Arash Aryani

Extracting salient sublexical units from written texts: “Emophon,” a corpus-based approach to phonological iconicity

A growing body of literature in psychology, linguistics, and the neurosciences has paid increasing attention to the understanding of the relationships between phonological representations of words and their meaning: a phenomenon also known as phonological iconicity. In this article, we investigate how a texts intended emotional meaning, particularly in literature and poetry, may be reflected at the level of sublexical phonological salience and the use of foregrounded elements. To extract such elements from a given text, we developed a probabilistic model to predict the exceeding of a confidence interval for specific sublexical units concerning their frequency of occurrence within a given text contrasted with a reference linguistic corpus for the German language. Implementing this model in a computational application, we provide a text analysis tool which automatically delivers information about sublexical phonological salience allowing researchers, inter alia, to investigate effects of the sublexical emotional tone of texts based on current findings on phonological iconicity.

Interplay of bigram frequency and orthographic neighborhood statistics in language membership decision

Language-specific orthography (i.e., letters or bigrams that exist in only one language) is known to facilitate language membership recognition. Yet the contribution of continuous sublexical and lexical statistics to language membership decisions during visual word processing is unknown. Here, we used pseudo-words to investigate whether continuous sublexical and lexical statistics bias explicit language decisions (Experiment 1) and language attribution during naming (Experiment 2). We also asked whether continuous statistics would have an effect in the presence of orthographic markers. Language attribution in both experiments was influenced by lexical neighborhood size differences between languages, even in presence of orthographic markers. Sublexical frequencies of occurrence affected reaction times only for unmarked pseudo-words in both experiments, with greater effects in naming. Our results indicate that bilinguals rely on continuous language-specific statistics at sublexical and lexical levels to infer language membership. Implications are discussed with respect to models of bilingual visual word recognition.

On the Relation between the General Affective Meaning and the Basic Sublexical, Lexical, and Inter-lexical Features of Poetic Texts—A Case Study Using 57 Poems of H. M. Enzensberger

The literary genre of poetry is inherently related to the expression and elicitation of emotion via both content and form. To explore the nature of this affective impact at an extremely basic textual level, we collected ratings on eight different general affective meaning scales—valence, arousal, friendliness, sadness, spitefulness, poeticity, onomatopoeia, and liking—for 57 German poems (“die verteidigung der wölfe”) which the contemporary author H. M. Enzensberger had labeled as either “friendly,” “sad,” or “spiteful.” Following Jakobsons (1960) view on the vivid interplay of hierarchical text levels, we used multiple regression analyses to explore the specific influences of affective features from three different text levels (sublexical, lexical, and inter-lexical) on the perceived general affective meaning of the poems using three types of predictors: (1) Lexical predictor variables capturing the mean valence and arousal potential of words; (2) Inter-lexical predictors quantifying peaks, ranges, and dynamic changes within the lexical affective content; (3) Sublexical measures of basic affective tone according to sound-meaning correspondences at the sublexical level (see Aryani et al., 2016). We find the lexical predictors to account for a major amount of up to 50% of the variance in affective ratings. Moreover, inter-lexical and sublexical predictors account for a large portion of additional variance in the perceived general affective meaning. Together, the affective properties of all used textual features account for 43–70% of the variance in the affective ratings and still for 23–48% of the variance in the more abstract aesthetic ratings. In sum, our approach represents a novel method that successfully relates a prominent part of variance in perceived general affective meaning in this corpus of German poems to quantitative estimates of affective properties of textual components at the sublexical, lexical, and inter-lexical level.

Bridges from affect to language Comment on "The quartet theory of human emotions: An integrative and neurofunctional model" by S. Koelsch et al.

The comprehensive Quartet Theory of Human Emotions proposed by Koelsch et al. [4] offers an exceptional synopsis regarding major developments in affective neuroscience, encompassing classical data based on animal studies as well as emotions generally classified as uniquely human. In doing so, it becomes apparent that while general anatomical grounds appear well covered mainly based on animal studies, neuroanatomical underpinnings of interactions between emotion and language may not be readily understood. An important insight accounting for this matter is advanced by Pessoa [3], who suggests high levels of functional integration of networks of brain areas forming the basis of complex cognitive–emotional behaviors such as language. With a clear functional segregation of brain areas becoming less likely with increasing distance from sensory input, dynamic coalitions of networks of brain areas exhibit high degrees of connectivity, thus integrating the flow of information across distant neuroanatomical areas of different domains while preserving the general hierarchical architecture found throughout lower processing states [3]. To start out with feeling states, Damasio and Carvalho [2] offer an excellent illustration of how a high degree of functional integration constitutes a necessary precondition for their emergence, making clearcut localizations of particular functions difficult. They suggest rerepresentations of simpler, phylogenetically older neural body maps originating largely from brain stem areas (as have been outlined by Koelsch et al.) via projections into continuously higher, phylogenetically younger cortical tissue. This idea of grounding complex feeling states into basic, necessarily valenced deviations from essential homeostatic states is mirrored e.g. in Rolls’ [5] approach, conceptualizing emotion as evolutions solution to flexibly setting goals for the organism that ultimately serve survival. Simple forms of communication of feeling states may already originate from brain stem areas and are thus to be found in phylogenetically older species as well. However, human language does not merely serve the expression of emotion but can rather be understood as evolutionary advancement of simpler forms of action selection and action planning towards highly abstract representations based on the use of symbolic code, though still following higher order goals. Accordingly, emotion constitutes a hub between sensory input and action, thus serving the same goals as the cognitive system

Activation patterns throughout the word processing network of l1-dominant bilinguals reflect language similarity and language decisions

A crucial aspect of bilingual communication is the ability to identify the language of an input. Yet, the neural and cognitive basis of this ability is largely unknown. Moreover, it cannot be easily incorporated into neuronal models of bilingualism, which posit that bilinguals rely on the same neural substrates for both languages and concurrently activate them even in monolingual settings. Here we hypothesized that bilinguals can employ language-specific sublexical (bigram frequency) and lexical (orthographic neighborhood size) statistics for language recognition. Moreover, we investigated the neural networks representing language-specific statistics and hypothesized that language identity is encoded in distributed activation patterns within these networks. To this end, German–English bilinguals made speeded language decisions on visually presented pseudowords during fMRI. Language attribution followed lexical neighborhood sizes both in first (L1) and second (L2) language. RTs revealed an overall tuning to L1 bigram statistics. Neuroimaging results demonstrated tuning to L1 statistics at sublexical (occipital lobe) and phonological (temporoparietal lobe) levels, whereas neural activation in the angular gyri reflected sensitivity to lexical similarity to both languages. Analysis of distributed activation patterns reflected language attribution as early as in the ventral stream of visual processing. We conclude that in language-ambiguous contexts visual word processing is dominated by L1 statistical structure at sublexical orthographic and phonological levels, whereas lexical search is determined by the structure of both languages. Moreover, our results demonstrate that language identity modulates distributed activation patterns throughout the reading network, providing a key to language identity representations within this shared network.

The Sound of Words Evokes Affective Brain Responses

The long history of poetry and the arts, as well as recent empirical results suggest that the way a word sounds (e.g., soft vs. harsh) can convey affective information related to emotional responses (e.g., pleasantness vs. harshness). However, the neural correlates of the affective potential of the sound of words remain unknown. In an fMRI study involving passive listening, we focused on the affective dimension of arousal and presented words organized in two discrete groups of sublexical (i.e., sound) arousal (high vs. low), while controlling for lexical (i.e., semantic) arousal. Words sounding high arousing, compared to their low arousing counterparts, resulted in an enhanced BOLD signal in bilateral posterior insula, the right auditory and premotor cortex, and the right supramarginal gyrus. This finding provides first evidence on the neural correlates of affectivity in the sound of words. Given the similarity of this neural network to that of nonverbal emotional expressions and affective prosody, our results support a unifying view that suggests a core neural network underlying any type of affective sound processing.

Affective Congruence between Sound and Meaning of Words Facilitates Semantic Decision

A similarity between the form and meaning of a word (i.e., iconicity) may help language users to more readily access its meaning through direct form-meaning mapping. Previous work has supported this view by providing empirical evidence for this facilitatory effect in sign language, as well as for onomatopoetic words (e.g., cuckoo) and ideophones (e.g., zigzag). Thus, it remains largely unknown whether the beneficial role of iconicity in making semantic decisions can be considered a general feature in spoken language applying also to “ordinary” words in the lexicon. By capitalizing on the affective domain, and in particular arousal, we organized words in two distinctive groups of iconic vs. non-iconic based on the congruence vs. incongruence of their lexical (meaning) and sublexical (sound) arousal. In a two-alternative forced choice task, we asked participants to evaluate the arousal of printed words that were lexically either high or low arousing. In line with our hypothesis, iconic words were evaluated more quickly and more accurately than their non-iconic counterparts. These results indicate a processing advantage for iconic words, suggesting that language users are sensitive to sound-meaning mappings even when words are presented visually and read silently.

Why 'piss' is ruder than 'pee'? The role of sound in affective meaning making

Most language users agree that some words sound harsh (e.g. grotesque) whereas others sound soft and pleasing (e.g. lagoon). While this prominent feature of human language has always been creatively deployed in art and poetry, it is still largely unknown whether the sound of a word in itself makes any contribution to the word’s meaning as perceived and interpreted by the listener. In a large-scale lexicon analysis, we focused on the affective substrates of words’ meaning (i.e. affective meaning) and words’ sound (i.e. affective sound); both being measured on a two-dimensional space of valence (ranging from pleasant to unpleasant) and arousal (ranging from calm to excited). We tested the hypothesis that the sound of a word possesses affective iconic characteristics that can implicitly influence listeners when evaluating the affective meaning of that word. The results show that a significant portion of the variance in affective meaning ratings of printed words depends on a number of spectral and temporal acoustic features extracted from these words after converting them to their spoken form (study1). In order to test the affective nature of this effect, we independently assessed the affective sound of these words using two different methods: through direct rating (study2a), and through acoustic models that we implemented based on pseudoword materials (study2b). In line with our hypothesis, the estimated contribution of words’ sound to ratings of words’ affective meaning was indeed associated with the affective sound of these words; with a stronger effect for arousal than for valence. Further analyses revealed crucial phonetic features potentially causing the effect of sound on meaning: For instance, words with short vowels, voiceless consonants, and hissing sibilants (as in ‘piss’) feel more arousing and negative. Our findings suggest that the process of meaning making is not solely determined by arbitrary mappings between formal aspects of words and concepts they refer to. Rather, even in silent reading, words’ acoustic profiles provide affective perceptual cues that language users may implicitly use to construct words’ overall meaning.