A joint spiking neural network and neurally-inspired evidence accumulation modeling approach to human lexical decision-making

The Lexical Decision Task (LDT) is a fundamental tool for understanding how the human brain processes and recognizes words, offering insight into the complex mechanisms of language comprehension. Human lexical decision-making, the process of distinguishing words from non-words, is influenced by various factors such as word frequency, length, and semantic priming. Existing decision-making models like the drift-diffusion model capture response time and accuracy effectively but lack the biological plausibility to explicitly represent these lexical properties. This research proposes a novel joint model that combines the strengths of spiking neural networks (SNNs) and the reflecting boundary race diffusion Model (RBRDM), a neurally-inspired evidence accumulation, to overcome the mentioned limitation. The SNN component simulates neural activity in brain regions involved in lexical processing, encoding information through spike timing and count. This allows for the explicit representation of lexical properties, such as word frequency, via variations in spike patterns. The RBRDM, drawing inspiration from biological decision-making processes, accumulates evidence for and against a word decision with non-negative firing rates (known as Reflecting Boundary) and employs separate accumulators for each option (Independent Accumulators). Our findings demonstrate a strong correlation between firing rates and the frequency of the associated word in context. This potential link may reflect participants' confidence levels (higher drift rate, lower response time). However, further research is crucial to establish causality. Importantly, neural activity and spiking patterns predicted participants' response times and inferred confidence levels (drift rates). This provides a bridge between these neural phenomena and the parameters of the sequential sampling model (drift rate and threshold). Regarding morphological similarity, our trained SNN showed selectivity, suggesting potential modulation of participants' confidence levels. However, further investigation is needed to elucidate the precise nature of this influence. Word perception remains a highly complex cognitive process, encompassing multiple lexical properties and neural mechanisms. This study specifically investigated the effect of word frequency on accuracy and response time, laying the groundwork for future research to delve deeper into the multifaceted nature of this process.