A typology of internet users based on comparative affective states: evidence from eight countries

Purpose - The role of affective states in consumer behaviour is well established. However, no study to date has empirically examined online affective states as a basis for constructing typologies of internet users and for assessing the invariance of clusters across national cultures. Design/methodology/approach - Four focus groups with internet users were carried out to adapt a set of affective states identified from the literature to the online environment. An online survey was then designed to collect data from internet users in four Western and four East Asian countries. Findings - Based on a cluster analysis, six cross-national market segments are identified and labelled "Positive Online Affectivists", "Offline Affectivists", "On/Off-line Negative Affectivists", "Online Affectivists", "Indistinguishable Affectivists", and "Negative Offline Affectivists". The resulting clusters discriminate on the basis of national culture, gender, working status and perceptions towards online brands. Practical implications - Marketers may use this typology to segment internet users in order to predict their perceptions towards online brands. Also, a standardised approach to e-marketing is not recommended on the basis of affective state-based segmentation. Originality/value - This is the first study proposing affective state-based typologies of internet users using comparable samples from four Western and four East Asian countries.

The role of negative maternal affective states and infant temperament in early interactions between infants with cleft lip and their mothers

OBJECTIVES: The study examined the early interaction between mothers and their infants with cleft lip, assessing the role of maternal affective state and expressiveness and differences in infant temperament. METHODS: Mother-infant interactions were assessed in 25 2-month-old infants with cleft lip and 25 age-matched healthy infants. Self-report and behavioral observations were used to assess maternal depressive symptoms and expressions. Mothers rated infant temperament. RESULTS: Infants with cleft lip were less engaged and their mothers showed more difficulty in interaction than control group dyads. Mothers of infants with cleft lip displayed more negative affectivity, but did not report more self-rated depressive symptoms than control group mothers. No group differences were found in infant temperament. CONCLUSIONS: In order to support the mother's experience and facilitate her ongoing parental role, findings highlight the importance of identifying maternal negative affectivity during early interactions, even when they seem have little awareness of their depressive symptoms.

Nature of autobiographical memory that facilitates improvement of negative moods

Sakaki, M. (2005). Persons in negative moods often recall their positive experiences to alleviate their affective states. This study investigated the nature of autobiographical memories that facilitate improvement of negative moods. More specifically, it examined the relations between participants' subjective moods and the three natures of recalled memories (the current positivity, the past positivity, and the importance). After 67 participants were induced either negative or neutral moods, they were asked to recall five positive experiences and to rate their affective states. Results indicated that the more important memories participants recalled, the more positive moods they reported after recollection, and that these effects of the importance of autobiographical memories were observed in both negative and neutral moods. In contrast, neither the current positivity of memories nor the past positivity of memories significantly influenced participant's moods in both negative and neutral moods. The mechanism by which the importance of autobiographical memory affects moods was discussed.

Automated identification of neural correlates of continuous variables

Background: The electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables. New method: A method is presented for the automated identification of features that differentiate two or more groups inneurologicaldatasets basedupona spectraldecompositionofthe feature set. Furthermore, the method is able to identify features that relate to continuous independent variables. Results: The proposed method is first evaluated on synthetic EEG datasets and observed to reliably identify the correct features. The method is then applied to EEG recorded during a music listening task and is observed to automatically identify neural correlates of music tempo changes similar to neural correlates identified in a previous study. Finally,the method is applied to identify neural correlates of music-induced affective states. The identified neural correlates reside primarily over the frontal cortex and are consistent with widely reported neural correlates of emotions. Comparison with existing methods: The proposed method is compared to the state-of-the-art methods of canonical correlation analysis and common spatial patterns, in order to identify features differentiating synthetic event-related potentials of different amplitudes and is observed to exhibit greater performance as the number of unique groups in the dataset increases. Conclusions: The proposed method is able to identify neural correlates of continuous variables in EEG datasets and is shown to outperform canonical correlation analysis and common spatial patterns.

Investigating affect in algorithmic composition systems

There has been a significant amount of work implementing systems for algorithmic composition with the intention of targeting specific emotional responses in the listener, but a full review of this work is not currently available. This gap creates a shared obstacle to those entering the field. Our aim is thus to give an overview of progress in the area of these affectively driven systems for algorithmic composition. Performative and transformative systems are included and differentiated where appropriate, highlighting the challenges these systems now face if they are to be adapted to, or have already incorporated, some form of affective control. Possible real-time applications for such systems, utilizing affectively driven algorithmic composition and biophysical sensing to monitor and induce affective states in the listener are suggested.

One’s own soundtrack: affective music synthesis

Computer music usually sounds mechanical; hence, if musicality and music expression of virtual actors could be enhanced according to the user’s mood, the quality of experience would be amplified. We present a solution that is based on improvisation using cognitive models, case based reasoning (CBR) and fuzzy values acting on close-to-affect-target musical notes as retrieved from CBR per context. It modifies music pieces according to the interpretation of the user’s emotive state as computed by the emotive input acquisition componential of the CALLAS framework. The CALLAS framework incorporates the Pleasure-Arousal-Dominance (PAD) model that reflects emotive state of the user and represents the criteria for the music affectivisation process. Using combinations of positive and negative states for affective dynamics, the octants of temperament space as specified by this model are stored as base reference emotive states in the case repository, each case including a configurable mapping of affectivisation parameters. Suitable previous cases are selected and retrieved by the CBR subsystem to compute solutions for new cases, affect values from which control the music synthesis process allowing for a level of interactivity that makes way for an interesting environment to experiment and learn about expression in music.