Preference Task Research Articles

Coalition Game of Radar Network for Multitarget Tracking via Model-Based Multiagent Reinforcement Learning

Task assignment is crucial for multitarget tracking of the radar network and is mainly solved by centralized optimization methods, which results in the issues of robustness deficiency, high computational cost, and inflexible adaptability in complex environments. To overcome these issues, task assignment of the radar network for multitarget tracking is formulated as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiagent decision-making and learning</i> problem, where each radar node in the network acts as an intelligent agent that can make the tracking decision according to its task preference and interact with other agents for the sake of the best network utility. To describe the decision references of radar nodes for various tasks, the criterion for the design of the utility function is presented, and a utility function under this criterion is devised based on the quality of service framework. Then, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coalition game with transferrable utility</i> is developed for task assignment, where the utility of the coalition is completely transferred to all coalition members. The existence of the stable coalition partition of the developed game is analyzed theoretically, and the model-based multiagent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random Fourier features reinforcement learning</i> (RFFRL) algorithm is proposed to solve the optimal solution to the game in the high dimensional state space, which is proven to be converged at a Nash-stable coalition partition. Some numerical simulation results are provided to illustrate the effectiveness of the proposed algorithm in terms of tracking performance and resource conservation.

If You Love Your Agents, Set Them Free: Task Discretion in Online Workplaces

In manufacturing and service operations, flexibility is beneficial for matching supply with demand, but it comes at a cost. In modern digital workplaces, agents have different skills associated with corresponding and variable task preferences. Some are inclined to give up part of their payment to avoid unfavorable matches, and the platform manager, in turn, gains extra freedom in allocating tasks by possibly charging servers for favorable assignments. Innovative marketplaces facilitate task discretion and seek novel and beneficial implementations in the platform design. This naturally leads to the problem of exploring and optimizing the task allocation process. We introduce an innovative mechanism for task assignment in the workplace and compare it to the traditional mechanism in which task routing is solely the platform’s decision. To improve the welfare of all users, agents are allowed some task discretion in exchange for a fee. In a multiserver system, a server may request flexibility and, considering the other agents’ behavior, choose the costly to the platform option, which may or may not be beneficial for the platform when the agents’ and the platform’s preferences are misaligned. We model different working environments and server preferences via different distributions and study how the agents’ preferences, task costs, and flexibility fee affect the equilibrium assignment. In every case, through pricing and offering flexibility, the platform can do at least as well as a scheme with no flexibility. An important conclusion is that pricing task discretion can often improve the agents’ welfare and the labor platform’s profit. This paper was accepted by Victor Martínez-de-Albéniz, operations management.

Who should see the patient? on deviations from preferred patient-provider assignments in hospitals.

In various organizations including hospitals, individuals are not forced to follow specific assignments, and thus, deviations from preferred task assignments are common. This is due to the conventional wisdom that professionals should be given the flexibility to deviate from preferred assignments as needed. It is unclear, however, whether and when this conventional wisdom is true. We use evidence on the assignments of generalist and specialists to patients in our partner hospital (a children's hospital), and generate insights into whether and when hospital administrators should disallow such flexibility. We do so by identifying 73 top medical diagnoses and using detailed patient-level electronic medical record (EMR) data of more than 4,700 hospitalizations. In parallel, we conduct a survey of medical experts and utilized it to identify the preferred provider type that should have been assigned to each patient. Using these two sources of data, we examine the consequence of deviations from preferred provider assignments on three sets of performance measures: operational efficiency (measured by length of stay), quality of care (measured by 30-day readmissions and adverse events), and cost (measured by total charges). We find that deviating from preferred assignments is beneficial for task types (patients' diagnosis in our setting) that are either (a) well-defined (improving operational efficiency and costs), or (b) require high contact (improving costs and adverse events, though at the expense of lower operational efficiency). For other task types (e.g., highly complex or resource-intensive tasks), we observe that deviations are either detrimental or yield no tangible benefits, and thus, hospitals should try to eliminate them (e.g., by developing and enforcing assignment guidelines). To understand the causal mechanism behind our results, we make use of mediation analysis and find that utilizing advanced imaging (e.g., MRIs, CT scans, or nuclear radiology) plays an important role in how deviations impact performance outcomes. Our findings also provide evidence for a "no free lunch" theorem: while for some task types, deviations are beneficial for certain performance outcomes, they can simultaneously degrade performance in terms of other dimensions. To provide clear recommendations for hospital administrators, we also consider counterfactual scenarios corresponding to imposing the preferred assignments fully or partially, and perform cost-effectiveness analyses. Our results indicate that enforcing the preferred assignments either for all tasks or only for resource-intensive tasks is cost-effective, with the latter being the superior policy. Finally, by comparing deviations during weekdays and weekends, early shifts and late shifts, and high congestion and low congestion periods, our results shed light on some environmental conditions under which deviations occur more in practice.

Benefits of repeated alternations – Task-specific vs. task-general sequential adjustments of dual-task order control

An important cognitive requirement in multitasking is the decision of how multiple tasks should be temporally scheduled (task order control). Specifically, task order switches (vs. repetitions) yield performance costs (i.e., task-order switch costs), suggesting that task order scheduling is a vital part of configuring a task set. Recently, it has been shown that this process takes specific task-related characteristics into account: task order switches were easier when switching to a preferred (vs. non-preferred) task order. Here, we ask whether another determinant of task order control, namely the phenomenon that a task order switch in a previous trial facilitates a task order switch in a current trial (i.e., a sequential modulation of task order switch effect) also takes task-specific characteristics into account. Based on three experiments involving task order switches between a preferred (dominant oculomotor task prior to non-dominant manual/pedal task) and a non-preferred (vice versa) order, we replicated the finding that task order switching (in Trial N) is facilitated after a previous switch (vs. repetition in Trial N - 1) in task order. There was no substantial evidence in favor of a significant difference when switching to the preferred vs. non-preferred order and in the analyses of the dominant oculomotor task and the non-dominant manual task. This indicates different mechanisms underlying the control of immediate task order configuration (indexed by task order switch costs) and the sequential modulation of these costs based on the task order transition type in the previous trial.

Task offloading based on edge collaboration in MEC-enabled IoV networks

Benefiting from its abundant computing resources and low computing latency, mobile edge computing (MEC) is a promising approach for enhancing the computing capacity of the 5G Internet of vehicles (IoV). Because of the high mobility, handover is frequent and inevitable in IoV networks. In this paper, we investigate an edge collaborative task offloading and splitting strategy in MEC-enabled IoV networks, in which the task is splitted on the edge and paralleling executed by each part of the task on several MEC servers when handover is occured. Applications in IoV networks have flexible requirements on latency and energy consumption. To realize the tradeoff between latency and energy consumption, we formulate the task offloading and splitting as an optimization problem with the aim of minimizing the total cost of latency and energy consumption by jointly optimizing the task splitting ratio and uplink transmit power of vehicle terminal (VT). Because the proposed problem is non-smooth and non-convex, we divide the original problem into two convex subproblems, and apply an alternate convex search (ACS) algorithm to obtain the optimized solution with low computational complexity. Numerical simulation results show that the proposed method can adjust the offloading strategy properly according to task preference, and obtain a lower total cost compared with the baseline algorithms.

Role of adenosine A2A receptors in hot and cold cognition: Effects of single-dose istradefylline in healthy volunteers

The role of the adenosine neurochemical system in human cognition is under-studied, despite such receptors being distributed throughout the brain. The aim of this study was to shed light on the role of the adenosine A2A receptors in human cognition using single-dose istradefylline. Twenty healthy male participants, aged 19–49, received 20 mg istradefylline and placebo, in a randomized, double-blind, placebo-controlled cross-over design. Cognition was assessed using computerized cognitive tests, covering both cold (non-emotional) and hot (emotion-laden) domains. Cardiovascular data were recorded serially. Cognitive effects of istradefylline were explored using repeated measures analysis of variance and paired t-tests as appropriate. On the EMOTICOM battery, there was a significant effect of istradefylline versus placebo on the Social Information Preference task (t = 2.50, p = 0.02, d=-0.59), indicating that subjects on istradefylline interpreted social situations more positively. No other significant effects were observed on other cognitive tasks, nor in terms of cardiovascular measures (pulse and blood pressure). De-briefing indicated that blinding was successful, both for participants and the research team. Further exploration of the role of adenosine A2A receptors in emotional processing may be valuable, given that abnormalities in related cognitive functions are implicated in neuropsychiatric disorders. The role of adenosine systems in human cognition requires further clarification, including with different doses of istradefylline and over different schedules of administration.

Is all mental effort equal? The role of cognitive demand-type on effort avoidance

Humans are often termed “cognitive misers” for their aversion to mental effort. Both in and outside the laboratory people often show preferences for low-effort tasks and are willing to forgo financial reward to avoid more demanding alternatives. Mental effort, however, does not seem to be ubiquitously avoided: people play crosswords, board games, and read novels, all as forms of leisure. While such activities undoubtedly require effort, the type of cognitive demands they impose appear markedly different from the tasks typically used in psychological research on mental effort (e.g., N-Back, Stroop Task, vigilance tasks). We investigate the effect disparate demands, such as tasks which require problem solving (e.g., solve the missing number: 1, 3, 7, 15, 31,?) compared to those which require rule-implementation (e.g., N-Back task), have on people's aversion to or preference for increased mental effort. Across four experiments using three different tasks, and a mixture of online and lab-based settings, we find that aversion to effort remains largely stable regardless of the types of cognitive demands a task imposes. The results are discussed in terms of other factors that might induce the pursuit of mental effort over and above the type of cognitive demands imposed by a task.

Learning progress mediates the link between cognitive effort and task engagement

While a substantial body of work has shown that cognitive effort is aversive and costly, a separate line of research on intrinsic motivation suggests that people spontaneously seek challenging tasks. According to one prominent account of intrinsic motivation, the learning progress motivation hypothesis, the preference for difficult tasks reflects the dynamic range that these tasks yield for changes in task performance (Kaplan & Oudeyer, 2007). Here we test this hypothesis, by asking whether greater engagement with intermediately difficult tasks, indexed by subjective ratings and objective pupil measurements, is a function of trial-wise changes in performance. In a novel paradigm, we determined each individual's capacity for task performance and used difficulty levels that are low, intermediately challenging or high for that individual. We demonstrated that challenging tasks resulted in greater liking and engagement scores compared with easy tasks. Pupil size tracked objective task difficulty, where challenging tasks were associated with greater pupil responses than easy tasks. Most importantly, pupil responses were predicted by trial-to-trial changes in average accuracy as well as learning progress (derivative of average accuracy), while greater pupil responses also predicted greater subjective engagement scores. Together, these results substantiate the learning progress motivation hypothesis stating that the link between task engagement and cognitive effort is mediated the dynamic range for changes in task performance.

Decreased medial prefrontal cortex activity related to impaired novel object preference task performance following GALR2 and Y1R agonists intranasal infusion

Different brain regions' interactions have been implicated in relevant neurological diseases, such as major depressive disorder (MDD), anxiety disorders, age-dependent cognitive decline, Alzheimer's disease (AD) and addiction. We aim to explore the role of the medial prefrontal cortex (mPFC) in the Neuropeptide Y (NPY) and Galanin (GAL) interaction since we have demonstrated specific NPY and GAL interactions in brain areas related to these brain diseases. We performed GALR2 and Y1R agonists intranasal infusion and analyzed the mPFC activation through c-Fos expression. To assess the associated cellular mechanism we studied the formation of Y1R-GALR2 heteroreceptor complexes with in situ proximity ligation assay (PLA) and the expression of the brain-derived neurotrophic factor (BDNF). Moreover, the functional outcome of the NPY and GAL interaction on the mPFC was evaluated in the novel object preference task. We demonstrated that the intranasal administration of both agonists decrease the medial prefrontal cortex activation as shown with the c-Fos expression. These effects were mediated by the decreased formation of Y1R-GALR2 heteroreceptor complexes without affecting the BDNF expression. The functional outcome of this interaction was related to an impaired performance on the novel object preference task. Our data may suggest the translational development of new heterobivalent agonist pharmacophores acting on Y1R–GALR2 heterocomplexes in the medial prefrontal cortex for the novel therapy on neurodegenerative and psychiatric diseases. Data Sharing and Data AccessibilityThe data that support the findings of this study are openly available in Institutional repository of the University of Malaga (RIUMA) and from the corresponding author upon reasonable request.

A matching game-based crowdsourcing framework for last-mile delivery: Ground-vehicles and Unmanned-Aerial Vehicles

This paper tackles the problem of allocating time-constrained last-mile delivery tasks to crowdsourced vehicles and Unmanned Aerial Vehicles (UAVs). With the increasing development of E-commerce, the demand for home deliveries is rapidly increasing. However, last-mile delivery remains the costliest part endured by shipping companies, particularly for time-critical deliveries. To mitigate this high cost, recently, several researchers and companies, such as Amazon and Mercedes-Benz, have adopted (1) crowdsourced and (2) UAVs-based delivery solutions. Nevertheless, crowdsourcing delivery tasks, where workers with vehicles are being recruited to deliver parcels, suffers from long delivery time and delays for time-critical tasks during peak traffic hours. Although UAVs are perceived currently as a trending solution for fast and timely delivery of time-critical delivery tasks, yet they are constrained by the flying time and the package weight. These limitations have a direct impact on the cost of delivery and the task accomplishment rate. In this paper, a hybrid framework that crowd-sources vehicles’ drivers and UAVs for delivery tasks is proposed. It aims at improving the delivery time and the allocation percentage mainly at peak hours, by the proposed, (1) task filtering, (2) worker quality estimation, and (3) task allocation and scheduling. The filtering of possible tasks, that each type of worker can accomplish is done based on the availability of the workers and their ability to reach the destination. The worker quality, with regard to the delivery task, is estimated using the Bayesian belief function that is utilized in the allocation. The allocation of tasks and workers is performed using the Gale–Shapley matching game to consider the preferences of both workers and tasks. The simulation results show that the hybrid framework outperforms the non-hybrid crowdsourcing model in terms of task allocation percentage, delivery time, and payoff.

Black-capped chickadees (Poecile atricapillus) discriminate between naturally-ordered and scramble-ordered chick-a-dee calls and individual preference is related to rate of learning

Though many forms of animal communication are not reliant on the order in which components of signals are combined to be effective, there is evidence that order does matter for some communication systems. In the light of differential responding to calls of varying note-order observed in black-capped chickadees in the field, we set out to determine whether chickadees recognize syntactically-ordered and incorrectly-ordered chick-a-dee calls as separate and distinct conceptual categories using both an auditory preference task and go/no-go operant conditioning paradigm. Results show that chickadees spent more time on the perch that did not produce sound (i.e., silent perch) than on either of the acoustic perches (i.e., natural and scrambled order chick-a-dee call playback) and visited the perch associated with naturally-ordered calls more often than the perch associated with scrambled-order calls. Birds in both the True natural- and scrambled-order call groups continued to respond according to the contingencies that they learned in Discrimination training, indicating that black-capped chickadees are capable of perceiving and acting upon the categories of natural- versus scrambled-ordered calls.

Maternal immune activation alters social affective behavior and sensitivity to corticotropin releasing factor in male but not female rats

Prenatal infection increases risk for neurodevelopmental disorders such as autism in offspring. In rodents, prenatal administration of the viral mimic Polyinosinic: polycytidylic acid (Poly I: C) allows for investigation of developmental consequences of gestational sickness on offspring social behavior and neural circuit function. Because maternal immune activation (MIA) disrupts cortical development and sociability, we examined approach and avoidance in a rat social affective preference (SAP) task. Following maternal Poly I:C (0.5 mg/kg) injection on gestational day 12.5, male adult offspring (PN 60–64) exhibited atypical social interactions with stressed conspecifics whereas female SAP behavior was unaffected by maternal Poly I:C. Social responses to stressed conspecifics depend upon the insular cortex where corticotropin releasing factor (CRF) modulates synaptic transmission and SAP behavior. We characterized insular field excitatory postsynaptic potentials (fEPSP) in adult offspring of Poly I:C or control treated dams. Male MIA offspring showed decreased sensitivity to CRF (300 nM) while female MIA offspring showed greater sensitivity to CRF compared to sham offspring. These sex specific effects appear to be behaviorally relevant as CRF injected into the insula of male and female rats prior to social exploration testing had no effect in MIA male offspring but increased social interaction in female MIA offspring. We examined the cellular distribution of CRF receptor mRNA but found no effect of maternal Poly I:C in the insula. Together, these experiments reveal sex specific effects of prenatal infection on offspring responses to social affective stimuli and identify insular CRF signaling as a novel neurobiological substrate for autism risk.

The effect of perceptual organization on numerical and preference-based decisions shows inter-subject correlation.

Individual differences in cognitive processing have been the subject of intensive research. One important type of such individual differences is the tendency for global versus local processing, which was shown to correlate with a wide range of processing differences in fields such as decision making, social judgments and creativity. Yet, whether these global/local processing tendencies are correlated within a subject across different domains is still an open question. To address this question, we develop and test a novel method to quantify global/local processing tendencies, in which we directly set in opposition the local and global information instead of instructing subjects to specifically attend to one processing level. We apply our novel method to two different domains: (1) a numerical cognition task, and (2) a preference task. Using computational modeling, we accounted for classical effects in choice and numerical-cognition. Global/local tendencies in both tasks were quantified using a salience parameter. Critically, the salience parameters extracted from the numerical cognition and preference tasks were highly correlated, providing support for robust perceptual organization tendencies within an individual.

Development of a Forced Choice Assessment of Appearance Importance: The Appearance Preference Task (APT)

Development of a Forced Choice Assessment of Appearance Importance: The Appearance Preference Task (APT)

Audio delivery of health information: An NLP study of information difficulty and bias in listeners

Health literacy is the ability to understand, process, and obtain health information and make suitable decisions about health care [3]. Traditionally, text has been the main medium for delivering health information. However, virtual assistants are gaining popularity in this digital era; and people increasingly rely on audio and smart speakers for health information. We aim to identify audio/text features that contribute to the difficulty of the information delivered over audio. We are creating a health-related audio corpus. We selected text snippets and calculated seven text features. Then, we converted the text snippets to audio snippets. In a pilot study with Amazon Mechanical Turk (AMT) workers, we measured the perceived and actual difficulty of the audio using the response of multiple choice and free recall questions. We collected demographic information as well as bias about doctors’ gender, task preference, and health information preference. Thirteen workers completed thirty audio snippets and related questions. We found a strong correlation between text features lexical chain, and the dependent variables, and multiple choice response, percentage of matching word, percentage of similar word, cosine similarity, and time taken (in seconds). In addition, doctors were generally perceived to be more competent than warm. How warm workers perceive male doctors correlated significantly with perceived difficulty.

A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers.

Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring functions are only applicable to a specific task, e.g., binding affinity prediction, binding pose prediction or virtual screening, suggesting that the development of a scoring function with balanced performance in all critical tasks remains a grand challenge. To this end, we propose a novel parameterization strategy by introducing an adjustable binding affinity term that represents the correlation between the predicted outcomes and experimental data into the training of mixture density network. The resulting residue-atom distance likelihood potential not only retains the superior docking and screening power over all the other state-of-the-art approaches, but also achieves a remarkable improvement in scoring and ranking performance. We emphatically explore the impacts of several key elements on prediction accuracy as well as the task preference, and demonstrate that the performance of scoring/ranking and docking/screening tasks of a certain model could be well balanced through an appropriate manner. Overall, our study highlights the potential utility of our innovative parameterization strategy as well as the resulting scoring framework in future structure-based drug design.

Personalized Multi-document Text Summarization using Deep Learning Techniques

The summary of a large piece of text or a document is a concise description of the same thing. It must retain most of the important points from the document and remove any kind of verbosity. The text summarization task is an information processing scheme that given a document or a set of documents, mines the most essential content from the source by considering the user or task at hand and presents the summary in a well-formed and brief text. Summarization can be done not just on one document, it can be done on a set of documents as well, which is called multi-document summarization. The proposed approach considers and in-corporates user preferences for the summarization task. Depending on the current user or current task, the summary could differ essentially. For an instance, a summary on ‘photosynthesis’, would be very different for the kid in 4th Grade vs. a student doing a bachelor's in Science. Therefore, while writing a summary, the reading history of the user is employed as a source of personalizing text summarization tasks. The proposed method is experimentally evaluated in the domain of news articles and obtained better summaries capable of extracting important concepts based on user preferences explained in the document when considering the relevant domain terms in the process of multi-document text summarization.

Offloading dependent tasks in MEC-enabled IoT systems: A preference-based hybrid optimization method

The rapid development of IoT-based services has resulted in an exponential increase in the number of connected smart mobile devices (SMDs). Processing the massive data generated by the large number of SMDs is becoming a big problem for mobile devices, servers, and wireless communication channels. A Multi-access Edge Computing (MEC) paradigm partially mitigates this problem by deploying edge server nodes at the edge of wireless networks nearby SMDs, but the challenge still remains due to the limited computation capacity of MEC servers and the bandwidth of wireless channels. In addition, the dependency of tasks generated by applications on SMDs increases the complexity of the problem. In this paper, we propose a constrained multiobjective computation offloading optimization solution to resolve the problem of task dependency under limited resources. This solution improves the Quality of Service (QoS) through minimizing the latency, energy consumption, and rate of task failure caused by limited resources. We propose a two-staged hybrid computation offloading optimization method to solve the problem. In the first stage, the computation offloading decisions are made based on the preferences of tasks. Then, in the second stage, nearly optimal solutions are found using the modified Non-Dominated Sorting Genetic Algorithm (NSGA-III). The overall efficiency of the proposed method is increased owing to the preference-based algorithm reinforcing the NSGA-III algorithm by generating a better initial population. The results of extensive experiments show that the efficiency of the proposed method is significantly better than the existing methods.

The predictive value of the cognitive scale of the Bayley Scales of Infant and Toddler Development-III

The measurement of cognitive development is of major importance within developmental psychology research. However, empirical findings from longitudinal studies using the Bayley Scales of Infant and Toddler Development-III (BSID-III) are scarce. Moreover, few studies have assessed the predictive relationship between the BSID-III and later IQ measurements using the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III). The aims of the current study were to investigate (1) the predictive relationship within the first, second, and third year of life using the Cognitive Scale of the BSID-III, (2) the predictive relationship between the Cognitive Scale of the BSID-III and the WPPSI-III Verbal IQ, Performance IQ and Full Scale IQ scores in the fifth year of life, and (3) the relationship of habituation speed and novelty preference with BSID-III and WPPSI-III scores. Therefore, 110 children were tested with the BSID-III at the ages of 6, 9, 18, and 26 months, with a habituation and novelty preference task at 9 months, and with the WPPSI-III at 50 months. Structural equation modeling revealed that BSID-III at all ages predicted the children’s IQ obtained using the WPPSI-III at 50 months. Furthermore, all BSID-III scores predicted subsequent BSID-III scores. These findings highlight the predictive value of the Cognitive Scale of the BSID-III. Habituation speed and novelty preference were not related to the BSID-III or WPPSI-III scores. Explanations for these findings, limitations, and ideas for future studies are discussed.

Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity

The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed "Electrical FingerPrint of rIFG"; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.