## 1 Introduction  
The influence of emotions on human decision-making has emerged as a critical area of research in both decision science and artificial intelligence [1, 2]. Particularly, the role of immediate emotions in dynamically regulating individual risk preferences and cooperation tendencies [3] has garnered increasing attention [4]. Immediate emotions refer to an individual's transient emotional responses in specific contexts, characterized by their short-term nature and dynamic variability. Research indicates that these emotions can alter subjective perceptions of risk and reward, influencing decision-making tendencies [5]. For example, [PERSON] et al. [6] found that positive emotion (PE) generally improves individuals' propensity to take risks, while negative emotion (NE) prompts more conservative choices. [PERSON] and [PERSON] [7] further noted that emotional states significantly modulate cooperative intentions, impacting team collaboration efficiency and decision outcomes. In human-machine collaboration, immediate emotions may determine the level of trust individuals place in machine-generated recommendations and their willingness to adopt such advice. This is crucial for designing effective and user-friendly interactive decision systems [8, 9]. Therefore, understanding the dynamic mechanisms of immediate emotionsand integrating emotion sensing and regulation technologies into human-machine interactive decision systems have become key challenges in this field [10; 11]. However, existing research has several limitations. First, traditional decision models, such as Expected Utility Theory and Prospect Theory, primarily focus on the impact of anticipated emotions on long-term decisions, neglecting the immediate effects of immediate emotions in short-term decision contexts [12; 13]. For example, in investment scenarios, investors' immediate emotions triggered by market fluctuations often lead to irrational behaviors, such as selling assets at low points or blindly increasing investments at high points, rather than making rational choices based on long-term profit maximization goals [14; 15]. Similarly, in medical decision-making, patients' fears or anxieties may cause them to overly rely on doctors' advice or even reject reasonable treatment plans, highlighting the need to understand the mechanisms of immediate emotions better. The choice of Kuhn poker as the experimental context was motivated by its ability to simulate decision-making under incomplete information, which aligns closely with the study's objective of investigating the impact of emotions on risk-taking behavior and cooperation levels. Kuhn poker is a zero-sum game typically involving two players, with a deck of only three cards. Players draw cards and make sequential decisions (e.g., betting or folding), with the final outcome determined by the relative strength of their hands. This structure, which incorporates elements of incomplete information and strategic interaction, makes Kuhn poker particularly suitable for studying how individuals adjust their decision-making behaviors based on emotional states in uncertain environments. In addition to Kuhn poker, other game-theoretic approaches, such as Bluffing Games, Rock-Paper-Scissors, and Schelling Points, were considered but ultimately deemed less suitable for this study. Compared to Bluffing Games, Kuhn poker avoids the excessive complexity and trust-related dynamics that could obscure the direct effects of emotions on decision-making. Rock-Paper-Scissors, while simple and widely understood, lacks the strategic depth and risk-reward trade-offs necessary to explore nuanced emotional influences on risk preferences and cooperation. Schelling Points, which focus on coordination and focal points in decision-making, are more relevant to cooperative scenarios rather than the competitive and risk-oriented context of this study.