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modelling and pRedicting Human decision-making Using Measures of subconscious Brain processes through mixed reality interfaces and biOmetric signals

Periodic Reporting for period 1 - RHUMBO (modelling and pRedicting Human decision-making Using Measures of subconscious Brain processes through mixed reality interfaces and biOmetric signals)

Reporting period: 2018-11-01 to 2020-10-31

Technology is changing the context and practice of businesses: companies are increasingly forced to operate in a changing world where they do not have any more the full control of the media and the message. Consumer behaviour is also changing: it is losing trust and becomes much more critical, smart, well informed and proactive than ever before.

RHUMBO will produce a set of tools and models to predict human decision-making in business environments. The goal of RHUMBO is to use mixed reality technologies (MRT) together with biometric signals, supported by artificial intelligence processing techniques to examine consumer behavioural patterns during dynamic, complex and realistic situations for a deeper understanding of internal human psychological states. Specific application in consumer behaviour has been selected to test the novel models:

(1) An adaptive virtual-commerce (V-commerce) prototype that will adapt in real-time both product and context.
(3) A neuro-architecture prototype using tools and models developed by RHUMBO to predict human decision-making for the optimal design of brick and mortars flagships stores.

RHUMBO produces young scientists addressing scientific, educational and training aspects. A better understanding of human decision-making in business environments will benefits the society by providing both companies and consumers with a deeper knowledge to be effective and informed participants in the economy. The results could also be valuable to decision makers in fast-paced operating environments, under stress and uncertainty, and could offer vital insights for policy makers and business leaders in disciplines beyond marketing (e.g. human resources management, education, economic decision making, clinical research or brain computer interfaces).

Training: RHUMBO provides a high-level personalised multidisciplinary training program with the long term aim to produce scientific leadership. Dissemination and outreach: The neuroscience-based business methods and tools developed by RHUMBO will be disseminated to a wide spectrum of stakeholders ranging from the scientific community to corporate users; create awareness in the general public about neuroscience and the fundamental role that it has in our daily decisions; to encourage neuroscience vocational careers among young students, with special emphasis on women.

Dissemination and outreach: The neuroscience-based business methods and tools developed by RHUMBO will be disseminated to a wide spectrum of stakeholders ranging from the scientific community to corporate users; create awareness in the general public about neuroscience and the fundamental role that it has in our daily decisions; to encourage neuroscience vocational careers among young students, with special emphasis on women.
The present deliverable, reports the activities that have been carried out in the RHUMBO project during the months 1-24, i.e. from November 2018 to October 2020. To summarize, here are the main accomplishments:

We have established the general experimental methodology that will adopt a Stimulus-Organism-Response (SOR) paradigm. In RHUMBO’s nomenclature, the affective and cognitive responses will be named as emotional and cognitive biomarkers (ECB) and the psychological responses will be named as consumer behavior biomarkers (CBB).

Based on the framework proposed, we have designed a set of experiments including a specific methodology for each of them, assessing a specific hypothesis of the project. The experiments are the following:
Cognitive-emotional states calibration (CE): The main aim is to calibrate the signal processing and machine learning algorithms.
Results: A calibration methodology for EEG signal has been defined

Shopper Profile Classifier Experiments without fMRI (SC): The main aim is to classify each subject according to the model for shopper genotype characterization.
Results: We have been able to classify consumers based on the big five personality domains and to assess impulsivity during a virtual purchase. Our results demonstrate that it is possible to classify several big five dimensions and impulsivity using eye gaze patterns, posture and interactions.

Shopper Profile Classifier Experiments with fMRI (FM): The main aim is to contribute to the classification of shopper characterization adding a new dimension related to the risk-taking decision styles.
Results: a cross-domain model for risk-taking decision has been defined. Data are being analized

Shopper Dimension Experiments (SD): The general objective is to characterize the virtual experience.
Results: The experimentation has been finished and data are under evaluation

Mediators Experiments (ME): The general objective is to analyze the influence of different mediators on the shopper behavior.

A total of 6 experiments has been conducted, and we are now in the process of specifying the ME experiments.
The experimental platform for the experiments has been developed.

As scientific contributions, 38 research papers have already been published in international conferences and journals. Several other submitted papers are under review.
RHUMBO has significantly advanced the state-of-the-art (SoA) in several research disciplines that can be summarized as:

Novel use of mixed reality technologies (MRT): Rhumbo has generated various real situations, including social situations, that elicitates embodied experiences in which body, environment, and brain are in close relationship.

Innovative biomedical signal processing: The project is generating novel signal processing techniques that use synchronised uptake of psychophysiological measures and brain activity (e.g. EEG, fNIRS, HRV, ET) to uncover specific signs of the implicit brain processes and related behaviour during decision-making.

Novel artificial intelligence and machine learning algorithms: The project is merging effectively knowledge quantified by multiple neurometrics gathered from different physiological dynamics, therefore obtaining unique representations of implicit processes of decision-making sustained by the central-autonomic nervous system.

The expected results until the end of the project are to conduct several mediators experiments and to build a comprehensive and complete model of human behaviour in the economic sphere using two case studies of special socio-economic relevance: the development of adaptive virtual-commerce sites and the neurodesign of physical stores and products.

These two case studies will integrate users’ differences, psychometrics, and behavioural patterns while interacting with virtual situations using MRT.

The research conducted will be applied to consumer behaviour, a key area where the application of decision-making computational models has the power to produce bigger impacts. The results could also be valuable to decision makers in fast-paced operating environments, under stress and uncertainty, and could offer vital insights for policy makers and business leaders in disciplines beyond marketing (e.g. human resources management, education, economic decision making, clinical research or brain computer interfaces).
Experimental room
Hardware development
RHUMBO Decision model
Experimental session