# Gender Differences in Visual Information Perception Ability: A Signal Detection Theory Approach
## 2 Signal Detection Theory  
Accurate perception of information in ambiguous or uncertain environments is essential for effective user information processing. Signal detection theory (SDT) provides a powerful theoretical framework to explain how users detect and respond to visual stimuli under such conditions [17]. SDT describes the decision-making process involved in distinguishing signal from noise and allows for the systematic analysis of perceptual accuracy and judgment criteria [18]. Originally developed in neurophysiology, SDT has since been widely applied in fields such as psychology, aviation, and behavioral and cognitive science [7,8].  
In psychology, SDT is frequently used to evaluate how individual or group characteristics affect perceptual tasks, such as the detection of social stimuli or facial emotion recognition [19,20]. For instance, SDT enables researchers to examine how different cognitive strategies are employed depending on task difficulty, emotional salience, or demographic variables.  
According to SDT, human responses to stimuli are categorized into four types: hits, false alarms, misses, and correct rejections (See Table 1). A false alarm occurs when a person detects a signal that is not present, while a miss refers to a failure to detect an actual signal [8]. These classifications provide insight into both the accuracy of perception and the underlying decision strategy.  
Signal detection performance is influenced by two core parameters: sensitivity (d\({}^{\prime}=|\,Z_{\text{N}}|\,+\,|Z_{\text{S}}|\)) and response bias (\(\beta=\text{P(N/S)/P(S/N)}\)). Sensitivity reflects an individual's ability to distinguish signal from noise and is typically calculated as the difference between the z-scores of the hit and false alarm rates [21]. In contrast, response bias indicates the individual's tendency to favor one type of response over another--whether they adopt a conservative or liberal decision criterion [2]. For instance, a conservative criterion may reduce false alarms but increase misses, whereas a liberal one may increase hits at the cost of more false alarms.  
Recent studies have applied SDT to a range of real-world contexts that demand visual decision-making, including medical image interpretation [9], military surveillance [22], driving safety and hazard alert detection [23], and digital interface design [24]. These applications demonstrate SDT's versatility in measuring expert-novice differences, analyzing the influence of stress and fatigue on performance, and informing system design for enhanced human-machine interaction.  
Against this backdrop, the present study adopts SDT as a methodological tool to quantify differences in visual information perception between genders. By measuring sensitivity and decision criteria in a signal detection task involving embedded visual stimuli, the study aims to examine whether meaningful differences exist in perceptual strategies and cognitive biases across male and female participants.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stimuli** & **Response** & **Noise** & **Signal** \\ \hline Noise & Correct rejection & False alarm \\ \hline Signal & Miss & Hit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Human responses in signal detection theory [8,18].