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We report two experiments and Bayesian modelling of the data collected. In both experiments, participants performed a long-lag primed picture naming task. Black-and-white line drawings were used as targets, which were overtly named by the participants. Their naming latencies were measured. In both experiments, primes consisted of past participle verbs (er tanzt/er hat getanzt "he dances/he has danced") and the relationship between primes and targets was either morphological or unrelated. Experiment 1 additionally had phonologically and semantically related prime-target pairs as well as present tense primes. Both in Experiment 1 and 2, participants showed significantly faster naming latencies for morphologically related targets relative to the unrelated verb primes. In Experiment 1, no priming effects were observed in phonologically and semantically related control conditions. In addition, the production latencies were not influenced by verb type.
Sentence comprehension requires that the comprehender work out who did what to whom. This process has been characterized as retrieval from memory. This review summarizes the quantitative predictions and empirical coverage of the two existing computational models of retrieval and shows how the predictive performance of these two competing models can be tested against a benchmark data-set. We also show how computational modeling can help us better understand sources of variability in both unimpaired and impaired sentence comprehension.