记忆监测识别测试(MMRT),一种刺激源监测的新测量方法:软件与理解
Memory Monitoring Recognition Test (MMRT), a new measurement of stimular source monitoring: Software and comprehension.
作者信息
Martínez-Suárez Pedro C, Valdevila Figueira José Alejandro, Luna-Cambi Joselyn M, Guerrero-Granda Carlos E, Santiesteban Rocío Valdevila
机构信息
Catholic University of Cuenca, Cuenca-Ecuador.
Psychology and Psychiatry Research Group (GIPSI), Ecuador.
出版信息
PLoS One. 2025 Apr 28;20(4):e0321991. doi: 10.1371/journal.pone.0321991. eCollection 2025.
BACKGROUND
Reality monitoring allows the evaluation and monitoring of reality through the assignment of information to internal or external sources, which is crucial to differentiate real events from imaginary ones. In schizophrenics, monitoring seems to be related to an error in the allocation processes, giving rise to false perceptions such as visual hallucinations, which are associated with a poor prognosis. This error can appear almost imperceptibly at an early age in life, making carrying out predictive or evaluation tests with paper and pencil unattractive. The computerization of technical resources that allow the monitoring of reality offers a new tool to evaluate the attribution process, in an effective and agile way and with easy understanding of cognitive deficits in a friendly environment.
OBJECTIVE
Computerize the Memory Monitoring and Recognition Test (MMRT) evaluate reality monitoring through verbal memory tasks, improving its implementation, optimizing interaction with the user and perfecting the recording of memory errors that could indicate psychotic symptoms.
METHOD
The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. The test is structured in stages, allows voice accessibility for people with visual disabilities and provides comprehensive user management. The test data is stored in the cloud using MongoDB as the database system. Additionally, the software incorporates speech recognition using the gTTS library and generates a performance report in PDF format, documenting external, internal and global attribution errors.
RESULT
The computerized version of the MMRT allowed the detection of specific errors in memory monitoring, as well as the performance of repeated measurements to evaluate long-term memory and working memory.
CONCLUSION
Preliminary applications suggest its usefulness in identifying early cognitive markers of schizophrenia, facilitating the measurement of reality monitoring through attribution errors. Developed with open-source technology and an interface adaptable to various platforms, the MMRT represents an accessible and efficient tool for psychological evaluation, with innovative potential in the study of reality monitoring.
背景
现实监测通过将信息分配到内部或外部来源来评估和监测现实,这对于区分真实事件和想象事件至关重要。在精神分裂症患者中,监测似乎与分配过程中的错误有关,从而导致诸如幻视等错误感知,而幻视与预后不良相关。这种错误在生命早期可能几乎难以察觉地出现,使得使用纸笔进行预测或评估测试缺乏吸引力。能够监测现实的技术资源的计算机化提供了一种新工具,以有效、灵活的方式评估归因过程,并在友好的环境中便于理解认知缺陷。
目的
将记忆监测与识别测试(MMRT)计算机化,通过言语记忆任务评估现实监测,改进其实施方式,优化与用户的交互,并完善可能表明精神症状的记忆错误记录。
方法
MMRT使用Python和Kivy开发,便于创建跨平台用户界面。该测试按阶段构建,为视力障碍者提供语音访问功能,并提供全面的用户管理。测试数据使用MongoDB作为数据库系统存储在云端。此外,该软件使用gTTS库集成语音识别,并生成PDF格式的性能报告,记录外部、内部和全局归因错误。
结果
MMRT的计算机化版本能够检测记忆监测中的特定错误,以及进行重复测量以评估长期记忆和工作记忆。
结论
初步应用表明其在识别精神分裂症早期认知标志物方面的有用性,便于通过归因错误来测量现实监测。MMRT采用开源技术开发,界面可适应各种平台,是一种便于使用且高效的心理评估工具,在现实监测研究中具有创新潜力。
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Neurosci Biobehav Rev. 2022-2
Eur Psychiatry. 2020-5-14
Eur Psychiatry. 2019-9-7
Acta Med Port. 2019-2-1
Cogn Neuropsychiatry. 2019-1
Front Hum Neurosci. 2018-3-6
Curr Opin Psychiatry. 2018-5
Neuroimage Clin. 2017-1-25
Rev Psiquiatr Salud Ment. 2016