scholarly journals Cyborg Groups Enhance Face Recognition in Crowded Environments

2018 ◽  
Author(s):  
Davide Valeriani ◽  
Riccardo Poli
Keyword(s):  
Decision Making ◽  
Face Recognition ◽  
Machine Vision ◽  
Decision Confidence ◽  
Computer Interfaces ◽  
Recognition Experiment ◽  
Or Groups ◽  
Individual Decisions ◽  
Human Participants ◽  
Crowded Environments

AbstractRecognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create “cyborgs” that improve decision making. Human participants and a ResNet undertook the same face-recognition experiment. BCIs were used to decode the decision confidence of humans from their EEG signals. Different types of cyborg groups were created, including either only humans (with or without the BCI) or groups of humans and the ResNet. Cyborg groups decisions were obtained weighing individual decisions by confidence estimates. Results show that groups of cyborgs are significantly more accurate (up to 35%) than the ResNet, the average participant, and equally-sized groups of humans not assisted by technology. These results suggest that melding humans, BCI, and machine-vision technology could significantly improve decision-making in realistic scenarios.

scholarly journals Anytime Collaborative Brain-Computer Interfaces for Enhancing Group Decision-Making in Realistic Environments

2020 ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Davide Valeriani ◽  
Caterina Cinel ◽  
Luca Citi ◽  
Riccardo Poli
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Brain Activity ◽  
Event Related Potentials ◽  
Brain Computer Interfaces ◽  
Appearance Time ◽  
Computer Interfaces ◽  
Related Potentials ◽  
Individual Decisions

Abstract In this paper we present and test collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of group decision-making in realistic situations. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.

scholarly journals Separable neural signatures of confidence during perceptual decisions

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Tarryn Balsdon ◽  
Pascal Mamassian ◽  
Valentin Wyart
Keyword(s):  
Decision Making ◽  
Neural Circuit ◽  
Spectral Power ◽  
Brain Activity ◽  
Neural Representation ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Decision Confidence ◽  
Electrical Brain Activity ◽  
Human Participants

Perceptual confidence is an evaluation of the validity of perceptual decisions. While there is behavioural evidence that confidence evaluation differs from perceptual decision-making, disentangling these two processes remains a challenge at the neural level. Here, we examined the electrical brain activity of human participants in a protracted perceptual decision-making task where observers tend to commit to perceptual decisions early whilst continuing to monitor sensory evidence for evaluating confidence. Premature decision commitments were revealed by patterns of spectral power overlying motor cortex, followed by an attenuation of the neural representation of perceptual decision evidence. A distinct neural representation was associated with the computation of confidence, with sources localised in the superior parietal and orbitofrontal cortices. In agreement with a dissociation between perception and confidence, these neural resources were recruited even after observers committed to their perceptual decisions, and thus delineate an integral neural circuit for evaluating perceptual decision confidence.

scholarly journals A new measure of group decision-making efficiency

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Cheng-Ju Hsieh ◽  
Mario Fifić ◽  
Cheng-Ta Yang
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Group Decision Making ◽  
Group Decision ◽  
Detection Task ◽  
Detection Sensitivity ◽  
System Level ◽  
Workload Capacity ◽  
Individual Decisions ◽  
Collective Benefit

Abstract It has widely been accepted that aggregating group-level decisions is superior to individual decisions. As compared to individuals, groups tend to show a decision advantage in their response accuracy. However, there has been a lack of research exploring whether group decisions are more efficient than individual decisions with a faster information-processing speed. To investigate the relationship between accuracy and response time (RT) in group decision-making, we applied systems’ factorial technology, developed by Townsend and Nozawa (Journal of Mathematical Psychology 39, 321–359, 1995) and regarded as a theory-driven methodology, to study the information-processing properties. More specifically, we measured the workload capacity CAND(t), which only considers the correct responses, and the assessment function of capacity AAND(t), which considers the speed-accuracy trade-off, to make a strong inference about the system-level processing efficiency. A two-interval, forced-choice oddball detection task, where participants had to detect which interval contains an odd target, was conducted in Experiment 1. Then, in Experiment 2, a yes/no Gabor detection task was adopted, where participants had to detect the presence of a Gabor patch. Our results replicated previous findings using the accuracy-based measure: Group detection sensitivity was better than the detection sensitivity of the best individual, especially when the two individuals had similar detection sensitivities. On the other hand, both workload capacity measures, CAND(t) and AAND(t), showed evidence of supercapacity processing, thus suggesting a collective benefit. The ordered relationship between accuracy-based and RT-based collective benefit was limited to the AAND(t) of the correct and fast responses, which may help uncover the processing mechanism behind collective benefits. Our results suggested that AAND(t), which combines both accuracy and RT into inferences, can be regarded as a novel and diagnostic tool for studying the group decision-making process.

The value of sociality

foresight ◽  
2014 ◽  
Vol 16 (4) ◽  
pp. 309-328 ◽  
Author(s):  
Evgeniya Lukinova ◽  
Mikhail Myagkov ◽  
Pavel Shishkin
Keyword(s):  
Decision Making ◽  
Utility Function ◽  
Social Relationship ◽  
Social Utility ◽  
Content Type ◽  
Individual Level ◽  
Know How ◽  
General Utility ◽  
The Social ◽  
Individual Decisions

Purpose – This paper aims to study the value of sociality. Recent experimental evidence has brought to light that the assumptions of the Prospect Theory by Kahneman and Tversky do not hold in the proposed substantive domain of “sociality”. In particular, the desire to be a part of the social environment, i.e. the environment where individuals make decisions among their peers, is not contingent on the framing. Evolutionary psychologists suggest that humans are “social animals” for adaptive reasons. However, entering a social relationship is inherently risky. Therefore, it is extremely important to know how much people value “sociality”, when the social outcomes are valued more than material outcomes and what kinds of adaptations people use. Design/methodology/approach – We develop a new theory and propose the general utility function that features “sociality” component. We test the theory in the laboratory experiments carried out in several countries. Findings – Our results suggest that when stakes are low the theory of “sociality” is successful in predicting individual decisions: on average, people do value “sociality” and it surpasses the monetary loss. Originality/value – The main contribution of this paper is the breakdown of the risk attitudes under low stakes and individual level of decision-making. Another advancement is the ability to formalize the social utility or the theory of “sociality” in an economic model; we use general utility function that we define both on the outcomes and on the process of the decision-making itself and test in laboratory studies.

scholarly journals Anytime collaborative brain–computer interfaces for enhancing perceptual group decision-making

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Davide Valeriani ◽  
Caterina Cinel ◽  
Luca Citi ◽  
Riccardo Poli
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Brain Activity ◽  
Event Related Potentials ◽  
Brain Computer Interfaces ◽  
Appearance Time ◽  
Perceptual Group ◽  
Computer Interfaces ◽  
Related Potentials

AbstractIn this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, in the two test environments, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.

scholarly journals Groups Make Better Self-Interested Decisions

2012 ◽  
Vol 26 (3) ◽  
pp. 157-176 ◽  
Author(s):  
Gary Charness ◽  
Matthias Sutter
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Economic Behavior ◽  
Economic Research ◽  
Individual Decision ◽  
Bottom Line ◽  
Individual Decision Making ◽  
Cognitive Limitations ◽  
Individual Decisions

In this paper, we describe what economists have learned about differences between group and individual decision-making. This literature is still young, and in this paper, we will mostly draw on experimental work (mainly in the laboratory) that has compared individual decision-making to group decision-making, and to individual decision-making in situations with salient group membership. The bottom line emerging from economic research on group decision-making is that groups are more likely to make choices that follow standard game-theoretic predictions, while individuals are more likely to be influenced by biases, cognitive limitations, and social considerations. In this sense, groups are generally less “behavioral” than individuals. An immediate implication of this result is that individual decisions in isolation cannot necessarily be assumed to be good predictors of the decisions made by groups. More broadly, the evidence casts doubts on traditional approaches that model economic behavior as if individuals were making decisions in isolation.

Slouching Toward Disaster

2011 ◽  
Author(s):  
Stephen A. Batzer ◽  
John S. Morse
Keyword(s):  
Decision Making ◽  
Gulf Of Mexico ◽  
Slow Motion ◽  
Deepwater Horizon ◽  
Complete Loss ◽  
Missing Information ◽  
Financial Loss ◽  
Low Profile ◽  
Decision Making Processes ◽  
Individual Decisions

“But, who cares, it’s done, end of story, [we] will probably be fine and we’ll get a good cement job.” This is an oft-repeated email quotation from one BP engineer to another on April 16, 2010, just four days before the Macondo well blew out in the Gulf of Mexico. Although these two men survived, 11 others did not. The well blowout also brought with it poisoning of the ecology and vast financial loss. This quote, part of a discussion about centralizers for the well (BP ended up with just six instead of the planned 16 or 21), seems to epitomize the attitude regarding a series of decisions made about the well’s design. The product of the decisions was complete loss and worse. However, the parties did not seem to be aware of the importance of their individual decisions or their consequences as they were making them. This disaster, like many others, seemed in retrospect to unfold in slow motion, and the players involved did not perceive the sheer cliff before them until they had transgressed its edge. This paper will examine decision-making processes in the Deepwater Horizon blowout and a series of other disasters, both high and low profile events. All of these preventable events stemmed from decision-making failures. These failures include disregarding existing information, failing to soberly extrapolate “what if?” when existing information contained uncertainty, failing to obtain vital missing information, failing to question decisions — particularly from those considered authoritative, and a cavalier attitude regarding rules because probably nothing will happen anyway. “Who cares?”

scholarly journals Neural encoding of perceived patch value during competitive and hazardous virtual foraging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Brian Silston ◽  
Toby Wise ◽  
Song Qi ◽  
Xin Sui ◽  
Peter Dayan ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Foraging ◽  
Survival Strategy ◽  
Neural Encoding ◽  
Individual Level ◽  
Hazardous Environments ◽  
Predatory Attack ◽  
Multidimensional Information ◽  
Human Participants ◽  
Foraging Task

AbstractNatural observations suggest that in safe environments, organisms avoid competition to maximize gain, while in hazardous environments the most effective survival strategy is to congregate with competition to reduce the likelihood of predatory attack. We probed the extent to which survival decisions in humans follow these patterns, and examined the factors that determined individual-level decision-making. In a virtual foraging task containing changing levels of competition in safe and hazardous patches with virtual predators, we demonstrate that human participants inversely select competition avoidant and risk diluting strategies depending on perceived patch value (PPV), a computation dependent on reward, threat, and competition. We formulate a mathematically grounded quantification of PPV in social foraging environments and show using multivariate fMRI analyses that PPV is encoded by mid-cingulate cortex (MCC) and ventromedial prefrontal cortices (vMPFC), regions that integrate action and value signals. Together, these results suggest humans utilize and integrate multidimensional information to adaptively select patches highest in PPV, and that MCC and vMPFC play a role in adapting to both competitive and predatory threats in a virtual foraging setting.

scholarly journals Longevity and reliability of chronic unit recordings using the Utah, intracortical multi-electrode arrays

2021 ◽  
Author(s):  
Caleb Scheffer Sponheim ◽  
Vasileios Papadourakis ◽  
Jennifer Collinger ◽  
John Downey ◽  
Jeffrey M Weiss ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Rhesus Macaques ◽  
Human Subjects ◽  
Electrode Arrays ◽  
Computer Interfaces ◽  
Somatosensory Cortices ◽  
Large Numbers ◽  
Electrode Length ◽  
Human Participants

Abstract Objective. Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have FDA clearance for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset. Approach. We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost 9 years of experiments, totaling 17 rhesus macaques (Macaca Mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time. Main Results. Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization.

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