The Language of Dreams: Application of Linguistics-Based Approaches for the Automated Analysis of Dream Experiences
Abstract
:1. Introduction
2. Graph Analysis: Exploring the Structural Properties of Mentation Reports
2.1. Overview
2.2. Description of the Approach
2.3. Applications in the Field of Dream Research
2.4. Methodological Considerations
3. Semantic Analysis for the Study of Dream Content
3.1. Overview
3.2. Description of the Approaches
3.2.1. Dictionary-Based Text Analysis
3.2.2. Distributional Semantics Approaches
3.3. Applications in the Field of Dream Research
3.3.1. Dictionary-Based Text Analysis
3.3.2. Distributional Semantics Approaches
3.4. Methodological Considerations
4. Automated Methods for Replacement of Manual Ratings
5. Limitations and Future Challenges
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Conflicts of Interest
References
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Category | Attribute | Description |
---|---|---|
General graph attributes | Total number of nodes | Total number of unique words |
Total number of edges | Total number of connections | |
Recurrence attributes | Repeated edges | Sum of all edges linking the same pair of nodes |
Parallel edges | Sum of parallel edges linking the same pair of nodes | |
Loops of one (or more) node | Sum of all edges linking a node with itself or sum of all loops containing two or more nodes | |
Connectivity attributes | Largest connected component (LCC) | Number of nodes in the maximal subgraph in which each pair of nodes is directly linked by an edge |
Largest strongly connected component (LSC) | Number of nodes in the in the maximal subgraph in which each pair of nodes has a mutually reachable path | |
General graph attributes | Average total degree | Given a node n, the sum of “in” and “out” edges represent its total degree; average total degree is the sum of total degree of all nodes divided by the number of nodes. |
Density | Number of edges (E) divided by the number of possible edges, according to the total number of nodes (N): (D = 2 × E/N × (N − 1)). | |
Diameter | Length of the longest shortest path between the node pairs of a network. | |
Average shortest path | Average length (number of steps along edges) of the shortest path between pairs of nodes of a network. | |
Clustering coefficient | The set of fractions of all node neighbors that are also neighbors of each other. |
First Author | Year | Population(s) | Analysis | Indices | Main Results |
---|---|---|---|---|---|
Mota, NB | 2012 | 8 schizophrenic patients; 8 manic patients; 8 healthy controls | Psychiatric populations’ vs. healthy controls’ dream reports | General graph attributes; Recurrence attributes; Connectivity attributes; Global attributes | Increased number of waking nodes and parallel edges in manics, reduced connectivity in schizophrenics |
Mota, NB | 2014 | 20 patients with Schizophrenia; 20 patients with Bipolar Disorder; 20 healthy controls. | Psychiatric populations’ vs. healthy controls’ dream reports | General graph attributes; Recurrence attributes; Connectivity attributes; Global attributes | Random-like connectedness more prevalent among schizophrenic patients |
Mota, NB | 2016 | 25 patients with Schizophrenia; 20 patients with Bipolar Disorder; 28 healthy controls | Psychotic lucid dreamers vs. non psychotic lucid dreamers; Lucid dreamers with psychotic symptoms vs. non lucid dreamers with psychotic symptoms | General graph attributes; Recurrence attributes; Connectivity attributes; Global attributes | Smaller clustering coefficient in schizophrenia lucid dreamers relative to non-lucid dreamers |
Mota, NB | 2017 | 21 subjects with recent-onset psychosis; 21 healthy controls | Subjects with recent-onset psychosis’ oral reports vs. healthy controls’ oral reports | Graph connectedness measures: Total number of edges; Largest connected component; Largest strongly connected component | Reduced connectivity in schizophrenia and bipolar disorder patients |
Martin, JM | 2020 | 20 healthy subjects | REM vs. NREM dream reports | Graph connectedness measures: Largest connected component; Largest strongly connected component | Increased graph connectedness in REM dream reports as compared to NREM dream reports |
Mota, NB | 2021 | 67 healthy subjects | Non-pandemic vs. pandemic dreaming | Graph connectedness measures: Largest connected component; Largest strongly connected component | No difference in graph connectedness between pandemic and non-pandemic dreams |
First Author | Year | Dataset | Analysis | Indices | Main Results |
---|---|---|---|---|---|
Schwartz, S | 2004 | Corpus of 1770 author’s dreams; Author’s 3-week diary of real events; Corpus of 1000 reports provided by 200 undergraduate students (100 males, 100 females) | Author’s dreams vs. author’s diary of real events; author’s dream vs. undergraduate students’ dreams | Correspondence Analysis (CoA); Cluster Analysis | Dreams were structured as “self-referential fiction”; Narrative of waking experiences resembled newspapers or essays; The author’s and students’ dreams were grouped into few principal clusters |
Altszyler, E | 2017 | DreamBank corpus: 19000 dreams reports from 59 subjects | LSA vs. word2vec for semantic analysis | Word embedding techniques (LSA, word2vec) | LSA outperformed word2vec in detecting semantic relatedness between dreams and waking reports |
Fogel, SM | 2018 | 24 healthy participants; Spatial navigation condition, N = 12; Tennis condition, N = 12 | Performance in motor or spatial tasks vs. degree of task incorporation into dreaming experience | Semantic similarity measured with WordNet | Significant relationship between participants’ performance in tasks and degree of incorporation into early dreams |
Sanz, C | 2018 | Erowid corpus—Dreamjournal corpus | Dreaming experience vs. phenomenological effects of psychedelic substances | Word embedding techniques (LSA) | LSD induced hallucinatory experiences more similar to highly lucid dreams |
Bulkeley, K | 2018 | SDDb Baseline Dreams corpora: 5208 baseline dreams; 625 reports of one’s worst nightmares; 388 dream reports of lucid self-awareness. | Baseline dreams vs. lucid dreams and nightmares | Word frequency counting with LIWC | Lucid dreams had frequent references to cognitive processes and few words referring to visual perception; Nightmares had more references to anxiety, anger and sadness than baseline dreams |
Mota, NB | 2020 | Control group (pre-pandemic dreams): 31 healthy participants; Experimental group (pandemic dreams): 31 healthy participants | Pandemic dreams vs. non-pandemic dreams | Word frequency counting with LIWC; Word embedding techniques (fastText) applied to external corpus | Pandemic dreams had the higher average semantic relatedness to the words “contamination” and “cleanness” |
Pesonen, AK | 2020 | Dream reports collected from 811 respondents | Dream content vs. sleep quality and perceived level of stress in pandemic lockdown | Analysis of word associations | Participants with an increased stress level reported a higher frequency of nightmares; 55% of bad dream clusters were related to pandemic-specific themes |
Mallett, R | 2021 | 54 healthy participants | Affective states in dreams vs. morning mood | Word frequency counting with LIWC | Dreams with higher degree of reference to anxiety, death, the body and first-person related to more negative morning mood; Dreams with positive emotion, leisure, ingestion and plural first-person references were associated with less negative morning mood. |
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Elce, V.; Handjaras, G.; Bernardi, G. The Language of Dreams: Application of Linguistics-Based Approaches for the Automated Analysis of Dream Experiences. Clocks & Sleep 2021, 3, 495-514. https://doi.org/10.3390/clockssleep3030035
Elce V, Handjaras G, Bernardi G. The Language of Dreams: Application of Linguistics-Based Approaches for the Automated Analysis of Dream Experiences. Clocks & Sleep. 2021; 3(3):495-514. https://doi.org/10.3390/clockssleep3030035
Chicago/Turabian StyleElce, Valentina, Giacomo Handjaras, and Giulio Bernardi. 2021. "The Language of Dreams: Application of Linguistics-Based Approaches for the Automated Analysis of Dream Experiences" Clocks & Sleep 3, no. 3: 495-514. https://doi.org/10.3390/clockssleep3030035