Pathological Imitative Behavior and Response Preparation in Schizophrenia

Dankinas, Denisas; Mėlynytė, Sigita; Šiurkutė, Aldona; Dapšys, Kastytis

doi:10.1093/arclin/acx034

Cited by 5 publications

(5 citation statements)

References 55 publications

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“…Finally, it is important to consider the implications of our results for imitative behavior in related psychiatric disorders. In particular, considering that schizophrenia can be seen as part of the autism spectrum (King & Lord, 2011), an interesting question is how intact automatic imitation in ASD can be reconciled with recent reports of disturbed automatic imitation in schizophrenic samples (Dankinas, Melynyte, Siurkute, & Dapsys, 2017; Nakagawa & Hoshiyama, 2015).…”

Section: Discussionmentioning

confidence: 99%

Automatic imitation: A meta-analysis.

Cracco

Bardi

Desmet

et al. 2018

Psychological Bulletin

202

273

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Automatic imitation is the finding that movement execution is facilitated by compatible and impeded by incompatible observed movements. In the past 15 years, automatic imitation has been studied to understand the relation between perception and action in social interaction. Although research on this topic started in cognitive science, interest quickly spread to related disciplines such as social psychology, clinical psychology, and neuroscience. However, important theoretical questions have remained unanswered. Therefore, in the present meta-analysis, we evaluated seven key questions on automatic imitation. The results, based on 161 studies containing 226 experiments, revealed an overall effect size of g = 0.95, 95% CI [0.88, 1.02]. Moderator analyses identified automatic imitation as a flexible, largely automatic process that is driven by movement and effector compatibility, but is also influenced by spatial compatibility. Automatic imitation was found to be stronger for forced choice tasks than for simple response tasks, for human agents than for nonhuman agents, and for goalless actions than for goal-directed actions. However, it was not modulated by more subtle factors such as animacy beliefs, motion profiles, or visual perspective. Finally, there was no evidence for a relation between automatic imitation and either empathy or autism. Among other things, these findings point toward actor-imitator similarity as a crucial modulator of automatic imitation and challenge the view that imitative tendencies are an indicator of social functioning. The current meta-analysis has important theoretical implications and sheds light on longstanding controversies in the literature on automatic imitation and related domains. (PsycINFO Database Record

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Section: Discussionmentioning

confidence: 99%

Automatic imitation: A meta-analysis.

Cracco

Bardi

Desmet

et al. 2018

Psychological Bulletin

202

273

View full text Add to dashboard Cite

show abstract

“…For instance, our previous studies confirmed a cognitive deficit of response preparation and inhibition of imitation in schizophrenia patients [2][3], which were investigated when engaging response probability [4] and stimulus-response compatibility [5] experimental methods. Therefore, in former researchers in general we assessed responses to visual stimuli of two types of probability (frequent and rare) and three types of compatibilities (imitative, neutral and counter-imitative) [2][3].…”

Section: Introductionmentioning

confidence: 72%

“…The dataset was engaged from our two previous studies of frontal lobe function impairments in schizophrenia (with adding of several new subjects). The information about participant recruitment and research procedures can be found in corresponding papers [2][3].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, current study contains two different datasets with assessments of neutral [2] and imitative/counter-imitative responses [3]. Neutral response dataset consists of 16 SZ subjects (6 males and 10 females) with average age of 38.9 (SD 13.2) years.…”

Section: Methodsmentioning

confidence: 99%

“…To summarize, the aim of the current study is to develop ML models using frontal lobe functions (response preparation and inhibition of imitation) assessments [2][3] and evaluate their efficiency to distinguish patients with schizophrenia from healthy controls.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Machine Learning Techniques for Distinguishing Schizophrenia Patients From Healthy Subjects Using Frontal Lobe Functions Assessments

Dankinas¹,

Budginaitė²,

Mėlynytė³

et al. 2023

Health Sciences

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Machine learning (ML) represents a set of artificial intelligence techniques that can assist in recognition of schizophrenia by classifying a person as belonging to either clinical or healthy subjects group. In the current study, we employed cognitive assessments of frontal lobe functions (the deficit of which is one of the prominent features of schizophrenia) for the training of ML models. Dataset for this research was engaged from our previous studies of two frontal lobe functions (response preparation and inhibition of imitation) in case of schizophrenia. According to our knowledge, all previous cognitive ML schizophrenia studies used only the data from standard neuropsychological test batteries. Nevertheless, we employed the cognitive data assessed with special experimental techniques that allowed us to engage Intra-individual reaction time variability (IIV) together with the classical reaction time (RT) assessment. It is important to emphasize that IIV is a cognitive measurement parameter that received vast attention of neuroscientists during the two last two decades and showed higher results in distinguishing of schizophrenia patients from healthy subjects than standard RT in the number of studies. The result revealed statistically significant accuracy for all ML models in current study. Moreover, ML classifier with the highest accuracy outperformed the accuracy of a number of best models previously trained with standard neuropsychological test batteries datasets. Thus, cognitive experimental assessments of frontal lobe functions (response preparation and inhibition of imitation) can be effectively employed in developing of ML classifiers for distinguishing schizophrenia patients from healthy subjects.

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