Word associations contribute to machine learning in automatic scoring of degree of emotional tones in dream reports

Amini, Reza; Sabourin, Catherine; Koninck, Joseph De

doi:10.1016/j.concog.2011.08.003

Cited by 11 publications

(9 citation statements)

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“…A major challenge to the study of dreams is that they are typically analyzed by rating and ranking the content of verbal dream reports along various dimensions by judges ( Hall and Van De Castle, 1966 ), or by the dreamers themselves. With the exception of a few recent studies ( Amini et al, 2011 ; Horikawa et al, 2013 ; Wong et al, 2016 ), the majority of dream research has been limited to the study of subjectively scored and interpreted dream reports. However, recent advances in language processing and machine learning techniques ( Amini et al, 2011 ; Horikawa et al, 2013 ; Wong et al, 2016 ) have made the objective analysis of dream reports possible.…”

Section: Discussionmentioning

confidence: 99%

“…With the exception of a few recent studies ( Amini et al, 2011 ; Horikawa et al, 2013 ; Wong et al, 2016 ), the majority of dream research has been limited to the study of subjectively scored and interpreted dream reports. However, recent advances in language processing and machine learning techniques ( Amini et al, 2011 ; Horikawa et al, 2013 ; Wong et al, 2016 ) have made the objective analysis of dream reports possible. The current study employed WordNet, a manually curated publicly available lexical database of the English language that can be used to derive the high-order meaning of words from a corpus of text, as well as the semantic distance between the higher-order senses of words with another corpus of text.…”

Section: Discussionmentioning

confidence: 99%

“…However, several methodological hurdles have presented a challenge to the objective, scientific investigation of dreams. For example: (1) only certain types of experiences (e.g., engaging, emotional, and autobiographical) are robustly incorporated into dreams ( Stickgold et al, 2000 ; Malinowski and Horton, 2014a , b ), (2) the type and timing of sleep when dream reports are collected is important for identifying learning-related incorporation ( Nielsen et al, 2004 ; van Rijn et al, 2015 ), (3) the objective quantification of dream content, until very recently ( Amini et al, 2011 ; Horikawa et al, 2013 ; Wong et al, 2016 ) has been limited to subjective assessment of verbal dream reports, and (4) the measurement of the semantic relationship between waking experiences and verbal dream reports (i.e., incorporation) has only been subjectively assessed by comparing behavior to dream reports, as opposed to comparing verbal reports of the learning experience to verbal reports of the subsequent dream experience ( Wamsley et al, 2010a , b ).…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Dream Content Analysis Reveals Links Between Learning-Related Dream Incorporation and Cognitive Abilities

et al. 2018

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Can dreams reveal insight into our cognitive abilities and aptitudes (i.e., “human intelligence”)? The relationship between dream production and trait-like cognitive abilities is the foundation of several long-standing theories on the neurocognitive and cognitive-psychological basis of dreaming. However, direct experimental evidence is sparse and remains contentious. On the other hand, recent research has provided compelling evidence demonstrating a link between dream content and new learning, suggesting that dreams reflect memory processing during sleep. It remains to be investigated whether the extent of learning-related dream incorporation (i.e., the semantic similarity between waking experiences and dream content) is related to inter-individual differences in cognitive abilities. The relationship between pre–post sleep memory performance improvements and learning-related dream incorporation was investigated (N = 24) to determine if this relationship could be explained by inter-individual differences in intellectual abilities (e.g., reasoning, short term memory (STM), and verbal abilities). The extent of dream incorporation using a novel and objective method of dream content analysis, employed a computational linguistic approach to measure the semantic relatedness between verbal reports describing the experience on a spatial (e.g., maze navigation) or a motor memory task (e.g., tennis simulator) with subsequent hypnagogic reverie dream reports and waking “daydream” reports, obtained during a daytime nap opportunity. Consistent with previous studies, the extent to which something new was learned was related (r = 0.47) to how richly these novel experiences were incorporated into the content of dreams. This was significant for early (the first 4 dream reports) but not late dreams (the last 4 dream reports). Notably, here, we show for the first time that the extent of this incorporation for early dreams was related (r = 0.41) to inter-individual differences in reasoning abilities. On the other hand, late dream incorporation was related (r = 0.46) to inter-individual differences in verbal abilities. There was no relationship between performance improvements and intellectual abilities, and thus, inter-individual differences in cognitive abilities did not mediate the relationship between performance improvements and dream incorporation; suggesting a direct relationship between reasoning abilities and dream incorporation. This study provides the first evidence that learning-related dream production is related to inter-individual differences in cognitive abilities.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Dream Content Analysis Reveals Links Between Learning-Related Dream Incorporation and Cognitive Abilities

et al. 2018

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“…In recent years, several methods of automated language analysis have been created to assist researchers with psychological inferences based on the words that people say, write, and type. In dreams research, examples exist in which researchers have developed limited and specialized computerized lexical codes for dreams for purposes such as neuropsychological modeling with brain scans (S. Schwartz & Maquet, 2002), dream cluster sequence multidimensional statistical modeling (S. Schwartz, 2004), and dream sentiment analysis (e.g., Amini, Sabourin, & DeKoninck, 2011;Razavi, Matwin, DeKoninck, & Amini, 2014). However, whereas these approaches often rely upon domainspecific psychological measurement from language, other methods exist that afford researchers more generalized psychological insights, typically achieved by measuring various classes of grammatical language (e.g., pronouns, conjunctions) and psychologically relevant categories of words (e.g., words related to active thinking and social processes).…”

Section: Automated Language Analysis Of Dream Narrativesmentioning

confidence: 99%

Such stuff as dreams are made on: Dream language, LIWC norms, and personality correlates.

Hawkins¹,

Boyd²

2017

Dreaming

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We describe the language features of dream narratives from 3 large samples of normal persons using Linguistic Inquiry Word Count (LIWC), a computer text analysis program. Compared with LIWC norms from waking narratives, LIWC dream narratives showed more use of function words in general, common words, past tense verbs, relativity (particularly space), inclusion, leisure, friend, and home words, and less use of second-person pronouns, present and future verbs, causation words, large words, and assent words. Dream narratives did not contain more negative emotion words. These patterns were consistent across investigators, samples gathered at different times from student and online sources, and instructions for dream reports (i.e., recent dream vs. important dream). Statistically significant correlations between dream language features and personality (as measured by the Ten-Item Personality Inventory and the Big Five Inventory) were few in number and small in effect sizes. We conclude with discussing the implications of computer text analysis of dreams in more systematic studies comparing linguistic features with dream themes in cross-cultural clinical populations, and the implications of these features for scientific understanding of the continuum of consciousness.

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“…When assessing the performance of neural networks, it is conventionally measured against the results achieved by humans as a 'gold standard' (Amini et al, 2011(Amini et al, : 1574DiMaggio, 2015: 1;Huang et al, 2012Huang et al, : 1601Jurafsky and Martin, 2018). Following this line of thinking, artificial creativity could be compared with human creativity.…”

Section: Areas In Which Neural Network Underperformmentioning

confidence: 99%

What are neural networksnotgood at? On artificial creativity

Олейник

2019

Big Data & Society

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This article discusses three dimensions of creativity: metaphorical thinking; social interaction; and going beyond extrapolation in predictions. An overview of applications of neural networks in these three areas is offered. It is argued that the current reliance on the apparatus of statistical regression limits the scope of possibilities for neural networks in general, and in moving towards artificial creativity in particular. Artificial creativity may require revising some foundational principles on which neural networks are currently built.

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Word associations contribute to machine learning in automatic scoring of degree of emotional tones in dream reports

Cited by 11 publications

References 11 publications

A Novel Approach to Dream Content Analysis Reveals Links Between Learning-Related Dream Incorporation and Cognitive Abilities

A Novel Approach to Dream Content Analysis Reveals Links Between Learning-Related Dream Incorporation and Cognitive Abilities

Such stuff as dreams are made on: Dream language, LIWC norms, and personality correlates.

What are neural networksnotgood at? On artificial creativity

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