Stability of and Changes in Implicit Motives. A Narrative Review of Empirical Studies

Denzinger, Ferdinand; Brandstätter, Veronika

doi:10.3389/fpsyg.2018.00777

Cited by 30 publications

(23 citation statements)

References 114 publications

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“…Delineation from motive dispositions -What differentiates aggregates of motivational states from explicit and implicit motives? A theoretically assumed feature of motive dispositions is a certain stability over time (see Denzinger & Brandstätter, 2018 for an overview on implicit motives). Although changes are not impossible, shifts would be assumed to occur moderately as a result of a slow situational adaptation (Bischof, 2008;Gubler & Bischof, 1991).…”

Section: Aggregated Motivational Statesmentioning

confidence: 99%

Motivated behavior in intimate relationships: Comparing the predictive value of motivational variables

Zygar–Hoffmann¹,

Pusch²,

Hagemeyer³

et al. 2020

Soc. Psychol. Bull.

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Motivational variables are considered fundamental factors influencing the occurrence of behavior. The current study compared different types of motivational variables (implicit and explicit motive dispositions, motivation as states and as aggregated person-level variables) in their ability to predict communal and agentic behavior reports in intimate relationships. 510 individuals completed measures of dispositional communion and agency motives and participated in a dyadic experience sampling study with five assessments per day across four weeks. They reported on their momentary communal and agentic motivation, as well as on their own and their partner’s behaviors. All examined types of motivational variables predicted certain behavior reports on the between-person or within-person level and had incremental effects beyond the other motivational variables in at least one motive domain. Directly replicating and conceptually extending prior research, the effects of motivational states and their aggregates were consistently found across behavioral outcomes, across self- and partner-reports and across the motive domains of communion and agency. Using the example of motivational states, the general value of assessing within-person variables for psychological phenomena in ESM-designs is discussed.

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Section: Aggregated Motivational Statesmentioning

confidence: 99%

Motivated behavior in intimate relationships: Comparing the predictive value of motivational variables

Zygar–Hoffmann¹,

Pusch²,

Hagemeyer³

et al. 2020

Soc. Psychol. Bull.

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“…To perform these experiments, we used the Simple Transformers library which allows us to easily implement this setup. 10 As main configuration parameters, we set the max_ length parameter to 90, 11 and we re-trained the models up to two epochs. From here after, we refer as Bert-(FT) to the fine-tuned experiments.…”

Section: Simple Transformermentioning

confidence: 99%

“…The most frequently used measures of implicit motives are the Picture Story Exercise (PSE) [7], the Thematic Apperception Test (TAT) [8], the Multi-Motive Grid (MMG) [9], and the Operant Motive Test (OMT) [10,11]. Generally speaking, these tests are based on the operant methods, i.e., participants are asked ambiguous questions or are shown simple images, which they have to describe.…”

Section: Introductionmentioning

confidence: 99%

Applying Attention-Based Models for Detecting Cognitive Processes and Mental Health Conditions

Villatoro-Tello

Parida

Kumar³

et al. 2021

Cogn Comput

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According to the psychological literature, implicit motives allow for the characterization of behavior, subsequent success, and long-term development. Contrary to personality traits, implicit motives are often deemed to be rather stable personality characteristics. Normally, implicit motives are obtained by Operant Motives, unconscious intrinsic desires measured by the Operant Motive Test (OMT). The OMT test requires participants to write freely descriptions associated with a set of provided images and questions. In this work, we explore different recent machine learning techniques and various text representation techniques for facing the problem of the OMT classification task. We focused on advanced language representations (e.g, BERT, XLM, and DistilBERT) and deep Supervised Autoencoders for solving the OMT task. We performed an exhaustive analysis and compared their performance against fully connected neural networks and traditional support vector classifiers. Our comparative study highlights the importance of BERT which outperforms the traditional machine learning techniques by a relative improvement of 7.9%. In addition, we performed an analysis of how the BERT attention mechanism is being modified. Our findings indicate that the writing style features acquire higher importance at the moment of accurately identifying the different OMT categories. This is the first time that a study to determine the performance of different transformer-based architectures in the OMT task is performed. Similarly, our work propose, for the first time, using deep supervised autoencoders in the OMT classification task. Our experiments demonstrate that transformer-based methods exhibit the best empirical results, obtaining a relative improvement of 7.9% over the competitive baseline suggested as part of the GermEval 2020 challenge. Additionally, we show that features associated with the writing style are more important than content-based words. Some of these findings show strong connections to previously reported behavioral research on the implicit psychometrics theory.

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“…Despite evidence of a relationship between achievement motive and various motivational constructs, research on a relationship between achievement motive and health has been limited. The research that exists has suggested that achievement motive is associated with health benefits including the attenuation of cortisol, release of vasopressin, reduced stress, and improved immune functioning (Denzinger & Brandstatter, 2018;McClelland, 1989;McClelland et al, 1987). Furthermore, people high in achievement motivation have been found to comply with and be more involved in their medical treatment (Hatta et al, 2016) and engage in healthier behaviors such as avoiding sun exposure and lowered risk-related sexual behavior (Aspden et al, 2012) and healthier eating behaviors (Job et al, 2010).…”

Section: Achievement Motivation and Healthmentioning

confidence: 99%

Marijuana Use and Achievement Motives: An Exploratory Analysis of Self-Reported Health Among Adult Cannabis Consumers

Gould

Greene

Donnelly

2020

Journal of Drug Issues

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The purpose of this study was to explore self-reported physical health and achievement motives in a sample of adult cannabis consumers. An online target sample ( n = 181) of adult cannabis consumers ( M = 38 yoa) was obtained from a cannabis tourism company in Colorado. Physical health and the primary benefit of cannabis use were self-reported and achievement motives were measured using the revised 10-item version of the Achievement Motives Scale (AMS). Results revealed five categories of benefit from cannabis consumption: stress/anxiety reduction (40%), pain/disease management (38.5%), thinking/introspection (11%), alternative drug (7.1%), and enhanced sociability (3%). t test results revealed no significant differences between the stress/anxiety and pain/disease groups in self-reported health (SRH), AMS dimensions, or cannabis consumption preferences. Regression results revealed that AMS factor fear of failure ( b = −0.224, p = .003), followed by cannabis spending per week ( b = −0.217, p = .024) best predicted SRH.

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Stability of and Changes in Implicit Motives. A Narrative Review of Empirical Studies

Cited by 30 publications

References 114 publications

Motivated behavior in intimate relationships: Comparing the predictive value of motivational variables

Motivated behavior in intimate relationships: Comparing the predictive value of motivational variables

Applying Attention-Based Models for Detecting Cognitive Processes and Mental Health Conditions

Marijuana Use and Achievement Motives: An Exploratory Analysis of Self-Reported Health Among Adult Cannabis Consumers

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