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It has been hypothesized recently that many social phenomena and consumer choices are characterized by a long-tail distribution. We investigated the preferences of 4- to 8-year-old children in the domain of television programs, and found a long-tail distribution of children's preference, characterized by Zipf's law. We also found a high awareness of parents about the preferences of their children, even about those that they judged negatively. Our findings might have useful implications for the study of consumers' behavior and for research on complex interactions between psychological and socioeconomic factors.

Conceptual knowledge is acquired through recurrent experiences, by extracting statistical regularities at different levels of granularity. At a fine level, patterns of feature co-occurrence are categorized into objects. At a coarser level, patterns of concept co-occurrence are categorized into contexts. We present and test CONCAT, a connectionist model that simultaneously learns to categorize objects and contexts. The model contains two hierarchically organized CALM modules (Murre, Phaf, & Wolters, 1992). The first module, the Object Module, forms object representations based on co-occurrences between features. These representations are used as input for the second module, the Context Module, which categorizes contexts based on object co-occurrences. Feedback connections from the Context Module to the Object Module send activation from the active context to those objects that frequently occur within this context. We demonstrate that context feedback contributes to the successful categorization of objects, especially when bottom-up feature information is degraded or ambiguous.

Background Alzheimer's Disease (AD) patients who have lost the ability to communicate verbally may benefit from a Brain-Computer Interface (BCI), which could allow them to convey basic thoughts and emotions, e.g. by associating affirmative and non-affirmative thinking to a positive and a negative emotion, respectively. One possibility to develop a BCI that can be used with AD patients is to modulate brain responses with a semantic classical conditioning paradigm, in order to associate congruent and incongruent word pairs with emotionally positive and negative stimuli, facilitating the discrimination between “affirmative” and “non-affirmative” thinking. Using a classical conditioning paradigm is convenient, since it does not require AD patients to put active effort in learning how the BCI works. Methods In our classical conditioning paradigm, already tested on 11 healthy subjects, the unconditioned stimuli consist of a positive and negative emotional sound (a baby laugh and a scream, respectively). The conditioned stimuli, presented aurally, are congruent (e.g. 'fruit-apple') and incongruent (e.g. 'fruit-dog') word pairs. During the conditioning acquisition, congruent and incongruent word pairs are associated to the baby laugh and the scream respectively. Functional magnetic resonance imaging (fMRI) will be performed on AD patients (Mini Mental State Examination score: 18-24; intact auditory system) on a 3T scanner. To classify the signals corresponding to congruent and incongruent word pairs, both univariate (General Linear Model) and multivariate (Support Vector Machine, SVM) analyses will be performed. Results Both univariate and multivatiate analyses on healthy subjects confirmed the possibility to discriminate between affirmative and non-affirmative responses. We hypothesize that a similar differentiation may be found in AD patients, since classical conditioning was demonstrated to be possible, at least to some extent, in these patients.The study with AD patients is ongoing, and preliminary results will be presented during the conference. Conclusions The encouraging results obtained with healthy subjects show that basic affirmative/non affirmative thinking discrimination is possible within an fMRI-BCI setting. The development of a BCI for AD patients could be an important step for allowing not only basic communication, but possibly lending a path for rehabilitation and diagnosis.

Background Alzheimer patients express their need of social interaction even when their communication abilities are highly impaired. (Mayhew et al., 2001). Brain-computer interfaces (BCI), already used with severely paralyzed patients, may be adapted for communicating with Alzheimer patients by shifting the paradigm from instrumental-operant learning to classical conditioning (Birbaumer, 2006), e.g. by associating “yes” thinking to a positive emotion and “no” thinking to a negative emotion. We designed an fMRI-based BCI setting aimed at conditioning subjects to associate positive and negative emotional stimuli with respectively congruent and incongruent word pairs, in view of a BCI application for basic yes/no communication. Methods fMRI was performed on 6 healthy subjects during a classical conditioning session, comprising the phases of habituation, acquisition and extinction. The unconditioned stimuli consisted of a positive (baby laughter) and negative (scream) emotional sound. The conditioned stimuli, presented aurally, were congruent (e.g. “animal-elephant”) and incongruent (e.g. “animal-Germany”) word pairs. During the conditioning acquisition phase, congruent word pairs were associated to the baby laughter and incongruent word pairs were associated to the scream. A linear Support Vector Machine was implemented to classify the BOLD signal corresponding to congruent and incongruent word pairs. To investigate the relative importance of distinct brain areas in decoding different brain states, feature vectors from the frontal cortex were also used as input to build a separate classifier. Results Participants rated in the Self Assessment Manikin (SAM) the scream and the laughter as having, respectively, negative and positive valence. Moreover, the scream was associated to higher arousal compared to the baby laughter. Classification of the BOLD signal as a response to the congruent and incongruent word pairs immediately followed by the emotional unconditioned stimuli showed above chance level performance (57-64%) in one subject, while the performance was chance level on the remaining subjects (44-56%). Conclusions We presented a possible paradigm for basic yes/no communication, based on the conditioning of BOLD signal by the repeated association of emotional and semantic stimuli. The performance of the classifier by feature selection needs to be improved before being tested with Alzheimer patients. Future developments also comprise an online implementation of the system.