Publications

Displaying 1 - 6 of 6
  • Bocanegra, B. R., Poletiek, F. H., & Zwaan, R. A. (2022). Language concatenates perceptual features into representations during comprehension. Journal of Memory and Language, 127: 104355. doi:10.1016/j.jml.2022.104355.

    Abstract

    Although many studies have investigated the activation of perceptual representations during language comprehension, to our knowledge only one previous study has directly tested how perceptual features are combined into representations during comprehension. In their classic study, Potter and Faulconer [(1979). Understanding noun phrases. Journal of Verbal Learning and Verbal Behavior, 18, 509–521.] investigated the perceptual representation of adjective-noun combinations. However, their non-orthogonal design did not allow the differentiation between conjunctive vs. disjunctive representations. Using randomized orthogonal designs, we observe evidence for disjunctive perceptual representations when participants represent feature combinations simultaneously (in several experiments; N = 469), and we observe evidence for conjunctive perceptual representations when participants represent feature combinations sequentially (In several experiments; N = 628). Our findings show that the generation of conjunctive representations during comprehension depends on the concatenation of linguistic cues, and thus suggest the construction of elaborate perceptual representations may critically depend on language.
  • Poletiek, F. H., Monaghan, P., van de Velde, M., & Bocanegra, B. R. (2021). The semantics-syntax interface: Learning grammatical categories and hierarchical syntactic structure through semantics. Journal of Experimental Psychology: Learning, Memory, and Cognition, 47(7), 1141-1155. doi:10.1037/xlm0001044.

    Abstract

    Language is infinitely productive because syntax defines dependencies between grammatical categories of words and constituents, so there is interchangeability of these words and constituents within syntactic structures. Previous laboratory-based studies of language learning have shown that complex language structures like hierarchical center embeddings (HCE) are very hard to learn, but these studies tend to simplify the language learning task, omitting semantics and focusing either on learning dependencies between individual words or on acquiring the category membership of those words. We tested whether categories of words and dependencies between these categories and between constituents, could be learned simultaneously in an artificial language with HCE’s, when accompanied by scenes illustrating the sentence’s intended meaning. Across four experiments, we showed that participants were able to learn the HCE language varying words across categories and category-dependencies, and constituents across constituents-dependencies. They also were able to generalize the learned structure to novel sentences and novel scenes that they had not previously experienced. This simultaneous learning resulting in a productive complex language system, may be a consequence of grounding complex syntax acquisition in semantics.
  • Poletiek, F. H. (2006). De dwingende macht van een Goed Verhaal [Boekbespreking van Vincent plast op de grond:Nachtmerries in het Nederlands recht door W.A. Wagenaar]. De Psycholoog, 41, 460-462.
  • Poletiek, F. H., & Chater, N. (2006). Grammar induction profits from representative stimulus sampling. In R. Sun (Ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (CogSci 2006) (pp. 1968-1973). Austin, TX, USA: Cognitive Science Society.
  • Poletiek, F. H. (2006). Natural sampling of stimuli in (artificial) grammar learning. In K. Fiedler, & P. Juslin (Eds.), Information sampling and adaptive cognition (pp. 440-455). Cambridge: Cambridge University Press.
  • Van den Bos, E. J., & Poletiek, F. H. (2006). Implicit artificial grammar learning in adults and children. In R. Sun (Ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (CogSci 2006) (pp. 2619). Austin, TX, USA: Cognitive Science Society.

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