The detection of faked identity using unexpected questions and mouse dynamics

Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions

In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals' performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.

Do privacy assurances work? a study of truthfulness in healthcare history data collection

Patients often provide untruthful information about their health to avoid embarrassment, evade treatment, or prevent financial loss. Privacy disclosures (e.g. HIPAA) intended to dissuade privacy concerns may actually increase patient lying. We used new mouse tracking-based technology to detect lies through mouse movement (distance and time to response) and patient answer adjustment in an online controlled study of 611 potential patients, randomly assigned to one of six treatments. Treatments differed in the notices patients received before health information was requested, including notices about privacy, benefits of truthful disclosure, and risks of inaccurate disclosure. Increased time or distance of device mouse movement and greater adjustment of answers indicate less truthfulness. Mouse tracking revealed a significant overall effect (p<0.001) by treatment on the time to reach their final choice. The control took the least time indicating greater truthfulness and the privacy + risk group took the longest indicating least truthfulness. Privacy, risk, and benefit disclosure statements led to greater lying. These differences were moderated by gender. Mouse tracking results largely confirmed the answer adjustment lie detection method with an overall treatment effect (p < .0001) and gender differences (p < .0001) on truthfulness. Privacy notices led to decreased patient honesty. Privacy notices should perhaps be administered well before personal health disclosure is requested to minimize patient untruthfulness. Mouse tracking and answer adjustment appear to be health care lie-detection methods to enhance optimal diagnosis and treatment.

Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the user is not an imposter who had access to the user credentials. Mouse dynamics is the behavior of a user’s mouse movements and is a biometric that has shown great promise for continuous authentication schemes. This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and three deep learning algorithms. Two evaluation scenarios are considered: binary classifiers are used for user authentication, with the top performer being a 1-dimensional convolutional neural network (1D-CNN) with a peak average test accuracy of 85.73% across the top-10 users. Multi-class classification is also examined using an artificial neural network (ANN) which reaches an astounding peak accuracy of 92.48%, the highest accuracy we have seen for any classifier on this dataset.

DepTSol: An Improved Deep-Learning- and Time-of-Flight-Based Real-Time Social Distance Monitoring Approach under Various Low-Light Conditions

Social distancing is an utmost reliable practice to minimise the spread of coronavirus disease (COVID-19). As the new variant of COVID-19 is emerging, healthcare organisations are concerned with controlling the death and infection rates. Different COVID-19 vaccines have been developed and administered worldwide. However, presently developed vaccine quantity is not sufficient to fulfil the needs of the world’s population. The precautionary measures still rely on personal preventive strategies. The sharp rise in infections has forced governments to reimpose restrictions. Governments are forcing people to maintain at least 6 feet (ft) of safe physical distance to stay safe. With summers, low-light conditions can become challenging. Especially in the cities of underdeveloped countries, where poor ventilated and congested homes cause people to gather in open spaces such as parks, streets, and markets. Besides this, in summer, large friends and family gatherings mostly take place at night. It is necessary to take precautionary measures to avoid more drastic results in such situations. To support the law and order bodies in maintaining social distancing using Social Internet of Things (SIoT), the world is considering automated systems. To address the identification of violations of a social distancing Standard Operating procedure (SOP) in low-light environments via smart, automated cyber-physical solutions, we propose an effective social distance monitoring approach named DepTSol. We propose a low-cost and easy-to-maintain motionless monocular time-of-flight (ToF) camera and deep-learning-based object detection algorithms for real-time social distance monitoring. The proposed approach detects people in low-light environments and calculates their distance in terms of pixels. We convert the predicted pixel distance into real-world units and compare it with the specified safety threshold value. The system highlights people violating the safe distance. The proposed technique is evaluated by COCO evaluation metrics and has achieved a good speed–accuracy trade-off with 51.2 frames per second (fps) and a 99.7% mean average precision (mAP) score. Besides the provision of an effective social distance monitoring approach, we perform a comparative analysis between one-stage object detectors and evaluate their performance in low-light environments. This evaluation will pave the way for researchers to study the field further and will enlighten the efficiency of deep-learning algorithms in timely responsive real-world applications.

Micro-Behavioral Accidental Click Detection System for Preventing Slip-Based Human Error

Accidentally clicking on a link is a type of human error known as a slip in which a user unintentionally performs an unintended task. The risk magnitude is the probability of occurrences of such error with a possible substantial effect to which even experienced individuals are susceptible. Phishing attacks take advantage of slip-based human error by attacking psychological aspects of the users that lead to unintentionally clicking on phishing links. Such actions may lead to installing tracking software, downloading malware or viruses, or stealing private, sensitive information, to list a few. Therefore, a system is needed that detects whether a click on a link is intentional or unintentional and, if unintentional, can then prevent it. This paper proposes a micro-behavioral accidental click detection system (ACDS) to prevent slip-based human error. A within-subject-based experiment was conducted with 20 participants to test the potential of the proposed system. The results reveal the statistical significance between the two cases of intentional vs. unintentional clicks using a smartphone. Random tree, random forest, and support vector machine classifiers were used, exhibiting 82.6%, 87.2%, and 91.6% accuracy in detecting unintentional clicks, respectively.

The detection of malingering in whiplash-related injuries: a targeted literature review of the available strategies

OBJECTIVE

The present review is intended to provide an up-to-date overview of the strategies available to detect malingered symptoms following whiplash. Whiplash-associated disorders (WADs) represent the most common traffic injuries, having a major impact on economic and healthcare systems worldwide. Heterogeneous symptoms that may arise following whiplash injuries are difficult to objectify and are normally determined based on self-reported complaints. These elements, together with the litigation context, make fraudulent claims particularly likely. Crucially, at present, there is no clear evidence of the instruments available to detect malingered WADs.

METHODS

We conducted a targeted literature review of the methodologies adopted to detect malingered WADs. Relevant studies were identified via Medline (PubMed) and Scopus databases published up to September 2020.

RESULTS

Twenty-two methodologies are included in the review, grouped into biomechanical techniques, clinical tools applied to forensic settings, and cognitive-based lie detection techniques. Strengths and weaknesses of each methodology are presented, and future directions are discussed.

CONCLUSIONS

Despite the variety of techniques that have been developed to identify malingering in forensic contexts, the present work highlights the current lack of rigorous methodologies for the assessment of WADs that take into account both the heterogeneous nature of the syndrome and the possibility of malingering. We conclude that it is pivotal to promote awareness about the presence of malingering in whiplash cases and highlight the need for novel, high-quality research in this field, with the potential to contribute to the development of standardised procedures for the evaluation of WADs and the detection of malingering.

Assessing the impact of previous experience on lie effects through a transfer paradigm

Influential lines of research propose dual processes-based explanations to account for both the cognitive cost implied in lying and for that entailed in the resolution of the conflict posited by Simon tasks. The emergence and consistency of the Simon effect has been proved to be modulated by both practice effects and transfer effects. Although several studies provided evidence that the lying cognitive demand may vary as a function of practice, whether and how transfer effects could also play a role remains an open question. We addressed this question with one experiment in which participants completed a Differentiation of Deception Paradigm twice (baseline and test sessions). Crucially, between the baseline and the test sessions, participants performed a training session consisting in a spatial compatibility task with incompatible (condition 1) or compatible (condition 2) mapping, a non-spatial task (condition 3) and a no task one (condition 4). Results speak in favour of a modulation of individual performances by means of an immediate prior experience, and specifically with an incompatible spatial training.

Brain-Machine Interfaces as Commodities: Exchanging Mind for Matter

Brain-machine interfaces (BMIs), which enable a two-way flow of signals, information, and directions between human neurons and computerized machines, offer spectacular opportunities for therapeutic and consumer applications, but they also present unique dangers to the safety, privacy, psychological health, and spiritual well-being of their users. The sale of these devices as commodities for profit exacerbates such issues and may subject the user to an unequal exchange with corporations. Catholic healthcare professionals and bioethicists should be especially concerned about the implications for the essential dignity of the persons using the new BMIs.

Summary

The commercial sale of brain-machine interfaces (BMIs) generates and exacerbates problems for end-users' safety, psychological health, and spiritual well-being.

The detection of faked identity using unexpected questions and choice reaction times

The identification of faked identities, especially within the Internet environment, still remains a challenging issue both for companies and researchers. Recently, however, latency-based lie detection techniques have been developed to evaluate whether the respondent is the real owner of a certain identity. Among the paradigms applied to this purpose, the technique of asking unexpected questions has proved to be useful to differentiate liars from truth-tellers. The aim of the present study was to assess whether a choice reaction times (RT) paradigm, combined with the unexpected question technique, could efficiently detect identity liars. Results demonstrate that the most informative feature in distinguishing liars from truth-tellers is the Inverse Efficiency Score (IES, an index that combines speed and accuracy) to unexpected questions. Moreover, to focus on the predictive power of the technique, machine-learning models were trained and tested, obtaining an out-of-sample classification accuracy of 90%. Overall, these findings indicate that it is possible to detect liars declaring faked identities by asking unexpected questions and measuring RTs and errors, with an accuracy comparable to that of well-established latency-based techniques, such as mouse and keystroke dynamics recording.

Use of mouse-tracking software to detect faking-good behavior on personality questionnaires: an explorative study

The aim of the present study was to explore whether kinematic indicators could improve the detection of subjects demonstrating faking-good behaviour when responding to personality questionnaires. One hundred and twenty volunteers were randomly assigned to one of four experimental groups (honest unspeeded, faking-good unspeeded, honest speeded, and faking-good speeded). Participants were asked to respond to the MMPI-2 underreporting scales (L, K, S) and the PPI-R Virtuous Responding (VR) scale using a computer mouse. The collected data included T-point scores on the L, K, S, and VR scales; response times on these scales; and several temporal and spatial mouse parameters. These data were used to investigate the presence of significant differences between the two manipulated variables (honest vs. faking-good; speeded vs. unspeeded). The results demonstrated that T-scores were significantly higher in the faking-good condition relative to the honest condition; however, faking-good and honest respondents showed no statistically significant differences between the speeded and unspeeded conditions. Concerning temporal and spatial kinematic parameters, we observed mixed results for different scales and further investigations are required. The most consistent finding, albeit with small observed effects, regards the L scale, in which faking-good respondents took longer to respond to stimuli and outlined wider mouse trajectories to arrive at the given response.

Knowledge of Depression and Malingering: An Exploratory Investigation

Malingering mental disorder for financial compensation can offer substantial rewards to those willing to do so. A recent review of UK medico-legal experts' practices for detecting claimants evidenced that they are not well equipped to detect those that do. This is not surprising, considering that very little is known regarding why individuals opt to malinger. A potential construct which may influence an individual's choice to malinger is their knowledge of the disorder, and when one considers the high levels of depression literacy within the UK, it is imperative that this hypothesis is investigated. A brief depression knowledge scale was devised and administered to undergraduate students (N = 155) alongside a series of questions exploring how likely participants were to malinger in both workplace stress and claiming for benefit vignettes. Depression knowledge did not affect the likelihood of engaging in any malingering strategy in either the workplace stress vignettes or the benefit claimant vignettes. Differences were found between the two vignettes providing evidence for the context-specific nature of malingering, and an individual's previous mental disorder was also influential.

A State Space Approach to Dynamic Modeling of Mouse-Tracking Data

Mouse-tracking recording techniques are becoming very attractive in experimental psychology. They provide an effective means of enhancing the measurement of some real-time cognitive processes involved in categorization, decision-making, and lexical decision tasks. Mouse-tracking data are commonly analyzed using a two-step procedure which first summarizes individuals' hand trajectories with independent measures, and then applies standard statistical models on them. However, this approach can be problematic in many cases. In particular, it does not provide a direct way to capitalize the richness of hand movement variability within a consistent and unified representation. In this article we present a novel, unified framework for mouse-tracking data. Unlike standard approaches to mouse-tracking, our proposal uses stochastic state-space modeling to represent the observed trajectories in terms of both individual movement dynamics and experimental variables. The model is estimated via a Metropolis-Hastings algorithm coupled with a non-linear recursive filter. The characteristics and potentials of the proposed approach are illustrated using a lexical decision case study. The results highlighted how dynamic modeling of mouse-tracking data can considerably improve the analysis of mouse-tracking tasks and the conclusions researchers can draw from them.

Population-scale hand tremor analysis via anonymized mouse cursor signals

Tremors are a common movement disorder with a spectrum of benign and pathological causes, including neurodegenerative disease, alcohol withdrawal, and physical overexertion. Studies of tremors in clinical practice are limited in size and scope and depend on explicit tracking of tremor characteristics by clinicians. Data drawn from small numbers of patients observed in short-duration sessions pose challenges for understanding the nature and distribution of tremors over a large population. Methods are presented to estimate hand tremors based on anonymized computer mouse cursor movement data collected from millions of users of a web search engine. To determine the feasibility of using this signal for the estimation of the prevalence of tremors over a large population, the characteristics of tremor-like movements are computed and compared against user data that can be interpreted as self-reports, the findings of published clinical studies, and a target selection study where participants self-report hand tremors and known causes. The results demonstrate significant alignment between estimated tremors and both self-reports and clinical findings. Those with cursor tremor events are more likely to report tremor-related search interests. Variations in cursor tremor quantity and cursor tremor frequency with demographics mirror those from clinical studies. Distributions of cursor tremor frequencies vary as expected for different medical conditions. Overall, the study finds evidence for the validity of harnessing anonymized mouse cursor motion as a population-scale tremor sensor for epidemiologic studies. Feasible future applications include opt-in services for screening and for monitoring the progression of illness.

Beyond reach: Do symmetric changes in motor costs affect decision making? A registered report

Executing an important decision can be as easy as moving a mouse cursor or reaching towards the preferred option with a hand. But would we decide differently if choosing required walking a few steps towards an option? More generally, is our preference invariant to the means and motor costs of reporting it? Previous research demonstrated that asymmetric motor costs can nudge the decision-maker towards a less costly option. However, virtually all traditional decision-making theories predict that increasing motor costs symmetrically for all options should not affect choice in any way. This prediction is disputed by the theory of embodied cognition, which suggests that motor behavior is an integral part of cognitive processes, and that motor costs can affect our choices. In this registered report, we investigated whether varying motor costs can affect response dynamics and the final choices in an intertemporal choice task: choosing between a readily available small reward and a larger but delayed reward. Our study compared choices reported by moving a computer mouse cursor towards the preferred option with the choices executed via a more motor costly walking procedure. First, we investigated whether relative values of the intertemporal choice options affect walking trajectories in the same way as they affect mouse cursor dynamics. Second, we tested a hypothesis that, in the walking condition, increased motor costs of a preference reversal would decrease the number of changes-of-mind and therefore increase the proportion of impulsive, smaller-but-sooner choices. We confirmed the hypothesis that walking trajectories reflect covert dynamics of decision making, and rejected the hypothesis that increased motor costs of responding affect decisions in an intertemporal choice task. Overall, this study contributes to the empirical basis enabling the decision-making theories to address the complex interplay between cognitive and motor processes.

The Detection of Malingered Amnesia: An Approach Involving Multiple Strategies in a Mock Crime

The nature of amnesia in the context of crime has been the subject of a prolonged debate. It is not uncommon that after committing a violent crime, the offender either does not have any memory of the event or recalls it with some gaps in its recollection. A number of studies have been conducted in order to differentiate between simulated and genuine amnesia. The recognition of probable malingering requires several inferential methods. For instance, it typically involves the defendant's medical records, self-reports, the observed behavior, and the results of a comprehensive neuropsychological examination. In addition, a variety of procedures that may detect very specific malingered amnesia in crime have been developed. In this paper, we investigated the efficacy of three techniques, facial thermography, kinematic analysis, and symptom validity testing in detecting malingering of amnesia in crime. Participants were randomly assigned to two different experimental conditions: a group was instructed to simulate amnesia after a mock homicide, and a second group was simply asked to behave honestly after committing the mock homicide. The outcomes show that kinematic analysis and symptom validity testing achieve significant accuracy in detecting feigned amnesia, while thermal imaging does not provide converging evidence. Results are encouraging and may provide a first step towards the application of these procedures in a multimethod approach on crime-specific cases of amnesia.

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

Background and Purpose. The use of machine learning (ML) models in the detection of malingering has yielded encouraging results, showing promising accuracy levels. We investigated the possible application of this methodology when trained on behavioral features, such as response time (RT) and time pressure, to identify faking behavior in self-report personality questionnaires. To do so, we reintroduced the article of Roma et al. (2018), which highlighted that RTs and time pressure are useful variables in the detection of faking; we then extended the number of participants and applied an ML analysis. Materials and Methods. The sample was composed of 175 subjects, of whom all were graduates (having completed at least 17 years of instruction), male, and Caucasian. Subjects were randomly assigned to four groups: honest speeded, faking-good speeded, honest unspeeded, and faking-good unspeeded. A software version of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) was administered. Results. Results indicated that ML algorithms reached very high accuracies (around 95%) in detecting malingerers when subjects are instructed to respond under time pressure. The classifiers' performance was lower when the subjects responded with no time restriction to the MMPI-2-RF items, with accuracies ranging from 75% to 85%. Further analysis demonstrated that T-scores of validity scales are ineffective to detect fakers when participants were not under temporal pressure (accuracies 55-65%), whereas temporal features resulted to be more useful (accuracies 70-75%). By contrast, temporal features and T-scores of validity scales are equally effective in detecting fakers when subjects are under time pressure (accuracies higher than 90%). Discussion. To conclude, results demonstrated that ML techniques are extremely valuable and reach high performance in detecting fakers in self-report personality questionnaires over more the traditional psychometric techniques. Validity scales MMPI-2-RF manual criteria are very poor in identifying under-reported profiles. Moreover, temporal measures are useful tools in distinguishing honest from dishonest responders, especially in a no time pressure condition. Indeed, time pressure brings out malingerers in clearer way than does no time pressure condition.

MMPI-2-RF Profiles in Child Custody Litigants

Background and Purpose: A psychological assessment of parents in post-divorce child custody disputes highlighted parents' motivation to appear as adaptive and responsible caregivers. The study hypothesized that personality self-report measures completed by child custody litigants (CCLs) during a parental skills assessment would show underreporting, rendering the measures worthless. The study also analyzed gender differences in a CCL sample, general CCL profiles, and the implicit structure of the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF) in the CCL sample. Materials and Methods: The sample comprised 400 CCLs undergoing personality evaluation as part of a parenting skills assessment. The mean age of the 204 mothers was 41.31 years (SD = 6.6), with an overall range of 24-59 years. Mothers had a mean educational level of 14.48 years (SD = 3.2). The 196 fathers were aged 20-59 years (M = 42.31; SD = 7.8), with an average of 14.48 years (SD = 3.9) of education. The MMPI-2-RF was administered. To test the hypotheses, multivariate analyses of variance (MANOVAs) and two-step cluster analyses were run. Results: CCL subjects reported higher scores in underreporting (L-r and K-r) and lower scores in overreporting [F-r, Fp-r, Fs-r, and response bias scale (RBS)] validity scales and restructured clinical (RC) scales, with the exception of RC2 and RC8. RC6 (Ideas of Persecution) was the most elevated. Intercorrelations within the RC scales significantly differed between CCL and normative samples. Women appeared deeply motivated to display a faking-good defensive profile, together with lower levels of cynicism and antisocial behaviors, compared to CCL men. Two-step cluster analyses identified three female CCL profiles and two male CCL profiles. Approximately 44% of the MMPI-2-RF profiles were deemed possibly underreporting and, for this reason, considered worthless. Discussion: The present study adds useful insight about which instruments are effective for assessing the personality characteristics of parents undergoing a parental skills assessment in the context of a child custody dispute. The results show that almost half of the MMPI-2-RF protocols in the CCL sample were worthless due to their demonstration of an underreporting attitude. This highlights the necessity to interpret CCL profiles in light of normative data collected specifically in a forensic setting and the need for new and promising methods of mainstreaming and administering the MMPI-2-RF.

A Review of Approaches to Detecting Malingering in Forensic Contexts and Promising Cognitive Load-Inducing Lie Detection Techniques

Malingering, the feigning of psychological or physical ailment for gain, imposes high costs on society, especially on the criminal-justice system. In this article, we review some of the costs of malingering in forensic contexts. Then the most common methods of malingering detection are reviewed, including those for feigned psychiatric and cognitive impairments. The shortcomings of each are considered. The article continues with a discussion of commonly used means for detecting deception. Although not traditionally used to uncover malingering, new, innovative methods are emphasized that attempt to induce greater cognitive load on liars than truth tellers, some informed by theoretical accounts of deception. As a type of deception, we argue that such cognitive approaches and theoretical understanding can be adapted to the detection of malingering to supplement existing methods.

Unanticipated questions can yield unanticipated outcomes in investigative interviews

Asking unanticipated questions in investigative interviews can elicit differences in the verbal behaviour of truth-tellers and liars: When faced with unanticipated questions, liars give less detailed and consistent responses than truth-tellers. Do such differences in verbal behaviour lead to an improvement in the accuracy of interviewers’ veracity judgements? Two empirical studies evaluated the efficacy of the unanticipated questions technique. Experiment 1 compared two types of unanticipated questions (questions regarding the planning of a task and questions regarding the specific spatial and temporal details associated with the task), assessing the veracity judgements of interviewers and verbal content of interviewees’ responses. Experiment 2 assessed veracity judgements of independent observers. Overall, the results provide little support for the technique. For interviewers, unanticipated questions failed to improve veracity judgement accuracy above chance. Reality monitoring analysis revealed qualitatively distinct information in the responses to the two unanticipated question types, though little distinction between the responses of truth-tellers and liars. Accuracy for observers was greater when judging transcripts of unanticipated questions, and this effect was stronger for spatial and temporal questions than planning questions. The benefits of unanticipated questioning appear limited to post-interview situations. Furthermore, the type of unanticipated question affects both the type of information gathered and the ability to detect deceit.

False Identity Detection Using Complex Sentences

The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90–95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models.

Freedom of Thought and Mental Integrity: The Moral Requirements for Any Neural Prosthesis

There are many kinds of neural prostheses available or being researched today. In most cases they are intended to cure or improve the condition of patients affected by some cerebral deficiency. In other cases, their goal is to provide new means to maintain or improve an individual's normal performance. In all these circumstances, one of the possible risks is that of violating the privacy of brain contents (which partly coincide with mental contents) or of depriving individuals of full control over their thoughts (mental states), as the latter are at least partly detectable by new prosthetic technologies. Given the (ethical) premise that the absolute privacy and integrity of the most relevant part of one's brain data is (one of) the most valuable and inviolable human right(s), I argue that a (technical) principle should guide the design and regulation of new neural prostheses. The premise is justified by the fact that whatever the coercion, the threat or the violence undergone, the person can generally preserve a "private repository" of thought in which to defend her convictions and identity, her dignity, and autonomy. Without it, the person may end up in a state of complete subjection to other individuals. The following functional principle is that neural prostheses should be technically designed and built so as to prevent such outcomes. They should: (a) incorporate systems that can find and signal the unauthorized detection, alteration, and diffusion of brain data and brain functioning; (b) be able to stop any unauthorized detection, alteration, and diffusion of brain data. This should not only regard individual devices, but act as a general (technical) operating principle shared by all interconnected systems that deal with decoding brain activity and brain functioning.

Covert lie detection using keyboard dynamics

Identifying the true identity of a subject in the absence of external verification criteria (documents, DNA, fingerprints, etc.) is an unresolved issue. Here, we report an experiment on the verification of fake identities, identified by means of their specific keystroke dynamics as analysed in their written response using a computer keyboard. Results indicate that keystroke analysis can distinguish liars from truth tellers with a high degree of accuracy - around 95% - thanks to the use of unexpected questions that efficiently facilitate the emergence of deception clues.

The Detection of Malingering: A New Tool to Identify Made-Up Depression

Major depression is a high-prevalence mental disease with major socio-economic impact, for both the direct and the indirect costs. Major depression symptoms can be faked or exaggerated in order to obtain economic compensation from insurance companies. Critically, depression is potentially easily malingered, as the symptoms that characterize this psychiatric disorder are not difficult to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and are thus easily faked. In this paper, we propose a new method to automatically detect the simulation of depression, which is based on the analysis of mouse movements while the patient is engaged in a double-choice computerized task, responding to simple and complex questions about depressive symptoms. This tool clearly has a key advantage over the other tools: the kinematic movement is not consciously controllable by the subjects, and thus it is almost impossible to deceive. Two groups of subjects were recruited for the study. The first one, which was used to train different machine-learning algorithms, comprises 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine-learning models, comprises 27 subjects (9 depressed patients and 18 healthy volunteers). In both groups, the healthy volunteers were randomly assigned to the liars and truth-tellers group. Machine-learning models were trained on mouse dynamics features, which were collected during the subject response, and on the number of symptoms reported by participants. Statistical results demonstrated that individuals that malingered depression reported a higher number of depressive and non-depressive symptoms than depressed participants, whereas individuals suffering from depression took more time to perform the mouse-based tasks compared to both truth-tellers and liars. Machine-learning models reached a classification accuracy up to 96% in distinguishing liars from depressed patients and truth-tellers. Despite this, the data are not conclusive, as the accuracy of the algorithm has not been compared with the accuracy of the clinicians; this study presents a possible useful method that is worth further investigation.