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The study aimed to examine whether three subtypes of anxiety (trait anxiety, state anxiety, and social anxiety) have different effects on recognition of common facial expressions\ud of emotion. One hundred and thirty-eight participants\ud were shown face images of six basic types of facial expressions with three levels of intensity (20%, 40%, 100%). They\ud matched each face image with one of the six emotion labels\ud (“happy”, “sad”, “fear”, “angry”, “disgust”, and “surprise”).\ud When we used conventional method of analysis, we found\ud significant correlations between each score of anxiety and\ud recognition performance for facial expressions. However,\ud when we used partial correlation to eliminate the complex\ud effect of each anxiety, only the correlation between anxiety\ud and categorization errors was significant. This was characterized by the tendency of miscategorizing a sad face as an angry\ud face in social anxiety, and a surprised face as a disgusted face\ud in trait anxiety and state anxiety. These results show that\ud 154 Perception 50(1S)\ud each subtype of anxiety differentially influenced on the\ud recognition of facial expressions especially when the intensity\ud of the expression is low. Our eye tracking data also show\ud that state anxiety may be associated with reduced fixations\ud on the eye regions of low-intensity sad or fearful faces.\ud Results from our partial correlation analyses cast some\ud doubts on some effects reported in the previous studies\ud because they are likely to reflect a mixture of influence\ud from the highly correlated anxiety subtypes.

One of the most significant anticipated consequences of global climate change is the change in frequency of hydrological extremes. Predictions of climate change impacts on the regime of hydrological extremes have traditionally been conducted by a top-down approach that involves a high degree of uncertainty associated with the temporal and spatial characteristics of general circulation model (GCM) outputs and the choice of downscaling technique. This study uses the inverse approach to model hydrological risk and vulnerability to changing climate conditions in the Seyhan River basin, Turkey. With close collaboration with the end users, the approach first identifies critical hydrological exposures that may lead to local failures in the Seyhan River basin. The Hydro-BEAM hydrological model is used to inversely transform the main hydrological exposures, such as floods and droughts, into corresponding meteorological conditions. The frequency of critical meteorological conditions is investigated under present and future climate scenarios by means of a weather generator based on the improved K-nearest neighbour algorithm. The weather generator, linked with the output of GCMs in the last step of the proposed methodology, allows for the creation of an ensemble of scenarios and easy updating when improved GCM outputs become available. Two main conclusions were drawn from the application of the inverse approach to the Seyhan River basin. First, floods of 100-, 200- and 300-year return periods under present conditions will have 102-, 293- and 1370-year return periods under the future conditions; that is, critical flood events will occur much less frequently under the changing climate conditions. Second, the drought return period will change from 5.3 years under present conditions to 2.0 years under the future conditions; that is, critical drought events will occur much more frequently under the changing climate conditions. Copyright © 2008 IAHS Press. Research Institute for Humanity and Nature Japan Society for the Promotion of Science Research Institute for Humanity and Nature Research Institute for Humanity and Nature Japan Society for the Promotion of Science: 19208022, 16380164, 1811748 1 Research Institute for Humanity and Nature (Japan Society for the Promotion of Science Research Fellow), 457-4 Motoyama, Kamigamo, Kita-ku, Kyoto 603-8047, Japan fujihara@chikyu.ac.jp Acknowledgements This research was financially supported by the Project “Impact of Climate Change on Agricultural Production System in the Arid Areas” (ICCAP), administered by the Research Institute for Humanity and Nature (RIHN) and the Scientific and Technical Research Council of Turkey (TÜBøTAK). Additional support was provided by the JSPS Grant-in-Aid nos 16380164, 1811748 and 19208022.