In human-machine trust, humans rely on a simple averaging strategy.

With the growing role of artificial intelligence (AI) in our lives, attention is increasingly turning to the way that humans and AI work together. A key aspect of human-AI collaboration is how people integrate judgements or recommendations from machine agents, when they differ from their own judgements. We investigated trust in human-machine teaming using a perceptual judgement task based on the judge-advisor system. Participants ( n = 89 ) estimated a perceptual quantity, then received a recommendation from a machine agent. The participants then made a second response which combined their first estimate and the machine's recommendation. The degree to which participants shifted their second response in the direction of the recommendations provided a measure of their trust in the machine agent. We analysed the role of advice distance in people's willingness to change their judgements. When a recommendation falls a long way from their initial judgement, do people come to doubt their own judgement, trusting the recommendation more, or do they doubt the machine agent, trusting the recommendation less? We found that although some participants exhibited these behaviours, the most common response was neither of these tendencies, and a simple model based on averaging accounted best for participants' trust behaviour. We discuss implications for theories of trust, and human-machine teaming.

Using group level factor models to resolve high dimensionality in model-based sampling.

Joint modeling of decisions and neural activation poses the potential to provide significant advances in linking brain and behavior. However, methods of joint modeling have been limited by difficulties in estimation, often due to high dimensionality and simultaneous estimation challenges. In the current article, we propose a method of model estimation that draws on state-of-the-art Bayesian hierarchical modeling techniques and uses factor analysis as a means of dimensionality reduction and inference at the group level. This hierarchical factor approach can adopt any model for the individual and distill the relationships of its parameters across individuals through a factor structure. We demonstrate the significant dimensionality reduction gained by factor analysis and good parameter recovery, and illustrate a variety of factor loading constraints that can be used for different purposes and research questions, as well as three applications of the method to previously analyzed data. We conclude that this method provides a flexible and usable approach with interpretable outcomes that are primarily data-driven, in contrast to the largely hypothesis-driven methods often used in joint modeling. Although we focus on joint modeling methods, this model-based estimation approach could be used for any high dimensional modeling problem. We provide open-source code and accompanying tutorial documentation to make the method accessible to any researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Using a cognitive model to understand crowdsourced data from citizen scientists.

Threatened species monitoring can produce enormous quantities of acoustic and visual recordings which must be searched for animal detections. Data coding is extremely time-consuming for humans and even though machine algorithms are emerging as useful tools to tackle this task, they too require large amounts of known detections for training. Citizen scientists are often recruited via crowd-sourcing to assist. However, the results of their coding can be difficult to interpret because citizen scientists lack comprehensive training and typically each codes only a small fraction of the full dataset. Competence may vary between citizen scientists, but without knowing the ground truth of the dataset, it is difficult to identify which citizen scientists are most competent. We used a quantitative cognitive model, cultural consensus theory, to analyze both empirical and simulated data from a crowdsourced analysis of audio recordings of Australian frogs. Several hundred citizen scientists were asked whether the calls of nine frog species were present on 1260 brief audio recordings, though most only coded a fraction of these recordings. Through modeling, characteristics of both the citizen scientist cohort and the recordings were estimated. We then compared the model's output to expert coding of the recordings and found agreement between the cohort's consensus and the expert evaluation. This finding adds to the evidence that crowdsourced analyses can be utilized to understand large-scale datasets, even when the ground truth of the dataset is unknown. The model-based analysis provides a promising tool to screen large datasets prior to investing expert time and resources.

Human Performance in Competitive and Collaborative Human-Machine Teams.

In the modern world, many important tasks have become too complex for a single unaided individual to manage. Teams conduct some safety-critical tasks to improve task performance and minimize the risk of error. These teams have traditionally consisted of human operators, yet, nowadays, artificial intelligence and machine systems are incorporated into team environments to improve performance and capacity. We used a computerized task modeled after a classic arcade game to investigate the performance of human-machine and human-human teams. We manipulated the group conditions between team members; sometimes, they were instructed to collaborate, compete, or work separately. We evaluated players' performance in the main task (gameplay) and, in post hoc analyses, participant behavioral patterns to inform group strategies. We compared game performance between team types (human-human vs. human-machine) and group conditions (competitive, collaborative, independent). Adapting workload capacity analysis to human-machine teams, we found performance under both team types and all group conditions suffered a performance efficiency cost. However, we observed a reduced cost in collaborative over competitive teams within human-human pairings, but this effect was diminished when playing with a machine partner. The implications of workload capacity analysis as a powerful tool for human-machine team performance measurement are discussed.

Complete sequence verification of plasmid DNA using the Oxford Nanopore Technologies' MinION device.

BACKGROUND: Sequence verification is essential for plasmids used as critical reagents or therapeutic products. Typically, high-quality plasmid sequence is achieved through capillary-based Sanger sequencing, requiring customized sets of primers for each plasmid. This process can become expensive, particularly for applications where the validated sequence needs to be produced within a regulated and quality-controlled environment for downstream clinical research applications. RESULTS: Here, we describe a cost-effective and accurate plasmid sequencing and consensus generation procedure using the Oxford Nanopore Technologies' MinION device as an alternative to capillary-based plasmid sequencing options. This procedure can verify the identity of a pure population of plasmid, either confirming it matches the known and expected sequence, or identifying mutations present in the plasmid if any exist. We use a full MinION flow cell per plasmid, maximizing available data and allowing for stringent quality filters. Pseudopairing reads for consensus base calling reduces read error rates from 5.3 to 0.53%, and our pileup consensus approach provides per-base counts and confidence scores, allowing for interpretation of the certainty of the resulting consensus sequences. For pure plasmid samples, we demonstrate 100% accuracy in the resulting consensus sequence, and the sensitivity to detect small mutations such as insertions, deletions, and single nucleotide variants. In test cases where the sequenced pool of plasmids contains subclonal templates, detection sensitivity is similar to that of traditional capillary sequencing. CONCLUSIONS: Our pipeline can provide significant cost savings compared to outsourcing clinical-grade sequencing of plasmids, making generation of high-quality plasmid sequence for clinical sequence verification more accessible. While other long-read-based methods offer higher-throughput and less cost, our pipeline produces complete and accurate sequence verification for cases where absolute sequence accuracy is required.

Selective B cell depletion upon intravenous infusion of replication-incompetent anti-CD19 CAR lentivirus.

Anti-CD19 chimeric antigen receptor (CAR)-T therapy for B cell malignancies has shown clinical success, but a major limitation is the logistical complexity and high cost of manufacturing autologous cell products. If engineered for improved safety, direct infusion of viral gene transfer vectors to initiate in vivo CAR-T transduction, expansion, and anti-tumor activity could provide an alternative, universal approach. To explore this approach we administered approximately 20 million replication-incompetent vesicular stomatitis virus G protein (VSV-G) lentiviral particles carrying an anti-CD19CAR-2A-GFP transgene comprising either an FMC63 (human) or 1D3 (murine) anti-CD19 binding domain, or a GFP-only control transgene, to wild-type C57BL/6 mice by tail vein infusion. The dynamics of immune cell subsets isolated from peripheral blood were monitored at weekly intervals. We saw emergence of a persistent CAR-transduced CD3+ T cell population beginning week 3-4 that reaching a maximum of 13.5% ± 0.58% (mean ± SD) and 7.8% ± 0.76% of the peripheral blood CD3+ T cell population in mice infused with ID3-CAR or FMC63-CAR lentivector, respectively, followed by a rapid decline in each case of the B cell content of peripheral blood. Complete B cell aplasia was apparent by week 5 and was sustained until the end of the protocol (week 8). No significant CAR-positive populations were observed within other immune cell subsets or other tissues. These results indicate that direct intravenous infusion of conventional VSV-G-pseudotyped lentiviral particles carrying a CD19 CAR transgene can transduce T cells that then fully ablate endogenous B cells in wild-type mice.

Time-evolving psychological processes over repeated decisions.

Many psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiments, time-on-task can have sizable effects on performance, changing the psychological processes under investigation. Most research has either ignored these changes, treating the underlying process as static, or sacrificed some psychological content of the models for statistical simplicity. We use particle Markov chain Monte-Carlo methods to study psychologically plausible time-varying changes in model parameters. Using data from three highly cited experiments, we find strong evidence in favor of a hidden Markov switching process as an explanation of time-varying effects. This embodies the psychological assumption of "regime switching," with subjects alternating between different cognitive states representing different modes of decision-making. The switching model explains key long- and short-term dynamic effects in the data. The central idea of our approach can be applied quite generally to quantitative psychological theories, beyond the models and datasets that we investigate. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes.

Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in-sample descriptive adequacy against model flexibility, with modern approaches advocating the use of marginal likelihood for hierarchical cognitive models. Cross-validation is another popular approach but its implementation remains out of reach for cognitive models evaluated in a Bayesian hierarchical framework, with the major hurdle being its prohibitive computational cost. To address this issue, we develop novel algorithms that make variational Bayes (VB) inference for hierarchical models feasible and computationally efficient for complex cognitive models of substantive theoretical interest. It is well known that VB produces good estimates of the first moments of the parameters, which gives good predictive densities estimates. We thus develop a novel VB algorithm with Bayesian prediction as a tool to perform model comparison by cross-validation, which we refer to as CVVB. In particular, CVVB can be used as a model screening device that quickly identifies bad models. We demonstrate the utility of CVVB by revisiting a classic question in decision making research: what latent components of processing drive the ubiquitous speed-accuracy tradeoff? We demonstrate that CVVB strongly agrees with model comparison via marginal likelihood, yet achieves the outcome in much less time. Our approach brings cross-validation within reach of theoretically important psychological models, making it feasible to compare much larger families of hierarchically specified cognitive models than has previously been possible. To enhance the applicability of the algorithm, we provide Matlab code together with a user manual so users can easily implement VB and/or CVVB for the models considered in this article and their variants. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Socioeconomic factors and parents' preferences for internet- and mobile-based parenting interventions to prevent youth mental health problems: A discrete choice experiment.

Background: The positive impact of parenting programs for youth mental health is undermined by difficulties engaging parents. Low engagement disproportionately impacts parents of lower-socioeconomic positions (SEPs). Internet- and mobile-based interventions hold potential for overcoming barriers to enrolment, but additional research is needed to understand how programs can appropriately meet the needs of parents across SEPs. Consumer preference methods such as discrete choice experiments may be valuable in this endeavour. Method: A discrete choice experiment was used to determine the relative influence of modifiable program features on parents' intent to enrol. 329 Australian parents of children aged 0-18 repeatedly selected their preferred program from randomized sets of hypothetical programs in an online survey. Each hypothetical program was unique, varying across four program features: module duration, program platform, user control, and program cost. Cumulative link models were used to predict choices, with education, household income, and community advantage used as indicators of SEP. Results: Overall, parents preferred cheaper programs and briefer modules. Parents' preferences differed based on their socioeconomic challenges. Lower-income parents preferred briefer modules, cheaper programs and application-based programs compared to higher-income parents. Parents with less education preferred briefer modules and a predefined module order. Parents living in areas of less advantage preferred website-based programs, user choice of module order, and more expensive programs. Conclusions: This study offers program developers evidence-based strategies for tailoring internet- and mobile-based parenting interventions to increase lower-SEP parent enrolment. Findings also highlight the importance of considering parents' socioeconomic challenges to ensure programs do not perpetuate existing mental health inequalities, as "one-size-fits-all" approaches are likely insufficient for reaching lower-SEP parents.

CLIC-01: Manufacture and distribution of non-cryopreserved CAR-T cells for patients with CD19 positive hematologic malignancies.

Access to commercial CD19 CAR-T cells remains limited even in wealthy countries like Canada due to clinical, logistical, and financial barriers related to centrally manufactured products. We created a non-commercial academic platform for end-to-end manufacturing of CAR-T cells within Canada's publicly funded healthcare system. We report initial results from a single-arm, open-label study to determine the safety and efficacy of in-house manufactured CD19 CAR-T cells (entitled CLIC-1901) in participants with relapsed/refractory CD19 positive hematologic malignancies. Using a GMP compliant semi-automated, closed process on the Miltenyi Prodigy, T cells were transduced with lentiviral vector bearing a 4-1BB anti-CD19 CAR transgene and expanded. Participants underwent lymphodepletion with fludarabine and cyclophosphamide, followed by infusion of non-cryopreserved CAR-T cells. Thirty participants with non-Hodgkin's lymphoma (n=25) or acute lymphoblastic leukemia (n=5) were infused with CLIC-1901: 21 males (70%), median age 66 (range 18-75). Time from enrollment to CLIC-1901 infusion was a median of 20 days (range 15-48). The median CLIC-1901 dose infused was 2.3 × 106 CAR-T cells/kg (range 0.13-3.6 × 106/kg). Toxicity included ≥ grade 3 cytokine release syndrome (n=2) and neurotoxicity (n=1). Median follow-up was 6.5 months. Overall response rate at day 28 was 76.7%. Median progression-free and overall survival was 6 months (95%CI 3-not estimable) and 11 months (95% 6.6-not estimable), respectively. This is the first trial of in-house manufactured CAR-T cells in Canada and demonstrates that administering fresh CLIC-1901 product is fast, safe, and efficacious. Our experience may provide helpful guidance for other jurisdictions seeking to create feasible and sustainable CAR-T cell programs in research-oriented yet resource-constrained settings. Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT03765177, identifier NCT03765177.

Clinical response to nivolumab in an INI1-deficient pediatric chordoma correlates with immunogenic recognition of brachyury.

Poorly differentiated chordoma (PDC) is a recently recognized subtype of chordoma characterized by expression of the embryonic transcription factor, brachyury, and loss of INI1. PDC primarily affects children and is associated with a poor prognosis and limited treatment options. Here we describe the molecular and immune tumour microenvironment profiles of two paediatric PDCs produced using whole-genome, transcriptome and whole-genome bisulfite sequencing (WGBS) and multiplex immunohistochemistry. Our analyses revealed the presence of tumour-associated immune cells, including CD8+ T cells, and expression of the immune checkpoint protein, PD-L1, in both patient samples. Molecular profiling provided the rationale for immune checkpoint inhibitor (ICI) therapy, which resulted in a clinical and radiographic response. A dominant T cell receptor (TCR) clone specific for a brachyury peptide-MHC complex was identified from bulk RNA sequencing, suggesting that targeting of the brachyury tumour antigen by tumour-associated T cells may underlie this clinical response to ICI. Correlative analysis with rhabdoid tumours, another INI1-deficient paediatric malignancy, suggests that a subset of tumours may share common immune phenotypes, indicating the potential for a therapeutically targetable subgroup of challenging paediatric cancers.

Enhancing Engagement of Fathers in Web-Based Preventive Parenting Programs for Adolescent Mental Health: A Discrete Choice Experiment.

Few fathers enrol in web-based preventive parenting programs for adolescent mental health, despite the evidence of the benefits associated with their participation. To inform the development of father-inclusive programs, this study used a discrete choice experiment (DCE) design to determine (a) the relative influence of number of sessions, program benefits, program participants, and user control over program content on fathers' preferences for web-based preventive parenting programs; and (b) whether selected father characteristics were associated with their preferences. One hundred and seventy-one fathers completed the DCE survey, which comprised 25 choices between hypothetical programs. Programs that included the participant's adolescent child (z = 10.06, p < 0.0001), or parenting partner (z = 7.30, p < 0.001) were preferred over those designed for fathers only. Participants also preferred program content that was recommended for them by experts (z = -4.31, p < 0.0001) and programs with fewer sessions (z = -2.94, p < 0.01). Program benefits did not predict fathers' choice of program. Prior use of a parenting program, level of education, perceived role of parenting for adolescent mental health, and being part of a dual-working family were associated with preferences. Application of these findings may improve paternal enrolment in web-based preventive parenting programs.

Modulation of the Host Cell Transcriptome and Epigenome by Fusobacterium nucleatum.

Fusobacterium nucleatum is a ubiquitous opportunistic pathogen with an emerging role as an oncomicrobe in colorectal cancer and other cancer settings. F. nucleatum can adhere to and invade host cells in a manner that varies across F. nucleatum strains and host cell phenotypes. Here, we performed pairwise cocultures between three F. nucleatum strains and two immortalized primary host cell types (human colonic epithelial [HCE] cells and human carotid artery endothelial [HCAE] cells) followed by transcriptome sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) to investigate transcriptional and epigenetic host cell responses. We observed that F. nucleatum-induced host cell transcriptional modulation involves strong upregulation of genes related to immune migration and inflammatory processes, such as TNF, CXCL8, CXCL1, and CCL20. Furthermore, we identified genes strongly upregulated in a cell line-specific manner. In HCE cells, overexpressed genes included UBD and DUOX2/DUOXA2, associated with p53 degradation-mediated proliferation and intestinal reactive oxygen species (ROS) production, respectively. In HCAE cells, overexpressed genes included EFNA1 and LIF, two genes commonly upregulated in colorectal cancer and associated with poor patient outcomes, and PTGS2 (COX2), a gene associated with the protective effect of aspirin in the colorectal cancer setting. Interestingly, we also observed downregulation of numerous histone modification genes upon F. nucleatum exposure. We used the ChIP-seq data to annotate chromatin states genome wide and found significant chromatin remodeling following F. nucleatum exposure in HCAE cells, with increased frequencies of active enhancer and low-signal/quiescent states. Thus, our results highlight increased inflammation and chemokine gene expression as conserved host cell responses to F. nucleatum exposure and extensive host cell epigenomic changes specific to host cell type. IMPORTANCE Fusobacterium nucleatum is a bacterium normally found in the healthy oral cavity but also has an emerging role in colorectal cancer and other cancer settings. The host-microbe interactions of F. nucleatum and its involvement in tumor initiation, progression, and treatment resistance are not fully understood. We explored host cell changes that occur in response to F. nucleatum. We identified key genes differentially expressed in response to various conditions of F. nucleatum exposure and determined that the conserved host cell response to F. nucleatum was dominated by increased inflammation and chemokine gene expression. Additionally, we found extensive host cell epigenomic changes as a novel aspect of host modulation associated with F. nucleatum exposure. These results extend our understanding of F. nucleatum as an emerging pathogen and highlight the importance of considering strain heterogeneity and host cell phenotypic variation when exploring pathogenic mechanisms of F. nucleatum.

Genome and Transcriptome Biomarkers of Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors.

PURPOSE: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of solid tumors with dramatic and durable responses seen across multiple tumor types. However, identifying patients who will respond to these drugs remains challenging, particularly in the context of advanced and previously treated cancers. EXPERIMENTAL DESIGN: We characterized fresh tumor biopsies from a heterogeneous pan-cancer cohort of 98 patients with metastatic predominantly pretreated disease through the Personalized OncoGenomics program at BC Cancer (Vancouver, Canada) using whole genome and transcriptome analysis (WGTA). Baseline characteristics and follow-up data were collected retrospectively. RESULTS: We found that tumor mutation burden, independent of mismatch repair status, was the most predictive marker of time to progression (P = 0.007), but immune-related CD8+ T-cell and M1-M2 macrophage ratio scores were more predictive for overall survival (OS; P = 0.0014 and 0.0012, respectively). While CD274 [programmed death-ligand 1 (PD-L1)] gene expression is comparable with protein levels detected by IHC, we did not observe a clinical benefit for patients with this marker. We demonstrate that a combination of markers based on WGTA provides the best stratification of patients (P = 0.00071, OS), and also present a case study of possible acquired resistance to pembrolizumab in a patient with non-small cell lung cancer. CONCLUSIONS: Interpreting the tumor-immune interface to predict ICI efficacy remains challenging. WGTA allows for identification of multiple biomarkers simultaneously that in combination may help to identify responders, particularly in the context of a heterogeneous population of advanced and previously treated cancers, thus precluding tumor type-specific testing.

A Broader Application of the Detection Response Task to Cognitive Tasks and Online Environments.

OBJECTIVE: The present research applied a well-established measure of cognitive workload in driving literature to an in-lab paradigm. We then extended this by comparing the in-lab version of the task to an online version. BACKGROUND: The accurate and objective measurement of cognitive workload is important in many aspects of psychological research. The detection response task (DRT) is a well-validated method for measuring cognitive workload that has been used extensively in applied tasks, for example, to investigate the effects of phone usage or passenger conversation on driving, but has been used sparingly outside of this field. METHOD: The study investigated whether the DRT could be used to measure cognitive workload in tasks more commonly used in experimental cognitive psychology and whether this application could be extended to online environments. We had participants perform a multiple object tracking (MOT) task while simultaneously performing a DRT. We manipulated the cognitive load of the MOT task by changing the number of dots to be tracked. RESULTS: Measurements from the DRT were sensitive to changes in the cognitive load, establishing the efficacy of the DRT for experimental cognitive tasks in lab-based situations. This sensitivity continued when applied to an online environment (our code for the online DRT implementation is freely available at https://osf.io/dc39s/), though to a reduced extent compared to the in-lab situation. CONCLUSION: The MOT task provides an effective manipulation of cognitive workload. The DRT is sensitive to changes in workload across a range of settings and is suitable to use outside of driving scenarios, as well as via online delivery. APPLICATION: Methodology shows how the DRT could be used to measure sources of cognitive workload in a range of human factors contexts.

Preferences for life expectancy discussions following diagnosis with a life-threatening illness: a discrete choice experiment.

PURPOSE: To explore in a sample of adult cancer patients: (1) the relative influence of initiation source, information format and consultation format on preferred approach to life expectancy disclosure using a discrete choice experiment (DCE); and (2) whether patient age, cancer type and perceived prognosis were associated with preferences within the three attributes. METHODS: A DCE survey of adult solid tumour and haematological cancer patients. Participants chose between three hypothetical scenarios about life expectancy disclosure consisting of three attributes: initiation source (i.e. doctor versus patient-initiated discussion), information content (i.e. estimate presented as best-worst-typical length of life case scenario versus median survival time) and consultation format (i.e. two 20-min versus one 40-min consultation). Respondents selected their most preferred scenario within each question. RESULTS: Three hundred and two patients completed the DCE (78% consent rate). Initiation source was the most influential predictor of patient choice. More preferred a doctor deliver life expectancy information as soon as it is available rather than waiting for the patient to ask (59% vs 41% z = - 7.396, p < 0.01). More patients preferred the two 20-min rather than the one 40-min consultation format (55% vs 45%, z = 4.284, p < 0.01). Information content did not influence choice. Age, cancer type, and patient-perceived prognosis were not associated with preferences. CONCLUSION: Healthcare professionals should assess cancer patients' preferences for engaging in life expectancy discussions as soon as they have this information, and ensure patients have adequate time to consider the information they receive, seek additional information and involve others if they wish.

Identifying relationships between cognitive processes across tasks, contexts, and time.

It is commonly assumed that a specific testing occasion (task, design, procedure, etc.) provides insights that generalize beyond that occasion. This assumption is infrequently carefully tested in data. We develop a statistically principled method to directly estimate the correlation between latent components of cognitive processing across tasks, contexts, and time. This method simultaneously estimates individual-participant parameters of a cognitive model at each testing occasion, group-level parameters representing across-participant parameter averages and variances, and across-task correlations. The approach provides a natural way to "borrow" strength across testing occasions, which can increase the precision of parameter estimates across all testing occasions. Two example applications demonstrate that the method is practical in standard designs. The examples, and a simulation study, also provide evidence about the reliability and validity of parameter estimates from the linear ballistic accumulator model. We conclude by highlighting the potential of the parameter-correlation method to provide an "assumption-light" tool for estimating the relatedness of cognitive processes across tasks, contexts, and time.

The Effects of Increased Visual Information on Cognitive Workload in a Helicopter Simulator.

OBJECTIVE: To test the effects of enhanced display information ("symbology") on cognitive workload in a simulated helicopter environment, using the detection response task (DRT). BACKGROUND: Workload in highly demanding environments can be influenced by the amount of information given to the operator and consequently it is important to limit potential overload. METHODS: Participants (highly trained military pilots) completed simulated helicopter flights, which varied in visual conditions and the amount of information given. During these flights, participants also completed a DRT as a measure of cognitive workload. RESULTS: With more visual information available, pilots' landing accuracy was improved across environmental conditions. The DRT is sensitive to changes in cognitive workload, with workload differences shown between environmental conditions. Increasing symbology appeared to have a minor effect on workload, with an interaction effect of symbology and environmental condition showing that symbology appeared to moderate workload. CONCLUSION: The DRT is a useful workload measure in simulated helicopter settings. The level of symbology-moderated pilot workload. The increased level of symbology appeared to assist pilots' flight behavior and landing ability. Results indicate that increased symbology has benefits in more difficult scenarios. APPLICATIONS: The DRT is an easily implemented and effective measure of cognitive workload in a variety of settings. In the current experiment, the DRT captures the increased workload induced by varying the environmental conditions, and provides evidence for the use of increased symbology to assist pilots.

How is multi-tasking different from increased difficulty?

With the advancement of technologies like in-car navigation and smartphones, concerns around how cognitive functioning is influenced by "workload" are increasingly prevalent. Research shows that spreading effort across multiple tasks can impair cognitive abilities through an overuse of resources, and that similar overload effects arise in difficult single-task paradigms. We developed a novel lab-based extension of the Detection Response Task, which measures workload, and paired it with a Multiple Object Tracking Task to manipulate cognitive load. Load was manipulated either by changing within-task difficulty or by the addition of an extra task. Using quantitative cognitive modelling we showed that these manipulations cause similar cognitive impairments through diminished processing rates, but that the introduction of a second task tends to invoke more cautious response strategies that do not occur when only difficulty changes. We conclude that more prudence should be exercised when directly comparing multi-tasking and difficulty-based workload impairments, particularly when relying on measures of central tendency.