Publications

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.

Abstract

Dealing with phonological variations is important for speech processing. This article addresses whether phonological variations introduced by assimilatory processes are compensated for at the pre-lexical or lexical level, and whether the nature of variation and the phonological context influence this process. To this end, Swedish nasal regressive place assimilation was investigated using the mismatch negativity (MMN) component. In nasal regressive assimilation, the coronal nasal assimilates to the place of articulation of a following segment, most clearly with a velar or labial place of articulation, as in utan mej “without me” > [ʉːtam mɛjː]. In a passive auditory oddball paradigm, 15 Swedish speakers were presented with Swedish phrases with attested and unattested phonological variations and contexts for nasal assimilation. Attested variations – a coronal-to-labial change as in utan “without” > [ʉːtam] – were contrasted with unattested variations – a labial-to-coronal change as in utom “except” > ∗[ʉːtɔn] – in appropriate and inappropriate contexts created by mej “me” [mɛjː] and dej “you” [dɛjː]. Given that the MMN amplitude depends on the degree of variation between two stimuli, the MMN responses were expected to indicate to what extent the distance between variants was tolerated by the perceptual system. Since the MMN response reflects not only low-level acoustic processing but also higher-level linguistic processes, the results were predicted to indicate whether listeners process assimilation at the pre-lexical and lexical levels. The results indicated no significant interactions across variations, suggesting that variations in phonological forms do not incur any cost in lexical retrieval; hence such variation is compensated for at the lexical level. However, since the MMN response reached significance only for a labial-to-coronal change in a labial context and for a coronal-to-labial change in a coronal context, the compensation might have been influenced by the nature of variation and the phonological context. It is therefore concluded that while assimilation is compensated for at the lexical level, there is also some influence from pre-lexical processing. The present results reveal not only signal-based perception of phonological units, but also higher-level lexical processing, and are thus able to reconcile the bottom-up and top-down models of speech processing.

Abstract

How listeners handle prosodic cues of linguistic and paralinguistic origin is a central question for spoken communication. In the present EEG study, we addressed this question by examining neural responses to variations in pitch accent (linguistic) and affective (paralinguistic) prosody in Swedish words, using a passive auditory oddball paradigm. The results indicated that changes in pitch accent and affective prosody elicited mismatch negativity (MMN) responses at around 200 ms, confirming the brain’s pre-attentive response to any prosodic modulation. The MMN amplitude was, however, statistically larger to the deviation in affective prosody in comparison to the deviation in pitch accent and affective prosody combined, which is in line with previous research indicating not only a larger MMN response to affective prosody in comparison to neutral prosody but also a smaller MMN response to multidimensional deviants than unidimensional ones. The results, further, showed a significant P3a response to the affective prosody change in comparison to the pitch accent change at around 300 ms, in accordance with previous findings showing an enhanced positive response to emotional stimuli. The present findings provide evidence for distinct neural processing of different prosodic cues, and statistically confirm the intrinsic perceptual and motivational salience of paralinguistic information in spoken communication.

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.