Processing non-adjacent dependencies is considered to be one of the hallmarks of human language. Assuming that sequence-learning tasks provide a useful way to tap natural-language-processing mechanisms, we cross-modally combined serial reaction time and artificial-grammar learning paradigms to investigate the processing of multiple nested (A 1 A 2 A 3 B 3 B 2 B 1 ) and crossed dependencies (A 1 A 2 A 3 B 1 B 2 B 3 ), containing either three or two dependencies. Both reaction times and prediction errors highlighted problems with processing the middle dependency in nested structures (A 1 A 2 A 3 B 3 _B 1 ), reminiscent of the 'missing-verb effect' observed in English and French, but not with crossed structures (A 1 A 2 A 3 B 1 _B 3 ). Prior linguistic experience did not play a major role: native speakers of German and Dutch-which permit nested and crossed dependencies, respectively-showed a similar pattern of results for sequences with three dependencies. As for sequences with two dependencies, reaction times and prediction errors were similar for both nested and crossed dependencies. The results suggest that constraints on the processing of multiple non-adjacent dependencies are determined by the specific ordering of the non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved (i.e. two or three). Furthermore, these constraints may not be specific to language but instead derive from limitations on structured sequence learning.Keywords: non-adjacent dependencies; sequence learning; artificial grammar learning; serial reaction time THEORETICAL BACKGROUNDThe natural-language phenomenon of recursion and its potential underlying processing mechanisms have attracted great interest from scholars of different fields, including psycholinguistics, biology, computer science and cognitive neuroscience. Most research so far has focused on sequence-learning tasks, assuming that they share cognitive mechanisms with language processing. The focus in the past decade was to determine whether such tasks could capture processing differences between specific language structures, often with their processing complexity defined in terms of the Chomsky hierarchy [1]. In the current paper, we identify two different sources of processing complexity, and we propose that processing differences are intrinsically tied to (i) the memory resources required and (ii) relevant processing experience [2]. We will empirically investigate this claim by focusing on non-adjacent dependency processing.(a) Non-adjacency in language Recursion has been suggested to be a hallmark of human language (cf.[3]). Recursion is an operation that permits a finite set of rules to generate an infinite number of expressions. In this paper, we concentrate on bounded recursive structures involving multiple overlapping non-adjacent dependencies. Their existence has been suggested by generative linguists to be one of the major challenges for empirically based approaches to language [4], as they may point to the lim...

The Stroop task is an excellent tool to test whether reading a word automatically activates its associated meaning, and it has been widely used in mono- and bilingual contexts. Despite of its ubiquity, the task has not yet been employed to test the automaticity of recently established word-concept links in novel-word-learning studies, under strict experimental control of learning and testing conditions. In three experiments, we thus paired novel words with native language (German) color words via lexical association and subsequently tested these words in a manual version of the Stroop task. Two crucial findings emerged: When novel word Stroop trials appeared intermixed among native-word trials, the novel-word Stroop effect was observed immediately after the learning phase. If no native color words were present in a Stroop block, the novel-word Stroop effect only emerged 24 h later. These results suggest that the automatic availability of a novel word's meaning depends either on supportive context from the learning episode and/or on sufficient time for memory consolidation. We discuss how these results can be reconciled with the complementary learning systems account of word learning.

Numerous studies have reported neurophysiological effects of semantic priming in electroencephalography (EEG) and in functional magnetic resonance imaging (fMRI). Because of differing methodological constraints, the comparability of the observed effects remains unclear. To directly compare EEG and fMRI effects and neural sources of semantic priming, we conducted a semantic word-picture priming experiment while measuring EEG and fMRI simultaneously. The visually presented primes were pseudowords, words unrelated to the target, semantically related words and the identical names of the target. Distributed source analysis of the event-related potentials (ERPs) successfully revealed a large effect of semantic prime-target relatedness (the N400 effect), which was driven by activations in a left-temporal source region. However, no significantly differing activations between priming conditions were found in the fMRI data. Our results support the notion that, for joint interpretations of existing EEG and fMRI studies of semantic priming, we need to fully appreciate the respective methodological limitations. Second, they show that simultaneous EEG-fMRI, including ERP source localization, is a feasible and promising methodological advancement for the investigation of higher-cognitive processes. Third, they substantiate the finding that, compared to fMRI, ERPs are often more sensitive to subtle cognitive effects.

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It is easier to indicate the ink color of a color-neutral noun when it is presented in the color in which it has frequently been shown before, relative to print colors in which it has been shown less often. This phenomenon is known as color-word contingency learning. It remains unclear whether participants actually learn semantic (word-color) associations and/or response (word-button) associations. We present a novel variant of the paradigm that can disentangle semantic and response learning, because word-color and word-button associations are manipulated independently. In four experiments, each involving four daily sessions, pseudowords—such as enas , fatu or imot —were probabilistically associated with either a particular color, a particular response-button position, or both. Neutral trials without color-pseudoword association were also included, and participants’ awareness of the contingencies was manipulated. The data showed no influence of explicit contingency awareness, but clear evidence both for response learning and for semantic learning, with effects emerging swiftly. Deeper processing of color information, with color words presented in black instead of color patches to indicate response-button positions, resulted in stronger effects, both for semantic and response learning. Our data add a crucial piece of evidence lacking so far in color-word contingency learning studies: Semantic learning effectively takes place even when associations are learned in an incidental way.