In the waking state, in the absence of meta-awareness, mind wandering with specific contents can lead to negative mood. Such negative mood can be incorporated into dreaming according to the continuity hypothesis of dreaming. In this paper we argue that in the presence of what we call ‘sustained phenomenal meta-awareness’, negative mood would not follow mind wandering in waking. Sustained phenomenal meta-awareness has a non-sensory, non-affective phenomenal character. It is essentially intransitive, prereflectively self-aware, non-propositional, non-conceptual and devoid of subject-object structure. (...) In other words, this unique kind of meta-awareness is non-representational. Evidence is then provided that such sustained phenomenal meta-awareness can be incorporated into the subsequent dream state as non-dual lucid dreaming in which, again, no negative mood would arise. Based on the latter observation, we have coined the term ‘mindful mind wandering’ and defined it as mind wandering in the presence of sustained phenomenal meta-awareness. We argue that not only does mindful mind wandering not lead to negative mood in waking, but also its incorporation into dreaming, as non-dual lucid dreaming, result in a state that is free of negative affection. (shrink)

In general, dreams are a novel but realistic simulation of waking social life, with a mixture of characters, motivations, scenarios, and positive and negative emotions. We propose that the sharing of dreams has an empathic effect on the dreamer and on significant others who hear and engage with the telling of the dream. Study 1 tests three correlations that are predicted by the theory of dream sharing and empathy: that trait empathy will be correlated with frequency of telling dreams to (...) others, with frequency of listening to others’ dreams, and with trait attitude towards dreams (for which higher scores indicate positive attitude). 160 participants completed online the Toronto Empathy Questionnaire and the Mannheim Dream Questionnaire. Pearson partial correlations were conducted, with age and sex partialled out. Trait empathy was found to be significantly associated with the frequency of listening to the dreams of others, frequency of telling one’s own dreams to others, and attitude towards dreams. Study 2 tests the effects of discussing dreams on state empathy, using an adapted version of the Shen (2010) state empathy scale, for 27 pairs of dream sharers and discussers. Dream discussion followed the stages of the Ullman (1996) dream appreciation technique. State empathy of the dream discusser towards the dream sharer was found to increase significantly as a result of the dream discussion, with a medium effect size, whereas the dream sharer had a small decrease in empathy towards the discusser. A proposed mechanism for these associations and effects is taken from the robust findings in the literature that engagement with literary fiction can induce empathy towards others. We suggest that the dream acts as a piece of fiction that can be explored by the dreamer together with other people, and can thus induce empathy about the life circumstances of the dreamer. We discuss the speculation that the story-like characteristics of adult human dreams may have been selected for in human evolution, including in sexual selection, as part of the selection for emotional intelligence, empathy, and social bonding. (shrink)

Scientific study of dreams requires the most objective methods to reliably analyze dream content. In this context, artificial intelligence should prove useful for an automatic and non subjective scoring technique. Past research has utilized word search and emotional affiliation methods, to model and automatically match human judges’ scoring of dream report’s negative emotional tone. The current study added word associations to improve the model’s accuracy. Word associations were established using words’ frequency of co-occurrence with their defining words as found in (...) a dictionary and an encyclopedia. It was hypothesized that this addition would facilitate the machine learning model and improve its predictability beyond those of previous models. With a sample of 458 dreams, this model demonstrated an improvement in accuracy from 59% to 63% on the negative emotional tone scale, and for the first time reached an accuracy of 77% on the positive scale. (shrink)