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The termprocess modelis widely used, but rarely agreed upon. This paper proposes a framework for characterizing and building cognitive process models. Process models model not only inputs and outputs but also model the ongoing information transformations at a given level of abstraction. We argue that the following dimensions characterize process models: They have a scope that includes different levels of abstraction. They specify a hypothesized mental information transformation. They make predictions not only for the behavior of interest but also for processes. The models' predictions for the processes can be derived from the input, without reverse inference from the output data. Moreover, the presumed information transformation steps are not contradicting current knowledge of human cognitive capacities. Lastly, process models require a conceptual scope specifying levels of abstraction for the information entering the mind, the proposed mental events, and the behavior of interest. This framework can be used for refining models before testing them or after testing them empirically, and it does not rely on specific modeling paradigms. It can be a guideline for developing cognitive process models. Moreover, the framework can advance currently unresolved debates about which models belong to the category of process models.
This dissertation addresses the question of how linguistic structures can be represented in working memory. We propose a memory-based computational model that derives offline and online complexity profiles in terms of a top-down parser for minimalist grammars (Stabler, 2011). The complexity metric reflects the amount of time an item is stored in memory. The presented architecture links grammatical representations stored in memory directly to the cognitive behavior by deriving predictions about sentence processing difficulty.
Results from five different sentence comprehension experiments were used to evaluate the model's assumptions about memory limitations. The predictions of the complexity metric were compared to the locality (integration and storage) cost metric of Dependency Locality Theory (Gibson, 2000). Both metrics make comparable offline and online predictions for four of the five phenomena. The key difference between the two metrics is that the proposed complexity metric accounts for the structural complexity of intervening material. In contrast, DLT's integration cost metric considers the number of discourse referents, not the syntactic structural complexity.
We conclude that the syntactic analysis plays a significant role in memory requirements of parsing. An incremental top-down parser based on a grammar formalism easily computes offline and online complexity profiles, which can be used to derive predictions about sentence processing difficulty.