Instance Memory Models as a General Computational Framework for Exploring Language Processing: Bringing the Lexicon to Life

Instance models have been successfully applied to a range of problems including memory,language, attention, learning, action, decision making, and categorization (see Jamieson, Johns,Vokey, & Jones, 2022). According to instance theory, the individual experience constitutes the fundamental unit of knowledge and knowledge of the general emerges during parallel retrieval from memory (Brooks, 1978, 1987). Until recently, applications of instance theory to the problem of language were constrained to small and contrived laboratory experiments (e.g., Jamieson & Mewhort, 2009). However, the approach has now been applied at scale to the large and messy problem of natural language (e.g., Jamieson et al., 2018; Johns & Jones, 2015; Johns et al., 2020). With those demonstrations now in hand, we argue that the framework can present an articulate mechanistic underbelly to the usage-based theory of language that highlights the role of specific language experience in general language behavior (Abbot-Smith & Tomasello, 2006; Tomasello, 2003). Overall, this article argues that instance memory models provide an ability to deepen our understanding of language as a dynamic, contextually embedded process, serving to bridge the gap between cognitive psychology and the language sciences.