The path of dishonesty: identification of mental processes with electrical neuroimaging.

Much research finds that lying takes longer than truth-telling. Yet, the source of this response time difference remains elusive. Here, we assessed the spatiotemporal evolution of electrical brain activity during honesty and dishonesty in 150 participants using a sophisticated electrical neuroimaging approach-the microstate approach. This uniquely positioned us to identify and contrast the entire chain of mental processes involved during honesty and dishonesty. Specifically, we find that the response time difference is the result of an additional late-occurring mental process, unique to dishonest decisions, interrupting the antecedent mental processing. We suggest that this process inhibits the activation of the truth, thus permitting the execution of the lie. These results advance our understanding of dishonesty and clarify existing theories about the role of increased cognitive load. More broadly, we demonstrate the vast potential of our approach to illuminate the temporal organization of mental processes involved in decision-making.

Behavioral signatures of the rapid recruitment of long-term memory to overcome working memory capacity limits.

Working- and long-term memory are often studied in isolation. To better understand the specific limitations of working memory, effort is made to reduce the potential influence of long-term memory on performance in working memory tasks (e.g., asking participants to remember artificial, abstract items rather than familiar real-world objects). However, in everyday life we use working- and long-term memory in tandem. Here, our goal was to characterize how long-term memory can be recruited to circumvent capacity limits in a typical visual working memory task (i.e., remembering colored squares). Prior work has shown that incidental repetitions of working memory arrays often do not improve visual working memory performance - even after dozens of incidental repetitions, working memory performance often shows no improvement for repeated arrays. Here, we used a whole-report working memory task with explicit rather than incidental repetitions of arrays. In contrast to prior work with incidental repetitions, in two behavioral experiments we found that explicit repetitions of arrays yielded robust improvement to working memory performance, even after a single repetition. Participants performed above chance at recognizing repeated arrays in a later long-term memory test, consistent with the idea that long-term memory was used to rapidly improve performance across array repetitions. Finally, we analyzed inter-item response times and we found a response time signature of chunk formation that only emerged after the array was repeated (inter-response time slowing after two to three items); thus, inter-item response times may be useful for examining the coordinated interaction of visual working and long-term memory in future work.

A Model-Based Approach to the Disentanglement and Differential Treatment of Engaged and Disengaged Item Omissions.

Item omissions in large-scale assessments may occur for various reasons, ranging from disengagement to not being capable of solving the item and giving up. Current response-time-based classification approaches allow researchers to implement different treatments of item omissions presumably going back to different mechanisms. These approaches, however, are limited in that they require a clear-cut decision on the underlying missingness mechanism and do not allow to take the uncertainty in classification into account. We present a response-time-based model-based mixture modeling approach that overcomes this limitation. The approach (a) facilitates disentangling item omissions stemming from disengagement from those going back to solution behavior, (b) considers the uncertainty in omission classification, (c) allows for omission mechanisms to vary on the item-by-examinee level, (d) supports investigating person and item characteristics associated with different types of omission behavior, and (e) gives researchers flexibility in deciding on how to handle different types of omissions. The approach exhibits good parameter recovery under realistic research conditions. We illustrate the approach on data from the Programme for the International Assessment of Adult Competencies 2012 and compare it against previous classification approaches for item omissions.

Adopting Stimulus Detection Tasks for Cognitive Workload Assessment: Some Considerations.

OBJECTIVE: This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload. BACKGROUND: Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic. METHOD: This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation. RESULTS: The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms. CONCLUSION: There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state. APPLICATION: Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.

A Mixture Fluency model using responses and response times with cognitive diagnosis model framework.

Random guessing behaviors are frequently observed in low-stakes assessments, often attributed to factors such as test-takers lacking motivation or experiencing time constraints and fatigue. Existing research suggests that responses stemming from random guessing behaviors introduce biases into the constructs and relationships of interest. This is particularly problematic when estimating the relationship between speed and ability. This study introduces a Mixture Fluency model designed to account for random guessing behaviors while utilizing valid response accuracy and response time to uncover students' latent attribute profiles. The model directly addresses a limitation present in the Fluency cognitive diagnostic model (Wang & Chen, Psychometrika, 85, 600-629, (2020), which assumes that test-takers consistently employ solution behaviors when answering questions. To investigate the effectiveness of the proposed Mixture Fluency model, we conducted a simulation study encompassing various simulation conditions. Results from this study not only confirm the model's ability to detect potential random guessing behaviors but also demonstrate its capacity to enhance the inference of targeted latent constructs within the assessment. Additionally, we showcase the practical utility of the proposed model through an application to real data.

Connecting process models to response times through Bayesian hierarchical regression analysis.

Process models specify a series of mental operations necessary to complete a task. We demonstrate how to use process models to analyze response-time data and obtain parameter estimates that have a clear psychological interpretation. A prerequisite for our analysis is a process model that generates a count of elementary information processing steps (EIP steps) for each trial of an experiment. We can estimate the duration of an EIP step by assuming that every EIP step is of random duration, modeled as draws from a gamma distribution. A natural effect of summing several random EIP steps is that the expected spread of the overall response time increases with a higher EIP step count. With modern probabilistic programming tools, it becomes relatively easy to fit Bayesian hierarchical models to data and thus estimate the duration of a step for each individual participant. We present two examples in this paper: The first example is children's performance on simple addition tasks, where the response time is often well predicted by the smaller of the two addends. The second example is response times in a Sudoku task. Here, the process model contains some random decisions and the EIP step count thus becomes latent. We show how our EIP regression model can be extended to such a case. We believe this approach can be used to bridge the gap between classical cognitive modeling and statistical inference and will be easily applicable to many use cases.

Longitudinal joint modeling for assessing parallel interactive development of latent ability and processing speed using responses and response times.

To measure the parallel interactive development of latent ability and processing speed using longitudinal item response accuracy (RA) and longitudinal response time (RT) data, we proposed three longitudinal joint modeling approaches from the structural equation modeling perspective, namely unstructured-covariance-matrix-based longitudinal joint modeling, latent growth curve-based longitudinal joint modeling, and autoregressive cross-lagged longitudinal joint modeling. The proposed modeling approaches can not only provide the developmental trajectories of latent ability and processing speed individually, but also exploit the relationship between the change in latent ability and processing speed through the across-time relationships of these two constructs. The results of two empirical studies indicate that (1) all three models are practically applicable and have highly consistent conclusions in terms of the changes in ability and speed in the analysis of the same data set, and (2) additional analysis of the RT data and acquisition of individual processing speed measurements can reveal the parallel interactive development phenomena that are difficult to detect using RA data alone. Furthermore, the results of our simulation study demonstrate that the proposed Bayesian Markov chain Monte Carlo estimation algorithm can ensure accurate model parameter recovery for all three proposed longitudinal joint models. Finally, the implications of our findings are discussed from the research and practice perspectives.

Testing cognitive models of decision-making: selected studies with starlings.

The behavioural sciences are home to controversies that have survived for centuries, notably about the relation between observable behaviour and theoretical constructs addressing out-of-sight processes in the agents' brains. There is no shared definition for cognition, but the very existence of a thriving journal called Animal Cognition proves that such controversies are still live and help to (a) promote research on the complexity of processes leading to action, and (b) nudge scholars to restrict their cognitive models to those that can be falsified experimentally. Here, we illustrate some of these issues in a limited arena, focusing on the construction and expression of subjective value and choice. Using mainly work from our own laboratory, we show that valuation of alternatives is sensitive to options' properties, to subject's state, and to background alternatives. These factors exert their influence at the time the subject learns about individual options, rather than at choice time. We also show that valuation can be experimentally dissociated from the cognitive representation of options' metrics and argue that experimental animals process options independently at the time of choice, without elaborated comparisons along different dimensions. The findings we report are not consistent with the hypothesis that preference is constructed at the time of choice, a prevalent view in human decision-making research. We argue that animal cognition, viewed as a research program at the crossroads of different behavioural sciences rather than as a debate about properties of mental life, is inspiring and solid, and a progressive and progressing paradigm.

The spatiotemporal gradient of intrusion errors in continuous outcome source memory: Source retrieval is affected by both guessing and intrusions.

Previous research has characterized source retrieval as a thresholded process, which fails on a proportion of trials and leads to guessing, as opposed to a continuous process, in which response precision varies across trials but is never zero. The thresholded view of source retrieval is largely based on the observation of heavy tailed distributions of response errors, thought to reflect a large proportion of "memoryless" trials. In this study, we investigate whether these errors might instead reflect systematic intrusions from other list items which can mimic source guessing. Using the circular diffusion model of decision making, which accounts for both response errors and RTs we found that intrusions account for some, but not all, errors in a continuous-report source memory task. We found that intrusion errors were more likely to come from items studied in nearby locations and times, and were well-described by a spatiotemporal gradient model, but not from semantically or perceptually similar cues. Our findings support a thresholded view of source retrieval but suggest that previous work has overestimated the proportion of guesses which have been conflated with intrusions.

Testing the drift-diffusion model.

The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals, where the decision maker accumulates evidence until the process hits either an upper or lower stopping boundary and then stops and chooses the alternative that corresponds to that boundary. In perceptual tasks, the drift of the process is related to which choice is objectively correct, whereas in consumption tasks, the drift is related to the relative appeal of the alternatives. The simplest version of the DDM assumes that the stopping boundaries are constant over time. More recently, a number of papers have used nonconstant boundaries to better fit the data. This paper provides a statistical test for DDMs with general, nonconstant boundaries. As a by-product, we show that the drift and the boundary are uniquely identified. We use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic based on finite samples.

Does mortality after trauma team activation peak at shift change?

BACKGROUND: Prior institutional data have demonstrated trauma mortality to be highest between 06:00-07:59 at our center, which is also when providers change shifts (07:00-07:30). The objective was definition of patient, provider, and systems variables associated with trauma mortality at shift change among patients arriving as trauma team activations (TTA). METHODS: All TTA patients at our ACS-verified Level I trauma center were included (01/2008-07/2019), excluding those with undocumented arrival time. Study groups were defined by arrival time: shift change (SC) (06:00-07:59) vs. non-shift change (NSC) (all other times). Univariable/multivariable analyses compared key variables. Propensity score analysis compared outcomes after matching. RESULTS: After exclusions, 6020 patients remained: 229 (4%) SC and 5791 (96%) NSC. SC mortality was 25% vs. 16% during NSC (p < 0.001). More SC patients arrived with SBP <90 (19% vs. 11%, p < 0.001) or GCS <9 (35% vs. 24%, p < 0.001). ISS was higher during SC (43[32-50] vs. 34[27-50], p < 0.001). Time to CT scan (36[23-66] vs. 38[23-61] minutes, p = 0.638) and emergent surgery (94[35-141] vs. 63[34-107] minutes, p = 0.071) were comparable. Older age (p < 0.001), SBP <90 (p < 0.001), GCS <9 (p < 0.001), need for emergent operative intervention (p = 0.044), and higher ISS (p < 0.001) were independently associated with mortality. After propensity score matching, mortality was no different between SC and NSC (p = 0.764). CONCLUSIONS: Early morning is a low-volume, high-mortality time for TTAs. Increased mortality at shift change was independently associated with patient/injury factors but not provider/systems factors. Ensuring ample clinical resource allocation during this high acuity time may be prudent to streamline patient care at shift change.

Vigilance Decrement During On-Road Partially Automated Driving Across Four Systems.

OBJECTIVE: This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems. BACKGROUND: Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven. METHOD: Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods. RESULTS: Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found. CONCLUSION: Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo. APPLICATION: The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.

Same same but different: Subtle but consequential differences between two measures to linearly integrate speed and accuracy (LISAS vs. BIS).

Condition-specific speed-accuracy trade-offs (SATs) are a pervasive issue in experimental psychology, because they sometimes render impossible an unambiguous interpretation of experimental effects on either mean response times (mean RT) or percentage of correct responses (PC). For between-participants designs, we have recently validated a measure (Balanced Integration Score, BIS) that integrates standardized mean RT and standardized PC and thereby controls for cross-group variation in SAT. Another related measure (Linear Integrated Speed-Accuracy Score, LISAS) did not fulfill this specific purpose in our previous simulation study. Given the widespread and seemingly interchangeable use of the two measures, we here illustrate the crucial differences between LISAS and BIS related to their respective choice of standardization variance. We also disconfirm the recently articulated hypothesis that the differences in the behavior of the two combined performance measures observed in our previous simulation study were due to our choice of a between-participants design and we demonstrate why a previous attempt to validate BIS (and LISAS) for within-participants designs has failed, pointing out several consequential issues in the respective simulations and analyses. In sum, the present study clarifies the differences between LISAS and BIS, demonstrates that the choice of the variance used for standardization is crucial, provides further guidance on the calculation and use of BIS, and refutes the claim that BIS is not useful for attenuating condition-specific SATs in within-participants designs.

Quantifying Entropy in Response Times (RT) Distributions Using the Cumulative Residual Entropy (CRE) Function.

Response times (RT) distributions are routinely used by psychologists and neuroscientists in the assessment and modeling of human behavior and cognition. The statistical properties of RT distributions are valuable in uncovering unobservable psychological mechanisms. A potentially important statistical aspect of RT distributions is their entropy. However, to date, no valid measure of entropy on RT distributions has been developed, mainly because available extensions of discrete entropy measures to continuous distributions were fraught with problems and inconsistencies. The present work takes advantage of the cumulative residual entropy (CRE) function-a well-known differential entropy measure that can circumvent those problems. Applications of the CRE to RT distributions are presented along with concrete examples and simulations. In addition, a novel measure of instantaneous CRE is developed that captures the rate of entropy reduction (or information gain) from a stimulus as a function of processing time. Taken together, the new measures of entropy in RT distributions proposed here allow for stronger statistical inferences, as well as motivated theoretical interpretations of psychological constructs such as mental effort and processing efficiency.

A comparison of simultaneously-obtained measures of listening effort: pupil dilation, verbal response time and self-rating.

OBJECTIVE: The aim of this study was to assess to what extent simultaneously-obtained measures of listening effort (task-evoked pupil dilation, verbal response time [RT], and self-rating) could be sensitive to auditory and cognitive manipulations in a speech perception task. The study also aimed to explore the possible relationship between RT and pupil dilation. DESIGN: A within-group design was adopted. All participants were administered the Matrix Sentence Test in 12 conditions (signal-to-noise ratios [SNR] of -3, -6, -9 dB; attentional resources focussed vs divided; spatial priors present vs absent). STUDY SAMPLE: Twenty-four normal-hearing adults, 20-41 years old (M = 23.5), were recruited in the study. RESULTS: A significant effect of the SNR was found for all measures. However, pupil dilation discriminated only partially between the SNRs. Neither of the cognitive manipulations were effective in modulating the measures. No relationship emerged between pupil dilation, RT and self-ratings. CONCLUSIONS: RT, pupil dilation, and self-ratings can be obtained simultaneously when administering speech perception tasks, even though some limitations remain related to the absence of a retention period after the listening phase. The sensitivity of the three measures to changes in the auditory environment differs. RTs and self-ratings proved most sensitive to changes in SNR.

An update of the Benton Facial Recognition Test.

The Benton Facial Recognition Test (BFRT) is a paper-and-pen task that is traditionally used to assess face perception skills in neurological, clinical and psychiatric conditions. Despite criticisms of its stimuli, the task enjoys a simple procedure and is rapid to administer. Further, it has recently been computerised (BFRT-c), allowing reliable measurement of completion times and the need for online testing. Here, in response to calls for repeat screening for the accurate detection of face processing deficits, we present the BFRT-Revised (BFRT-r): a new version of the BFRT-c that maintains the task's basic paradigm, but employs new, higher-quality stimuli that reflect recent theoretical advances in the field. An initial validation study with typical participants indicated that the BFRT-r has good internal reliability and content validity. A second investigation indicated that while younger and older participants had comparable accuracy, completion times were longer in the latter, highlighting the need for age-matched norms. Administration of the BFRT-r and BFRT-c to 32 individuals with developmental prosopagnosia resulted in improved sensitivity in diagnostic screening for the BFRT-r compared to the BFRT-c. These findings are discussed in relation to current diagnostic screening protocols for face perception deficits. The BFRT-r is stored in an open repository and is freely available to other researchers.

Challenging response latencies in faking detection: The case of few items and no warnings.

AbstractFaking detection is an ongoing challenge in psychological assessment. A notable approach for detecting fakers involves the inspection of response latencies and is based on the congruence model of faking. According to this model, respondents who fake good will provide favorable responses (i.e., congruent answers) faster than they provide unfavorable (i.e., incongruent) responses. Although the model has been validated in various experimental faking studies, to date, research supporting the congruence model has focused on scales with large numbers of items. Furthermore, in this previous research, fakers have usually been warned that faking could be detected. In view of the trend to use increasingly shorter scales in assessment, it becomes important to investigate whether the congruence model also applies to self-report measures with small numbers of items. In addition, it is unclear whether warning participants about faking detection is necessary for a successful application of the congruence model. To address these issues, we reanalyzed data sets of two studies that investigated faking good and faking bad on extraversion (n = 255) and need for cognition (n = 146) scales. Reanalyses demonstrated that having only a few items per scale and not warning participants represent a challenge for the congruence model. The congruence model of faking was only partly confirmed under such conditions. Although faking good on extraversion was associated with the expected longer latencies for incongruent answers, all other conditions remained nonsignificant. Thus, properties of the measurement and properties of the procedure affect the successful application of the congruence model.

Delay to initiation of out-of-hospital cardiac arrest EMS treatments.

BACKGROUND: Time to initial treatment is important in any response to out-of-hospital cardiac arrest (OHCA). The purpose of this paper was to quantify the time delay for providing initial EMS treatments supplemented by comparison with those of other EMS systems conducting clinical trials. METHODS: Data were collected between 1/1/16-2/15/19. Dispatched, EMS-worked, adult OHCA cases occurring before EMS arrival were included and compared with published treatment time data. Response time and time-to-treatment intervals were profiled in both groups. Time intervals were calculated by subtracting the following timepoints from 9-1-1 call receipt: ambulance in route; at curb; patient contact; first defibrillation; first epinephrine; and first antiarrhythmic. RESULTS: 342 subjects met study inclusion/exclusion. Mean time intervals (min [95%CI]) from 9-1-1 call receipt to the following EMS endpoints were: dispatch 0.1 [0.05-0.2]; at curb 5.0 [4.5, 5.5]; at patient 6.7 [6.1, 7.2];, first defibrillation initially shockable 11.7 [10.1, 13.3]; first epinephrine (initially shockable 15.0 [12.8, 17.2], initially non-shockable 14.8 [13.5, 15.9]), first antiarrhythmic 25.1 [22.0, 28.2]. These findings were similar to data in 5 published clinical trials involving 12,954 subjects. CONCLUSIONS: Delay to EMS treatments are common and may affect clinical outcomes. Neither Utstein out-of-hospital guidelines [1] nor U.S. Cardiac Arrest Registry to Enhance Survival (CARES) databases require capture of these elements. EMS is often not providing treatments quickly enough to optimize clinical outcomes. Further regulatory change/research are needed to determine whether OHCA outcome can be improved by novel changes such as enhancing bystander effectiveness through drone-delivered drugs/devices & real-time dispatcher direction on their use.

A novel combined visual scanning and verbal cuing intervention improves facial affect recognition after chronic severe traumatic brain injury: A single case design.

A single case design (AB with follow up) was used to determine the effect of a combined visual scanning and verbal cuing intervention technique in improving facial affect recognition after traumatic brain injury (TBI). A 35-year-old male (BR) with impaired ability to recognize facial emotions as a result of severe TBI participated in the study over a 3-month duration. BR's mean accuracy across six universal static facial expressions of emotion improved significantly during intervention and was maintained at follow up. BR was most impaired in labelling negative (sad, angry, disgusted, anxious) versus positive facial expressions (surprised, happy). BR's accuracy to negative facial affect significantly improved during intervention. No further improvement was possible for positive expressions because a ceiling effect was observed at baseline. Overall BR's mean response times across emotions was reduced at baseline but increased significantly during intervention. This was also recorded for both positive and negative expressions, respectively. This novel combined intervention has potential to improve facial affect recognition after TBI. Further evaluation using a multiple-baseline design is recommended. Additional research is needed to determine whether improved facial affect recognition following training translates to improvements in social function and communication in people with TBI.

Velocity influences the relative contributions of visual and vestibular cues to self-acceleration.

Self-motion perception is based on the integration of visual (optic flow) and vestibular (inertial) sensory information. Previous research has shown that the relative contribution of visual and vestibular cues can change in real time based on the reliability of that information. The present study assessed whether initial velocity and acceleration magnitude influence the relative contribution of these cues to the detection of self-acceleration. Participants performed a simple response time task with visual and vestibular self-acceleration cues as targets. Visual optic flow was presented at three possible initial velocities of 3, 9, or 15 m/s, and accelerated to result in three possible final velocities of 21, 27, or 33 m/s. Corresponding vestibular cues were presented at magnitudes between 0.01 and 0.04 g. The self-acceleration cues were presented at three possible stimulus onset asynchronies (SOAs): visual-first (by 100 ms), in-sync, and vestibular-first (by 100 ms). We found that presenting the cues in-sync resulted in the fastest responses across all velocities and acceleration magnitudes. Interestingly, presenting the visual cue first resulted in a relative advantage over vestibular-first at the slowest initial velocity of 3 m/s, and vice versa for the fastest initial velocity of 15 m/s. The fastest overall responses for visual-first and in-sync were observed at 9 m/s. The present results support the hypothesis that velocity of optic flow can alter the relative contribution of visual and vestibular cues to the detection of self-acceleration.