Musical structure modulates semantic priming in vocal music

It has been shown that harmonic structure may influence the processing of phonemes whatever the extent of participants' musical expertise [Bigand, E., Tillmann, B., Poulin, B., D'Adamo, D. A., & Madurell, F. (2001). The effect of harmonic context on phoneme monitoring in vocal music. Cognition, 81, B11-B20]. The present study goes a step further by investigating how musical harmony may potentially interfere with the processing of words in vocal music. Eight-chord sung sentences were presented, their last word being either semantically related (La girafe a un tres grand cou, The giraffe has a very long neck) or unrelated to the previous linguistic context (La girafe a un tres grand pied, The giraffe has a very long foot). The target word was sung on a chord that acted either as a referential tonic chord or as a congruent but less referential subdominant chord. Participants performed a lexical decision task on the target word. A significant interaction was observed between semantic and harmonic relatedness suggesting that music modulates semantic priming in vocal music. Following Jones' dynamic attention theory, we argue that music can modulate semantic priming in vocal music, by modifying the allocation of attentional resource necessary for linguistic computation.     

Re-framing the characteristics of concepts and their relation to learning and cognition in artificial agents

In this work, the problems of knowledge acquisition and information processing are explored in relation to the definitions of concepts and conceptual processing, and their implications for artificial agents.The discussion focuses on views of cognition as a dynamic property in which the world is actively represented in grounded mental states which only have meaning in the action context. Reasoning is understood as an emerging property consequence of actions-environment couplings achieved through experience, and concepts as situated and dynamic phenomena enabling behaviours.Re-framing the characteristics of concepts is considered crucial to overcoming settled beliefs and reinterpreting new understandings in artificial systems.The first part presents a review of concepts from cognitive sciences. Support is found for views on grounded and embodied cognition, describing concepts as dynamic, flexible, context-dependent, and distributedly coded.That is argued to contrast with many technical implementations assuming concepts as categories, whilst explains limitations when grounding amodal symbols, or in unifying learning, perception and reasoning.The characteristics of concepts are linked to methods of active inference, self-organization, and deep learning to address challenges posed and to reinterpret emerging techniques.In a second part, an architecture based on deep generative models is presented to illustrate arguments elaborated. It is evaluated in a navigation task, showing that sufficient representations are created regarding situated behaviours with no semantics imposed on data. Moreover, adequate behaviours are achieved through a dynamic integration of perception and action in a single representational domain and process.     

Mapping One Strong ‘Ohana: Using Network Analysis and GIS to Enhance the Effectiveness of a Statewide Coalition to Prevent Child Abuse and Neglect

Network analysis and GIS enable the presentation of meaningful data about organizational relationships and community characteristics, respectively. Together, these tools can provide a concrete representation of the ecological context in which coalitions operate, and may help coalitions identify opportunities for growth and enhanced effectiveness. This study uses network analysis and GIS mapping as part of an evaluation of the One Strong ‘Ohana (OSO) campaign. The OSO campaign was launched in 2012 via a partnership between the Hawai‘i Children’s Trust Fund (HCTF) and the Joyful Heart Foundation. The OSO campaign uses a collaborative approach aimed at increasing public awareness of child maltreatment and protective factors that can prevent maltreatment, as well as enhancing the effectiveness of the HCTF Coalition. This study focuses on three elements of the OSO campaign evaluation: (1) Network analysis exploring the relationships between 24 active Coalition member organizations, (2) GIS mapping of responses to a randomized statewide phone survey (n = 1,450) assessing awareness of factors contributing to child maltreatment, and (3) Combined GIS maps and network data, illustrating opportunities for geographically-targeted coalition building and public awareness activities.     

How useful are corpus-based methods for extrapolating psycholinguistic variables?

Subjective ratings for age of acquisition, concreteness, affective valence, and many other variables are an important element of psycholinguistic research. However, even for well-studied languages, ratings usually cover just a small part of the vocabulary. A possible solution involves using corpora to build a semantic similarity space and to apply machine learning techniques to extrapolate existing ratings to previously unrated words. We conduct a systematic comparison of two extrapolation techniques: k-nearest neighbours, and random forest, in combination with semantic spaces built using latent semantic analysis, topic model, a hyperspace analogue to language (HAL)-like model, and a skip-gram model. A variant of the k-nearest neighbours method used with skip-gram word vectors gives the most accurate predictions but the random forest method has an advantage of being able to easily incorporate additional predictors. We evaluate the usefulness of the methods by exploring how much of the human performance in a lexical decision task can be explained by extrapolated ratings for age of acquisition and how precisely we can assign words to discrete categories based on extrapolated ratings. We find that at least some of the extrapolation methods may introduce artefacts to the data and produce results that could lead to different conclusions that would be reached based on the human ratings. From a practical point of view, the usefulness of ratings extrapolated with the described methods may be limited.     

Cognitive–emotional distinctiveness: Separating emotions from non-emotions in the representation of a stressful memory

Current theories on autobiographical memory and recent neurological evidence suggest that emotional and non-emotional features of a memory may be retrieved by separate systems. To test this notion, 207 participants who had experienced the break-up of a significant romantic relationship in the last 12 months completed a Multidimensional Scaling (MDS) procedure in relation to the previous relationship. The resulting MDS model revealed two dimensions: a valence and an emotional/non-emotional dimension. Further, participants who associated a high level of distress with their relationship break-up perceived less dissimilarity between emotional and non-emotional features than participants who associated a low level of distress with their relationship break-up. Theoretical and methodological implications for stress and memory are discussed.