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This section presents work that has been carried out in recent years to develop neurotechnologies that can enhance cognitive abilities, with a focus on BCI applications for individual section 3. Decision-making has been intensively studied in social and cognitive sciences to understand the processes, dynamics, biases, and strategies that lead to optimal decisions Edwards, ; Janis and Mann, ; Sniezek, ; Plous, ; Cannon-Bowers and Salas, , both when made by an individual or a group.
A decision is affected by, and is the result of, a number of processes and mechanisms that include—but are not limited to—early perceptual processes, attention and working memory processing, all of which are critical to an optimal decision. Advances in neuroscience have provided a deeper understanding of neural processes related to decision-making.
For example, the amplitude of the N1—a large negative ERP occurring between 80 and ms after the onset of an unpredictable stimulus in the absence of task demands—decreases as the attentional level decreases Parasuraman and Beatty, ; Parasuraman et al. The difficulty of a task also affects amplitude and timing of the P Hagen et al.
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These ERPs are typically associated with early perceptual and cognitive processing of events, and can reveal fatigue in perceptual decision-making. For instance, this is signaled by a reduction of the amplitude and an increase of the latency of the P Uetake and Murata, ; Murata et al.
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Other, later ERPs are instead associated with decision processes preceding, for example, the overt response of a decision maker. For instance, the contingent negative variation —a slow negative wave related to the preparation for a motor response and stimulus anticipation—is smaller before incorrect responses than before correct ones in a task where information necessary to identify a target letter is conveyed to participants only a few hundred milliseconds before two potential targets are presented Padilla et al. The error related negativity —an ERP occurring 50—80 ms after an incorrect response—can also provide information about levels of confidence of decision-making as it is affected by confidence in own performance Selimbeyoglu et al.
This happens even when participants are unaware of the error Nieuwenhuis et al. Moreover, neural correlates of individual decisions can be detected hundreds of milliseconds before an explicit response is given— e. Error related negativity can also be used to automatically improve the speed of communication in BCI spellers Dal Seno et al. Recent advances in neuroscience have also shed light on how individuals approach decision-making, their strategies and their aptitude to risk-taking behavior Doya, ; Rushworth and Behrens, For example, there is evidence showing a large involvement of the prefrontal cortex in decision-making; in particular, its activation varies according to the level of risk taking Tobler et al.
However, to date this knowledge has not been exploited for human augmentation. Thanks to this plethora of neuro-scientific knowledge related to information and decision processing, it would seem reasonable to attempt exploiting it to improve decision-making.
However, the most practical non-invasive sources of information on brain activity are extremely noisy, which makes it very hard to reliably provide information on or aid individual decisions. Indeed, the aforementioned reports base their findings on averaging the signals resulting from many repetitions of each event. As shown in the next section, this limitation can be overcome if during the decision making process information is gathered from multiple brains.
In the last few years, researchers have started evaluating the possibilities offered by ERP-based single-trial collaborative BCIs. These integrate perceptual experiences, intentions and decisions from multiple non-communicating users to achieve improved joint performance over single-user BCIs and non-BCI systems. Alternatively, one can first extract meaningful features from the EEG data of each participant and then concatenate them to build a feature vector for the group, which is then passed to a single classifier.
Finally, users may have individual BCIs that predict their intentions, which a voting system integrates to compute the group's decision. Various studies Wang and Jung, ; Matran-Fernandez et al.
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Wang et al. In Yuan et al. Decisions were faster, but accuracy was substantially lower, than for non-BCI users. Eckstein et al. They found that cBCIs not only improve accuracy, but can also make the decisions faster than the average human.
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However, at least seven individuals were required to achieve the behavioral performance of the average single observer. Yuan et al. Cecotti and Rivet found that combining data from multiple participants provides more advantages in terms of accuracy than combining data from the same participant over time. Moreover, they showed that with the collaborative approach every group member makes a contribution to the overall performance of the group.
A different approach has been used by Poli et al. Instead of predicting the user's response, this cBCI used neural signals and response times to estimate the decision confidence group members and weigh their behavioral responses accordingly to build the group decision. This paradigm was tested with various tasks, including visual matching Poli et al. In all cases, it was found that the cBCI reduced error rates by up to a third with groups of only two users when compared with traditional equally-sized non-BCI groups using the standard majority, indicating that hybrid cBCIs for decision-making are promising.
For instance, Matran-Fernandez et al. The N2pc, an ERP that appears approximately ms after stimulus presentation on the opposite side of the scalp with respect to the visual hemispace where an object of interest is located, has been used for BCIs for determining the location rather than the presence of targets in aerial pictures Matran-Fernandez and Poli, a.
Collaborative BCIs have also been used to control robots Iturrate et al. Neuro-stimulation techniques, such as tES and TMS, can be used to improve performance in different cognitive domains, including perception, learning and memory, attention and decision making some of which will be reviewed in sections 3. Several studies have shown how the ability to detect e. For example, performance in visual search was improved in a tDCS study Nelson et al.
Results showed that anodal stimulation slightly improved performance. Similar results were obtained in a more realistic, complex threat-detection task with tES Clark et al. In that study, observers were presented with a short video clip recorded from a virtual reality environment and had to decide whether a possible threat was present or not.
In the two experiments conducted in the study, the use of tES significantly and consistently improved performance. Multiple object tracking is another task often associated with and preceding complex decision-making in many situations and where tES can augment human abilities. In Blumberg et al. The circles were then moved around for 8 s and then participants were asked to manually select which circles were the target. This required users to track multiple moving objects. Results indicated that tES significantly improved performance of participants in the high-load condition, but only marginally improved performance in the low-load condition.
Risk-taking behavior can also be affected by tES. In particular, Sela et al. However, the improvement in reading brought by tDCS seems only to apply to certain tasks, such as sight word efficiency Younger et al. Finally, brain stimulation could also be used to optimize cortical oscillations e. For example, tDCS stimulation has been observed to improve: implicit learning of sequential motor sequences Nitsche et al. In these studies, particularly effective seems to be the stimulation of the dorsolateral prefrontal cortex, which is known to be a critical locus for working memory functions Levy and Goldman-Rakic, However, the evidence is mixed Teo et al.
Studies with invasive stimulation neurotechnologies have also shown promising results. Recent successes include the development of neuroprosthesis that can improve memory encoding and retention. The neuroprostheses have demonstrated that real-time manipulation of the encoding process can restore and even enhance mnemonic processes in rodents Berger et al.
In particular, the pattern of activation predicted by the MIMO model from the activation of the neurons in CA3 is artificially applied via electrical stimulation to neurons in area CA1. The application of the model in rats' hippocampus has allowed the transference of memories between animals Deadwyler et al.
More recently, the first successful implementation of the neuroprosthesis, based on the MIMO model, in human subjects has been demonstrated Hampson et al. DBS in the hippocampus and the entorhinal cortex has also been successful at improving memory Hamani et al. An increasing number of studies and technologies are aimed at monitoring cognitive performance and capacity, for example working memory capacity or attention, in real time Durantin et al.
Even when such systems are not directly aimed at augmenting performance, monitoring the mental state of users makes it possible to enhance their performance by adapting the interface they interact with, with so called adaptive interfaces. For instance, Wilson and Russell described a neuroadaptive system where the users' task is to detect a target in an environment and where the mental workload is varied according to the feedback given by EEG and other physiological measures.
So, many of the studies described below may enable indirect human cognitive augmentation. There is a vast literature on methods for monitoring changes in the level of attention. In general, the literature makes a distinction between vigilance i. Tasks used to monitor vigilant attention include simple reaction-time tasks, stimulus-discrimination tasks and target counting.
In all these cases vigilance is gauged using reaction times. Apart from the type of task, the duration of sustained attention without breaks is a major determinant of performance Davies and Parasuraman, Changes in patterns of EEG activity that accompany the awake-sleep transition can also reveal decreases in attention see Oken et al.
The most consistent of such measures are an increased theta activity and decreased beta activity Belyavin and Wright, ; Parasuraman and Rizzo, The amplitude of the P ERP is also known to be related to the mental workload and the level of attention devoted to a task. This has also been examined in complex flight and driving simulation tasks where it has been shown that the P can provide an assessment of workload Fu and Parasuraman, Other, earlier, ERPs can be modulated by attentional allocation.
For example, it is known that the N1 amplitude is modulated by allocation of attention to both visual and auditory stimuli in high-load conditions Hink et al. These are different from the processes where the cognitive load is high, and attention has to be maintained in order to process all the information needed to perform correctly a given, often demanding, task. This ability is often investigated in tasks where there are high demands on working memory.
For example, in Gevins and Smith participants were asked to perform a task consisting of viewing a continuous sequence of stimuli and having to indicate when the current stimulus matches the one from n steps earlier in the sequence while EEG was recorded. It was found that as the difficulty of the task n increased, there was a corresponding increase in theta rhythm and a decrease in the alpha rhythm around the anterior-midline cortex.
Given that changes in attention correspond to specific, detectable patterns of EEG activity, over the years, scientists have tried to developed methods—and applications—to monitor sustained attention and the ability to respond to high workload. For example, methods have been developed to detect drowsiness see Gevins and Smith, , based on the amplitude of different rhythms, in tasks similar to those one might face in real-world environments.
Studies using transcranial Doppler echography and fNIRS also suggest that temporal variations in vigilance and changes in mental workload are accompanied by variations in the cerebral blood flow e. They also suggest a critical role of the right parietal lobe in the control of vigilance as also seen in the EEG studies discussed above.
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Changes in mental workload can also be monitored by measuring cerebral hemodynamic changes using fNIRS in real-world environments Ayaz et al. Some research has also been devoted to decoding the spatial orienting of attention Astrand et al.