Striatal and Behavioral Responses to Reward Vary by Socioeconomic Status in Adolescents.

Disparities in socioeconomic status (SES) lead to unequal access to financial and social support. These disparities are believed to influence reward sensitivity, which in turn are hypothesized to shape how individuals respond to and pursue rewarding experiences. However, surprisingly little is known about how SES shapes reward sensitivity in adolescence. Here, we investigated how SES influenced adolescent responses to reward, both in behavior and the striatum-a brain region that is highly sensitive to reward. We examined responses to both immediate reward (tracked by phasic dopamine) and average reward rate fluctuations (tracked by tonic dopamine) as these distinct signals independently shape learning and motivation. Adolescents (n = 114; 12-14 years; 58 female) performed a gambling task during functional magnetic resonance imaging. We manipulated trial-by-trial reward and loss outcomes, leading to fluctuations between periods of reward scarcity and abundance. We found that a higher reward rate hastened behavioral responses, and increased guess switching, consistent with the idea that reward abundance increases response vigor and exploration. Moreover, immediate reward reinforced previously rewarding decisions (win-stay, lose-switch) and slowed responses (postreward pausing), particularly when rewards were scarce. Notably, lower-SES adolescents slowed down less after rare rewards than higher-SES adolescents. In the brain, striatal activations covaried with the average reward rate across time and showed greater activations during rewarding blocks. However, these striatal effects were diminished in lower-SES adolescents. These findings show that the striatum tracks reward rate fluctuations, which shape decisions and motivation. Moreover, lower SES appears to attenuate reward-driven behavioral and brain responses.

Fast response times signal social connection in conversation.

Clicking is one of the most robust metaphors for social connection. But how do we know when two people "click"? We asked pairs of friends and strangers to talk with each other and rate their felt connection. For both friends and strangers, speed in response was a robust predictor of feeling connected. Conversations with faster response times felt more connected than conversations with slower response times, and within conversations, connected moments had faster response times than less-connected moments. This effect was determined primarily by partner responsivity: People felt more connected to the degree that their partner responded quickly to them rather than by how quickly they responded to their partner. The temporal scale of these effects (<250 ms) precludes conscious control, thus providing an honest signal of connection. Using a round-robin design in each of six closed networks, we show that faster responders evoked greater feelings of connection across partners. Finally, we demonstrate that this signal is used by third-party listeners as a heuristic of how well people are connected: Conversations with faster response times were perceived as more connected than the same conversations with slower response times. Together, these findings suggest that response times comprise a robust and sufficient signal of whether two minds "click."

High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity.

It is a widely held belief that people's choices are less sensitive to changes in value as value increases. For example, the subjective difference between $11 and $12 is believed to be smaller than between $1 and $2. This idea is consistent with applications of the Weber-Fechner Law and divisive normalization to value-based choice and with psychological interpretations of diminishing marginal utility. According to random utility theory in economics, smaller subjective differences predict less accurate choices. Meanwhile, in the context of sequential sampling models in psychology, smaller subjective differences also predict longer response times. Based on these models, we would predict decisions between high-value options to be slower and less accurate. In contrast, some have argued on normative grounds that choices between high-value options should be made with less caution, leading to faster and less accurate choices. Here, we model the dynamics of the choice process across three different choice domains, accounting for both discriminability and response caution. Contrary to predictions, we mostly observe faster and more accurate decisions (i.e., higher drift rates) between high-value options. We also observe that when participants are alerted about incoming high-value decisions, they exert more caution and not less. We rule out several explanations for these results, using tasks with both subjective and objective values. These results cast doubt on the notion that increasing value reduces discriminability.

Task functional networks predict individual differences in the speed of emotional facial discrimination.

Every individual experiences negative emotions, such as fear and anger, significantly influencing how external information is perceived and processed. With the gradual rise in brain-behavior relationship studies, analyses investigating individual differences in negative emotion processing and a more objective measure such as the response time (RT) remain unexplored. This study aims to address this gap by establishing that the individual differences in the speed of negative facial emotion discrimination can be predicted from whole-brain functional connectivity when participants were performing a face discrimination task. Employing the connectome predictive modeling (CPM) framework, we demonstrated this in the young healthy adult group from the Human Connectome Project-Young Adults (HCP-YA) dataset and the healthy group of the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset. We identified distinct network contributions in the adult and adolescent predictive models. The highest represented brain networks involved in the adult model predictions included representations from the motor, visual association, salience, and medial frontal networks. Conversely, the adolescent predictive models showed substantial contributions from the cerebellum-frontoparietal network interactions. Finally, we observed that despite the successful within-dataset prediction in healthy adults and adolescents, the predictive models failed in the cross-dataset generalization. In conclusion, our study shows that individual differences in the speed of emotional facial discrimination can be predicted in healthy adults and adolescent samples using their functional connectivity during negative facial emotion processing. Future research is needed in the derivation of more generalizable models.

Heavy-intensity priming exercise extends the V̇o2max plateau and increases peak-power output during ramp-incremental exercise.

This study investigated whether a heavy-intensity priming exercise precisely prescribed within the heavy-intensity domain would lead to a greater peak-power output (POpeak) and a longer maximal oxygen uptake (V̇o2max) plateau. Twelve recreationally active adults participated in this study. Two visits were required: 1) a step-ramp-step test [ramp-incremental (RI) control], and 2) an RI test preceded by a priming exercise within the heavy-intensity domain (RI primed). A piecewise equation was used to quantify the V̇o2 plateau duration (V̇o2plateau-time). The mean response time (MRT) was computed during the RI control condition. The delta (Δ) V̇o2 slope (S; mL·min-1·W-1) and V̇o2-Y intercept (Y; mL·min-1) within the moderate-intensity domain between conditions (RI primed minus RI control) were also assessed using a novel graphical analysis. V̇o2plateau-time (P = 0.001; d = 1.27) and POpeak (P = 0.003; d = 1.08) were all greater in the RI primed. MRT (P < 0.001; d = 2.45) was shorter in the RI primed compared with the RI control. A larger ΔV̇o2plateau-time was correlated with a larger ΔMRT between conditions (r = -0.79; P = 0.002). This study demonstrated that heavy-intensity priming exercise lengthened the V̇o2plateau-time and increased POpeak. The overall faster RI-V̇o2 responses seem to be responsible for the longer V̇o2plateau-time. Specifically, a shorter MRT, but not changes in RI-V̇o2-slopes, was associated with a longer V̇o2plateau-time following priming exercise.NEW & NOTEWORTHY It remains unclear whether priming exercise extends the maximal oxygen uptake (V̇o2max) plateau and increases peak-power output (POpeak) during ramp-incremental (RI) tests. This study demonstrates that a priming exercise, precisely prescribed within the heavy-intensity domain, extends the plateau at V̇o2max and leads to a greater POpeak. Specifically, the extended V̇o2max plateau was associated with accelerated RI-V̇o2 responses.

Do we intervene less and slower in the epilepsy monitoring unit for psychogenic seizures?

PURPOSE: Individuals with psychogenic non-epileptic seizures (PNES) can be stigmatized in healthcare settings. We aimed to compare intervention rate (IR), intervention time (IT), and adverse event (AE) rate between PNES and epileptic seizures (ES) in the epilepsy monitoring unit (EMU). METHODS: We used a prospective database of consecutive admissions to our centre's EMU between August 2021 and September 2022. We excluded purely electric seizures and vague, minor spells with no EEG correlate. We therefore only included electroclinical seizures and PNES. We compared the IR, IT, and AE rate between PNES and ES, as diagnosed by an epileptologist during EEG monitoring. We performed the same comparisons between spells occurring in people admitted with a high vs low suspicion of PNES (HSP vs LSP). We also verified if ITs became longer with repeated PNES. RESULTS: We analyzed 586 spells: 43 PNES vs 543 ES, or 133 HSP vs 453 LSP. Our univariate analyses showed that IR was higher for PNES than for ES (93 % vs 61 %, p <.001) but that IT and AE rate were similar across groups. This higher IR was only apparent outside weekday daytime hours, when EEG technologists were not present. HSP did not differ from LSP in terms of IR, IT, and AE rate. As PNES accumulated in individual patients, IT tended to be longer (Spearman's correlation = 0.42; p =.012). SIGNIFICANCE: Our EMU staff did not intervene less or slower for PNES. Rather, IR was higher for PNES than for ES, but IT tended to be longer with repeat PNES.

On-scene time delays for epileptic seizures in developed community-based integrated care system regions.

BACKGROUND: Delayed on-scene time by emergency medical services (EMS) can have detrimental effects on critical cases for people with epilepsy (PWE). In preparation for a super-aged society, a Community-based Integrated Care System is crucial to manage healthcare costs. However, sufficient coordination irrespective of sociomedical changes among medical providers is challenging. AIM: This study aimed to evaluate on-scene time delays in the treatment of PWE, identify factors associated with such delays, and clarify regional differences. The focus was on the volume of acute care beds in regions with a developed Community-based Integrated Care System. METHODS: This population-based observational study evaluated on-scene time delays in the treatment of PWE across six major cities in western Japan between 2017 and 2021. In addition, we also evaluated the association between regional differences focusing on volume of acute care beds ("Reduced region" and "Preserved region", as cities with numbers of acute care beds per 1,000 people below and above the national average, respectively) along with sociomedical factors associated with on-scene time delays. RESULTS: This study included 8,737 PWE transported by EMS, with a mean on-scene time for EMS ranging from 12.9 ± 6.8 min to 21.7 ± 10.6 min. On-scene time delays were evident in Reduced regions, with an increase of 1.45 min (95 % confidence interval 0.86-2.03 min, p < 0.001). A high total EMS call volume independently influenced on-scene time delays during the middle period of the pandemic in Reduced regions. CONCLUSION: Optimal coordination must be facilitated to ensure the effective functioning of the Community-based Integrated Care System, particularly during unusual circumstances.

How important are delays in treatment for health outcomes? The case of ambulance response time and cardiovascular events.

The cost effectiveness of medical treatments is not precisely known due to the compounding effect of multiple determining factors. Ambulance response time (RT) to emergency calls is exploited to learn more about the effect of the timing of treatment on health outcomes. This causal relation is identified by exploiting rainfall at the time of the ambulance run as a shock to RT. The analysis focuses on patients who have undergone a cardiac event and shows that a one-minute increase in average RT leads to 105 more deaths each year in one Italian region. Finally, the economic value of the lives that would be saved by reducing RT is quantified to facilitate policymaking.

Patient adherence and response time in electronic patient-reported outcomes: insights from three longitudinal clinical trials.

PURPOSE: Patient-reported outcome measures (PROMs) are used to collect data on disease symptoms in support of clinical trial endpoints. Clinical studies can last a year or more, and the patients' adherence and response time to daily at-home questionnaires may vary significantly over time. The aim of this study was to understand patterns and changes in patients' completion of daily PROMs during longitudinal clinical studies. METHODS: Data were collected from 1342 patients randomized into three respiratory clinical trials (NCT03401229, NCT03347279, and NCT03406078). PROMs were completed by patients using electronic handheld devices that collected the starting and completion times. A Bayesian generalized linear mixed-effects model was used to identify unbiased coefficients associated with PROM adherence and response time using patient, site, and calendar features as covariates. RESULTS: Adherence decreased over time after randomization, and the rate of decrease was higher in younger patients. The 14-day pre-randomization adherence was correlated with adherence throughout the study. Patients were also more adherent during working days compared to non-working days. Oldest patients took twice as long to complete PROMs throughout the study; however, the response time for all patients decreased during the first month of the study regardless of age. Response time increased 7 days before and after the date of a scheduled clinic visit and when a patient-reported higher symptom burden. CONCLUSION: Detailed analyses of adherence and response time for daily PROMs in clinical trials can provide significant insights about trends of patient behavior in longitudinal clinical studies with high baseline adherence.

Ambulance and helicopter response time. Association with patient outcome and illness severity: Protocol of a systematic literature review and meta-analysis.

BACKGROUND: Response time for emergency medical service units is a key performance indicator. Studies have shown reduced response time association with improved outcome for specific critical conditions. To achieve short response time, emergency vehicles utilize lights and sirens, and crews are allowed to be non-compliant with traffic rules, posing a risk for accident. The purpose of the systematic review and meta-analysis is to provide an overview of the current body of evidence regarding the association, if any, between ambulance and helicopter response time and major complications and mortality in patients conveyed by ambulance and/or helicopter. Our secondary aim will be to enhance knowledge in the field of criteria-based dispatch to provide decision makers with evidence to optimize dispatch of limited resources. RESEARCH QUESTIONS: What is the association between overall emergency medical services unit response time and patient outcomes, major complications, and time-critical conditions? What is the internal and external validity of the included literature? METHODS: We plan the systematic review and meta-analysis to be in accordance with the Cochrane Handbook and Joanna Briggs Institute Manual for Systematic Reviews. The methodology will include formulating the review questions using a Population, Exposure, and Outcome framework. Every study design is eligible, including qualitative, quantitative, and mixed-methods designs. We will include all articles in English, Scandinavian, German, French and Portuguese in this systematic review. RESULTS: We will publish results from the systematic review and meta-analysis in a peer-reviewed journal and we will present the results at scientific conferences and meetings. Results will also be available at www.ahrtemis.dk. CONCLUSION: We will base our conclusions on the findings of the review and meta-analysis.

A study of prehospital EMS response time and influencing factors in the main urban area of Chongqing, China.

Shortening the prehospital emergency medical service (EMS) response time is crucial for saving lives and lowering mortality and disability rates in patients with sudden illnesses. Descriptive analyses of prehospital EMS response time and each component were conducted separately using ambulance trip data from the 120 Dispatch Command Centre in the main urban area of Chongqing in 2021, and then, logistic regression analyses were used to explore the influencing factors. The median prehospital EMS response time in the main urban area of Chongqing was 14.52 min and the mean was 16.14 min. A 44.89% of prehospital EMS response time exceeded 15 min. Response time was more likely to surpass this threshold during peak hours and in high population density areas. Conversely, lower probabilities exceeding 15 min were observed during the night shift, summer, and autumn seasons, and areas with a high density of emergency station. 33.28% of preparation time was >3 min, with the night shift and high population density areas more likely to be >3 min, while for the summer and autumn seasons, high Gross National Product (GDP) per capita areas had a lower likelihood of having preparation time >3 min. 45.52% of travel time was >11 min, with peak hours, summer and autumn, and high GDP per capita areas more likely to have had a travel time >11 min, while night shift and high emergency station density areas had a lower likelihood of travel time >11 min. The primary factors influencing prehospital EMS response time were shifts, traffic scenarios, seasons, GDP per capita, emergency station density, and population density. Relevant departments can devise effective interventions to reduce response time through resource allocation and department coordination, staff training and work arrangement optimisation, as well as public participation and education, thereby enhancing the efficiency of prehospital emergency medical services.

Investigation of the Performance of Various Low-Cost Radon Monitors under Variable Environmental Conditions.

A comparison of low-cost radon monitors was conducted at the Laboratory of Natural Radiation (LNR). The monitors we evaluated were EcoQube, RadonEye, RadonEye Plus2, Spirit, ViewPlus, ViewRadon and WavePlus. An AlphaGUARD monitor calibrated at the Laboratory of Environmental Radioactivity of the University of Cantabria (LaRUC), accredited for testing and calibration according to ISO/IEC 17025, provided the reference value of radon concentration. The temporal stability of the monitors was studied, obtaining a percentage of missing records ranged from 1% to 19% of the data. The main technical characteristics studied were temporal stability, measurement ranges, accuracy, correlation and response time. The main results show that the measurement ranges align with those specified by their manufacturers, with percentage differences with respect to the reference monitor of between 5% and 16%. The diversity found for response time is remarkable, with values ranging from 1 to 15 h, with Pearson correlation factors between 0.63 and 0.90.

Call to Action: Investigating Interaction Delay in Smartphone Notifications.

Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users' interaction delay. In this paper, we present the results of a comprehensive study that identified the factors that influence users' interaction delay to their smartphone notifications. We analyzed almost 10 million notifications collected in-the-wild from 922 users and computed their response times with regard to their demographics, their Big Five personality trait scores and the device's charging state. Depending on the app category, the following tendencies can be identified over the course of the day: Most notifications were logged in late morning and late afternoon. This number decreases in the evening, between 8 p.m. and 11 p.m., and at the same time exhibits the lowest average interaction delays at daytime. We also found that the user's sex and age is significantly associated with the response time. Based on the results of our study, we encourage developers to incorporate more information on the user and the executing device in their notification strategy to notify users more effectively.

Applications of Alpha Neurofeedback Processes for Enhanced Mental Manipulation of Unfamiliar Molecular and Spatial Structures.

This study explores a novel approach to enhancing cognitive proficiency by targeting neural mechanisms that facilitate science and math learning, especially mental rotation. The study specifically examines the relationship between upper alpha intensity and mental rotation skills. Although prior neurofeedback research for increasing upper alpha highlights this correlation, mostly with familiar objects, novel chemistry and math learning prompts envisioning unfamiliar objects which question the persistence of this correlation. This study revisits the upper alpha and mental rotation relationship in the context of unfamiliar objects with a single neurofeedback session and examines the efficiency of manual and automatic neurofeedback protocols. Results will provide a basis for integrating neurofeedback protocols into learning applications for enhanced learning. Our study encompassed three cohorts: Group 1 experienced an automatic neurofeedback protocol, Group 2 received a manual neurofeedback protocol, and the control group had no neurofeedback intervention. The experimental phases involved EEG measurement of individual upper alpha (frequency of maximal power + 2 Hz) intensity, mental rotation tasks featuring geometric and unfamiliar molecular stimuli, one neurofeedback session for applicable groups, post-treatment upper alpha level assessments, and a mental rotation retest. The neurofeedback groups exhibited increased levels of upper alpha power, which was correlated with improved response time in mental rotation, regardless of stimulus type, compared to the control group. Both neurofeedback protocols achieved comparable results. This study advocates integrating neurofeedback into learning software for optimal learning experiences, highlighting a single session's efficacy and the substantial neurofeedback protocol's impact in enhancing upper alpha oscillations.

Intraindividual Variability in Attention-Deficit/Hyperactivity Disorder: An Ex-Gaussian Approach.

Higher intraindividual variability (IIV) of response times is consistently noted in children with attention-deficit/hyperactivity disorder (ADHD). The current study investigated whether an ex-Gaussian estimate of IIV in children ages 6-13 years-old could differentiate between children with and without ADHD. Children completed a computerized go/no-go task to estimate trial-by-trial IIV and a continuous performance test (CPT) to estimate inattention and hyperactivity/impulsivity. Parents completed questionnaires assessing inattention and hyperactive/impulsive behaviors. IIV, commission errors, and attention problems as rated by parents were significantly greater in the ADHD group. Groups did not differ on errors of omission, but IIV was predictive of omission errors and parent ratings of inattention and hyperactivity/impulsivity. IIV predicted group membership (ADHD vs Control) whereas errors of omission did not. However, IIV did not improve diagnostic accuracy when parent ratings were used, such that parent ratings were superior at determining diagnosis. Current results support the use of IIV, based on the ex-Gaussian approach, as an objective measure of attention problems over omission errors on sustained attention CPT-type tasks. Additionally, while parent ratings of attention impairment remain the best predictor of ADHD diagnostic status, IIV may be helpful in determining when further assessment is required in the absence of those ratings.

Long-term comparison and performance study of consumer grade electronic radon integrating monitors.

This study reports the performance of 7 types of consumer grade passive Electronic Radon Integrating Monitors, ERIM (AlphaE, AER Plus, Canary, Corentium Pro, Radon Scout Home, Ramon and Wave) and passive etched track radon detectors. All monitors and passive radon detectors were exposed side by side for 2 periods of 3 months under controlled conditions in the UKHSA radon chamber and in a stainless steel container to an average radon concentration of 4781 Bq m-3 and 166 Bq m-3, respectively. The performance of each individual monitor was compared with Atmos 12DPX and AlphaGUARD P30 reference instruments. The performance of the monitors was evaluated by estimating the biased, precision and measurement errors of each type. It was found that UKHSA passive radon detectors showed excellent performance (measurement error < 10%) at both higher and lower exposures. The AlphaE, Canary and Ramon showed excellent performance, with measurement error < 10%, when they were exposed to radon concentrations between 4000 Bq m-3 and 6000 Bq m-3 in the UKHSA radon chamber. However, when the monitors were exposed to radon levels below the UK radon Action Level of 200 Bq m-3, the only ERIM which had a measurement error <10% was the Radon Scout Home. All other monitors showed a significant decrease in their performance with measurement errors ranging between 20% and 50%. The calibration factor, which is the ratio between the measured value (background is subtracted) and the reference value, was also studied. It was found that the calibration factors of individual monitors changed significantly. Calibration measurements in 2019 and in 2023 found that the percentage change varied between -46% and +63%. This shows the importance of initial and regular calibration, and maintenance of the monitors. .

Predicting response time on self-report rating scale assessments of noncognitive constructs.

Methodological studies of response time on noncognitive assessments have separately demonstrated the relevance of content trait level and response styles as predictive factors. In this paper we examine the simultaneous relevance of both factors as well as the potential for omitted predictor bias when ignoring either factor. Using response time data from several different noncognitive assessments, we demonstrate how a multilevel regression model that attends simultaneously to content and response style factors leads to consistent findings that support the simultaneous relevance of both factors. The average effects of response style consistently emerge as stronger, although also show greater respondent-level variability, possibly due to the multiple different underlying causes of response style behavior. Some implications for the use of response times in noncognitive measurement are considered.

Timed picture naming norms for 800 photographs of 200 objects in English.

The present study presents picture-naming norms for a large set of 800 high-quality photographs of 200 natural objects and artefacts spanning a range of categories, with four unique images per object. Participants were asked to provide a single, most appropriate name for each image seen. We report recognition latencies for each image, and several normed variables for the provided names: agreement, H-statistic (i.e. level of naming uncertainty), Zipf word frequency and word length. Rather than simply focusing on a single name per image (i.e. the modal or most common name), analysis of recognition latencies showed that it is important to consider the diversity of labels that participants may ascribe to each pictured object. The norms therefore provide a list of candidate labels per image with weighted measures of word length and frequency per image that incorporate all provided names, as well as modal measures based on the most common name only.

Using mixture modeling to examine differences in perceptual decision-making as a function of the time and method of participant recruitment.

We examine whether perceptual decision-making differs as a function of the time in the academic term and whether the participant is an undergraduate participating for course credit, a paid in-person participant, or a paid online participant recruited via Amazon Mechanical Turk. We use a mixture modeling approach within an evidence accumulation framework that separates stimulus-driven responses from contaminant responses, allowing us to distinguish between performance when a participant is engaged in the task and the consistency in this task focus. We first report a survey showing cognitive psychologists expect performance and response caution to be lower among undergraduate participants recruited at the end of the academic term compared to those recruited near the start, and highest among paid in-person participants. The findings from two experiments using common paradigms revealed very little evidence of time-of-semester effects among course credit participants on accuracy, response time, efficiency of information processing (when engaged in the task), caution, and non-decision time, or consistency in task focus. However, paid in-person participants did tend to be more accurate than the other two groups. Groups showed similar effects of speed/accuracy emphasis on response caution and of discrimination difficulty on information processing efficiency, but the effect of speed/accuracy emphasis on information processing efficiency was less consistent among groups. We conclude that online crowdsourcing platforms can provide quality perceptual decision-making data, but recommend that mixture modeling be used to adequately account for data generated by processes other than the psychological phenomena under investigation.

A spatially continuous diffusion model of visual working memory.

We present results from five visual working memory (VWM) experiments in which participants were briefly shown between 2 and 6 colored squares. They were then cued to recall the color of one of the squares and they responded by choosing the color on a continuous color wheel. The experiments provided response proportions and response time (RT) measures as a function of angle for the choices. Current VWM models for this task include discrete models that assume an item is either within working memory or not and resource models that assume that memory strength varies as a function of the number of items. Because these models do not include processes that allow them to account for RT data, we implemented them within the spatially continuous diffusion model (SCDM, Ratcliff, 2018) and use the experimental data to evaluate these combined models. In the SCDM, evidence retrieved from memory is represented as a spatially continuous normal distribution and this drives the decision process until a criterion (represented as a 1-D line) is reached, which produces a decision. Noise in the accumulation process is represented by continuous Gaussian process noise over spatial position. The models that fit best from the discrete and resource-based classes converged on a common model that had a guessing component and that allowed the height of the normal memory-strength distribution to vary with number of items. The guessing component was implemented as a regular decision process driven by a flat evidence distribution, a zero-drift process. The combination of choice and RT data allows models that were not identifiable based on choice data alone to be discriminated.