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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. [ABSTRACT FROM AUTHOR]

Independent racing evidence-accumulator models have proven fruitful in advancing understanding of rapid decisions, mainly in the case of binary choice, where they can be relatively easily estimated and are known to account for a range of benchmark phenomena. Typically, such models assume a one-to-one mapping between accumulators and responses. We explore an alternative independent-race framework where more than one accumulator can be associated with each response, and where a response is triggered when a sufficient number of accumulators associated with that response reach their thresholds. Each accumulator is primarily driven by the difference in evidence supporting one versus another response (i.e., that response's "advantage"), with secondary inputs corresponding to the total evidence for both responses and a constant term. We use Brown and Heathcote's (2008) linear ballistic accumulator (LBA) to instantiate the framework in a mathematically tractable measurement model (i.e., a model whose parameters can be successfully recovered from data). We show this "advantage LBA" model provides a detailed quantitative account of a variety of benchmark binary and multiple choice phenomena that traditional independent accumulator models struggle with; in binary choice the effects of additive versus multiplicative changes to input values, and in multiple choice the effects of manipulations of the strength of lure (i.e., nontarget) stimuli and Hick's law. We conclude that the advantage LBA provides a tractable new avenue for understanding the dynamics of decisions among multiple choices. [ABSTRACT FROM AUTHOR]

The "law of practice"--a simple nonlinear function describing the relationship between mean response time (RT) and practice--has provided a practically and theoretically useful way of quantifying the speed-up that characterizes skill acquisition. Early work favored a power law, but this was shown to be an artifact of biases caused by averaging over participants who are individually better described by an exponential law. However, both power and exponential functions make the strong assumption that the speedup always proceeds at a steadily decreasing rate, even though there are sometimes clear exceptions. We propose a new law that can both accommodate an initial delay resulting in a slower-faster-slower rate of learning, with either power or exponential forms as limiting cases, and which can account for not only mean RT but also the effect of practice on the entire distribution of RT. We evaluate this proposal with data from a broad array of tasks using hierarchical Bayesian modeling, which pools data across participants while minimizing averaging artifacts, and using inference procedures that take into account differences in flexibility among laws. In a clear majority of paradigms our results supported a delayed exponential law. [ABSTRACT FROM AUTHOR]

Evidence accumulation models of decision-making have led to advances in several different areas of psychology. These models provide a way to integrate response time and accuracy data, and to describe performance in terms of latent cognitive processes. Testing important psychological hypotheses using cognitive models requires a method to make inferences about different versions of the models which assume different parameters to cause observed effects. The task of model-based inference using noisy data is difficult, and has proven especially problematic with current model selection methods based on parameter estimation. We provide a method for computing Bayes factors through Monte-Carlo integration for the linear ballistic accumulator (LBA; Brown and Heathcote, 2008), a widely used evidence accumulation model. Bayes factors are used frequently for inference with simpler statistical models, and they do not require parameter estimation. In order to overcome the computational burden of estimating Bayes factors via brute force integration, we exploit general purpose graphical processing units; we provide free code for this. This approach allows estimation of Bayes factors via Monte-Carlo integration within a practical time frame. We demonstrate the method using both simulated and real data. We investigate the stability of the Monte-Carlo approximation, and the LBA’s inferential properties, in simulation studies. [ABSTRACT FROM AUTHOR]

Motivation: In T-cell lymphoma, malignant T cells arising from a founding clone share an identical T cell receptor (TCR) and can be identified by the over-representation of this TCR relative to TCRs from the patient's repertoire of normal T cells. Here, we demonstrate that TCR information extracted from RNA-seq data can provide a higher resolution view of peripheral T cell lymphomas (PTCLs) than that provided by conventional methods. Results: For 60 subjects with PTCL, flow cytometry/FACS was used to identify and sort aberrant T cell populations fromdiagnostic lymph node cell suspensions. For samples that did not appear to contain aberrant T cell populations, T helper (TH), T follicular helper (TFH) and cytotoxic T lymphocyte (CTL) subsets were sorted. RNA-seq was performed on sorted T cell populations, and TCR alpha and beta chain sequences were extracted and quantified directly from the RNA-seq data. 96% of the immunophenotypically aberrant samples had a dominant T cell clone readily identifiable by RNA-seq. Of the samples where no aberrant population was found by flow cytometry, 80% had a dominant clone by RNA-seq. This demonstrates the increased sensitivity and diagnostic ability of RNA-seq over flow cytometry and shows that the presence of a normal immunophenotype does not exclude clonality. [ABSTRACT FROM AUTHOR]

Due to advances in sequencing technology, somatically mutated cancer antigens, or neoantigens, are now readily identifiable and have become compelling targets for immunotherapy. In particular, neoantigen-targeted vaccines have shown promise in several pre-clinical and clinical studies. However, to date, neoantigen-targeted vaccine studies have involved tumors with exceptionally high mutation burdens. It remains unclear whether neoantigen-targeted vaccines will be broadly applicable to cancers with intermediate to low mutation burdens, such as ovarian cancer. To address this, we assessed whether a derivative of the murine ovarian tumor model ID8 could be targeted with neoantigen vaccines. We performed whole exome and transcriptome sequencing on ID8-G7 cells. We identified 92 somatic mutations, 39 of which were transcribed, missense mutations. For the 17 top predicted MHC class I binding mutations, we immunized mice subcutaneously with synthetic long peptide vaccines encoding the relevant mutation. Seven of 17 vaccines induced robust mutation-specific CD4 and/or CD8 T cell responses. However, none of the vaccines prolonged survival of tumor-bearing mice in either the prophylactic or therapeutic setting. Moreover, none of the neoantigen-specific T cell lines recognized ID8-G7 tumor cells in vitro, indicating that the corresponding mutations did not give rise to bonafide MHC-presented epitopes. Additionally, bioinformatic analysis of The Cancer Genome Atlas data revealed that only 12% (26/220) of HGSC cases had a ≥90% likelihood of harboring at least one authentic, naturally processed and presented neoantigen versus 51% (80/158) of lung cancers. Our findings highlight the limitations of applying neoantigen-targeted vaccines to tumor types with intermediate/low mutation burdens. [ABSTRACT FROM AUTHOR]

Deep sequencing of recombined T cell receptor (TCR) genes and transcripts has provided a view of T cell repertoire diversity at an unprecedented resolution. Beyond profiling peripheral blood, analysis of tissueresident T cells provides further insight into immunerelated diseases. We describe the extraction of TCR sequence information directly from RNA-sequencing data from 6738 tumor and 604 control tissues, with a typical yield of 1 TCR per 10 million reads. This method circumvents the need for PCR amplification of the TCR template and provides TCR information in the context of global gene expression, allowing integrated analysis of extensive RNA-sequencing data resources. [ABSTRACT FROM AUTHOR]

Trueblood, Brown, and Heathcote (2014) developed a new model, called the multiattribute linear ballistic accumulator (MLBA), to explain contextual preference reversals in multialtemative choice. MLBA was shown to provide good accounts of human behavior through both qualitative analyses and quantitative fitting of choice data. Tsetsos, Chater, and Usher (2015) investigated the ability of MLBA to simultaneously capture 3 prominent context effects (attraction, compromise, and similarity). They concluded that MLBA must set a "fine balance" of competing forces to account for all 3 effects simultaneously and that its predictions are sensitive to the position of the stimuli in the attribute space. Through a new experiment, we show that the 3 effects are very fragile and that only a small subset of people shows all 3 simultaneously. Thus, the predictions that Tsetsos et al. generated from the MLBA model turn out to match closely real data in a new experiment. Support for these predictions provides strong evidence for the MLBA. A corollary is that a model that can "robustly" capture all 3 effects simultaneously is not necessarily a good model. Rather, a good model captures patterns found in human data, but cannot accommodate patterns that are not found. [ABSTRACT FROM AUTHOR]

Context effects occur when a choice between 2 options is altered by adding a 3rd alternative. Three major context effects--similarity, compromise, and attraction--have wide-ranging implications across applied and theoretical domains, and have driven the development of new dynamic models of multiattribute and multialternative choice. We propose the multiattribute linear ballistic accumulator (MLBA), a new dynamic model that provides a quantitative account of all 3 context effects. Our account applies not only to traditional paradigms involving choices among hedonic stimuli, but also to recent demonstrations of context effects with nonhedonic stimuli. Because of its computational tractability, the MLBA model is more easily applied than previous dynamic models. We show that the model also accounts for a range of other phenomena in multiattribute, multialternative choice, including time pressure effects, and that it makes a new prediction about the relationship between deliberation time and the magnitude of the similarity effect, which we confirm experimentally. [ABSTRACT FROM AUTHOR]

Whenever someone in a team tries to help others, it is crucial that they have some understanding of other team members' goals. In modern teams, this applies equally to human and artificial ("bot") assistants. Understanding when one does not know something is crucial for stopping the execution of inappropriate behavior and, ideally, attempting to learn more appropriate actions. From a statistical point of view, this can be translated to assessing whether none of the hypotheses in a considered set is correct. Here we investigate a novel approach for making this assessment based on monitoring the maximum a posteriori probability (MAP) of a set of candidate hypotheses as new observations arrive. Simulation studies suggest that this is a promising approach, however, we also caution that there may be cases where this is more challenging. The problem we study and the solution we propose are general, with applications well beyond human–bot teaming, including for example the scientific process of theory development. [ABSTRACT FROM AUTHOR]

Context effects—preference changes that depend on the availability of other options—have attracted a great deal of attention among consumer researchers studying high-level decision tasks. In the experiments reported here, we showed that these effects also arise in simple perceptual-decision-making tasks. This finding casts doubt on explanations limited to consumer choice and high-level decisions, and it indicates that context effects may be amenable to a general explanation at the level of the basic decision process. We demonstrated for the first time that three important context effects from the preferential-choice literature—similarity, attraction, and compromise effects—all occurred within a single perceptual-decision task. Not only do our results challenge previous explanations for context effects proposed by consumer researchers, but they also challenge the choice rules assumed in theories of perceptual decision making. [ABSTRACT FROM PUBLISHER]

For decisions between many alternatives, the benchmark result is Hick's Law: that response time increases log-linearly with the number of choice alternatives. Even when Hick's Law is observed for response times, divergent results have been observed for error rates-sometimes error rates increase with the number of choice alternatives, and sometimes they are constant. We provide evidence from two experiments that error rates are mostly independent of the number of choice alternatives, unless context effects induce participants to trade speed for accuracy across conditions. Error rate data have previously been used to discriminate between competing theoretical accounts of Hick's Law, and our results question the validity of those conclusions. We show that a previously dismissed optimal observer model might provide a parsimonious account of both response time and error rate data. The model suggests that people approximate Bayesian inference in multi-alternative choice, except for some perceptual limitations. [ABSTRACT FROM AUTHOR]

The division between cognitive research that employs neuroimaging data and cognitive research that excludes such data has led to extreme views on both sides. As in most conflicts, these extreme views reinforce each other. They are further deepened by some poor science and a cavalier attitude towards the interpretation of neuroimaging data by some practitioners. I discuss two of the most problematic aspects of the interpretation of neuroimaging data: the logical fallacy of affirming the consequent; and the difficulties of linking assumptions which link the data with theoretical constructs. Both of these problems are not unique to neuroimaging research, and errors with them certainly occur in standard behavioural psychology. Nevertheless, when executed carefully, and when interpreted cautiously, neuroimaging has the potential to contribute to the understanding of cognition. [ABSTRACT FROM AUTHOR]

Trial-to-trial variability in decision making can be caused by variability in information processing as well as by variability in response caution. In this paper, we study which neural components code for trial-to-trial adjustments in response caution using a new computational approach that quantifies response caution on a single-trial level. We found that the frontostriatal network updates the amount of response caution. In particular, when human participants were required to respond quickly, we found a positive correlation between trial-to-trial fluctuations in response caution and the hemodynamic response in the presupplementary motor area and dorsal anterior cingulate. In contrast, on trials that required a change from a speeded response mode to a more accurate response mode or vice versa, we found a positive correlation between response caution and hemodynamic response in the anterior cingulate proper. These results indicate that for each decision, response caution is set through corticobasal ganglia functioning, but that individual choices differ according to the mechanisms that trigger changes in response caution. [ABSTRACT FROM AUTHOR]

Identification accuracy for sets of perceptually discriminable stimuli ordered on a single dimension (e.g., line length) is remarkably low, indicating a fundamental limit on information processing capacity. This surprising limit has naturally led to a focus on measuring and modeling choice probability in absolute identification research. We show that choice response time (RT) results can enrich our understanding of absolute identification by investigating dissociation between RT and accuracy as a function of stimulus spacing. The dissociation is predicted by the SAMBA model of absolute identification (Brown, Marley, Dockin, & Heathcote, ), but cannot easily be accommodated by other theories. We show that SAMBA provides an accurate, parameter free, account of the dissociation that emerges from the architecture of the model and the physical attributes of the stimuli, rather than through numerical adjustment. This violation of the pervasive monotonic relationship between RT and accuracy has implications for model development, which are discussed. [ABSTRACT FROM AUTHOR]

Abstract: When required to predict sequential events, such as random coin tosses or basketball free throws, people reliably use inappropriate strategies, such as inferring temporal structure when none is present. We investigate the ability of observers to predict sequential events in dynamically changing environments, where there is an opportunity to detect true temporal structure. In two experiments we demonstrate that participants often make correct statistical decisions when asked to infer the hidden state of the data generating process. However, when asked to make predictions about future outcomes, accuracy decreased even though normatively correct responses in the two tasks were identical. A particle filter model accounts for all data, describing performance in terms of a plausible psychological process. By varying the number of particles, and the prior belief about the probability of a change occurring in the data generating process, we were able to model most of the observed individual differences. [Copyright &y& Elsevier]

Psychological experiments often collect choice responses using buttonpresses. However, spoken responses are useful in many cases--for example, when working with special clinical populations, or when a paradigm demands vocalization, or when accurate response time measurements are desired. In these cases, spoken responses are typically collected using a voice key, which usually involves manual coding by experimenters in a tedious and error-prone manner. We describe ChoiceKey, an open-source speech recognition package for MATLAB. It can be optimized by training for small response sets and different speakers. We show ChoiceKey to be reliable with minimal training for most participants in experiments with two different responses. Problems presented by individual differences, and occasional atypical responses, are examined, and extensions to larger response sets are explored. The ChoiceKey source files and instructions may be downloaded as supplemental materials for this article from brm.psychonomic-journals.org/content/supplemental. [ABSTRACT FROM AUTHOR]

Abstract: We propose a linear ballistic accumulator (LBA) model of decision making and reaction time. The LBA is simpler than other models of choice response time, with independent accumulators that race towards a common response threshold. Activity in the accumulators increases in a linear and deterministic manner. The simplicity of the model allows complete analytic solutions for choices between any number of alternatives. These solutions (and freely-available computer code) make the model easy to apply to both binary and multiple choice situations. Using data from five previously published experiments, we demonstrate that the LBA model successfully accommodates empirical phenomena from binary and multiple choice tasks that have proven difficult for other theoretical accounts. Our results are encouraging in a field beset by the tradeoff between complexity and completeness. [Copyright &y& Elsevier]

Recent theoretical developments in the field of absolute identification have stressed differences between relative and absolute processes, that is, whether stimulus magnitudes are judged relative to a shorter term context provided by recently presented stimuli or a longer term context provided by the entire set of stimuli. The authors developed a model (SAMBA: selective attention, mapping, and ballistic accumulation) that integrates shorter and longer term memory processes and accounts for both the choices made and the associated response time distributions, including sequential effects in each. The model's predictions arise as a consequence of its architecture and require estimation of only a few parameters with values that are consistent across numerous data sets. The authors show that SAMBA provides a quantitative account of benchmark choice phenomena in classical absolute identification experiments and in contemporary data involving both choice and response time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The self-immunopeptidome is the repertoire of all self-peptides that can be presented by the combination of MHC variants carried by an individual, defined by their HLA genotype. Each MHC variant presents a distinct set of self-peptides, and the number of peptides in a set is variable. Subjects carrying MHC variants that present fewer self-peptides should also present fewer mutated peptides, resulting in decreased immune pressure on tumor cells. To explore this, we predicted peptide-MHC binding values using all unique 8-11mer human peptides in the human proteome and all available HLA class I allelic variants, for a total of 134 billion unique peptide—MHC binding predictions. From these predictions, we observe that most peptides are able to be presented by relatively few (< 250) MHC, while some can be presented by upwards of 1,500 different MHC. There is substantial overlap among the repertoires of peptides presented by different MHC and no relationship between the number of peptides presented and HLA population frequency. Nearly 30% of self-peptides are presentable by at least one MHC, leaving 70% of the human peptidome unsurveyed by T cells. We observed similar distributions of predicted self-immunopeptidome sizes in cancer subjects compared to controls, and within the pan-cancer population, predicted self-immunopeptidome size combined with mutational load to predict survival. Self-immunopeptidome analysis revealed evidence for tumor immunoediting and identified specific peptide positions that most influence immunogenicity. Because self-immunopeptidome size is defined by HLA genotypes and approximates neoantigen load, HLA genotyping could offer a rapid predictive biomarker for response to immunotherapy. [ABSTRACT FROM AUTHOR]

Theories of perceptual decision making have been dominated by the idea that evidence accumulates in favor of different alternatives until some fixed threshold amount is reached, which triggers a decision. Recent theories have suggested that these thresholds may not be fixed during each decision but change as time passes. These collapsing thresholds can improve performance in particular decision environments, but reviews of data from typical decision-making paradigms have failed to support collapsing thresholds. We designed three experiments to test collapsing threshold assumptions in decision environments specifically tailored to make them optimal. An emphasis on decision speed encouraged the adoption of collapsing thresholds—most strongly through the use of response deadlines but also through instruction to a lesser extent—but setting an explicit goal of reward rate optimality through both instructions and task design did not. Our results suggest that collapsing thresholds models of decision-making are inconsistent with human behaviour even in some situations where there are normative motivations for these models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models. [ABSTRACT FROM AUTHOR]

Decision-making involves a tradeoff between pressures for caution and urgency. Previous research has investigated how well humans optimize this tradeoff, and mostly concluded that people adopt a sub-optimal strategy that over-emphasizes caution. This emphasis reduces how many decisions can be made in a fixed time, which reduces the "reward rate". However, the strategy that is optimal depends critically on the timing properties of the experiment design: the slower the rate of decision opportunities, the more cautious the optimal strategy. Previous studies have almost uniformly adopted very fast designs, which favor very urgent decision-making. This raises the possibility that previous findings—that humans adopt strategies that are too cautious—could either be ascribed to human caution, or to the experiments' design. To test this, we used a slowed-down decision-making task in which the optimal strategy was quite cautious. With this task, and in contrast to previous findings, the average strategy adopted across participants was very close to optimal, with about equally many participants adopting too-cautious as too-urgent strategies. Our findings suggest that task design can play a role in inferences about optimality, and that previous conclusions regarding human sub-optimality are conditional on the task settings. This limits claims about human optimality that can be supported by the available evidence. [ABSTRACT FROM AUTHOR]

Objectives: To explore in a sample of medical oncology outpatients and their nominated support persons (SPs): (1) the relative influence of pain, consciousness and life extension on end-of-life choices using a discrete choice experiment (DCE); (2) the extent to which SPs can predict the choices of index patients and (3) whether having a previous end-of-life discussion was associated with dyad agreement.Methods: Adult medical oncology patients and their SPs were approached for consent to complete a survey containing a DCE. Participants chose between three unlabelled care scenarios characterised by three attributes: pain (mild, moderate or severe), consciousness (some, half or most of time) and extension of life (1, 2 or 3 weeks). Respondents selected (1) most-preferred and (2) least-preferred scenarios within each question. SPs answered the same questions but from patient's perspective.Results: A total of 110 patients and 64 SPs responded overall (42 matched patient-SP dyads). For patients, pain was the most influential predictor of most- and least-preferred scenarios (z = 12.5 and z = 12.9). For SPs, pain was the only significant predictor of most and least-preferred scenarios (z = 9.7 and z = 11.5). Dyad agreement was greater for choices about least- (69%) compared to most-preferred scenarios (55%). Agreement was slightly higher for dyads reporting a previous EOL discussion (68 versus 48%; p = 0.065).Conclusion: Patients and SPs place significant value on avoiding severe pain when making end-of-life choices, over and above level of consciousness or life extension. People's views about end-of-life scenarios they most as well as least prefer should be sought. [ABSTRACT FROM AUTHOR]

Abstract: Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process—such as cognitive processing speed, response caution, and motor execution speed—in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision‐making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies—that the heritability of cognitive ability is based on cognitive processing speed—may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement. [ABSTRACT FROM AUTHOR]

Markov Chain Monte-Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference. This article provides a very basic introduction to MCMC sampling. It describes what MCMC is, and what it can be used for, with simple illustrative examples. Highlighted are some of the benefits and limitations of MCMC sampling, as well as different approaches to circumventing the limitations most likely to trouble cognitive scientists. [ABSTRACT FROM AUTHOR]

Listeners rely on multiple acoustic cues to recognize any phoneme. The relative contribution of these cues to listeners׳ perception is typically inferred from listeners׳ categorization of sounds in a two-alternative forced-choice task. Here we advocate the use of an evidence accumulation model to analyze categorization as well as response time data from such cue weighting paradigms in terms of the processes that underlie the listeners׳ categorization. We tested 30 Dutch listeners on their categorization of speech sounds that varied between typical /ɑ/ and /aː/ in vowel quality (F1 and F2) and duration. Using the linear ballistic accumulator model, we found that the changes in spectral quality and duration lead to changes in the speed of information processing, and the effects were larger for spectral quality. In addition, for stimuli with atypical spectral information, listeners accumulate evidence faster for /ɑ/ compared to /aː/. Finally, longer durations of sounds did not produce longer estimates of perceptual encoding time. Our results demonstrate the utility of evidence accumulation models for learning about the latent processes that underlie phoneme categorization. The implications for current theory in speech perception as well as future directions for evidence accumulation models are discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Speed-accuracy tradeoff (SAT) is an adaptive process balancing urgency and caution when making decisions. Computational cognitive theories, known as “evidence accumulation models”, have explained SATs via a manipulation of the amount of evidence necessary to trigger response selection. New light has been shed on these processes by single-cell recordings from monkeys who were adjusting their SAT settings. Those data have been interpreted as inconsistent with existing evidence accumulation theories, prompting the addition of new mechanisms to the models. We show that this interpretation was wrong, by demonstrating that the neural spiking data, and the behavioural data are consistent with existing evidence accumulation theories, without positing additional mechanisms. Our approach succeeds by using the neural data to provide constraints on the cognitive model. Open questions remain about the locus of the link between certain elements of the cognitive models and the neurophysiology, and about the relationship between activity in cortical neurons identified with decision-making vs. activity in downstream areas more closely linked with motor effectors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A portion of Stroop interference is thought to arise from a failure to maintain goal-oriented behaviour (or goal neglect). The aim of the present study was to investigate whether goal- relevant primes could enhance goal maintenance and reduce the Stroop interference effect. Here it is shown that primes related to the goal of responding quickly in the Stroop task (e.g. fast, quick, hurry) substantially reduced Stroop interference by reducing reaction times to incongruent trials but increasing reaction times to congruent and neutral trials. No effects of the primes were observed on errors. The effects on incongruent, congruent and neutral trials are explained in terms of the influence of the primes on goal maintenance. The results show that goal priming can facilitate goal-oriented behaviour and indicate that automatic processing can modulate executive control. [ABSTRACT FROM AUTHOR]

In most of the long history of the study of absolute identification-since Miller's (1956) seminal article-a severe limit on performance has been observed, and this limit has resisted improvement even by extensive practice. In a startling result, Rouder, Morey, Cowan, and Pfaltz (2004) found substantially improved performance with practice in the absolute identification of line lengths, albeit for only 3 participants and in a somewhat atypical paradigm. We investigated the limits of this effect and found that it also occurs in more typical paradigms, is not limited to a few virtuoso participants or due to relative judgment strategies, and generalizes to some (e.g., line inclination and tone frequency) but not other (e.g., tone loudness) dimensions. We also observed, apart from differences between dimensions, 2 unusual aspects of improvement with practice: (a) a positive correlation between initial performance and the effect of practice and (b) a large reduction in a characteristic trial-to-trial decision bias with practice. [ABSTRACT FROM AUTHOR]

Jones and Dzhafarov (2014) provided a useful service in pointing out that some assumptions of modem decision-making models require additional scrutiny. Their main result, however, is not surprising: If an infinitely complex model was created by assigning its parameters arbitrarily flexible distributions, this new model would be able to fit any observed data perfectly. Such a hypothetical model would be unfalsifiable. This is exactly why such models have never been proposed in over half a century of model development in decision making. Additionally, the main conclusion drawn from this result--that the success of existing decision-making models can be attributed to assumptions about parameter distributions--is wrong. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: To explore the preferences of people with cancer regarding the timing and format of information provision about emotional concerns that may occur when undergoing medical procedures.Methods: Eligible cancer survivors were mailed a survey containing discrete choice scenarios examining their timing and format preferences for information about potential emotional concerns associated with an upcoming hypothetical medical procedure.Results: Of 356 eligible patients, 271 (76 %) completed the survey. Both face-to-face discussion and written materials were preferred as the mode of information delivery over access to a website. In order of descending preference, participants preferred to receive the information 1 week, 3 days and the day of the procedure. There were no differences in preferences for timing or format between subgroups based on age, gender, education and cancer type.Conclusion: This study has demonstrated that cancer patients prefer receiving information about emotional concerns that might be experienced as part of a medical procedure in either written or via face-to-face format, and one week before the procedure.Practice Implications: In order to provide patient-centred care, clinicians and the healthcare system more broadly should consider patient preferences for information delivery about upcoming medical procedures. INFORMATION: preparation for medical procedures; discrete choice; oncology; patient preference; emotional response. [ABSTRACT FROM AUTHOR]

A standard assumption of most sequential sampling models is that decision-makers rely on a decision criterion that remains constant throughout the decision process. However, several authors have recently suggested that, in order to maximize reward rates in dynamic environments, decision-makers need to rely on a decision criterion that changes over the course of the decision process. We used dynamic programming and simulation methods to quantify the reward rates obtained by constant and dynamic decision criteria in different environments. We further investigated what influence a decision-maker's uncertainty about the stochastic structure of the environment has on reward rates. Our results show that in most dynamic environments, both types of decision criteria yield similar reward rates, across different levels of uncertainty. This suggests that a static decision criterion might provide a robust default setting. [ABSTRACT FROM AUTHOR]

Neoantigens present unique and specific targets for personalized cancer immunotherapy strategies. Given the low mutational burden yet immunotherapy responsiveness of malignant mesothelioma (MM) when compared to other carcinogen-induced malignancies, identifying candidate neoantigens and T cells that recognize them has been a challenge. We used pleural effusions to gain access to MM tumor cells as well as immune cells in order to characterize the tumor-immune interface in MM. We characterized the landscape of potential neoantigens from SNVs identified in 27 MM patients and performed whole transcriptome sequencing of cell populations from 18 patient-matched pleural effusions. IFNγ ELISpot was performed to detect a CD8+ T cell responses to predicted neoantigens in one patient. We detected a median of 68 (range 7–258) predicted neoantigens across the samples. Wild-type non-binding to mutant binding predicted neoantigens increased risk of death in a model adjusting for age, sex, smoking status, histology and treatment (HR: 33.22, CI: 2.55–433.02, p =.007). Gene expression analysis indicated a dynamic immune environment within the pleural effusions. TCR clonotypes increased with predicted neoantigen burden. A strong activated CD8+ T-cell response was identified for a predicted neoantigen produced by a spontaneous mutation in the ROBO3 gene. Despite the challenges associated with the identification of bonafide neoantigens, there is growing evidence that these molecular changes can provide an actionable target for personalized therapeutics in difficult to treat cancers. Our findings support the existence of candidate neoantigens in MM despite the low mutation burden of the tumor, and may present improved treatment opportunities for patients. [ABSTRACT FROM AUTHOR]

Establishing linkages between light detection and ranging (lidar) data, produced from interrogating forest canopies, to the highly complex forest structures, composition, and traits that such forests contain, remains an extremely difficult problem. Radiative transfer models have been developed to help solve this problem and test new sensor platforms in a virtual environment. Many forest canopy studies include the major assumption of isotropic (Lambertian) reflecting and transmitting leaves or non-transmitting leaves. Here, we study when these assumptions may be valid and evaluate their associated impacts/effects on the lidar waveform, as well as its dependence on wavelength, lidar footprint, view angle, and leaf angle distribution (LAD), by using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) remote sensing radiative transfer simulation model. The largest effects of Lambertian assumptions on the waveform are observed at visible wavelengths, small footprints, and oblique interrogation angles relative to the mean leaf angle. For example, a 77% increase in return signal was observed with a configuration of a 550 nm wavelength, 10 cm footprint, and 45° interrogation angle to planophile leaves. These effects are attributed to (i) the bidirectional scattering distribution function (BSDF) becoming almost purely specular in the visible, (ii) small footprints having fewer leaf angles to integrate over, and (iii) oblique angles causing diminished backscatter due to forward scattering. Non-transmitting leaf assumptions have the greatest error for large footprints at near-infrared (NIR) wavelengths. Regardless of leaf angle distribution, all simulations with non-transmitting leaves with a 5 m footprint and 1064 nm wavelength saw around a 15% reduction in return signal. We attribute the signal reduction to the increased multiscatter contribution for larger fields of view, and increased transmission at NIR wavelengths. Armed with the knowledge from this study, researchers will be able to select appropriate sensor configurations to account for or limit BSDF effects in forest lidar data. [ABSTRACT FROM AUTHOR]

An important feature of human cognition is the ability to flexibly and efficiently adapt behavior in response to continuously changing contextual demands. We leverage a large-scale dataset from Lumosity, an online cognitive-training platform, to investigate how cognitive processes involved in cued switching between tasks are affected by level of task practice across the adult lifespan. We develop a computational account of task switching that specifies the temporal dynamics of activating task-relevant representations and inhibiting task-irrelevant representations and how they vary with extended task practice across a number of age groups. Practice modulates the level of activation of the task-relevant representation and improves the rate at which this information becomes available, but has little effect on the task-irrelevant representation. While longterm practice improves performance across all age groups, it has a greater effect on older adults. Indeed, extensive task practice can make older individuals functionally similar to lesspracticed younger individuals, especially for cognitive measures that focus on the rate at which task-relevant information becomes available. [ABSTRACT FROM AUTHOR]

Abstract: How does the process of information transmission affect the cultural or linguistic products that emerge? This question is often studied experimentally and computationally via iterated learning, a procedure in which participants learn from previous participants in a chain. Iterated learning is a powerful tool because, when all participants share the same priors, the stationary distributions of the iterated learning chains reveal those priors. In many situations, however, it is unreasonable to assume that all participants share the same prior beliefs. We present four simulation studies and one experiment demonstrating that when the population of learners is heterogeneous, the behavior of an iterated learning chain can be unpredictable and is often systematically distorted by the learners with the most extreme biases. This results in group‐level outcomes that reflect neither the behavior of any individuals within the population nor the overall population average. We discuss implications for the use of iterated learning as a methodological tool as well as for the processes that might have shaped cultural and linguistic evolution in the real world. [ABSTRACT FROM AUTHOR]

In cued task switching, performance relies on proactive and reactive control processes. Proactive control is evident in the reduction in switch cost under conditions that promote advance preparation. However, the residual switch cost that remains under conditions of optimal proactive control indicates that, on switch trials, the target continues to elicit interference that is resolved using reactive control. We examined whether posttarget interference varies as a function of trial-by-trial variability in preparation. We investigated target congruence effects on behavior and targetlocked ERPs extracted across the response time (RT) distribution, using orthogonal polynomial trend analysis (OPTA). Early N2, late N2, and P3b amplitudes were differentially modulated across the RT distribution. There was a large congruence effect on late N2 and P3b, which increased with RT for P3b amplitude, but did not vary with trial type. This suggests that target properties impact switch and repeat trials equally and do not contribute to residual switch cost. P3b amplitude was larger, and latency later, for switch than repeat trials, and this difference became larger with increasing RT, consistent with sustained carryover effects on highly prepared switch trials. These results suggest that slower, less prepared responses are associated with greater target-related interference during target identification and processing, as well as slower, more difficult decision processes. They also suggest that neither general nor switch-specific preparation can ameliorate the effects of target-driven interference. These findings highlight the theoretical advances achieved by integrating RT distribution analyses with ERP and OPTA to examine trial-by-trial variability in performance and brain function. [ABSTRACT FROM AUTHOR]

Summary We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes—wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant—characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower ( CTNNB1 , NRAS , or IDH1 ) or higher ( BRAF , TP53 , or CASP8 ) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field. [ABSTRACT FROM AUTHOR]

Recently, Veksler, Myers, and Gluck (2015) proposed model flexibility analysis as a method that "aids model evaluation by providing a metric for gauging the persuasiveness of a given fit" (p. 755) Model flexibility analysis measures the complexity of a model in terms of the proportion of all possible data patterns it can predict. We show that this measure does not provide a reliable way to gauge complexity, which prevents model flexibility analysis from fulfilling either of the 2 aims outlined by Veksler et al. (2015): absolute and relative model evaluation. We also show that model flexibility analysis can even fail to correctly quantify complexity in the most clear cut case, with nested models. We advocate for the use of well-established techniques with these characteristics, such as Bayes factors, normalized maximum likelihood, or cross-validation, and against the use of model flexibility analysis. In the discussion, we explore 2 issues relevant to the area of model evaluation: the completeness of current model selection methods and the philosophical debate of absolute versus relative model evaluation. [ABSTRACT FROM AUTHOR]