Publications

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  • Zora, H., Rudner, M., & Montell Magnusson, A. (2020). Concurrent affective and linguistic prosody with the same emotional valence elicits a late positive ERP response. European Journal of Neuroscience, 51(11), 2236-2249. doi:10.1111/ejn.14658.

    Abstract

    Change in linguistic prosody generates a mismatch negativity response (MMN), indicating neural representation of linguistic prosody, while change in affective prosody generates a positive response (P3a), reflecting its motivational salience. However, the neural response to concurrent affective and linguistic prosody is unknown. The present paper investigates the integration of these two prosodic features in the brain by examining the neural response to separate and concurrent processing by electroencephalography (EEG). A spoken pair of Swedish words—[ˈfɑ́ːsɛn] phase and [ˈfɑ̀ːsɛn] damn—that differed in emotional semantics due to linguistic prosody was presented to 16 subjects in an angry and neutral affective prosody using a passive auditory oddball paradigm. Acoustically matched pseudowords—[ˈvɑ́ːsɛm] and [ˈvɑ̀ːsɛm]—were used as controls. Following the constructionist concept of emotions, accentuating the conceptualization of emotions based on language, it was hypothesized that concurrent affective and linguistic prosody with the same valence—angry [ˈfɑ̀ːsɛn] damn—would elicit a unique late EEG signature, reflecting the temporal integration of affective voice with emotional semantics of prosodic origin. In accordance, linguistic prosody elicited an MMN at 300–350 ms, and affective prosody evoked a P3a at 350–400 ms, irrespective of semantics. Beyond these responses, concurrent affective and linguistic prosody evoked a late positive component (LPC) at 820–870 ms in frontal areas, indicating the conceptualization of affective prosody based on linguistic prosody. This study provides evidence that the brain does not only distinguish between these two functions of prosody but also integrates them based on language and experience.
  • Zora, H., Riad, T., & Ylinen, S. (2019). Prosodically controlled derivations in the mental lexicon. Journal of Neurolinguistics, 52: 100856. doi:10.1016/j.jneuroling.2019.100856.

    Abstract

    Swedish morphemes are classified as prosodically specified or prosodically unspecified, depending on lexical or phonological stress, respectively. Here, we investigate the allomorphy of the suffix -(i)sk, which indicates the distinction between lexical and phonological stress; if attached to a lexically stressed morpheme, it takes a non-syllabic form (-sk), whereas if attached to a phonologically stressed morpheme, an epenthetic vowel is inserted (-isk). Using mismatch negativity (MMN), we explored the neural processing of this allomorphy across lexically stressed and phonologically stressed morphemes. In an oddball paradigm, participants were occasionally presented with congruent and incongruent derivations, created by the suffix -(i)sk, within the repetitive presentation of their monomorphemic stems. The results indicated that the congruent derivation of the lexically stressed stem elicited a larger MMN than the incongruent sequences of the same stem and the derivational suffix, whereas after the phonologically stressed stem a non-significant tendency towards an opposite pattern was observed. We argue that the significant MMN response to the congruent derivation in the lexical stress condition is in line with lexical MMN, indicating a holistic processing of the sequence of lexically stressed stem and derivational suffix. The enhanced MMN response to the incongruent derivation in the phonological stress condition, on the other hand, is suggested to reflect combinatorial processing of the sequence of phonologically stressed stem and derivational suffix. These findings bring a new aspect to the dual-system approach to neural processing of morphologically complex words, namely the specification of word stress.
  • Zormpa, E., Meyer, A. S., & Brehm, L. (2019). Slow naming of pictures facilitates memory for their names. Psychonomic Bulletin & Review, 26(5), 1675-1682. doi:10.3758/s13423-019-01620-x.

    Abstract

    Speakers remember their own utterances better than those of their interlocutors, suggesting that language production is beneficial to memory. This may be partly explained by a generation effect: The act of generating a word is known to lead to a memory advantage (Slamecka & Graf, 1978). In earlier work, we showed a generation effect for recognition of images (Zormpa, Brehm, Hoedemaker, & Meyer, 2019). Here, we tested whether the recognition of their names would also benefit from name generation. Testing whether picture naming improves memory for words was our primary aim, as it serves to clarify whether the representations affected by generation are visual or conceptual/lexical. A secondary aim was to assess the influence of processing time on memory. Fifty-one participants named pictures in three conditions: after hearing the picture name (identity condition), backward speech, or an unrelated word. A day later, recognition memory was tested in a yes/no task. Memory in the backward speech and unrelated conditions, which required generation, was superior to memory in the identity condition, which did not require generation. The time taken by participants for naming was a good predictor of memory, such that words that took longer to be retrieved were remembered better. Importantly, that was the case only when generation was required: In the no-generation (identity) condition, processing time was not related to recognition memory performance. This work has shown that generation affects conceptual/lexical representations, making an important contribution to the understanding of the relationship between memory and language.
  • Zormpa, E., Brehm, L., Hoedemaker, R. S., & Meyer, A. S. (2019). The production effect and the generation effect improve memory in picture naming. Memory, 27(3), 340-352. doi:10.1080/09658211.2018.1510966.

    Abstract

    The production effect (better memory for words read aloud than words read silently) and the picture superiority effect (better memory for pictures than words) both improve item memory in a picture naming task (Fawcett, J. M., Quinlan, C. K., & Taylor, T. L. (2012). Interplay of the production and picture superiority effects: A signal detection analysis. Memory (Hove, England), 20(7), 655–666. doi:10.1080/09658211.2012.693510). Because picture naming requires coming up with an appropriate label, the generation effect (better memory for generated than read words) may contribute to the latter effect. In two forced-choice memory experiments, we tested the role of generation in a picture naming task on later recognition memory. In Experiment 1, participants named pictures silently or aloud with the correct name or an unreadable label superimposed. We observed a generation effect, a production effect, and an interaction between the two. In Experiment 2, unreliable labels were included to ensure full picture processing in all conditions. In this experiment, we observed a production and a generation effect but no interaction, implying the effects are dissociable. This research demonstrates the separable roles of generation and production in picture naming and their impact on memory. As such, it informs the link between memory and language production and has implications for memory asymmetries between language production and comprehension.

    Additional information

    pmem_a_1510966_sm9257.pdf
  • Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O'Donnell, T. J. O., Sainburgh, T., & Gentner, T. Q. (2020). Five ways in which computational modeling can help advance cognitive science: Lessons from artificial grammar learning. Topics in Cognitive Science, 12(3), 925-941. doi:10.1111/tops.12474.

    Abstract

    There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques—even simple ones that are straightforward to use—can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space.

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