Towards human-robot interaction: A framing effect experiment

Souza, Paulo Eduardo Ubaldino de; Chanel, Caroline Ponzoni Carvalho; Dehais, Frédéric; Givigi, Sidney N.

doi:10.1109/smc.2016.7844521

Cited by 8 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, by comparing the results of different scenarios, the influence of emotional commitment on the subject's decisions is observed. These statistical effects, related with the emotional commitment, were also observed in our previous work (Souza et al, 2016), but for only 14 subjects. Interestingly, each curve presented in Figure 3 is strictly increasing function that satisfies f (0) = 0 and f (1) = 1, which is required by PT.…”

Section: Definition Of the Probability Weighting Functionw(•)supporting

confidence: 84%

Predicting Human Operator’s Decisions Based on Prospect Theory

Souza

Chanel

Mailliez

et al. 2020

Interacting With Computers

View full text Add to dashboard Cite

The aim of this work is to predict human operator’s (HO) decisions in a specific operational context, such as a cooperative human-robot mission, by approximating his/her utility function based on prospect theory (PT). To this aim, a within-subject experiment was designed in which the HO has to decide with limited time and incomplete information. This experiment also involved a framing effect paradigm, a typical cognitive bias causing people to react differently depending on the context. Such an experiment allowed to acquire data concerning the HO’s decisions in two different mission scenarios: search and rescue and Mars rock sampling. The framing was manipulated (e.g. positive vs. negative) and the probability of the outcomes causing people to react differently depending on the context. Statistical results observed for this experiment supported the hypothesis that the way the problem was presented (positively or negatively framed) and the emotional commitment affected the HO’s decisions. Thus, based on the collected data, the present work is willed to propose: (i) a formal approximation of the HO’s utility function founded on the prospect theory and (ii) a model used to predict the HO’s decisions based on the economics approach of multi-dimensional consumption bundle and PT. The obtained results, in terms of utility function fit and prediction accuracy, are promising and show that similar modeling and prediction method should be taken into account when an intelligent cybernetic system drives human–robot interaction. The advantage of predicting the HO’s decision, in this operational context, is to anticipate his/her decision, given the way a question is framed to the HO. Such a predictor lays the foundation for the development of a decision-making system capable of choosing how to present the information to the operator while expecting to align his/her decision with the given operational guideline.

show abstract

Section: Definition Of the Probability Weighting Functionw(•)supporting

confidence: 84%

Predicting Human Operator’s Decisions Based on Prospect Theory

Souza

Chanel

Mailliez

et al. 2020

Interacting With Computers

View full text Add to dashboard Cite

show abstract

“…Following this perspective, Dehais et al (2010Dehais et al ( , 2012, Imbert et al (2014) andSaint Lot et al (2020) have demonstrated that very brief (∼200 ms) and located information removal was an efficient mean to mitigate perseveration by forcing disengagement from non-relevant tasks. Souza et al (2016) demonstrated that digital nudging (see Weinmann et al, 2016) could be used to mitigate poor decision making and cognitive bias associated with perseveration. Imbert et al (2014) designed attention-grabbing stimuli grounded on vision research and demonstrated that yellow chevrons pulsing at a cycle of 1 Hz can re-orientate attention and mitigate inattentional blindness.…”

Section: Adaptation Of the User Interfacementioning

confidence: 99%

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

et al. 2020

View full text Add to dashboard Cite

The assessment and prediction of cognitive performance is a key issue for any discipline concerned with human operators in the context of safety-critical behavior. Most of the research has focused on the measurement of mental workload but this construct remains difficult to operationalize despite decades of research on the topic. Recent advances in Neuroergonomics have expanded our understanding of neurocognitive processes across different operational domains. We provide a framework to disentangle those neural mechanisms that underpin the relationship between task demand, arousal, mental workload and human performance. This approach advocates targeting those specific mental states that precede a reduction of performance efficacy. A number of undesirable neurocognitive states (mind wandering, effort withdrawal, perseveration, inattentional phenomena) are identified and mapped within a twodimensional conceptual space encompassing task engagement and arousal. We argue that monitoring the prefrontal cortex and its deactivation can index a generic shift from a nominal operational state to an impaired one where performance is likely to degrade. Neurophysiological, physiological and behavioral markers that specifically account for these states are identified. We then propose a typology of neuroadaptive countermeasures to mitigate these undesirable mental states.

show abstract

“…Stakeholders may frame certain aspects of a project in a positive or negative light to influence perspectives on that specific aspect. In HMT and Artificial Intelligence (AI), humans placed in human-AI teams may have their trust and decisions impacted by the framing effect [27,30,31]. Hindsight bias [28] may impact stakeholders as they tend to see past events as predictable at the time they occurred.…”

Section: General Stakeholder Biasesmentioning

confidence: 99%

Biases in Stakeholder Elicitation as a Precursor to the Systems Architecting Process

Yeazitzis,

Weger,

Mesmer

et al. 2023

Systems

View full text Add to dashboard Cite

Many systems engineering projects begin with the involvement of stakeholders to aid in decision-making processes. As an application of systems engineering, systems architecture involves the documentation of stakeholder needs gathered via elicitation and the transformation of these needs into requirements for a system. Within human–machine teaming, systems architecture allows for the creation of a system with desired characteristics elicited from stakeholders involved with the project or system. Though stakeholders can be excellent sources for expert opinion, vested interests in a project may potentially bias stakeholders and impact decision-making processes. These biases may influence the design of the system architecture, potentially resulting in a system that is developed with unbalanced and misrepresented stakeholder preferences. This paper presents an activity analysis of the Stakeholder Needs and Requirements Process as described in the Systems Engineering Body of Knowledge (SEBoK) to identify potential biases associated with this elicitation process. As part of the research presented in this paper, a workshop was conducted where currently practicing systems architects provided feedback regarding perceptions of biases encountered during the elicitation process. The findings of this research will aid systems architects, developers, and users in understanding how biases may impact stakeholder elicitation within the architecting process.

show abstract

Towards human-robot interaction: A framing effect experiment

Cited by 8 publications

References 22 publications

Predicting Human Operator’s Decisions Based on Prospect Theory

Predicting Human Operator’s Decisions Based on Prospect Theory

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

Biases in Stakeholder Elicitation as a Precursor to the Systems Architecting Process

Contact Info

Product

Resources

About