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Word associations contribute to machine learning in automatic scoring of degree of emotional tones in dream reports
Abstract
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% (kappa=.485) on the negative emotional tone scale, and for the first time reached an accuracy of 77% (kappa=.520) on the positive scale.
... 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). ...
... 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. ...
... 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. ...
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.
... The search term (a word or a sentence) also represents a vector. Thus, the higher the Cosine coefficient between the search term and a document, the closer the document's contents to what is searched for (Khan et al. 2017, p. 860;Evans and Aveces 2016, p. 32;Zhai and Massung 2016, p. 280;Amini et al. 2011Amini et al. , p. 1572Grossman andFrieder 2004, p. 19, Salton andMcGill 1983, p. 203). This model may be further improved by normalizing word frequency by inverse document frequency, TF-IDF 7 (Bruggeman et al. 2012(Bruggeman et al. , p. 1056Evans et al. 2007Evans et al. , p. 1017Grossman and Frieder 2004, p. 2;Salton and McGill 1983, pp. ...
The algorithms underpinning information retrieval shape its outcomes and have epistemological, social and political consequences. On the one hand, the Web search algorithms place a specific actor—the Web librarian (cataloguer), the document’s creator, the expert (“authority”), the user or the service provider (developer and operator of a search engine)—in the position of a decision-maker. Each of them has distinctive criteria of relevance in information retrieval. On the other hand, the application of those criteria determines what information the user receives. Content-based search places emphasis on the contents of retrievable documents whereas collaborative search shifts the focus of attention to opinions of experts and other users. The outcomes of content-based and collaborative searches diverge as a result. Depending on the information provided to the user, the development of her knowledge and socialization proceeds differently. A plea for customized Web search is made. It is argued that the user should be given an opportunity for selecting a combination of content-based and collaborative search that matches her interests and the context of a search query.
... 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. ...
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.
... 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 where researchers have developed limited and specialized computerized lexical codes for dreams for purposes such as neuropsychological modeling with brain scans (Schwartz & Maquet, 2002), dream cluster sequence multidimensional statistical modeling (Schwartz, 2004), and dream sentiment analysis (e.g., Amini, Sabourin, & DeKoninck, 2011;Razavi, Matwin, DeKoninck, & Amini, 2014). However, whereas these approaches often rely upon domain-specific psychological measurement from language, other methods exist that afford researchers more generalized psychological insights, ...
We describe the language features of dream narratives from three large samples of
normal persons using Linguistic Inquiry Word Count (LIWC), a computer text analysis
program. Compared to 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.
... With regard to a variety of dream research questions, empirical linguistic analysis may close the gap between existing approaches to the analysis of dream phenomenology: It combines the advantages of the high objectivity of standardized or even automatic counting systems (e.g. Amini, Sabourin, & De Koninck, 2011;Bulkeley, 2009;Domhoff & Schneider, 2008) with the flexibility of human rating and scoring (e.g. Fosse, Stickgold, & Hobson, 2001) -a flexibility that is necessary when we are working with language in the context of subjective verbal reports. ...
Research has proven that having high level of emotional intelligence (EI) can reduce the chance of getting mental illness. EI, and its component, can be improved with training, but currently the process is less flexible and very time-consuming. Machine learning (ML), on the other hand, can analyse huge amount of data to discover useful trends and patterns in shortest time possible. Despite the benefits, ML usage in EI training is scarce. In this paper, we studied 92 journal articles to discover the trend of the ML utilisation in the study of EI and its components. This survey aims to pave way for future studies that could lead to implementation of ML in EI training, and to rope in researchers in psychology and computer science to find possibilities of having a generic ML algorithm for every EI’s components. Our findings show an increasing trend to apply ML on EI components, and Support Vector Machine and Neural Network are the two most popular ML algorithms used in those researches. We also found that social skill and empathy are the least exposed EI components to ML. Finally, we provide recommendations for future research direction of ML in EI domain, and EI in ML.
There is a reproducibility crisis in science. There are many potential contributors to replication failure in research across the translational continuum. In this perspective piece, we focus on the narrow topic of inferential reproducibility. Although replication of methods and results is necessary to demonstrate reproducibility, it is not sufficient. Also fundamental is consistent interpretation in the Discussion section. Current deficiencies in the Discussion sections of manuscripts might limit the inferential reproducibility of scientific research. Lack of contextualisation using systematic reviews, overinterpretation and misinterpretation of results, and insufficient acknowledgement of limitations are common problems in Discussion sections; these deficiencies can harm the translational process. Proposed solutions include eliminating or not reading Discussions, writing accompanying editorials, and post-publication review and comments; however, none of these solutions works very well. A second Discussion written by an independent author with appropriate expertise in research methodology is a new testable solution that could help probe inferential reproducibility, and address some deficiencies in primary Discussion sections.
A computer program was developed in an attempt to differentiate the dreams of males from females. Hypothesized gender predictors were based on previous literature concerning both dream content and written language features. Dream reports from home-collected dream diaries of 100 male (144 dreams) and 100 female (144 dreams) adolescent Anglophones were matched for equal length. They were first scored with the Hall and Van de Castle (HVDC) scales and quantified using DreamSAT. Two male and two female undergraduate students were asked to read all dreams and predict the dreamer's gender. They averaged a pairwise percent correct gender prediction of 75.8% (κ=0.516), while the Automatic Analysis showed that the computer program's accuracy was 74.5% (κ=0.492), both of which were higher than chance of 50% (κ=0.00). The prediction levels were maintained when dreams containing obvious gender identifiers were eliminated and integration of HVDC scales did not improve prediction.
A computer program was developed in an attempt to differentiate the dreams of males from females. Hypothesized gender predictors were based on previous literature concerning both dream content and written language features. Dream reports from home-collected dream diaries of 100 male (144 dreams) and 100 female (144 dreams) adolescent Anglophones were matched for equal length. They were first scored with the Hall and Van de Castle (HVDC) scales and quantified using DreamSAT. Two male and two female undergraduate students were asked to read all dreams and predict the dreamer's gender. They averaged a pairwise percent correct gender prediction of 75.8% (j = 0.516), while the Automatic Analysis showed that the computer program's accuracy was 74.5% (j = 0.492), both of which were higher than chance of 50% (j = 0.00). The prediction levels were maintained when dreams containing obvious gender identifiers were eliminated and integration of HVDC scales did not improve prediction.
Dreams have fascinated humans from the earliest of times. Yet modern research is still struggling to understand the nature and functions of dreaming. It has been observed that sleep mentation tends to be in continuity with waking mentation but that the memory sources of dreams are significantly transformed into new expressions of past experience and current concerns. Some dreams are creative and useful. Dreams can also be used to increase self-knowledge or as complement in psychotherapy. Negative emotions prevail in dreams and can culminate in nightmares. Fortunately, dreams can be controlled by suggestion, imagery rehearsal, and lucid dreaming. Electrophysiological and neuroimaging studies suggest that the unique features of dreaming are due to the fact that key brain structures are activated and interact differently in REM sleep than in waking. While many dream function theories have been proposed, more rigorous scientific research is needed to determine whether dreaming by itself serves an adaptive function.
p>This study extends previous research on the relationship among dream content, waking day mood and waking day anxiety while examining these variables between Italian and Canadian participants. One-hundred Canadian females from Trent University, Canada (M = 23.4, SD = 1.8) and 100 Italian females from Tor Vergata University, Rome (M = 25.6, SD = 1.9) volunteered one dream report and completed measures of waking day anxiety (BAI) and mood disturbance (POMS-SF). Dream imagery was categorized with Hall and Van de Castle’s method of Content Analysis and conducted via computer textual analyses. Significant differences between Italian and Canadian females were found for dream content as well as waking day measures while the predictive value of dreams was demonstrated. The findings have clinical implications for Canadians and Italians as well as other cultures. Limitations of the study and future research directions are discussed in terms of waking day mood and dreams.
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When researchers are interested in the influence of long-term knowledge on performance, printed word frequency is typically
the variable of choice. Despite this preference, we know little about what frequency norms measure. They ostensibly index
how often and how recently words are experienced, but words appear in context, so frequency potentially reflects an influence
of connections with other words. This paper presents the results of a large free association study as well as the results
of experiments designed to evaluate the hypothesis that common words have stronger connectionsto other words. The norms indicate that common words tend to be more concrete but they do not appear to have more associates,
stronger associates, or more connections among their associates. Two extralist cued recall experiments showed that, with other
attributes being equal, high- and low-frequency words were equally effective as test cues. These results suggest that frequency
does not achieve its effects because of stronger or greater numbers of connectionsto other words, as implied in SAM. Other results indicated that common words have more connectionsfrom other words, including their associates, and that free association provides a valid index of associative strength.
We describe a project undertaken by an interdisciplinary team of researchers in sleep and in and machine learning. The goal is sentiment extraction from a corpus containing short textual descriptions of dreams. Dreams are categorized in a four-level scale of affections. The approach is based on a novel representation, taking into account the leading themes of the dream and the sequential unfolding of associated affective feelings during the dream. The dream representation is based on three combined parts, two of which are automatically produced from the description of the dream. The first part consists of co-occurrence vectors, which ---unlike the standard Bag-of-words model ---capture non-local relationships between meanings of word in a corpus. The second part introduces the dynamic representation that captures the change in affections throughout the progress of the dream. The third part is the self-reported assessment of the dream by the dreamer according to eight given attributes. The three representations are subject to aggressive feature selection. Using an ensemble of classifiers and the combined 3-partite representation, we have achieved 64% accuracy, which is in the range of human experts' consensus in that domain.
In this position paper, we propose a first step toward automatic analysis of sentiments in dreams. 100 dreams were sampled from a dream bank created for a normative study of dreams. Two human judges assigned a score to describe dream sentiments. We ran four baseline algorithms in an attempt to automate the rating of sentiments in dreams. Particularly, we compared the General Inquirer (GI) tool, the Linguistic Inquiry and Word Count (LIWC), a weighted version of the GI lexicon and of the HM lexicon and a standard bag-of-words. We show that machine learning allows automating the human judgment with accuracy superior to majority class choice. Dans le présent exposé de position, nous proposons une première étape d'analyse automatique des sentiments éprouvés dans les rêves. Dans une banque de rêves créée en vue d'une étude normative sur les rêves, 100 rêves ont été échantillonnés. Deux juges humains ont accordé une note permettant de décrire les sentiments éprouvés dans les rêves. Nous avons exécuté quatre algorithmes de base afin de tenter d'automatiser la cote à attribuer aux sentiments. Plus particulièrement, nous avons comparé entre eux l'outil General Inquirer (GI), le Linguistic Inquiry and Word Count (LIWC), une version allégée du lexique GI et du lexique HM ainsi qu'un groupe de mots standard. Nous démontrons que l'apprentissage machine permet d'automatiser le jugement humain selon un niveau de précision supérieur au choix des classes de la majorité.
No consensus has been reached on the characteristics of emotional experience during rapid eye movement sleep (REM). Thus, the relationship between the emotional brain activation and mental activity in REM remains unclear. Our objective is to characterize emotional experience in REM in order to facilitate understanding of brain-mind correlations in this state.
We combined instrumental awakenings from REM with the subjects' own ratings of the occurrence and intensity of discrete emotion types for each line in their REM mentation reports.
The study was performed in the subjects' own homes over three consecutive nights using ambulatory polysomnography.
Nine normal healthy subjects, age 31-60 (mean=43.0).
Awakenings 5-15 minutes into REM periods across the night.
Emotions were found in 74% of 88 mentation reports, with a balanced proportion of positive and negative emotions. Among the reports scored for emotions, 14% contained one emotion and 86% contained two or more different emotion types. Joy/elation was the most frequent emotion, found in 36% of the reports, followed by surprise (24%), anger (17%), anxiety/fear (11%), and sadness (10%). Anxiety/fear was significantly less intense than joy/elation, anger, and surprise. Except for surprise, no specific emotion type changed from the first to the second half of the night. Negative emotions and surprise but not positive emotions varied significantly across subjects.
The analysis of subject reports of emotions following instrumental awakenings demonstrate a balanced and widespread occurrence of both positive and negative emotions in REM sleep dreams. Emotions in REM are likely to be powerfully modulated by the neurobiological processes which differentiate REM from waking.
The Scientific Study of Dream Content. The Hall/Van de Castle System of Content Analysis. The Quality of the Data. Normative Findings on American College Students. Age Differences in Dream Reports. Crosscultural Similarities and Differences. Consistency and Change in Long Dream Series. The Continuity between Dreams and Waking Life in Individuals and Groups. The Repetition Dimension in Dreams and Waking Cognition. Appendix A: The Hall/Van de Castle Coding Rules. Appendix B: The Coding of a Sample Dream Series. Appendix C: Instructions for Reporting Dreams in Written Form. Appendix D: Statistical Appendix. Appendix E: Normative Tables. Index.
Reviews the book, The Scientific Study of Dreams by G. William Domhoff (see record 2002-06753-000). This book presents what Domhoff calls a neurocognitive model of dreaming. The first aspect of Domhoff's review is the extent of the neuronal network and the mechanisms of its activation in sleep. The second aspect is Domhoff's emphasis on the development of dreaming in humans. The third aspect is Domhoff's approach to the quantitative description of dream content. The book will appeal to those who share Domhoff's views about what a science of dreaming should accomplish. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Most of the studies on dreams have used time consuming coding systems that depend on a rater’s judgement. It is of interest to develop an efficient mean of scoring dreams that can be used with large data banks and reproduced across laboratories. We report on our exploration of dream’s emotional content using automatic analysis tools.
A sample of 776 dreams, reported by 274 individuals of varied age and sex, was used for word-correlation analysis. A subset of 477 was rated by a judge using two 0-3 scales describing negatively and positively orientation of the dream. LIWC dictionary was used to identify affective words while CMU Link Grammar Parser was used to identify adverbs. Hence, the LIWC reported affect, was modified for better representation. We also attempted to develop a novel dynamic representation of changes in affect with respect to dream progression.
This book presents a theory of dreaming based on many years of psychological and biological research. Critical to this theory is the concept of a Central Image; this book describes his repeated finding that dreams of being swept away by a tidal wave are common among people who have recently experienced a trauma of some kind-a fire, an attack, or a rape. Dreams with these Central Images are not dreams of the traumatic experience itself, but rather the Central Image reveals the emotional response to the experience. Dreams with a potent Central Image, like the tidal wave, vary in intensity along with the severity of the trauma; this pattern was shown quite powerfully in a systematic study of dreams occurring before and after the September 11 attacks in New York. This book's theory comprises three fundamental elements: dreaming is simply one form of mental functioning, occurring along a continuum from focused waking thought to reverie, daydreaming, and fantasy. Second, dreaming is hyperconnective, linking material more fluidly and making connections that aren't made as readily in waking thought. Finally, the connections that are made are not random, but rather are guided by the dreamer's emotions or emotional concerns-and the more powerful the emotion, the more intense the Central Image.
Building on previous investigations of waking–dreaming continuities using word search technology (Bulkeley 2009a, 2009b; Domhoff & Schneider, 2008), we demonstrate that a blind analysis of a dream series using only word search methods can accurately predict many important aspects of the individual's waking life, including personality attributes, relationships, activities, and cultural preferences. Results from a study of the “Van” dream series (N = 192) show that blind inferences drawn from a word search analysis were almost entirely accurate according to the dreamer. After presenting these findings we discuss several remaining shortcomings and suggest ways of improving the method for use by other researchers involved in the search for a more systematic understanding of meaning in dreams. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
A method is described for the identification of possible links among dream sources and the study of their possible significance. The analysis is based on the automatic recognition of word root recurrences in text files, including dream reports and associations. Two tools are then applied: graph representation and grammar analysis. Graph representation of the detected links provides a quantitative description of some of their basic features. Grammar changes for recurrent word roots can imply remarkable context changes. A plausible explanation of the identified context changes can evidence interesting phenomena connected to the significance of links among dream sources. Two examples of application of the method are given, 1 taken from the literature and the other from sleep lab data. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Describes search of associative memory (SAM), a general theory of retrieval from long-term memory that combines features of associative network models and random search models. It posits cue-dependent probabilistic sampling and recovery from an associative network, but the network is specified as a retrieval structure rather than a storage structure. A quantitative computer simulation of SAM was developed and applied to the part-list cuing paradigm. When free recall of a list of words was cued by a random subset of words from that list, the probability of recalling one of the remaining words was less than if no cues were provided at all. SAM predicted this effect in all its variations by making extensive use of interword associations in retrieval, a process that previous theorizing has dismissed. (55 ref)
Words become associated following repeated co-occurrence episodes. This process might be further determined by the semantic characteristics of the words. The present study focused on how semantic and episodic factors interact in incidental formation of word associations. First, we found that human participants associate semantically related words more easily than unrelated words; this advantage increased linearly with repeated co-occurrence. Second, we developed a computational model, SEMANT, suggesting a possible mechanism for this semantic-episodic interaction. In SEMANT, episodic associations are implemented through lateral connections between nodes in a pre-existent self-organized map of word semantics. These connections are strengthened at each instance of concomitant activation, proportionally with the amount of the overlapping activity waves of activated nodes. In computer simulations SEMANT replicated the dynamics of associative learning in humans and led to testable predictions concerning normal associative learning as well as impaired learning in a diffuse semantic system like that characteristic of schizophrenia.
This paper systematizes the word search potential of DreamBank.net (Domhoff & Schneider, 2008a, 2008b) by formulating and testing a set of word strings that can be used as default analytic categories in future investigations. The word strings are applied to the 981 dream reports of college students gathered by Hall and Van de Castle (1966) and the 136 dream reports of an 80-year old male gathered by Bulkeley (2008a). The results show a basic compatibility with the frequencies identified by Hall and Van de Castle's labor-intensive method of content analysis employing teams of human coders. These findings support the expanded use of word search technologies for the scientific study of dream content and its relation to forms of waking consciousness.
This paper shows how the dream archive and search engine on DreamBank.net, a Web site containing over 22,000 dream reports, can be used to generate new findings on dream content, some of which raise interesting questions about the relationship between dreaming and various forms of waking thought. It begins with studies that draw dream reports from DreamBank.net for studies of social networks in dreams, and then demonstrates the usefulness of the search engine by employing word strings relating to religious and sexual elements. Examples from two lengthy individual dream series are used to show how the dreams of one person can be studied for characters, activities, and emotions. A final example shows that accurate inferences about a person's religious beliefs can be made on the basis of reading through dreams retrieved with a few keywords. The overall findings are similar to those in studies using traditional forms of content analysis.
The amygdala is important in processing emotion and in the acquisition and expression of fear and anxiety. It also appears to be involved in the regulation of sleep and wakefulness. The purpose of this study was to assess the effects of, fiber-sparing lesions of the amygdala on sleep in rhesus monkeys (Macaca mulatta). We recorded sleep from 18 age-matched male rhesus monkeys, 11 of which had previously received ibotenic acid lesions of the amygdala and seven of which were normal controls. Surface electrodes for sleep recording were attached and the subjects were seated in a restraint chair (to which they had been adapted) for the nocturnal sleep period. Despite adaptation, control animals had sleep patterns characterized by frequent arousals. Sleep was least disrupted in animals with large bilateral lesions of the amygdala. They had more sleep and a higher proportion of rapid-eye-movement (REM) sleep than did either animals with smaller lesions or control animals. Based on these results, it seems likely that, in the primate, the amygdala plays a role in sleep regulation and may be important in mediating the effects of emotions/stress on sleep. These findings may also be relevant to understanding sleep disturbances associated with psychopathology.
Several theories claim that dreaming is a random by-product of REM sleep physiology and that it does not serve any natural function. Phenomenal dream content, however, is not as disorganized as such views imply. The form and content of dreams is not random but organized and selective: during dreaming, the brain constructs a complex model of the world in which certain types of elements, when compared to waking life, are underrepresented whereas others are over represented. Furthermore, dream content is consistently and powerfully modulated by certain types of waking experiences. On the basis of this evidence, I put forward the hypothesis that the biological function of dreaming is to simulate threatening events, and to rehearse threat perception and threat avoidance. To evaluate this hypothesis, we need to consider the original evolutionary context of dreaming and the possible traces it has left in the dream content of the present human population. In the ancestral environment human life was short and full of threats. Any behavioral advantage in dealing with highly dangerous events would have increased the probability of reproductive success. A dream-production mechanism that tends to select threatening waking events and simulate them over and over again in various combinations would have been valuable for the development and maintenance of threat-avoidance skills. Empirical evidence from normative dream content, children's dreams, recurrent dreams, nightmares, post traumatic dreams, and the dreams of hunter-gatherers indicates that our dream-production mechanisms are in fact specialized in the simulation of threatening events, and thus provides support to the threat simulation hypothesis of the function of dreaming.
The dream is tackled sometimes from the neurobiological viewpoint, sometimes from the neuropsychological angle, or from the positions of experimental and psychoanalytical psychology. Interest in dreams started with psychoanalysis in 1900, and 53 years later the discovery of REM sleep by Aserinski and Kleitman, and subsequent psychophysiological findings took the dream into the realm of biology. The dichotomous model of REM and non-REM sleep is described, as a basis for thought-like activity (non-REM sleep) and dreaming (REM sleep). This led to Hobson and McCarley's theory of activation-synthesis, suggesting that the mind while dreaming is simply the brain self-activated in REM sleep. Psychophysiological research has shown that people dream in all phases of sleep, from falling asleep to waking, but that the characteristics of the dreams may differ in the different phases. Bio-imaging studies indicate that during REM sleep there is activation of the pons, the amygdala bilaterally, and the anterior cingulate cortex, and disactivation of the posterior cingulate cortex and the prefrontal cortex. The images suggest there is a neuroanatomical frame within which dreams can be generated and then forgotten. Psychoanalysis studies the dream from a completely different angle. Freud believed it was the expression of hallucinatory satisfaction of repressed desires. Today it is interpreted as the expression of a representation of the transference in the hic et nunc of the session. At the same time it also has symbol-generating functions which provide an outlet by which affective experiences and fantasies and defences stored as parts of an unrepressed unconscious in the implicit memory can be represented in pictorial terms, then thought and rendered verbally. From the psychoanalytical point of view, the dream transcends neurobiological knowledge, and looks like a process of internal activation that is only apparently chaotic, but is actually rich in meanings, arising from the person's affective and emotional history.
Dimensions of dreams
- C Winget
- M Kramer
Winget, C., & Kramer, M. (1979). Dimensions of dreams. Gainesville: University of Florida Press.
An examination of syntagmatic and paradigmatic associations using behavioral and electrophysiological measures of priming tasks Dissertation Abstracts International: Section B: The Sciences and Engineering
- H T Gomes
Gomes, H. T. (1995). An examination of syntagmatic and paradigmatic associations using behavioral and electrophysiological measures of priming tasks. (Doctoral dissertation City University of New York, 1995). Dissertation Abstracts International: Section B: The Sciences and Engineering, 55, 5103.
Automatic emotion annotation of dream diaries. In Analyzing social media to represent collective knowledge workshop at K-CAP 2009, The fifth international conference on knowledge capture
- E Frantova
- S Bergler
Frantova, E., & Bergler, S., (2009). Automatic emotion annotation of dream diaries. In Analyzing social media to represent collective knowledge workshop at K-CAP 2009, The fifth international conference on knowledge capture.
Dreams as cognitions Neuroimaging of REM sleep dreaming The new science of dreaming
- C Cavallero
- D Foulkes
- Harvester Wheatsheaf
- T T Dang-Vu
- M Schabus
- M Desseilles
- S Schwarts
- P Maquet
Cavallero, C., & Foulkes, D. (1993). Dreams as cognitions. New York: Harvester Wheatsheaf. Dang-Vu, T. T., Schabus, M., Desseilles, M., Schwarts, S., & Maquet, P. (2007). Neuroimaging of REM sleep dreaming. In D. Barrett & P. McNamara (Eds.). The new science of dreaming (Vol. 1, pp. 95–114). London: Praeger.
A new resource for content analysis
- G W Domhoff
Domhoff, G. W. (2003a). A new resource for content analysis (pp. 95–105)