A sequential sampling account of semantic relatedness decisions
- Author(s): Kraemer, Peter M;
- Wulff, Dirk;
- Gluth, Sebastian
- et al.
Semantic relatedness decisions – decisions whether two concepts are semantically related or not – depend on cognitive processes of semantic memory and decision making. However, behavioral findings are mostly interpreted in the light of memory retrieval as spreading activation but neglects decision components. We propose that sequential sampling models (SSM) of decision making as a novel computational account of choice and response time data. In a simulation study, we generate data from basic SSM versions. We show if and how these models can account for two established behavioral benchmarks: The inverted-U shape of response times and the relatedness effect. Further, one of the SSM, the Leaky Competing Accumulator model makes a novel prediction: The relatedness reverses for weakly related word pairs. Reanalyzing a publicly available data set, we found credible evidence that this prediction holds empirically. Our results provide strong support for SSM as a viable computational account of semantic relatedness decisions.