Abstract
The meaning of the word “romantic”, according to Oxford Dictionary, is
having a quality that motivates emotions. It makes you think about love. One would
find the meaning of love either in the poetry of Ravindra Nath Tagore or in the poems
of Rumi. W. B. Eats defined beauty as a thing of eternal joy, which keeps on
increasing. It has no upper bound. The branch of neurosciences that connects us to the
study of literature, paintings, and movies is cognitive neuroscience. Cognition is the
process by which knowledge and understanding are developed in the mind, i.e., how
does the brain enable the mind? Now, what is mind? Mind is reflected in occurrences
such as sensations, perceptions, emotions, memories, desires, and many more. The aim
of this chapter is to elucidate the link between neuroscience and romanticism.
Language plays a central role in cognition. The effects of language on visual perception
were explored by Lupyan et al. [1]. Our ability to recognize perceptual stimuli is
dependent on the physical features of the stimuli and our prior experiences. Language
influences visual processing both offline and online. These effects are the result of a
predictive processing approach to perception. The history of English (both British and
American) literature is discussed. Poets from South Asia and the Middle East are also
covered. The chapter impresses upon the reader that poets such as Rumi, Khalid
Gibran, Mirza Ghalib and others guide us to the path of eternality. The reader will find
a few stanzas from their poems in this chapter.
Theories of action and perception are discussed in Chapter 1 under the heading
‘Reward’. Sensations, emotions, desires and empathy are the subject matter of this
chapter. This chapter will attempt to establish a link between the occurrences and the
quality, which is known as empathy. A piece of literature devoid of this quality does
not qualify to be literature. Some recent developments in neurosciences are reviewed.
Free-energy formulation of inference and learning schemas based on generative models
of the world is given. The free-energy principle provides a useful framework to
investigate neural computation and probabilistic world models.