A sequential sampling account of semantic relatedness decisions

Semantic relatedness, the degree to which a pair of concepts is related, is a key variable in modeling semantic memory. Researchers have been assessing this variable with semantic relatedness decision tasks (SRDT). In SRDT, participants judge within a 2-alternative-forced choice setting whether they consider two concepts to be semantically related or not. Choices and response times in the SRDT are usually interpreted in the light of spreading activation in semantic networks. However, spreading activation alone is insufficient to explain critical behavioral benchmarks. These include the inverted U shape of response times as a function of semantic relatedness (Kenett et al., 2017) and the relatedness effect according to which “related” choices are generally faster than “unrelated” choices (Balota & Black, 1997). Here we propose that sequential sampling models of decision making, which draw on spreading activation dynamics, and on decision aspects from signal detection theory, can account for the two benchmarks. In a simulation study, we obtained behavioral predictions for three sequential sampling models, the Race model, the Leaky Competing Accumulator model (LCA) and the Drift Diffusion Model (DDM). We found that the LCA and DDM can predict both benchmarks. Interestingly, the LCA predicted that the relatedness effect reverses for weakly related concepts, implicating faster “unrelated” choices than “related” choices. This inverted relatedness effect describes a novel prediction, not yet reported in the literature. Testing this prediction on a data set by Kumar et al. (2019), we found empirical support for the inverted relatedness effect. Overall, our work highlights the importance of considering decision-related processes when studying semantic memory. Sequential sampling models constitute a productive modeling framework for semantic decision tasks.