An Introduction to Artificial Psychology

Farahani, Hojjatollah; Blagojević, Marija; Azadfallah, Parviz; Watson, Peter; Esrafilian, Forough; Saljoughi, Sara

doi:10.1007/978-3-031-31172-7

Cited by 3 publications

(3 citation statements)

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“…Both cognitive psychology and cybernetics depended on a view of information as independent of its medium of circulation and thus prefigured visions of humans as "data subjects" long before the advent of the Internet (Hayles, 1999). As will become evident, this technoscientific relationship is only deepening as psychology turns increasingly to highly sophisticated data processing and complex engineering for its methods, and computer scientists attempt to model or approximate human subjectivity (e.g., Crowder & Friess, 2012;Farahani et al, 2023). Yet, while the limitations of information processing as metaphor for human cognition have become increasingly apparent (e.g., Gallagher, 2018), comparatively little attention has been given to the assessment of information technology as medium of psychological inquiry, though these go hand in hand (see Hayles, 1999).…”

Section: Digital Methods In An Emerging Data Politicsmentioning

confidence: 99%

The social life of digital methods in psychology: Situating digital methods in the new data politics

Yen

2023

Social & Personality Psych

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In this paper I present some preliminary analyses of what is at stake in the growing use of digital methods in psychology. Their exponential rise in the discipline has scientific consequences, because such methods embody unarticulated assumptions that derive from their cultural, technical, or commercial origins. Such methods also rearticulate researcher‐participant relations in new ways, and reframe what it means to take part in psychological research. Additionally, strong calls for psychologists to exploit the potentials of Big Data to analyze and influence human action in real time signal the growing entanglement of psychology, computer science, and the accumulative tactics of the digital economy. As part of a larger project to trace the social life of digital methods in psychology, my aim here is to link disparate literature in psychology, science and technology studies, and critical data studies, to situate such methods in the broader context of technologically afforded shifts in modes of economic and knowledge production. I argue that digital methods are in urgent need of analysis, not only in terms of the interpretive frames, modes of participation, and courses of action they afford, but as research media that circulate in a larger digital and political economic ecosystem, and with associations that span multiple sociotechnical assemblages.

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Section: Digital Methods In An Emerging Data Politicsmentioning

confidence: 99%

The social life of digital methods in psychology: Situating digital methods in the new data politics

Yen

2023

Social & Personality Psych

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“…One of the strengths of the present study was the use of network analysis as an innovative method to create new clinical insights into CPTSD symptoms, emotional processing, and dissociative experiences after traumatic experiences in different developmental stages. The network analysis approach in psychopathology has many advantages, including 1) Network analysis provides a robust framework for exploring complex interrelationships among psychological variables, offering insights into how symptoms, behaviors, or traits influence each other within a system ( 35 , 77 , 78 ); 2) It provides a visual representation of relationships, facilitating the identification of central nodes, clusters, and pathways. This information can inform intervention strategies and theoretical models ( 35 , 64 , 77 ); 3) Network analysis can identify critical players or influential nodes in a system, which can help researchers and clinicians focus on specific targets for intervention ( 35 , 77 , 78 ).…”

Section: Limitationsmentioning

confidence: 99%

“…The network analysis approach in psychopathology has many advantages, including 1) Network analysis provides a robust framework for exploring complex interrelationships among psychological variables, offering insights into how symptoms, behaviors, or traits influence each other within a system ( 35 , 77 , 78 ); 2) It provides a visual representation of relationships, facilitating the identification of central nodes, clusters, and pathways. This information can inform intervention strategies and theoretical models ( 35 , 64 , 77 ); 3) Network analysis can identify critical players or influential nodes in a system, which can help researchers and clinicians focus on specific targets for intervention ( 35 , 77 , 78 ). While network analysis is a valuable approach, past network studies of psychopathology have certain limitations that need to be addressed in future research.…”

Section: Limitationsmentioning

confidence: 99%

A network analysis of ICD-11 Complex PTSD, emotional processing, and dissociative experiences in the context of psychological trauma at different developmental stages

Mohammadi,

Dehghani,

Fathali Lavasani

et al. 2024

Front. Psychiatry

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ObjectiveTraumatic experiences are a significant risk factor for psychological disturbances, including disorders such as complex posttraumatic stress disorder, emotion-processing problems, and trauma-related dissociative experiences. The present investigation examined the coexistence of these symptoms using a network analysis model.MethodThis study included a sample of 406 people referred to comprehensive health centers in Tehran from September to December 2023 with psychopathological syndromes. Variables were assessed using The International Trauma Questionnaire, International Measurement of Exposure to Traumatic Event checklist, Baker Emotional Processing Questionnaire, and Dissociative Experiences. A regularized partial correlation network and Glasso algorithm, in combination with Extended Bayesian information criteria, were applied to estimate the network structure.ResultsSigns of unprocessed emotions and disturbance in self-organization symptoms were the most important symptoms in the symptom network, forming strong connections with other nodes. Thereby, these two symptoms can be regarded as the most important clinical manifestations in the symptom network following traumatic experiences. Three distinct symptom communities were identified: the community of traumatic experiences (childhood, adolescence, adulthood), the community of dissociative experiences (amnesia, depersonalization/derealization, and absorption), and the community of emotional processing (suppression, unpleasant emotional experience, Signs of unprocessed emotions, avoidance, and emotional control, posttraumatic stress disorder symptoms and disturbance in self-organization symptoms). The strongest edges observed were between childhood trauma-adolescence trauma (0.473) in the community of traumatic experiences, between amnesia and depersonalization/derealization (0.644) in the community of dissociative experiences, and between disturbance in self-organization symptoms and unprocessed emotions (0.324) in the community of emotional processing, indicating the recurrent occurrence of these symptoms.ConclusionIn this study, disturbance in self-organization symptoms was identified as the central psychopathologic symptom in individuals experiencing traumas at different developmental stages. It seems that adolescent trauma and not childhood trauma plays a more decisive role in the symptoms that a person manifests after traumatic experiences. Also, posttraumatic stress disorder symptoms and disturbance in self-organization symptoms were recognized in the cluster of emotional processing symptoms and can have substantial roles in prioritizing therapeutic measures.

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Predictors of Successful Maintenance Practices in Companies Using Fluid Power Systems: A Model-Agnostic Interpretation

Orošnjak,

Beker,

Brkljač

et al. 2024

Applied Sciences

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The study identifies critical factors influencing companies’ operational and sustainability performance utilising fluid power systems. Firstly, the study performs Machine Learning (ML) modelling using variables extracted from survey instruments in the West Balkan region. The dataset comprises 115 companies (38.75% response rate). The survey data consist of 22 predictors, including meta-data and three target variables. The K-Nearest Neighbours algorithm offers the highest predictive accuracy compared to the other seven ML models, including Ridge Regression, Support Vector Regression, and ElasticNet Regression. Next, using a model-agnostic interpretation, we assess feature importance using mean dropout loss. After extracting the most essential features, we test hypotheses to understand individual variables’ local and global interpretation of maintenance performance metrics. The findings suggest that Failure Analysis Personnel, data analytics, and the usage of advanced technological solutions significantly impact the availability and sustainability of these systems.

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An Introduction to Artificial Psychology

Cited by 3 publications

References 0 publications

The social life of digital methods in psychology: Situating digital methods in the new data politics

The social life of digital methods in psychology: Situating digital methods in the new data politics

A network analysis of ICD-11 Complex PTSD, emotional processing, and dissociative experiences in the context of psychological trauma at different developmental stages

Predictors of Successful Maintenance Practices in Companies Using Fluid Power Systems: A Model-Agnostic Interpretation

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