The threat simulation theory of the evolutionary function of dreaming: Evidence from dreams of traumatized children

Valli, Katja; Revonsuo, Antti; Pälkäs, Outi; Ismail, Kamaran Hassan; Ali, Karzan Jalal; Punamäki, Raija‐Leena

doi:10.1016/s1053-8100(03)00019-9

Cited by 117 publications

(104 citation statements)

References 39 publications

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“…We found that in waking life corpora run is linked with words associated with a big variety of contexts, such as sports, means of transport and programming, while in dreams, run is directly related with words associated with threatening events and chase/ escape situations. This result is consistent with the Threat Simulation Theory, which propose that dream production mechanism evolve to simulate threatening events (Revonsuo, 2000;Valli et al, 2005). figure legend, the reader is referred to the web version of this article.)…”

Section: Ukwacsupporting

confidence: 88%

“…This theory brings the analysis of dream contents centerstage, as a natural approach for the investigation of their functionality. For example, Valli et al (2005) tested the Threat Simulation Theory by comparing the content of the dreams reported by traumatized and non-traumatized children. Their results show an increased number of threatening dream events in traumatized population, thus giving support to the Threat Simulation Theory.…”

Section: Introductionmentioning

confidence: 99%

“…We chose this word because of its high frequency in dreams, and the great variety of contexts in which it can be used. For example, run may be associated to sports activities or with the chase/escape situation, which is reported to be one of the most typical dreams (Griffith, Miyagi, & Tago, 1958;Malcolm-Smith et al, 2012;Nielsen et al, 2003) and is a central element in the Threat Simulation Theory (Revonsuo, 2000;Valli et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text

Altszyler

Ribeiro

Sigman

et al. 2017

Consciousness and Cognition

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A B S T R A C TComputer-based dreams content analysis relies on word frequencies within predefined categories in order to identify different elements in text. As a complementary approach, we explored the capabilities and limitations of word-embedding techniques to identify word usage patterns among dream reports. These tools allow us to quantify words associations in text and to identify the meaning of target words. Word-embeddings have been extensively studied in large datasets, but only a few studies analyze semantic representations in small corpora. To fill this gap, we compared Skip-gram and Latent Semantic Analysis (LSA) capabilities to extract semantic associations from dream reports. LSA showed better performance than Skip-gram in small size corpora in two tests. Furthermore, LSA captured relevant word associations in dream collection, even in cases with low-frequency words or small numbers of dreams. Word associations in dreams reports can thus be quantified by LSA, which opens new avenues for dream interpretation and decoding.

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Section: Ukwacsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text

Altszyler

Ribeiro

Sigman

et al. 2017

Consciousness and Cognition

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“…Extending this view to affective processes, Revonsuo and Valli recently proposed that the realistic representation of fear in dreams and nightmares might serve an evolutionary sound and adaptive function of ''threat simulation'', by affording the rehearsal of threat perception and avoidance in an entirely harmless situation and without any detrimental consequences (Revonsuo, 2000;Valli et al, 2005). This proposal is compatible with psychological models suggesting that dreaming is a process of internal activation, arising from a person's affective and emotional history and serving an emotion-regulation function (e.g.…”

Section: Functions Of Dreamingmentioning

confidence: 99%

Cognitive and emotional processes during dreaming: A neuroimaging view

Desseilles

Dang‐Vu

Sterpenich

et al. 2011

Consciousness and Cognition

141

121

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a b s t r a c tDream is a state of consciousness characterized by internally-generated sensory, cognitive and emotional experiences occurring during sleep. Dream reports tend to be particularly abundant, with complex, emotional, and perceptually vivid experiences after awakenings from rapid eye movement (REM) sleep. This is why our current knowledge of the cerebral correlates of dreaming, mainly derives from studies of REM sleep. Neuroimaging results show that REM sleep is characterized by a specific pattern of regional brain activity. We demonstrate that this heterogeneous distribution of brain activity during sleep explains many typical features in dreams. Reciprocally, specific dream characteristics suggest the activation of selective brain regions during sleep. Such an integration of neuroimaging data of human sleep, mental imagery, and the content of dreams is critical for current models of dreaming; it also provides neurobiological support for an implication of sleep and dreaming in some important functions such as emotional regulation.

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“…In the recent cognitive and neurological literature the mechanisms and functions of dreaming have received much attention; e.g., [19][20][21][22][23], [28][29][30][31][32]. Dreaming makes use of memory elements for sensory representations (mental images) and their associated emotions to generate 'virtual simulations'; e.g., [20], pp.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Agent Model for the Emergence of Recurring Dream Scripts Based on Hebbian Learning

Kemp¹,

Treur²

2012

Studies in Computational Intelligence

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Abstract. In this paper an adaptive agent model is presented that models how recurrent dreams such as nightmares occur. The approach addresses dreaming as internal simulation incorporating memory elements in the form of sensory representations and their associated emotions. In the agent model recurring dreams emerge as scripts of connected dream episodes in which the connections between the episodes strengthen over time by Hebbian learning. The model was evaluated by a number of simulation experiments for different scenarios.

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The threat simulation theory of the evolutionary function of dreaming: Evidence from dreams of traumatized children

Cited by 117 publications

References 39 publications

The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text

The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text

Cognitive and emotional processes during dreaming: A neuroimaging view

An Adaptive Agent Model for the Emergence of Recurring Dream Scripts Based on Hebbian Learning

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