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New Behavioral EXperimental approaches to complexity perception and stress assessment in cognitive research

Data inizio : 2022

Data fine : 2024

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Breve descrizione

many smartwatches are able to record precise variations in earth-beat rate (VBR) and blood oxygenation (spO2) which have been recognized to be useful in many health tracking conditions (Li et al. 2017). Even though such measures are less responsive than standard eye tracking and ERPs, they correlate with the stress level induced by an increasing complexity in cognitive tasks (Hughes et al 2019). On the one hand, few preliminary studies approached these problems (though obtaining encouraging results, Gjoreski etal 2020), on the other many projects are now under scrutiny of the international research community (Can etal 2019). While the correlation between ERPs components, N400/P600-800, and syntactic processing of sentences are well known, recent studies showed a correlation between the VBR and cognitive-attentive processes (Forte et al. 2019). This is then a frontier project supported by other parallel, though independent, research activities (EVALITA 2020 – Accompl-IT).

Our goal is to develop this methodology into working solution to reduce the necessity of in-lab experiments while controlling for individual subject and task dependent variability during measurements of the recorded VBR and spO2 as a correlate of cognitive task complexity.

The project requires state-of-the-art programming, algorithm development and working with highly dynamic and sensitive new web technologies, together with considerable scientific expertise, which constitutes a high-risk/reward project suitable for initial public funding.

 

Descrizione

PROBLEM

Smart working presents challenges for researchers running experimental studies in the laboratory environment. Psychologists, psycholinguists, as well as neurologists, collect behavioral responses by using eye-tracking, EEG, fMRI and other advanced methods, traditionally measured in laboratory conditions and thus requiring close interaction between a number of people, participants, assistants and the lead researchers. This activity came into halt during the lock-down phase seriously limiting the research activity. The problem will persist during phase two and beyond, first because of the difficulties in maintaining social distancing but, more importantly, because we already see psychological factors in play making participants hesitant to participate in studies in the “post covid” realities requiring access to small rooms, close social interaction, and use of devices used by other experimental subjects before. This presents a challenge for scientific research, especially for those who work with human participants.

 

PROPOSAL

To solve the issue, we consider to expand traditional measurement paradigms by developing and testing new, remote experimental behavioral data collection approaches which track physiological and behavioral data by using new, experimental web-based platforms that offer microsecond-precision solutions (e.g. jsPsych, De Leeuw 2015) and allow recording of physiological data using widely used devices already owned by the potential experimental subjects, i.e. smartwatches. The idea of such “smart lab” methodologies is to lower the threshold for participation, reduce lab and infrastructure costs, gather data more efficiently and in greater volume, and to utilize crowd sourcing (e.g. Prolific).

In details, many smartwatches are able to record precise variations in earth-beat rate (VBR) and blood oxygenation (spO2) which have been recognized to be useful in many health tracking conditions (Li et al. 2017). Even though such measures are less responsive than standard eye tracking and ERPs, they correlate with the stress level induced by an increasing complexity in cognitive tasks (Hughes et al 2019). On the one hand, few preliminary studies approached these problems (though obtaining encouraging results, Gjoreski etal 2020), on the other many projects are now under scrutiny of the international research community (Can etal 2019). While the correlation between ERPs components, N400/P600-800, and syntactic processing of sentences are well known, recent studies showed a correlation between the VBR and cognitive-attentive processes (Forte et al. 2019). This is then a frontier project supported by other parallel, though independent, research activities (EVALITA 2020 – Accompl-IT).

Our goal is to develop this methodology into working solution to reduce the necessity of in-lab experiments while controlling for individual subject and task dependent variability during measurements of the recorded VBR and spO2 as a correlate of cognitive task complexity.

The project requires state-of-the-art programming, algorithm development and working with highly dynamic and sensitive new web technologies, together with considerable scientific expertise, which constitutes a high-risk/reward project suitable for initial public funding.

BENEFITS

  1. Develop smart approaches to data collection and data processing for behavioral experimental sciences (language comprehension, risk perception, economical choices, rational reasoning etc.) based on wearable technologies;
  2. Benefit from widely used devices with spO2/VBR sensors by reducing lab and infrastructure costs;
  3. Become a reference point for sensors testing/development for better capturing physiological data and maximize their cognitive utility and readability;
  4. Open source technology development for facilitating research accessibility and evaluation and minimize privacy concerns, then promoting a ethic and efficient standard in this delicate new field.

 

Persone coinvolte

Direttore di ricerca: Prof. Cristiano Chesi

Collaboratori esterni: Prof. Davide Crepaldi (SISSA)

Partners

Centro Interuniversitario di Studi Cognitivi sul Linguaggio (CISCL) - Università di Siena

 

Finanziato

PRO3

 

Pubblicazioni

Can, Y. S., Arnrich, B., & Ersoy, C. (2019). Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. Journal of biomedical informatics, 92, 103139.

Brattico, P., & Chesi, C. (2020). A top-down, parser-friendly approach to pied-piping and operator movement. Lingua, 233, 102760.

Chesi, C. (2015). On directionality of phrase structure building. Journal of psycholinguistic research, 44(1), 65-89.

Chesi, C., & Canal, P. (2019). Person features and lexical restrictions in Italian clefts. Frontiers in Psychology, 10, 2105.

De Leeuw, J. R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a Web browser. Behavior research methods, 47(1), 1-12.

Gjoreski, M., Kolenik, T., Knez, T., Luštrek, M., Gams, M., Gjoreski, H., & Pejović, V. (2020). Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits. Applied Sciences, 10(11), 3843.

Grimaldi, M. (2012). Toward a neural theory of language: old issues and new perspectives. Journal of Neurolinguistics, 25(5), 304-327.

Hughes, A. M., Hancock, G. M., Marlow, S. L., Stowers, K., & Salas, E. (2019). Cardiac measures of cognitive workload: a meta-analysis. Human factors61(3), 393-414.

Mancini, S., Molinaro, N., Rizzi, L., & Carreiras, M. (2011). A person is not a number: Discourse involvement in subject-verb agreement computation. Brain Research, 1410, 64-76. Doi:10.1016/j.brainres.2011.06.055

Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126-1177.

Li, X., Dunn, J., Salins, D., Zhou, G., Zhou, W., Rose, S. M. S. F., ... & Sonecha, R. (2017). Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS biology15(1), e2001402.fit