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BackgroundThe Mnemonic Similarity Task (MST) is a popular memory task designed to assess hippocampal integrity. We assessed whether analyzing MST performance using a multinomial processing tree (MPT) cognitive model could detect individuals with elevated Alzheimer's disease (AD) biomarker status prior to cognitive decline.MethodWe analyzed MST data from >200 individuals (young, cognitively healthy older adults and individuals with mild cognitive impairment [MCI]), a subset of which also had existing cerebrospinal fluid (CSF) amyloid beta (Aβ) and phosphorylated tau (pTau) data using both traditional and model-derived approaches. We assessed how well each could predict age group, memory ability, MCI status, Aβ, and pTau status using receiver operating characteristic analyses.ResultsBoth approaches predicted age group membership equally, but MPT-derived metrics exceeded traditional metrics in all other comparisons.DiscussionA MPT model of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for screening and monitoring older adults during the asymptomatic phase of AD.HighlightsThe MST, along with cognitive modeling, identifies individuals with memory deficits and cognitive impairment. Cognitive modeling of the MST identifies individuals with increased AD biomarkers prior to changes in cognitive function. The MST is a digital biomarker that identifies individuals at high risk of AD.

We apply cognitive modeling to improve the wisdom of the crowd in a spatial knowledge task. Participants provided point estimates for where 48 US cities are located and then, using the point estimate as a center point, chose a radius large enough that they believed the resulting circle was certain to contain the city’s location. Simple and radius-weighted arithmetic averages of the individuals’ point estimates produced more accurate group answers than the majority of individuals. These statistical aggregates, however, assume there are no differences in individual expertise nor in the difficulty of locating different cities. Accordingly, we develop a set of cognitive models to infer group estimates that make various assumptions about individual expertise and differences in city difficulty. The model-based estimates generally outperform the statistical averages. The models are especially accurate if they allow for individual differences in expertise that can vary city by city. We replicate this finding by applying the same cognitive models to data reported by Mayer and Heck (2023) in which participants provided point estimates for the locations of European cities.

The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings. We develop new cognitive models, based on the multinomial processing tree framework, for two versions of the MST. The models are implemented as generative probabilistic models and applied to behavioral data using Bayesian graphical modeling methods. We demonstrate how the combination of cognitive modeling and Bayesian methods allows for flexible and powerful inferences about performance on the MST. These demonstrations include latent-mixture extensions for identifying individual differences in decision strategies, and hierarchical extensions that measure fine-grained differences in the ability to detect lures. One key finding is that the availability of a "similar" response in the MST reduces individual differences in decision strategies and allows for more direct measurement of recognition memory.

Wills and Pothos (2012) reviewed approaches to evaluating formal models of categorization, raising a series of worthwhile issues, challenges, and goals. Unfortunately, in discussing these issues and proposing solutions, Wills and Pothos (2012) did not consider Bayesian methods in any detail. This means not only that their review excludes a major body of current work in the field, but also that it does not consider the body of work that provides the best current answers to the issues raised. In this comment, we argue that Bayesian methods can be--and, in most cases, already have been--applied to all the major model evaluation issues raised by Wills and Pothos (2012). In particular, Bayesian methods can address the challenges of avoiding overfitting, considering qualitative properties of data, reducing dependence on free parameters, and testing empirical breadth. (Contains 1 footnote.)

ObjectiveTo demonstrate measurement precision of cognitive domains in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set.MethodParticipants with normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD) were included from all ADNI waves. We used data from each person's last study visit to calibrate scores for memory, executive function, language, and visuospatial functioning. We extracted item information functions for each domain and used these to calculate standard errors of measurement. We derived scores for each domain for each diagnostic group and plotted standard errors of measurement for the observed range of scores.ResultsAcross all waves, there were 961 people with NC, 825 people with MCI, and 694 people with AD at their most recent study visit (data pulled February 25, 2019). Across ADNI's battery there were 34 memory items, 18 executive function items, 20 language items, and seven visuospatial items. Scores for each domain were highest on average for people with NC, intermediate for people with MCI, and lowest for people with AD, with most scores across all groups in the range of -1 to +1. Standard error of measurement in the range from -1 to +1 was highest for memory, intermediate for language and executive functioning, and lowest for visuospatial.ConclusionModern psychometric approaches provide tools to help understand measurement precision of the scales used in studies. In ADNI, there are important differences in measurement precision across cognitive domains. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

D. Trafimow presented an analysis of null hypothesis significance testing (NHST) using Bayes's theorem. Among other points, he concluded that NHST is logically invalid, but that logically valid Bayesian analyses are often not possible. The latter conclusion reflects a fundamental misunderstanding of the nature of Bayesian inference. This view needs correction, because Bayesian methods have an important role to play in many psychological problems where standard techniques are inadequate. This comment, with the help of a simple example, explains the usefulness of Bayesian inference for psychology.

The circular drift-diffusion model (CDDM) is a sequential sampling model designed to account for decisions and response times in decision-making tasks with a circular set of choice alternatives. We present and demonstrate a fully Bayesian implementation and extension of the CDDM. This development allows researchers to apply the CDDM to data from complex experiments and draw conclusions about targeted hypotheses. The Bayesian implementation relies on a custom JAGS module. We describe the module and demonstrate its adequacy through a simulation study. We then illustrate the advantages of the implementation by revisiting data from a continuous orientation judgment task. We develop a graphical model for the analysis that is based on the CDDM but extends it with hierarchical and latent-mixture structures. We then demonstrate how these extensions are used to accommodate the design of the experiment and to implement psychological assumptions about individual differences, the difficulty of different stimulus conditions, and the impact of cues on decision making. Finally, we demonstrate how the computational Bayesian inference enabled by JAGS allows these assumptions to be tested and addresses psychological research questions about people’s decision making.

The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings, where it has been used to identify hippocampal dysfunction associated with healthy aging, dementia, schizophrenia, depression, and other disorders. As with any test used in a clinical setting, it is especially important for the MST to be administered as efficiently as possible. We apply adaptive design optimization methods (Lesmes et al., 2015; Myung et al., 2013) to optimize the presentation of test stimuli in accordance with previous responses. This optimization is based on a signal-detection model of an individual's memory capabilities and decision-making processes. We demonstrate that the adaptive design optimization approach generally reduces the number of test stimuli needed to provide these measures.

Chronic health conditions (CHCs) are common and associated with functional limitations. Acceptance and commitment therapy (ACT) shows promise in improving functioning, quality of life, and distress across several CHCs. The purpose of this study was to conduct a systematic review of technology-supported ACT for CHCs and perform a meta-analysis on functioning and ACT process outcomes. Multiple databases were systematically searched for randomized controlled trials. A total of 20 unique studies with 2,430 randomized participants were included. CHCs addressed in these studies were chronic pain (k = 9), obesity/overweight (k = 4), cancer (k = 3), hearing loss (k = 1), HIV (k = 1), multiple sclerosis (k = 1), and tinnitus (k = 1). Internet and telephone were the most used technology platforms. All studies included therapist contact with considerable heterogeneity between studies. Random effects meta-analyses found medium effect sizes showing technology-supported ACT outperformed comparator groups on measures of function at post-treatment (Hedges' g = -0.49; p = 0.002) and follow-up (Hedges' g = -0.52; p = 0.02), as well as ACT process outcomes at post-treatment (Hedges' g = 0.48; p

Pulmonary arterial hypertension is an incurable disease marked by dysregulated metabolism, both at the cellular level in the pulmonary vasculature, and at the whole-body level characterized by impaired exercise oxygen consumption. Though both altered pulmonary vascular metabolism and abnormal exercise physiology are key markers of disease severity and pulmonary arterial remodeling, their precise interactions are relatively unknown. Herein we review normal pulmonary vascular physiology and the current understanding of pulmonary vascular cell metabolism and cardiopulmonary response to exercise in Pulmonary arterial hypertension. We additionally introduce a newly developed international collaborative effort aimed at quantifying exercise-induced changes in pulmonary vascular metabolism, which will inform about underlying pathophysiology and clinical management. We support our investigative approach by presenting preliminary data and discuss potential future applications of our research platform.

We study the wisdom of the crowd in the context of spatial knowledge, asking participants to identify US states and African countries on unlabeled tile maps. We use two question frames, asking participants to select where the target is present or eliminate where it is absent. Participants generally display overconfidence, often selecting small regions that do not include the target. We find strong wisdom of the crowd effects by aggregating participants' responses, especially by weighting the individual responses according to the size of their selection. The weighted crowd outperforms all but a few participants for the US states and all participants for the African countries. We also find wisdom of the crowd within effects, by aggregating the present and absent frames for the same participant. We discuss the implications of our findings for understanding how people express uncertain spatial knowledge and the potential use of crowd aggregation in real-world applications.

Most studies of human category learning involve category structures that do not change, or that change in a way that is independent of people's categorization behavior. We consider the situation in which successful category learning causes categories to change. In an experiment, participants learned from feedback whether animals are healthy or diseased. Once their categorization accuracy was near-perfect, the category structure changed so that different animals became diseased. Based on exploratory data analysis and the application of two category learning models, we argue that, once they detect a category change, people retain what they have learned about healthy animals, but reset what they have learned about diseased animals. We discuss future modeling goals and emphasize the need for learning models to study situations in which people's behavior impacts the dynamics of the environment in which learning takes place.

The free recall of triadic comparisons, a task used in clinical settings, presents a unique analysis challenge for many memory models, because learning occurs incidentally and items are presented multiple times in triads. To account for this design, we extend the SIMPLE (Brown et al., 2007) model of memory, which assumes to-be-remembered items are stored as separate logarithmically-compressed temporal traces. The ability to retrieve these traces depends on the acuity of memory probes and the semantic similarity between the items represented by the traces. We applied this model to a real-world clinical data set including healthy controls, people with mild cognitive impairment (MCI), and people with Alzheimer’s dementia. We found that people with MCI had lower acuity than healthy controls, but both groups placed roughly equal weight on temporal and semantic cues. People with dementia had both lower acuity and placed much more weight on temporal cues than semantic cues.

RationaleChronic health conditions (CHCs) are costly and difficult to manage. Patients often struggle with behavioral adherence to complex treatment regimens and experience psychiatric distress. Acceptance and Commitment Therapy (ACT) is a transdiagnostic behavioral approach that aims to improve functioning and quality of life (QoL), which are important treatment outcomes for this population. Preliminary efficacy of multi-session ACT in patients with CHCs has been demonstrated, and single-session ACT interventions have since been developed to increase feasibility, acceptability, and accessibility. The purpose of this systematic review and meta-analysis was to describe the literature on single-session ACT intervention studies in CHC populations with regards to (1) study design and methodology, (2) patient characteristics and conditions targeted, and (3) efficacy for outcomes across various domains, using narrative and quantitative methods.MethodsPsycINFO, PubMed, and Web of Science were systematically searched in August 2020. Studies of single-session ACT interventions in adult patients with CHCs that reported quantitative outcomes in any of the following domains were included: (a) functioning and related domains (e.g., disability, QoL, well-being); (b) mental health; (c) physical health; (d) ACT processes. Both controlled and uncontrolled studies were included. Study quality was assessed using the Psychotherapy Outcome Study Methodology Rating Scale (POMRF). Between-group random effects meta-analysis was conducted on general functioning outcomes.ResultsFourteen manuscripts reporting outcomes from 13 studies (N = 793) met inclusion criteria. Ten studies were identified by their authors as pilot or feasibility trials. Eight studies used comparison or control groups. Twelve studies delivered the ACT content in workshop format. Studies recruited for a variety of conditions. Narrative review found that between- and within-group effect sizes showed generally positive results favoring single-session ACT overall (69%), especially for measures of functioning and related domains (88%), mental health (67%), and ACT processes (73%). Meta-analysis found that ACT did not significantly outperform comparison groups on measures of general functioning (Hedges' g: -0.51, 95% confidence interval: [-1.19, 0.16]; I 2 = 86%; K = 5) despite a medium-sized pooled effect.DiscussionUse of single-session ACT interventions in CHC populations is an emergent field. There is preliminary evidence for the acceptability, feasibility, and efficacy of these interventions, which provides support for further testing in fully-powered RCTs. Additional RCTs will enable larger meta-analyses and stronger conclusions about efficacy. Recommendations for future trials are provided.

Depression is a debilitating disorder with high prevalence and socioeconomic cost, but the brain-physiological processes that are altered during depressive states are not well understood. Here, we build on recent findings in macaques that indicate a direct causal relationship between pupil dilation and anterior cingulate cortex mediated arousal during anticipation of reward. We translated these findings to human subjects with concomitant pupillometry/fMRI in a sample of unmedicated participants diagnosed with major depression and healthy controls. We could show that the upregulation and maintenance of arousal in anticipation of reward was disrupted in patients in a symptom-load dependent manner. We could further show that the failure to maintain reward anticipatory arousal showed state-marker properties, as it tracked the load and impact of depressive symptoms independent of prior diagnosis status. Further, group differences of anticipatory arousal and continuous correlations with symptom load were not traceable only at the level of pupillometric responses, but were mirrored also at the neural level within salience network hubs. The upregulation and maintenance of arousal during reward anticipation is a novel translational and well-traceable process that could prove a promising gateway to a physiologically informed patient stratification and targeted interventions.

Approximately 30% of older adults exhibit the neuropathological features of Alzheimer's disease without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively unimpaired in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modelling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5108 participants from a clinical trial of Alzheimer's disease and three longitudinal cohort studies of cognitive ageing. All analyses were run across all participants and repeated restricting the sample to individuals with unimpaired cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (P-values < 2.5 × 10-20), and we observed novel correlations with neuropsychiatric conditions (P-values < 7.9 × 10-4). Notably, neither resilience metric was genetically correlated with clinical Alzheimer's disease (P-values > 0.42) nor associated with APOE (P-values > 0.13). In single variant analyses, we observed a genome-wide significant locus among participants with unimpaired cognition on chromosome 18 upstream of ATP8B1 (index single nucleotide polymorphism rs2571244, minor allele frequency = 0.08, P = 2.3 × 10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; P = 2 × 10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical Alzheimer's disease, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

The target article on robust modeling (Lee et al.) generated a lot of commentary. In this reply, we discuss some of the common themes in the commentaries; some are simple points of agreement while others are extensions of a practical or abstract nature. We also address a small number of disagreements or confusions.

We apply the "wisdom of the crowd" idea to human category learning, using a simple approach that combines people's categorization decisions by taking the majority decision. We first show that the aggregated crowd category learning behavior found by this method performs well, learning categories more quickly than most or all individuals for 28 previously collected datasets. We then extend the approach so that it does not require people to categorize every stimulus. We do this using a model-based method that predicts the categorization behavior people would produce for new stimuli, based on their behavior with observed stimuli, and uses the majority of these predicted decisions. We demonstrate and evaluate the model-based approach in two case studies. In the first, we use the general recognition theory decision-bound model of categorization (Ashby & Townsend, ) to infer each person's decision boundary for two categories of perceptual stimuli, and we use these inferences to make aggregated predictions about new stimuli. In the second, we use the generalized context model exemplar model of categorization (Nosofsky, ) to infer each person's selective attention for face stimuli, and we use these inferences to make aggregated predictions about withheld stimuli. In both case studies, we show that our method successfully predicts the category of unobserved stimuli, and we emphasize that the aggregated crowd decisions arise from psychologically interpretable processes and parameters. We conclude by discussing extensions and potential real-world applications of the approach.

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.

The development of cognitive models involves the creative scientific formalization of assumptions, based on theory, observation, and other relevant information. In the Bayesian approach to implementing, testing, and using cognitive models, assumptions can influence both the likelihood function of the model, usually corresponding to assumptions about psychological processes, and the prior distribution over model parameters, usually corresponding to assumptions about the psychological variables that influence those processes. The specification of the prior is unique to the Bayesian context, but often raises concerns that lead to the use of vague or non-informative priors in cognitive modeling. Sometimes the concerns stem from philosophical objections, but more often practical difficulties with how priors should be determined are the stumbling block. We survey several sources of information that can help to specify priors for cognitive models, discuss some of the methods by which this information can be formalized in a prior distribution, and identify a number of benefits of including informative priors in cognitive modeling. Our discussion is based on three illustrative cognitive models, involving memory retention, categorization, and decision making.

People often interact with environments that can provide only a finite number of items as resources. Eventually a book contains no more chapters, there are no more albums available from a band, and every Pokémon has been caught. When interacting with these sorts of environments, people either actively choose to quit collecting new items, or they are forced to quit when the items are exhausted. Modeling the distribution of how many items people collect before they quit involves untangling these two possibilities, We propose that censored geometric models are a useful basic technique for modeling the quitting distribution, and, show how, by implementing these models in a hierarchical and latent-mixture framework through Bayesian methods, they can be extended to capture the additional features of specific situations. We demonstrate this approach by developing and testing a series of models in two case studies involving real-world data. One case study deals with people choosing jokes from a recommender system, and the other deals with people completing items in a personality survey.

Although the Kendall distance is a standard metric in computer science, it is less widely used in psychology. We demonstrate the usefulness of the Kendall distance for analyzing psychological data that take the form of ranks, lists, or orders of items. We focus on weighted extensions of the metric that allow for heterogeneity of item importance, item position, and item similarity, as well showing how the metric can accommodate missingness in the form of top-k lists. To demonstrate how the Kendall distance can help address research questions in psychology, we present four applications to previous data. These applications involve the recall of events on September 11, people's preference rankings for the months of the year, people's free recall of animal names in a clinical setting, and expert predictions involving American football outcomes.

Interval estimates - estimates of parameters that include an allowance for sampling uncertainty - have long been touted as a key component of statistical analyses. There are several kinds of interval estimates, but the most popular are confidence intervals (CIs): intervals that contain the true parameter value in some known proportion of repeated samples, on average. The width of confidence intervals is thought to index the precision of an estimate; CIs are thought to be a guide to which parameter values are plausible or reasonable; and the confidence coefficient of the interval (e.g., 95 %) is thought to index the plausibility that the true parameter is included in the interval. We show in a number of examples that CIs do not necessarily have any of these properties, and can lead to unjustified or arbitrary inferences. For this reason, we caution against relying upon confidence interval theory to justify interval estimates, and suggest that other theories of interval estimation should be used instead.

This paper comments on the response offered by Trafimow on Lee and Wagenmakers comments on Trafimow's original article. It seems our comment should have made it clear that the objective Bayesian approach we advocate views probabilities neither as relative frequencies nor as belief states, but as degrees of plausibility assigned to propositions in a rational way, entirely (and uniquely) determined by the available information. Instead of addressing the substantial issues raised in our comment, Trafimow provides a series of consternations making it clear that Bayesian methods cannot work miracles for psychological researchers and that researchers must be resigned to using them to provide coherent and rational analyses of available information.

BackgroundAbnormalities in the corpus callosum have been reported in individuals with autism spectrum disorder (ASD), but few studies have evaluated young children. Sex differences in callosal organization and diffusion characteristics have also not been evaluated fully in ASD.MethodsStructural and diffusion-weighted images were acquired in 139 preschool-aged children with ASD (112 males/27 females) and 82 typically developing (TD) controls (53 males/29 females). Longitudinal scanning at two additional annual time points was carried out in a subset of these participants. Callosal organization was evaluated using two approaches: 1) diffusion tensor imaging (DTI) tractography to define subregions based on cortical projection zones and 2) as a comparison to previous studies, midsagittal area analysis using Witelson subdivisions. Diffusion measures of callosal fibers were also evaluated.ResultsAnalyses of cortical projection zone subregions revealed sex differences in the patterns of altered callosal organization. Relative to their sex-specific TD counterparts, both males and females with ASD had smaller regions dedicated to fibers projecting to superior frontal cortex, but patterns differed in callosal subregions projecting to other parts of frontal cortex. While males with ASD had a smaller callosal region dedicated to the orbitofrontal cortex, females with ASD had a smaller callosal region dedicated to the anterior frontal cortex. There were also sex differences in diffusion properties of callosal fibers. While no alterations were observed in males with ASD relative to TD males, mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were all increased in females with ASD relative to TD females. Analyses of Witelson subdivisions revealed a decrease in midsagittal area of the corpus callosum in both males and females with ASD but no regional differences in specific subdivisions. Longitudinal analyses revealed no diagnostic or sex differences in the growth rate or change in diffusion measures of the corpus callosum from 3 to 5 years of age.ConclusionsThere are sex differences in the pattern of altered corpus callosum neuroanatomy in preschool-aged children with ASD.

Diffusion models are widely-used and successful accounts of the time course of two-choice decision making. Most diffusion models assume constant boundaries, which are the threshold levels of evidence that must be sampled from a stimulus to reach a decision. We summarize theoretical results from statistics that relate distributions of decisions and response times to diffusion models with time-varying boundaries. We then develop a computational method for finding time-varying boundaries from empirical data, and apply our new method to two problems. The first problem involves finding the time-varying boundaries that make diffusion models equivalent to the alternative sequential sampling class of accumulator models. The second problem involves finding the time-varying boundaries, at the individual level, that best fit empirical data for perceptual stimuli that provide equal evidence for both decision alternatives. We discuss the theoretical and modeling implications of using time-varying boundaries in diffusion models, as well as the limitations and potential of our approach to their inference.

We study how people terminate their search for information when making decisions in a changing environment. In 3 experiments, differing in the cost of search, participants made a sequence of 2-alternative decisions, based on the information provided by binary cues they could search. Whether limited or extensive search was required to maintain accurate decisions changed across the course of the experiment, but was not indicated to participants. We find large individual differences but that, in general, the extent of search is changed in response to environmental change, and is not necessarily triggered by a reduction in accuracy. We then examine the ability of 4 models to account for individual participant behavior, using a generalization measure that tests model predictions. Two of the models use reinforcement learning, and differ in whether they use error or both error and effort signals to control how many cues are searched. The other 2 models use sequential sampling processes, and differ in the regulatory mechanisms they use to adjust the decision thresholds that control the extent of search. We find that error-based reinforcement learning is usually an inadequate account of behavior, especially when search is costly. We also find evidence in the model predictions for the use of confidence as a regulatory variable. This provides an alternative theoretical approach to balancing error and effort, and highlights the possibility of hierarchical regulatory mechanisms that lead to delayed and abrupt changes in the extent of search.

PurposeAsian Americans have consistently reported poorer communication with physicians compared with non-Hispanic Whites (NHW). This qualitative study sought to elucidate the similarities and differences in communication with physicians between Chinese and NHW breast cancer survivors.MethodsForty-four Chinese and 28 NHW women with early stage breast cancer (stage 0-IIa) from the Greater Bay Area Cancer Registry participated in focus group discussions or individual interviews. We oversampled Chinese women because little is known about their cancer care experiences. In both interview formats, questions explored patients' experiences and feelings when communicating with physicians about their diagnosis, treatment, and follow-up care.ResultsPhysician empathy at the time of diagnosis was important to both ethnic groups; however, during treatment and follow-up care, physicians' ability to treat cancer and alleviate physical symptoms was a higher priority. NHW and US-born Chinese survivors were more likely to assert their needs, whereas Chinese immigrants accepted physician advice even when it did not alleviate physical problems (e.g., pain). Patients viewed all physicians as the primary source for information about cancer care. Many Chinese immigrants sought additional information from primary care physicians and stressed optimal communication over language concordance.ConclusionsPhysician empathy and precise information were important for cancer patients. Cultural differences such as the Western emphasis on individual autonomy vs. Chinese emphasis on respect and hierarchy can be the basis for the varied approaches to physician communication we observed. Interventions based on cultural understanding can foster more effective communication between immigrant patients and physicians ultimately improving patient outcomes.

In The Price Is Right game show, players compete to win a prize, by placing bids on its price. We ask whether it is possible to achieve a "wisdom of the crowd" effect, by combining the bids to produce an aggregate price estimate that is superior to the estimates of individual players. Using data from the game show, we show that a wisdom of the crowd effect is possible, especially by using models of the decision-making processes involved in bidding. The key insight is that, because of the competitive nature of the game, what people bid is not necessarily the same as what they know. This means better estimates are formed by aggregating latent knowledge than by aggregating observed bids. We use our results to highlight the usefulness of models of cognition and decision-making in studying the wisdom of the crowd, which are often approached only from non-psychological statistical perspectives.

Despite affirmation that students with disabilities should have equal access to education, individuals with disabilities are still not participating to the same degree as individuals without disabilities, particularly within postsecondary institutions. Students in Health and Human Service (HHS) programs experience many unique challenges and disadvantages. In-depth focus groups and interviews were conducted with 14 university stakeholders in HHS programs regarding their perceptions and experiences of working with students with disabilities. We found that the rhetoric of citizenship, specifically notions of rationality, autonomy, and productivity, interacts with beliefs about students with disabilities to allow stakeholders to justify their exclusion or limited participation. Our findings demonstrate how taken-for-granted beliefs can limit the inclusion of students with disabilities in ways that seem natural and unproblematic. [ABSTRACT FROM AUTHOR]

The article shares the author's experience of being a surgical intern having fear in attending to a surgeon in surgery room, wherein it mentions the curbing aggressive behaviors of surgeons which is unacceptable. It mentions that having a surgeon with aggressive behavior puts patient care at risk since the communication between them and others attending the surgery has been compromised.

To die by suicide, one must think about suicide, make a plan, and then carry it out. Prior research has examined the presence and predictors of these outcomes; however, virtually no studies have characterized how these steps unfold along the pathway to suicide. A novel instrument was administered to 30 recent suicide attempters. Results revealed that although the median onset for suicidal ideation occurs 1 to 5 years prior to attempting, the median for 6 of the 10 steps measured was within 6 hours of attempting. Overall, 86.5% of proximal planning steps took place within 1 week of attempting and 66.6% occurred within 12 hours. [ABSTRACT FROM AUTHOR]