The Cognitive Social Network in Dreams: Transitivity, Assortativity, and Giant Component Proportion Are Monotonic

For five individuals, a social network was constructed from a series of his or her dreams. Three important network measures were calculated for each network: transitivity, assortativity, and giant component proportion. These were monotonically related; over the five networks as transitivity increased, assortativity increased and giant component proportion decreased. The relations indicate that characters appear in dreams systematically. Systematicity likely arises from the dreamer's memory of people and their relations, which is from the dreamer's cognitive social network. But the dream social network is not a copy of the cognitive social network. Waking life social networks tend to have positive assortativity; that is, people tend to be connected to others with similar connectivity. Instead, in our sample of dream social networks assortativity is more often negative or near 0, as in online social networks. We show that if characters appear via a random walk, negative assortativity can result, particularly if the random walk is biased as suggested by remote associations.

[1]  J. Hobson,et al.  Sleep-Induced Changes in Associative Memory , 1999, Journal of Cognitive Neuroscience.

[2]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[3]  Raymond A. Mar,et al.  How daydreaming relates to life satisfaction, loneliness, and social support: The importance of gender and daydream content , 2012, Consciousness and Cognition.

[4]  D. Cai,et al.  REM, not incubation, improves creativity by priming associative networks , 2009, Proceedings of the National Academy of Sciences.

[5]  Vladimir Batagelj,et al.  Pajek - Program for Large Network Analysis , 1999 .

[6]  M. Newman,et al.  Random graphs with arbitrary degree distributions and their applications. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Joshua B. Tenenbaum,et al.  The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth , 2001, Cogn. Sci..

[8]  D. Foulkes Dreaming: A Cognitive Psychological Analysis , 1985 .

[9]  C. E. Veni Madhavan,et al.  A Network Analysis Approach to Understand Human-wayfinding Problem , 2011, CogSci.

[10]  P. Holme,et al.  Exploring the assortativity-clustering space of a network's degree sequence. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Thomas T. Hills,et al.  Optimal foraging in semantic memory. , 2012, Psychological review.

[12]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[13]  Amber Wutich,et al.  Conceptual and Empirical Arguments for Including or Excluding Ego from Structural Analyses of Personal Networks , 2005 .

[14]  Thomas T. Hills,et al.  Dynamic search and working memory in social recall. , 2012, Journal of experimental psychology. Learning, memory, and cognition.

[15]  Michael Schredl,et al.  Continuity between waking activities and dream activities , 2003, Consciousness and Cognition.

[16]  Vittorio Loreto,et al.  Complex Structures and Semantics in Free Word Association , 2012, Adv. Complex Syst..

[17]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[18]  M. Newman Properties of highly clustered networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Bradford C. Dickerson,et al.  Intrinsic Amygdala–Cortical Functional Connectivity Predicts Social Network Size in Humans , 2012, The Journal of Neuroscience.

[20]  David Krackhardt,et al.  Cognitive social structures , 1987 .

[21]  Massimo Franceschet,et al.  The large-scale structure of journal citation networks , 2011, J. Assoc. Inf. Sci. Technol..

[22]  S. D. Berkowitz,et al.  Social Structures: A Network Approach , 1989 .

[23]  Nick Chater,et al.  Networks in Cognitive Science , 2013, Trends in Cognitive Sciences.

[24]  Sau-chin Chen,et al.  Creating false memories: Remembering words not presented in lists. , 2018 .

[25]  R. Adolphs,et al.  Annals of the New York Academy of Sciences What Does the Amygdala Contribute to Social Cognition? , 2022 .

[26]  C. Degueldre,et al.  Functional neuroanatomy of human rapid-eye-movement sleep and dreaming , 1996, Nature.

[27]  K. Christoff,et al.  Dreaming as mind wandering: evidence from functional neuroimaging and first-person content reports , 2013, Front. Hum. Neurosci..

[28]  T. Griffiths,et al.  Google and the Mind , 2007, Psychological science.

[29]  G. William Domhoff,et al.  Studying dream content using the archive and search engine on DreamBank.net , 2008, Consciousness and Cognition.

[30]  Peter Grassberger,et al.  Clustering Drives Assortativity and Community Structure in Ensembles of Networks , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Soon-Hyung Yook,et al.  Biased random walk sampling on assortative networks , 2010 .

[32]  R. Schweickert Properties of the organization of memory for people: Evidence from dream reports , 2007, Psychonomic bulletin & review.

[33]  S. Roodenrys,et al.  Complex network structure influences processing in long-term and short-term memory. , 2012, Journal of memory and language.

[34]  K. McDermott,et al.  Creating false memories: Remembering words not presented in lists. , 1995 .

[35]  S. Mednick,et al.  The Remote Associates Test , 1968 .

[36]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[37]  Richard Schweickert,et al.  Metamorphosed Characters in Dreams: Constraints of Conceptual Structure and Amount of Theory of Mind , 2009, Cogn. Sci..

[38]  M. Serrano,et al.  Percolation and epidemic thresholds in clustered networks. , 2006, Physical review letters.

[39]  M. Vitevitch What can graph theory tell us about word learning and lexical retrieval? , 2008, Journal of speech, language, and hearing research : JSLHR.

[40]  Mark Blagrove,et al.  Trait and neurobiological correlates of individual differences in dream recall and dream content. , 2010, International review of neurobiology.

[41]  Michael S. Vitevitch,et al.  Network Structure Influences Speech Production , 2010, Cogn. Sci..

[42]  Andreas Wree,et al.  The Intrinsic Connectome of the Rat Amygdala , 2012, Front. Neural Circuits.

[43]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[44]  J. Hobson,et al.  The brain as a dream state generator: an activation-synthesis hypothesis of the dream process. , 1977, The American journal of psychiatry.

[45]  G. Cecchi,et al.  Scale-free brain functional networks. , 2003, Physical review letters.

[46]  Ernesto Estrada,et al.  Combinatorial study of degree assortativity in networks. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[47]  Errata to , 1980 .

[48]  G. Domhoff,et al.  Chapter 50 – Dream Content: Quantitative Findings , 2011 .

[49]  M. Linden,et al.  Phenomenal characteristics associated with projecting oneself back into the past and forward into the future: Influence of valence and temporal distance , 2004, Consciousness and Cognition.

[50]  Haibo Hu,et al.  Disassortative mixing in online social networks , 2009, 0909.0450.

[51]  W. Batchelder,et al.  Systematic Biases in Social Perception , 1994, American Journal of Sociology.

[52]  Raina A. Brands Cognitive social structures in social network research: A review , 2013 .

[53]  K. Sneppen,et al.  Detection of topological patterns in complex networks: correlation profile of the internet , 2002, cond-mat/0205379.

[54]  J. Antrobus,et al.  The effects of external stimuli applied prior to and during sleep on sleep experience. , 1991 .

[55]  Sidarta Ribeiro,et al.  Graph analysis of dream reports is especially informative about psychosis , 2014, Scientific Reports.

[56]  P. Killworth,et al.  Informant Accuracy in Social Network Data , 1976 .

[57]  R. Stickgold,et al.  A “Jekyll and Hyde” Within , 2005, Psychological science.

[58]  M. Newman,et al.  Why social networks are different from other types of networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[59]  D. Pisoni,et al.  Recognizing Spoken Words: The Neighborhood Activation Model , 1998, Ear and hearing.

[60]  M. Keeling,et al.  The effects of local spatial structure on epidemiological invasions , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[61]  Lael J. Schooler,et al.  Mapping the Structure of Semantic Memory , 2013, Cogn. Sci..

[62]  M E J Newman Assortative mixing in networks. , 2002, Physical review letters.

[63]  Michael Schredl,et al.  Characteristics and contents of dreams. , 2010, International review of neurobiology.