Measuring individual differences in implicit cognition: The implicit association test.

Greenwald, Anthony G.; McGhee, Debbie E.; Schwartz, Jordan L. K.

doi:10.1037//0022-3514.74.6.1464

Cited by 2,182 publications

(82 citation statements)

References 21 publications

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“…Actual and desired multicultural experiences were measured using the 15-item MEQ (Narvaez & Hill, 2010). The MEQ utilizes several scale ranges (e.g., Content: read the three assigned articles (Fazio & Olson, 2003;Greenwald et al, 1998;Kirwin Institute, 2013) ). Structure: students with last names that end in A through O will post on the odd-numbered units (1, 3, 5, 7, etc.)…”

Section: Multicultural Experiencesmentioning

confidence: 99%

Shifting student attitudes while minimizing grading bias: Pedagogical considerations in a multicultural psychology course.

Hicks,

Alvarez,

Domenech Rodríguez

2024

Translational Issues in Psychological Science

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Section: Multicultural Experiencesmentioning

confidence: 99%

Shifting student attitudes while minimizing grading bias: Pedagogical considerations in a multicultural psychology course.

Hicks,

Alvarez,

Domenech Rodríguez

2024

Translational Issues in Psychological Science

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“…We used data from four IATs (Cutting, Suicide, Death, Death brief IAT [BIAT]) to obtain estimates of the implicit association (Greenwald et al, 1998) between the self on the one hand, and cutting, suicide, or death on the other hand, respectively. Displayed one by one at the center of a screen were words relating to the self or others, images related to cutting (i.e., images of forearms cut), or words relating to suicide and death, as well as control images or words unrelated to these topics (i.e., life, alive, thrive, breathing).…”

Section: Implicit Associationsmentioning

confidence: 99%

How do explicit, implicit, and sociodemographic measures relate to concurrent suicidal ideation? A comparative machine learning approach

Freichel,

Kahveci,

O’Shea

2023

Suicide & Life Threat Behav

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IntroductionSuicide is a leading cause of death, and decades of research have identified a range of risk factors, including demographics, past self‐injury and suicide attempts, and explicit suicide cognitions. More recently, implicit self‐harm and suicide cognitions have been proposed as risk factors for the prospective prediction of suicidal behavior. However, most studies have examined these implicit and explicit risk factors in isolation, and little is known about their combined effects and interactions in the prediction of concurrent suicidal ideation.MethodsIn an online community sample of 6855 participants, we used different machine learning techniques to evaluate the utility of measuring implicit self‐harm and suicide cognitions to predict concurrent desire to self‐harm or die.ResultsDesire to self‐harm was best predicted using gradient boosting, achieving 83% accuracy. However, the most important predictors were mood, explicit associations, and past suicidal thoughts and behaviors; implicit measures provided little to no gain in predictive accuracy.ConclusionConsidering our focus on the concurrent prediction of explicit suicidal ideation, we discuss the need for future studies to assess the utility of implicit suicide cognitions in the prospective prediction of suicidal behavior using machine learning approaches.

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“…To compare different methods, we used multiclass metrics involving accuracy as well as macroaveraged recall, F 1 , and precision [46], as shown in equations ( 9) and (10).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…For the measurement of cognitive biases, attitude towards height questionnaire [8] and agoraphobic cognition questionnaire [9] are most popular. Besides, some studies used the implicit association test [10] to observe people's implicit attitudes to height. For the measurement of subjective feeling, the subjective units of distress scales (SUDS) [11] is the most commonly used questionnaires.…”

Section: Introductionmentioning

confidence: 99%

Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia

Wang

et al. 2021

J. Neural Eng.

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Objective. The prevalence of acrophobia is high, especially with the rise of many high-rise buildings. In the recent few years, researchers have begun to analyze acrophobia from the neuroscience perspective, especially to improve the virtual reality exposure therapy (VRET). Electroencephalographic (EEG) is an informative neuroimaging technique, but it is rarely used for acrophobia. The purpose of this study is to evaluate the effectiveness of using EEGs to identify the degree of acrophobia objectively. Approach. EEG data were collected by virtual reality (VR) exposure experiments. We classified all subjects’ degrees of acrophobia into three categories, where their questionnaire scores and behavior data showed significant differences. Using synchronization likelihood, we computed the functional connectivity between each pair of channels and then obtained complex networks named functional brain networks (FBNs). Basic topological features and community structure features were extracted from the FBNs. Statistical results demonstrated that FBN features can be used to distinguish different groups of subjects. We trained machine learning (ML) algorithms with FBN features as inputs and trained convolutional neural networks (CNNs) with FBNs directly as inputs. Main results. It turns out that using FBN to identify the severity of acrophobia is feasible. For ML algorithms, the community structure features of some cerebral cortex regions outperform typical topological features of the whole brain, in terms of classification accuracy. The performances of CNN algorithms are better than ML algorithms. The CNN with ResNet performs the best (accuracy reached 98.46 ± 0.42%). Significance. These observations indicate that community structures of certain cerebral cortex regions could be used to identify the degree of acrophobia. The proposed CNN framework can provide objective feedback, which could help build closed-loop VRET portable systems.

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Measuring individual differences in implicit cognition: The implicit association test.

Cited by 2,182 publications

References 21 publications

Shifting student attitudes while minimizing grading bias: Pedagogical considerations in a multicultural psychology course.

Shifting student attitudes while minimizing grading bias: Pedagogical considerations in a multicultural psychology course.

How do explicit, implicit, and sociodemographic measures relate to concurrent suicidal ideation? A comparative machine learning approach

Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia

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