Publications

Abstract

The purpose of this study is to shed light to the linguistic deficit in populations with dementia, and more specifically with Mild Cognitive Impairment and Alzheimer’s Disease; by examining the assignment of thematic roles (θ-roles) in sentences including psychological verbs.
The interest in types of dementia and its precursor is due to the relevance of the disease in present-day world society (Caloi, 2017). 47 millions of people worldwide were reported by the World Alzheimer Report in 2016 (Prince et al. 2016) as people with a type of dementia. This number surpasses the number of inhabitants in Spain, a whole country, and it is expected, according to the report, to triplicate until 2050 reaching the number of 131 million. The impact of this disease is observed not only at the social level but also in the economic one, because of their need for assistance in their everyday life. What is worrying, is the lack of total treatment once the disease has started. Despite the efforts of medicine, dementia is problematic in terms of its diagnosis, because a variety of cognitive abilities is assessed in combination with medical workup. Language is a crucial component in the procedure of diagnosis as linguistic deficits are among the first symptoms that accompany the onset of the disease. Therefore, further investigation of linguistic impairment is a necessity in order to enhance the diagnostic techniques used nowadays. Furthermore, the lack of efficient drugs for the treatment of the disease has necessitated the development of training programs for maintenance and increase of the cognitive abilities in people with either Mild Cognitive Impairment or a type of dementia …

Abstract

In informal interaction, speakers rarely thank a person who has complied with a request. Examining data from British English, German, Italian, Polish, and Telugu, we ask when speakers do thank after compliance. The results show that thanking treats the other’s assistance as going beyond what could be taken for granted in the circumstances. Coupled with the rareness of thanking after requests, this suggests that cooperation is to a great extent governed by expectations of helpfulness, which can be long-standing, or built over the course of a particular interaction. The higher frequency of thanking in some languages (such as English or Italian) suggests that cultures differ in the importance they place on recognizing the other’s agency in doing as requested.

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

Neural correlates of lexical stress were studied using the mismatch negativity (MMN) component in event-related potentials. The MMN responses were expected to reveal the encoding of stress information into long-term memory and the contributions of prosodic features such as fundamental frequency (F0) and intensity toward lexical access. In a passive oddball paradigm, neural responses to changes in F0, intensity, and in both features together were recorded for words and pseudowords. The findings showed significant differences not only between words and pseudowords but also between prosodic features. Early processing of prosodic information in words was indexed by an intensity-related MMN and an F0-related P200. These effects were stable at right-anterior and mid-anterior regions. At a later latency, MMN responses were recorded for both words and pseudowords at the mid-anterior and posterior regions. The P200 effect observed for F0 at the early latency for words developed into an MMN response. Intensity elicited smaller MMN for pseudowords than for words. Moreover, a larger brain area was recruited for the processing of words than for the processing of pseudowords. These findings suggest earlier and higher sensitivity to prosodic changes in words than in pseudowords, reflecting a language-related process. The present study, therefore, not only establishes neural correlates of lexical stress but also confirms the presence of long-term memory traces for prosodic information in the brain.

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.