Argument structure and the representation of abstract semantics

According to the dual coding theory, differences in the ease of retrieval between concrete and abstract words are related to the exclusive dependence of abstract semantics on linguistic information. Argument structure can be considered a measure of the complexity of the linguistic contexts that accompany a verb. If the retrieval of abstract verbs relies more on the linguistic codes they are associated to, we could expect a larger effect of argument structure for the processing of abstract verbs. In this study, sets of length-and frequency-matched verbs including 40 intransitive verbs, 40 transitive verbs taking simple complements, and 40 transitive verbs taking sentential complements were presented in separate lexical and grammatical decision tasks. Half of the verbs were concrete and half were abstract. Similar results were obtained in the two tasks, with significant effects of imageability and transitivity. However, the interaction between these two variables was not significant. These results conflict with hypotheses assuming a stronger reliance of abstract semantics on linguistic codes. In contrast, our data are in line with theories that link the ease of retrieval with availability and robustness of semantic information

Attribution and Expression of Incentive Salience Are Differentially Signaled by Ultrasonic Vocalizations in Rats

During Pavlovian incentive learning, the affective properties of rewards are thought to be transferred to their predicting cues. However, how rewards are represented emotionally in animals is widely unknown. This study sought to determine whether 50-kHz ultrasonic vocalizations (USVs) in rats may signal such a state of incentive motivation to natural, nutritional rewards. To this end, rats learned to anticipate food rewards and, across experiments, the current physiological state (deprived vs. sated), the type of learning mechanism recruited (Pavlovian vs. instrumental), the hedonic properties of UCS (low vs. high palatable food), and the availability of food reward (continued vs. discontinued) were manipulated. Overall, we found that reward-cues elicited 50-kHz calls as they were signaling a putative affective state indicative of incentive motivation in the rat. Attribution and expression of incentive salience, however, seemed not to be an unified process, and could be teased apart in two different ways: 1) under high motivational state (i.e., hunger), the attribution of incentive salience to cues occurred without being expressed at the USVs level, if reward expectations were higher than the outcome; 2) in all experiments when food rewards were devalued by satiation, reward cues were still able to elicit USVs and conditioned anticipatory activity although reward seeking and consumption were drastically weakened. Our results suggest that rats are capable of representing rewards emotionally beyond apparent, immediate physiological demands. These findings may have translational potential in uncovering mechanisms underlying aberrant and persistent motivation as observed in drug addiction, gambling, and eating disorders.Deutscher Akademischer Austauschdienst/[Schw 559/10-1]/DAAD/GermanyUniversidad de Costa Rica/[]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Sociales::Instituto de Investigaciones Psicológicas (IIP

Big data analytics in healthcare: A cloud based framework for generating insights

With exabytes of data being generated from genome sequencing, a whole new science behind genomic big data has emerged. As technology improves, the cost of sequencing a human genome has gone down considerably increasing the number of genomes being sequenced. Huge amounts of genomic data along with a vast variety of clinical data cannot be handled using existing frameworks and techniques. It is to be efficiently stored in a warehouse where a number of things have to be taken into account. Firstly, the genome data is to be integrated effectively and correctly with clinical data. The other data sources along with their formats have to be identified. Required data is then extracted from these other sources (such as clinical datasets) and integrated with the genome. The main challenge here is to be able to handle the integration complexity as a large number of datasets are being integrated with huge amounts of genome. Secondly, since the data is captured at disparate locations individually by clinicians and scientists, it brings the challenge of data consistency. It has to be made sure that the data consistency is not compromised as it is passed along the warehouse. Checks have to be put in place to make sure the data remains consistent from start to finish. Thirdly, to carry this out effectively, the data infrastructure has to be in the correct order. How frequently the data is accessed plays a crucial role here. Data in frequent use will be handled differently than data which is not in frequent use. Lastly, efficient browsing mechanisms have to put in place to allow the data to be quickly retrieved. The data is then iteratively analysed to get meaningful insights. The challenge here is to perform analysis very quickly. Cloud Computing plays an important role as it is used to provide scalability.N/