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2,584 results for “Mentalization”
"Mapping specific mental content during musical imagery" - Musical stimuli
<p>Six instrumental melodies composed by Joe Hisaishi, used as stimuli in the paper "<strong>Mapping the contents of consciousness during musical imagery"</strong></p>
European Study on Risk Factors for Violence in Mental Disorder and Forensic Care: a multicentre project. The EU-Viormed Dataset
<p>The aims of this project, through a series of interlinked work packages, designed by researchers and practicing clinicians, and lead by an experienced management team, is to explore and map these differences, test new risk assessment tools and identify and share best practice where it exists. It aims are fourfold. Firstly to describe forensic psychiatry services as they exist today in 2017 across the European Union. Secondly to identify risk factors for violence in a unique international forensic sample and thirdly to test for the very first time in a related EU sample two contrasting methods of violence risk assessment. Finally it explored what works for these often marginalised patients, their families and their carers, at an operational, clinical and ethical level.<br> These aims were achieved through two studies designs:<br> Study 1: case-control retrospective design in which forensic patients with Schizophrenia<br> Spectrum Disorders (SSDs) who have a history of interpersonal violence and live in forensic<br> units will be compared to non-violent patients with SSDs living in the community.<br> Study 2: prospective cohort study, with 6 and 12 month follow-up, to test the predictive validity<br> of the leading structured professional clinical judgement guide for violence prediction, the<br> HCR-20v3, the Forensic Psychiatry and Violence Tool (FoVOx) and the Mental Illness and<br> Suicide Tool (OXMIS, https://oxrisk.com/)</p>
Mental Models
<p>Raw survey data from 3 studies of academic teachers regarding Mental Models towards online teaching</p>
Dataset for the paper "Mental Health and Burnout during Medical School: Longitudinal Evolution and Covariates" published in PLOS ONE (2024)
<p><strong>Full reference of the paper:</strong></p> <p>Carrard V, Berney S, Bourquin C, Ranjbar S, Castelao E, Schlegel K, et al. (2024) Mental health and burnout during medical school: Longitudinal evolution and covariates. PLoS ONE 19(4): e0295100. <a href="https://doi.org/10.1371/journal.pone.0295100">https://doi.org/10.1371/journal.pone.0295100</a></p>
Gender differences in preferences for mental health apps in the general popu-lation – a Choice-based Conjoint Analysis from Germany
<p><em><span>Background:</span></em><span> Men and women differ in the mental health issues they typically face. This study aims to describe gender differences in preferences for mental health treatment options and specifically tries to identify participants who prefer AI-based therapy over traditional face-to-face therapy. </span></p> <p><em><span>Method:</span></em><span> A nationally representative sample of 2</span><span>,</span><span>108 participants (53% female) aged 18 to 74 </span><span>years </span><span>completed a </span><span>CBCAs</span><span>. Within the CBCA</span><span>,</span><span> participants evaluated twenty choice sets, each describing three treatment variants in terms of provider, content, costs, and waiting time. </span></p> <p><em><span>Results:</span></em><span> Costs (</span><span>relative importance</span><span> [RI] = 55%) emerged as the most critical factor when choosing between treatment options, followed by provider (RI= 31%), content (RI = 10%), and waiting time (RI = 4%). Small yet statistically significant differences were observed between women and men. Women placed </span><span>greater</span><span> importance on the provider</span><span>,</span><span> while men placed </span><span>greater</span><span> importance on cost and waiting time. Age and previous experience with psychotherapy and with mental health apps were systematically related to individual preferences but did not alter gender effects. Only a minority </span><span>(approximately 8%)</span><span> of participants preferred AI-based treatment to traditional therapy. </span></p> <p><em><span>Conclusions:</span></em><span> Overall, affordable mental health treatments </span><span>performed</span><span> by human therapists are consistently favored by both men and women. AI-driven mental health apps should align with user preferences to address psychologist shortages. However, it is uncertain whether they alone can meet the rising demand, highlighting the need for alternative solutions.</span></p> <p><em><span>Keywords: </span></em><span>gender preferences; discrete choice experiment; mental health treatment; artificial intelligence</span></p>
Data and Code: Familial transmission of neural representations for mental arithmetic across two generations
<p>Here we provide anonymized behavioral data, individual beta maps and analyses codes used in "Familial transmission of neural representations for mental arithmetic across two generations".</p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu/">GDPR</a>), we cannot provide raw MRI data. <br>Therefore, the fMRI data consists of individual beta maps from the first-level analysis, which correspond to the brain activity associated with increases in problem size for each operation (addition and subtraction). Maps are normalized into the MNI template. See paper for details about the preprocessing and first-level analysis.</p> <p>The dataset consists of mother-child dyads. Mothers are assigned codes of 200 or higher. Children are assigned codes below 200. Each child's code is exactly 200 less than their mother's code.</p> <p>The analyses codes require Python version 3.8.8 and Nilearn version 0.8.1.</p> <p>If you have any questions, please send an email to charlotte.constant@inserm.fr. </p> <p> </p>
Mental health, physical health, training load and subjective performance during the COVID-19 pandemic – a Swiss elite athletes' cohort study
<p>Dataset of Swiss elite athletes (n=203) participating in a repeated online survey evaluating mental and physical health factors, as well as training and performance related metrics. After the first survey during the first lockdown between April and May 2020, there were monthly follow-up surveys over a 6-month period.</p>
Family Type, Mental and Physical Health of in-school Female Adolescents in Ado-Ekiti, Nigeria
<p>This dataset is based on a cross-sectional survey conducted in Ado-Ekiti, Nigeria among in-school female adolescents. The general objective of the study was to examine the influence of family type on the physical and mental health of in-school female adolescents. The data were obtained in 2017 from in-school female adolescents aged 10-18 years old. The respondents were randomly selected from four purposively selected secondary schools. Two schools were selected from the government-owned public schools and two from schools owned by individuals or private organizations. Nigeria operates a six-year secondary education; three years at the junior secondary school (JSS 1-3) and three years at the senior secondary (SS 1-3). The population for this study excluded students in SS3 because they had just concluded their terminal examination at the time of this study. Using the formula proposed by (Krejcie & Morgan, 1970) for deriving a small sample when the population is known, a sample size of 383 was derived from a population of 1656 female students in the four schools. With 10% added to adjust for non-response, a total of 421 students were involved in the study. The number of respondents allocated to each school was proportional to the size of the female students' population in each school. In each school, respondents were assigned to classes proportional to the size, and the particular respondents were identified using a random number. Data were collected with a self-administered structured questionnaire which was piloted before the actual survey. The data is saved in Stata format. </p> <p>A scale for measuring self-reported mental well was designed using questions adapted from Ross, Mirowsky, & Goldsteen (1990) and (Langton & Berger, 2011). The scale reliability coefficient was 0.85. The students were asked to state how often they experience 22 conditions such as feeling sad, discouraged, lonely, hopeless, worthless, wishing you were dead, having trouble concentrating, having difficulty sleeping, crying, and worried among others. </p> <p>Physical health was measured using a scale generated with questions adapted from (Langton & Berger, 2011; Ross et al., 1990). The students were asked to state the frequency of experiencing seven conditions in the month before the survey: feeling sick, tired, dizzy, having chest pain, a headache, muscle or joint pain, and stomach ache. The response options were every day, more than two times, two times, once, and never, graded 1-5.</p> <p>Family type was categorised in four ways: 1) two-parent and single parent. Two parents comprised of couples who were legally married and those who were living together in a consensual union whereas single parents comprised never married, widowed, divorced, and separated mother or father. 2) two-parent (monogamous), two-parent (polygynous), and single parent 3) two-parent, single father, and single mother 4) two-parent, never married, widowed, divorced, and separated</p>
Figure 11. Comparison of MultiNet and MST representations-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>In the sentence (7) there are two clauses describing two hypothetical situations. These two<br> situations are connected by the relation COND in MultiNet representation (Figure 11).</p>
Figure 10. Comparison of MultiNet and MST representations.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Helbig [18, p. 507] defines the expression (s MCONT c) as “a specification of the<br> informational or mental content c of a mental or informational process s… By default, the second<br> argument c is assumed to be a hypothetical object or situation.”<br> MCONT relation properties are roughly equivalent to those of subject-verb complexes<br> (SVSBs) of MST. MCONT relation can generally be treated as capturing the idea behind what<br> philosophers call propositional attitudes in representational theories of mind [12] or opaque<br> contexts [10] in semantics. For example, consider the sentence in (3) rewritten below as (6) and<br> semantic representation of which is given in Figure 10.</p>
Figure 8. Comparison of MultiNet and MST representations-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Figure 8 shows the representation of the<br> first reading of the sentence (5) in MWR. The MultiNet is shown at top of the figure, and the corresponding mental space representation is shown at the bottom side. Blue circles mark<br> hypothetical or nonreal objects and situations and red circles mark real objects and situations.<br> Facticity value for John is real, for unicorn it is nonreal, and for the process of riding, marked with<br> blue broken circle, it is non-real as well.</p>
Figure 7. The mental spaces set up by the sentence If John buys the car, he will drive to Berlin.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>In the sentence (4) there are three proper names which constitute elements of the base space<br> or reality space (R). The CNSB if sets up the hypothetical space (H) with elements identical to those<br> of reality space (Figure 7). Every space’s internal structure is presented in the boxes next to them.<br> In the next part, we will see how MultiNet’s built-in meaning representation mechanisms are<br> capable of representing the basic principles of mental space building outlined above.</p>
Figure 6. The mental spaces set up by the sentence Mary thinks that John smokes.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>The proper nouns Mary and John setup a base space (B). By the help of background<br> knowledge and activated frames we know that they are names of female and male humans. Not<br> having access to the previous discourse, we also consider their existence presupposed. The SVSB<br> Mary thinks that sets up a belief space (L) relative to space B (Figure 6). The identity connector<br> maintains the referential link between elements a and a ׳ both referring to the same person.</p>
Figure 9. Comparison of MultiNet and MST representations-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Second interpretation of the sentence (5) can be represented with changing John’s Facticity<br> attribute-value from [FACT = real] to [FACT = nonreal] making the whole situation and its<br> elements non-real (Figure 9).</p>
Figure 5. The mental spaces set up by the second interpretation of the sentence in the film, John is riding a unicorn.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Second interpretation: The proper name John exists only in film space without having a<br> counterpart in base space. Therefore, the second interpretation of the sentence is: John is a<br> film character who is riding a unicorn in the film (Figure 5).</p>
Figure 4. The mental spaces set up by the first interpretation of the sentence in the film, John is riding a unicorn-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>First interpretation: The reality space (let’s call it R) contains an element a associated with<br> the proper name John. The noun phrase a unicorn introduces an element b ׳ to the film space<br> (call it F). I is the connector linking a in the space B to a ׳ in the space F (Figure 4). Since the<br> elements of both mental spaces are co-referential, this connector is an identity connector.<br> The rectangles represent the internal structure of the spaces next to them. The dashed line<br> indicates that the space F is set up in relation to R and that it is subordinate to R in<br> discourse.</p>
Figure 1. The upper ontology of sorts in MultiNet (after Helbig [17])-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>One of the distinguishing features of MultiNet is its commitment to the Cognitive Adequacy<br> requirement. According this requirement [9], semantic representations and knowledge<br> representations should be centered around concepts. Concepts2 are represented by nodes in the<br> graphical representation of the network. Every node belongs to a specific sort defined by the<br> MultiNet’s ontology of sorts (Figure 2).</p>
Figure 1. Mental space representation of "In the play, Mary is excited"-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Thus an entity can have a variable reality status depending on the mental<br> space to which it belongs. The mental space constructed by the sentence Poirot is a Belgian<br> detective is a non-real imaginary story space of which Poirot is an element. But when we say in<br> reality, Poirot is not Belgian the constructed space is reality space in which Poirot (the actor, not<br> the character) does not have the fictional nationality.</p>
Figure 2. Semantic representation of the sentence Peter finished the discussion in MultiNet after Helbig [8, p. 447].-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>In Figure 3, semantic frame of the concept Finish realized in the form of the verb finish<br> requires two C-roles: An agent represented by the relation AGT, and an affected entity represented<br> by the relation AFF. Here agent is Peter and the affected entity is an abstract object ([SORT = ad]<br> means the concept is a dynamic abstraction).</p>
Dataset: Tema Neuroscience And Mental Health ETF (MNTL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.