning it cannot "read the room" the way humans do.
In short, while AI excels at logical tasks and pattern recognition, it faces a significant, possibly insurmountable gap in navigating the high-dimensional, nuanced, and emotional landscape of true human interaction.