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Abstract
This article explores intermediality and synaesthesia in James Joyce's Ulysses, particularly focusing on the "Sirens" chapter. It examines how Joyce, akin to Johann Sebastian Bach's innovative musical techniques and Richard Wagner's Gesamtkunstwerk concept, weaves a rich tapestry of sensory experiences through his narrative. Joyce's use of intermedial techniques transcends traditional literary forms, creating a synaesthetic experience that blends visual and auditory elements. With this approach, he manages to convey a sense of simultaneity and polyphony that resembles a musical harmony, which is demonstrated through a detailed analysis of the structure, motifs, and thematic elements of the chapter. The article highlights how Joyce's own innovative narrative techniques mirror musical composition techniques by Bach and Wagner, particularly in their use of fragmented polyphony, Klangfarbenmelodie, the alliterative preparation of words, leitmotifs, and thematic development, offering a reading experience that challenges and expands the boundaries of literary expression.

The Model of Action Phases ( Gollwitzer, 1990 , 2012 ) distinguishes an implemental mindset evoked by planning goal-directed actions, from a deliberative mindset evoked by pondering pros/cons of adopting a particular goal. The present research demonstrates that mindsets affect time predictions. In Study 1 (N = 151), participants in an implemental mindset made shorter time predictions with respect to completing personal goals than participants in a deliberative mindset. In Study 2 (N = 78), we tested a mediating mechanism for the mindset effect by varying the motivation to return a report. The mindset effect was replicated in the low- but not in the high-motivation condition with participants in an implemental mindset returning the report earlier, and being as accurate in their predictions as participants in a deliberative mindset. Results indicate that an implemental (vs. deliberative) mindset increases motivation and therefore leads to shorter, but equally biased time predictions.