Data collection was conducted during a single session. All participants or their legally acceptable representatives signed a written consent form agreeing to take part in this study. After that, all participants underwent face-to-face interviews conducted by a trained staff member to collect the demographic information (eg, age and gender). We administered the Montreal Cognitive Assessment (MoCA) and the go/no-go tasks in an interleaved and alternating manner using a number-calling strategy, with at least a 30-minute interval between the 2 assessments. The MoCA assessment was conducted by trained research personnel.

The MoCA is a well-validated, multidomain, paper-and-pencil test that assesses a range of cognitive functions, including visuospatial and executive functions, naming, memory, attention, abstraction, and orientation [35]. We defined participants’ cognitive status using the following MoCA cutoff scores (adjusted education; the MoCA should be scored based on educational level to reduce bias. According to the standard MoCA scoring rules, one point is added to the total score for participants with less than 6 years of education): a score of 23 or higher was categorized as no cognitive impairment (NCI), a score ranging from 14 to 22 was categorized as MCI, and a score of less than 14 was categorized as dementia [5].

The task design was built on previous research and finalized through consensus among all authors [24]. This study comprised 6 go/no-go tasks (Figure 1), including visual, auditory, and audiovisual integration tasks. Every task consisted of 5 runs of 16 trials each (80 total trials). The total completion time for the 6 tasks was approximately 18 minutes (170 seconds per task). Participants were allowed a 1-minute break after every task. The tasks were conducted in a quiet environment (≤45 dB), verified using a decibel meter (model DLX-PS02303; Delixi Electric). Participants sat at approximately 58 cm from a 14-inch laptop computer (ThinkPad T480s-i7; Lenovo). Auditory stimuli were presented through wired headphones (model HD350BT; Sennheiser Electronic SE & Co). Volume was adjusted to a level clearly audible to participants before testing commenced. Visual materials were sourced from a canonical set of line drawings [36], and auditory materials were obtained from the Aigei website. As prior research has suggested that humans are more sensitive to biological sounds compared to nonbiological sounds, the auditory stimuli were designed as animal sound recognition tasks [37]. Visual and auditory stimulus materials underwent processing to standardize the image size and sound pressure level before being inserted into E-Prime (version 2.0). The sequence of stimuli in each task was fixed. A single-key keyboard (model K1-LY-WX; Yingyu IoT) was connected to the computer and positioned in front of the participant’s dominant hand. Each stimulus was presented for 500 ms, with a 1500-ms intertrial interval during which a blank screen with a fixation cross was displayed. Each task was preceded by 12 practice trials at the same pace as the formal study trials (go/no-go ratio of 3:1) to ensure participant comprehension of task requirements. The go/no-go metrics evaluated were as follows:

A lower score reflects better inhibitory control by integrating speed and accuracy. All go/no-go metrics were standardized into Z-scores before performing regression analysis.

Following completion of the MoCA and modified go/no-go tasks, we conducted informal interviews lasting approximately 3 to 5 minutes with participants. Semistructured questions included the following: “How did you feel about the overall testing process of Go/No-go?” “How was your experience with the Go/No-go task compared with the MoCA questionnaire?” “How do you feel about implementing this Go/No-go task for cognitive decline screening in a community setting?” Interviewers recorded key content words related to feelings, with each unique word recorded only once per participant.

This study provides empirical evidence that the increased cognitive load imposed by integrating visual-auditory stimuli and semantic categorization into a go/no-go task effectively amplifies performance differences across cognitive states, thereby supporting its utility as a discriminative tool. This novel approach indirectly assessed the episodic buffer’s ability to form coherent episodic representations, enabling early detection of cognitive decline. Our findings showed that the task complexity incrementally increased from unimodal stimuli (visual or auditory) to multimodal stimuli (audiovisual integration) and from a basic level (concrete objects such as “dog”) to a superordinate level (abstract categories such as “animal”), consistent with our hypothesis. Older adults with cognitive decline exhibited progressively worse performance in these incrementally difficult tasks.

Unimodal tasks (task 1 and 3), which relied on basic sensory discrimination, did not demonstrate superior overall performance in discriminating between cognitive statuses. Such an observation is likely due to dependence on automatic processing and low-level perception, yielding similar performance across groups. In contrast, both cross-modal matching tasks (tasks 5 and 6) performed well; in particular, they outperformed the auditory-only MoCA-attention cognitive domain (AUC=88.7%, 95% CI 88.0%‐89.4%), suggesting that multisensory integration is a reliable biomarker for cognitive impairment [39]. This is supported by findings from The Irish Longitudinal Study on Ageing, which reported broad associations between multisensory integration and multiple cognitive domains rather than links to any specific subdomain [40]. This implies that measuring multisensory integration could serve as a window into global cognition. Furthermore, as noted previously, multisensory stimulation may serve as a compensatory strategy for older adults, enhancing behavioral performance in those with NCI [26]. However, this compensatory mechanism is limited as behavioral responses are governed by the central executive system, which may account for the more pronounced deficits observed in individuals with progressive cognitive decline. Bayesian integration theory further supports this premise, proposing that perceptual reliability weighting affects audiovisual integration. In other words, the brain optimally combines sensory inputs based on their relative reliability, but cognitive decline may change this weighting process [41]. Apart from that, some scholars regard temporal synchrony and functional connectivity as 2 primary mechanisms that facilitate audiovisual integration [42]. The integration time window posits 2 sequential, interdependent stages: initial parallel peripheral processing of stimuli followed by central processes involving audiovisual integration and motor response initiation [43]. This process may help explain impairments observed in cognitive decline. Previous research indicates that patients with MCI and Alzheimer disease exhibit delayed audiovisual integration compared to older adults with normal cognitive function, with MCI populations perceiving more sound-induced flash illusions [27]. However, patients with MCI and Alzheimer disease experience attentional deficits, and these patients become distracted by irrelevant information, demonstrating impaired attention-switching speed [44]. This aligns with the theory by Baddeley [10]. Impaired performance of the episodic buffer may contribute to working memory deficit, particularly in cognitively declining populations [10]. Therefore, we propose that assessment methods involving multisensory processing can evaluate central executive performance and better identify cognitive decline.

Task 5, rather than the more cognitively demanding task 6, exhibited the most stable discriminative performance across cognitive states. This suggests that excessive complexity may overwhelm the limitations of the episodic buffer and central executive system. A study indicated that older adults tend to adopt conservative action strategies when performing complex tasks, reflecting speed-accuracy trade-offs. Specifically, they adjust their decision boundaries to increase response caution, which results in prolonged RT as a means to enhance or maintain accuracy [45]. However, a study by Chen et al [46] showed different results, with MCI and dementia populations potentially exhibiting random performance without deliberation, resulting in shorter RTs when compared to normal groups. Nonetheless, the adaptive training trials at the beginning of each task in this study could, to some extent, strengthen participants’ understanding of the tasks and avoid performance variability. However, such training for task 6, which placed the highest demands on central executive resources, may still have been insufficient, leading participants to adopt response strategies that influenced the outcomes. According to the Yerkes-Dodson law (an arousal-modulated mechanism) [47], task 5 likely reaches the peak of the inverted U-shaped curve, enhancing arousal and performance discrimination, whereas the increased cognitive load imposed by task 6 exceeds this optimum, resulting in overload and diminished utility. This may also explain why task 5 demonstrated greater stability than task 6. The findings further suggest that task difficulty must be carefully calibrated to avoid overloading the episodic buffer, which could undermine measurement consistency. Additionally, the fixed task order (task 1 to task 6) used in this study means that the decline in performance on task 6 may be partially attributable to time-on-task effects, manifested as decreased attention and less effective decision-making [48]. Previous research has shown that tasks shorter than 15 minutes are unlikely to induce significant fatigue, whereas subjective fatigue increases after 20 minutes of task engagement [49]. Although rest breaks were provided between tasks, this potential confounder should be considered when interpreting the results. Future studies should use task randomization or repeated measurements to rule out this alternative explanation.