Neural responses appear to synchronize with sentence structure. However, researchers have debated whether this response in the delta band (0.5–3 Hz) really reflects hierarchical information, or simply lexical regularities. Computational simulations in which sentences are represented simply as sequences of high-dimensional numeric vectors that encode lexical information seem to give rise to power spectra similar to those observed for sentence synchronization, suggesting that sentence-level cortical tracking findings may reflect sequential lexical or part-of-speech information, and not necessarily hierarchical syntactic information. Using electroencephalography (EEG) data and the frequency-tagging paradigm, we develop a novel experimental condition to tease apart the predictions of the lexical and the hierarchical accounts of the attested low-frequency synchronization. Under a lexical model, synchronization should be observed even when words are reversed within their phrases (e.g. “sheep white grass eat” instead of “white sheep eat grass”), because the same lexical items are preserved at the same regular intervals. Critically, such stimuli are not syntactically well-formed; thus a hierarchical model does not predict synchronization of phrase- and sentence-level structure in the reversed phrase condition. Computational simulations confirm these diverging predictions. EEG data from N = 31 native speakers of Mandarin show robust delta synchronization to syntactically well-formed isochronous speech. Importantly, no such pattern is observed for reversed phrases, consistent with the hierarchical, but not the lexical, accounts.

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This paper develops a formal definition of Minimal Search to evaluate the idea that Agree and Labeling could be reduced to Minimal Search. Different aspects of the search algorithm in Minimal Search, i.e., breadth-first vs. depth-first search, parallel vs. serial search, global vs. modular search are compared, and reasons for choosing between each of these pairs are given based on detailed examinations of their theoretical and empirical consequences. This paper argues, based on the formal definition of Minimal Search, that Agree and Labeling can only be partially unified by Minimal Search: the search algorithms in Agree and Labeling can be unified by Minimal Search, but the values of the search targets and search domains are determined by Agree and Labeling independently. This paper (re)defines Agree and Labeling based on Minimal Search to capture both the similarities and differences between these two operations.

This paper explores empirical merits of a version of Agree that is defined based on Minimal Search (MS-Agree). Compared to the standard Agree, MS-Agree, essentially a search algorithm, uniquely allows the independent assignment of its search target and search domain. This unique feature enables MS-Agree to accommodate both upward and downward agreement phenomena, and offers a unified downward search analysis for negative concord, inflection doubling, multiple case-assignment, cyclic agreement, and complementizer agreement observed across languages. This paper thus argues that these core empirical data that have served as the main motivation for Upward Agree can be successfully reanalyzed with MS-Agree. It is also argued that the proposed MS-Agree analysis makes better predictions than Upward Agree regarding intervention effects in apparent upward agreement phenomena.