## 1 Introduction  
The emergence of Large Reasoning Models (LRMs) has renewed longstanding debates on whether artificial systems can reason, or whether they simply simulate reasoning through surface-level statistical patterns. This discussion has been fueled in particular by [PERSON] et al. 's [1] study, \"The Illusion of Thinking,\" (commonly referred to as \"the apple paper\" in public discourse), which argued that the apparent cognitive behaviors of LRMs are misleading and superficial. They observed that LRMs outperform standard LLMs at medium problem complexity but then collapse entirely as complexity increases, even when computational resources are not exhausted. This performance trajectory was framed as an illusion, suggesting that LRMs mimic thinking without engaging in genuine reasoning. In this paper, I challenge that interpretation. Drawing from dual-process theory in cognitive psychology, I argue that these behaviors should not be seen as illusions or failures but rather as authentic manifestations of bounded rationality. This theory, first formalized in psychology by [PERSON] and [PERSON] [2; 3; 4] and further developed in cognitive science by [PERSON] and [PERSON] [5], proposes a model in which human reasoning operates through two systems: a fast, automatic, intuitive process (System 1) and a slower, effortful, deliberate one (System 2). I suggest that LRM behaviors mirror this dual structure, with resource-intensive reasoning being selectively applied and strategically withdrawn, depending on perceived problem tractability. **This paper is presented as a hypothesis. ** Its goal is not to prove that LRMs reason as humans do, but rather to offer a testable framework grounded in decades of research in human cognition. In Section 4, I propose concrete experimental protocols to assess whether LRM behavior aligns with physiological and behavioral markers observed in human reasoning, such as pupillometry-based indicators of effort and disengagement [6; 7]. [PERSON] et al. [1] showed that LRMs exhibit three distinct performance regimes. At low complexity, standard LLMs outperform LRMs. At moderate complexity, LRMs excel due to their Chain-of-Thought reasoning. At high complexity, both systems collapse. Most notably, reasoning effort, measured in inference tokens, rises with complexity and then falls abruptly when the model reaches a threshold. This pattern aligns with an inverted-U curve characteristic of human effort, observed in both pupil dilation and cognitive control studies [8]. Rather than interpreting this collapse as failure, I argue it represents a rational withdrawal of effort under high cognitive load. While [PERSON] et al. viewed these behaviors as superficial, other studies suggest a deeper mechanism. [PERSON] [9] reanalyzed their findings and attributed many failures to poor benchmark design, such as unsolvable problems and artificial token limits. [PERSON] et al. [10] extended this critique by replicating and refining the original tasks, finding that while design flaws explained some collapses, LRMs still struggled at higher task complexity, reinforcing the idea that resource allocation, not capability, was the limiting factor. Meanwhile, cognitive research has shown that human reasoning behavior is strongly influenced by effort-related cost-benefit tradeoffs. [PERSON] et al. proposed [11] the expected-value-of-control model, which describes how humans regulate effort based on perceived task utility. LRM token allocation can be interpreted similarly: when models anticipate diminishing returns from additional computation, they withdraw effort, even when resources are available. This pattern has also been observed in modern pupillometry research, which shows that effort markers increase with difficulty until a threshold is reached, after which disengagement occurs [7]. Other critiques of LLM reasoning, such as those by [PERSON] et al. [12] and [PERSON] et al. [13], argue that models succeed by exploiting token-level heuristics rather than formal logic.