Pupillary responses are a well-known indicator of emotional arousal but have not yet been systematically investigated in response to music. Here, we measured pupillary dilations evoked by short musical excerpts normalized for intensity and selected for their stylistic uniformity. Thirty participants (15 females) provided subjective ratings of music-induced felt arousal, tension, pleasantness, and familiarity for 80 classical music excerpts. The pupillary responses evoked by these excerpts were measured in another thirty participants (15 females). We probed the role of listener-specific characteristics such as mood, stress reactivity, self-reported role of music in life, liking for the selected excerpts, as well as of subjective responses to music, in pupillary responses. Linear mixed model analyses showed that a greater role of music in life was associated with larger dilations, and that larger dilations were also predicted for excerpts rated as more arousing or tense. However, an interaction between arousal and liking for the excerpts suggested that pupillary responses were modulated less strongly by arousal when the excerpts were particularly liked. An analogous interaction was observed between tension and liking. Additionally, males exhibited larger dilations than females. Overall, these findings suggest a complex interplay between bottom-up and top-down influences on pupillary responses to music.

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We describe a parser for robust and flexible interpretation of user utterances in a multi-modal system for web search in newspaper databases. Users can speak or type, and they can navigate and follow links using mouse clicks. Spoken or written queries may combine search expressions with browser commands and search space restrictions. In interpreting input queries, the system has to be fault-tolerant to account for spontanous speech phenomena as well as typing or speech recognition errors which often distort the meaning of the utterance and are difficult to detect and correct. Our parser integrates shallow parsing techniques with knowledge-based text retrieval to allow for robust processing and coordination of input modes. Parsing relies on a two-layered approach: typical meta-expressions like those concerning search, newspaper types and dates are identified and excluded from the search string to be sent to the search engine. The search terms which are left after preprocessing are then grouped according to co-occurrence statistics which have been derived from a newspaper corpus. These co-occurrence statistics concern typical noun phrases as they appear in newspaper texts.