The role of emotional mediation in musical and vocal sound-color correspondence

This study investigates the role of emotional mediation in sound-color cross-modal correspondence, using twocomplementary sets of validated stimuli: the Montreal Affective Voices (MAV; Belin et al., 2008), and Musical EmotionalBursts (MEB; Paquette et al., 2013). These stimuli were presented to participants for color associations, emotional associations,and rated for arousal and valence. The results demonstrated that the same pattern of color association applied across both vocaland musical sounds, which strongly correlated with the perceived emotional connotation of the sound. Sounds across bothdomains that were rated as high arousal/negative valence were associated with red (anger), sounds rated as high arousal/positivevalence were associated with yellow (happiness), and sounds rated as low arousal/negative valence were associated with blue(sadness). The results thus replicate previous research indicating that arousal and valence govern sound-color correspondence,suggesting that cross-modal associations may reflect reciprocal interactions between the connotative meanings of differentstimuli

Statistically based chunking of nonadjacent dependencies.

How individuals learn complex regularities in the environment and generalize them to new instances is a key question in cognitive science. Although previous investigations have advocated the idea that learning and generalizing depend upon separate processes, the same basic learning mechanisms may account for both. In language learning experiments, these mechanisms have typically been studied in isolation of broader cognitive phenomena such as memory, perception, and attention. Here, we show how learning and generalization in language is embedded in these broader theories by testing learners on their ability to chunk nonadjacent dependencies—a key structure in language but a challenge to theories that posit learning through the memorization of structure. In two studies, adult participants were trained and tested on an artificial language containing nonadjacent syllable dependencies, using a novel chunking-based serial recall task involving verbal repetition of target sequences (formed from learned strings) and scrambled foils. Participants recalled significantly more syllables, bigrams, trigrams, and nonadjacent dependencies from sequences conforming to the language’s statistics (both learned and generalized sequences). They also encoded and generalized specific nonadjacent chunk information. These results suggest that participants chunk remote dependencies and rapidly generalize this information to novel structures. The results thus provide further support for learning-based approaches to language acquisition, and link statistical learning to broader cognitive mechanisms of memory

Testing the limits of non-adjacent dependency learning:Statistical segmentation and generalization across domains

Achieving linguistic proficiency requires identifying words from speech, and discovering the constraints that govern the way those words are used. In a recent study of non-adjacent dependency learning, Frost and Monaghan (2016) demonstrated that learners may perform these tasks together, using similar statistical processes — contrary to prior suggestions. However, in their study, non-adjacent dependencies were marked by phonological cues (plosive- continuant-plosive structure), which may have influenced learning. Here, we test the necessity of these cues by comparing learning across three conditions; fixed phonology, which contains these cues, varied phonology, which omits them, and shapes, which uses visual shape sequences to assess the generality of statistical processing for these tasks. Participants segmented the sequences and generalized the structure in both auditory conditions, but learning was best when phonological cues were present. Learning was around chance on both tasks for the visual shapes group, indicating statistical processing may critically differ across domains

Statistical learning as chunking: Domain general computations in language acquisition

245 pagesUnderstanding the computations involved in language acquisition is a central topic in cognitive science. This dissertation presents four empirical papers that investigate the role of domain general cognitive processes in the learning of linguistic structure. The first paper describes the contribution of chunking—a basic memory process—to the phenomenon known as statistical learning, which describes learners’ ability to leverage the regularities present in the environment to form concrete representations of the input, such as finding the words in speech. The second paper extends these findings by showing how chunking can also account for the statistical learning and generalization of non-adjacent dependencies, a key feature of many linguistic systems. The third paper demonstrates that individual differences in statistically-based chunking of artificial language statistics significantly predicts sensitivity to comparable statistical structures in natural language. The final paper presents a meta-analysis of nearly 500 peer-reviewed studies on statistical learning in infants, children, and adults, tests its utility across different language properties, and proposes several methodological considerations that may benefit future experimentation. Together, these studies highlight the fundamental contribution of basic, domain general computations to language—and how they may even shape the evolution of linguistic structure over time.2023-09-1

The Color of Music: Synesthesia or emotion-mediated cross-modal associations?

The cross-modal literature posits a weak-to-strong continuum of synesthesia. One extreme views cross-modal associations as idiosyncratic and unique to synesthetes. The other extreme suggests that cross-modal associations follow a general pattern across individuals, and are mediated by emotional associations. We tested these views by examining differences between music-color synesthetes and non-synesthetes in their consistency of color associations and memory for music. We find that music-color associations follow the same general pattern across these groups. A two-dimensional mapping is found to mode (major/minor) and tempo. Slow-minor music (thought to convey sadness) is associated with blue, fast-minor with red (anger), fast-major with yellow (happiness), and slow-major with green (calmness). Both groups are consistent in their associations over time, and synesthesia has no effect on memory. We conclude that music-color synesthesia may be an extension of normal psychological processes that govern cross-modal associations, with individuals aligning music and color based on emotional congruence

Chunking Ability Shapes Sentence Processing at Multiple Levels of Abstraction

Several recent empirical findings have reinforced the notion that a basic learning and memory skill—chunking—plays a fundamental role in language processing. Here, we provide evidence that chunking shapes sentence processing at multiple levels of linguistic abstraction, consistent with a recent theoretical proposal by Christiansen and Chater (2016). Individual differences in chunking ability at two different levels is shown to predict on-line sentence processing in separate ways: i) phonological chunking ability, as assessed by a variation on the non-word repetition task, predicts processing of complex sentences featuring phonological overlap; ii) multiword chunking ability, as assessed by a variation on the serial recall task, is shown to predict reading times for sentences featuring long-distance number agreement with locally distracting number-marked nouns. Together, our findings suggest that individual differences in chunking ability shape language processing at multiple levels of abstraction, consistent with the notion of language acquisition as learning to process