Abstract
We propose that feature bundles in syntactic computations coactivate subset vocabulary items in content-based access. Thus, in French, an inflectional node for the future tense bearing [Number: Plural] activates third-person singular -a underspecified for [Number: Ø] and third- person plural -ont specified for [Number: Plural]. These activations compete in externalization. [Number: Ø/Plural] defines a <-ont, -a> scale where plural-marked -ont carries more information than -a, allowing scalar inferencing in form selection. Given an activated Plural-marked -ont, -a (3ps.sg) is automatically interpreted as [–Plural] via scalar inferencing and becomes unsuitable for insertion. Thus, -ont (3ps.pl) must be selected when -a (3ps.sg) is eliminated. We tested our model assuming an interaction of cross-domain inferencing with morphological selection, using two experiments. In forced-paced reading and listening tasks, 19 native speaker subjects per task classified picture probes accompanying matching and mismatching subject-verb future tense agreement. Classification times for pictures semantically linked to the verb probed for an interaction between the processing of agreement morphology and the ongoing conceptual processing of the sentence. Classification times were modulated by the type of morphological mismatch. Singular verb form mangera (eat-fut.3ps.sg) in plural contexts slowed down picture classifications, whereas plural verb form mangeront (eat-fut.3ps.pl) in singular contexts did not. This specific interaction between purely formal agreement and conceptual-structure processing is unexplained by superset models of late insertion, or by interface relations, frequency, load of stored information, and phonological cohort activation. It suggests that domain-general principles of inference enrich domain-specific feature-based computations accessing vocabulary items through the coactivation of features.
Highlights
A long-standing body of syntactic research has fruitfully explored the hypothesis that complex words are derived as part of syntactic computations
Where several subset vocabulary items could be inserted, a selection criterion requires that the item specified for the greatest number of features represented in syntax be selected in accordance with the Elsewhere Principle (Halle 1997; Embick & Noyer 2007)
We argued that the Subset Principle of Distributed Morphology (DM) with insertion and selection conditions can be profitably seen as following from basic cognitive processes: content-based access in feature coactivation by syntactic computations and conceptual processes of scalar inference that enable the selection of the most informative item from a set of alternatives ordered on an information scale
Summary
A long-standing body of syntactic research has fruitfully explored the hypothesis that complex words are derived as part of syntactic computations (see Baker 1988; Li 1990; Lasnik 1995; Marantz 1997; Ackema & Neeleman 2004; Kornfilt & Whitman 2011; Bobaljik 2017; Starke 2010; 2018). In Distributed Morphology (DM) (Halle & Marantz 1993), vocabulary items can be inserted when their specifications contain a subset of the grammatical features in the syntactic representation. Insertion is excluded when the vocabulary item contains specifications of features that are not present in the syntactic representation. Where several subset vocabulary items could be inserted, a selection criterion requires that the item specified for the greatest number of features represented in syntax be selected in accordance with the Elsewhere Principle (see the Subset Principle of DM) (Halle 1997; Embick & Noyer 2007). Where several items meet the superset condition for insertion, a selection criterion requires that the item with the fewest features unspecified in the structure be selected, under the general principle “minimize junk”, as a general prohibition against unnecessary/unwarranted information (Starke 2010: 4)
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