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Eye-gaze tracking is traditionally used to analyze ocular parameters for investigating visual psychology, marketing study, behavior analysis, and so on. Currently, eye-gaze trackers are also being used to control electronic interfaces in assistive technology, automobile control, and even consumer electronic products like smartphones and tablets. However, there are not many attempts to combine these two streams of research on active and passive uses of eye-gaze trackers. This article compares a few ocular parameters to estimate users’ cognitive load in eye-gaze-controlled interfaces. It was found that average velocity of a particular type of microsaccadic eye movement called Saccadic Intrusion is most indicative of users’ cognitive load compared to pupil dilation and eye-blink-based parameters. Results from the study can be used to develop new metrics of cognitive load measurement, as well as to design intelligent gaze-controlled interfaces that respond to users’ cognitive load. [ABSTRACT FROM AUTHOR]

Objective: To determine the effects of an adversary's behavior on the defender's accurate and timely detection of network threats.Background: Cyber attacks cause major work disruption. It is important to understand how a defender's behavior (experience and tolerance to threats), as well as adversarial behavior (attack strategy), might impact the detection of threats. In this article, we use cognitive modeling to make predictions regarding these factors.Method: Different model types representing a defender, based on Instance-Based Learning Theory (IBLT), faced different adversarial behaviors. A defender's model was defined by experience of threats: threat-prone (90% threats and 10% nonthreats) and nonthreat-prone (10% threats and 90% nonthreats); and different tolerance levels to threats: risk-averse (model declares a cyber attack after perceiving one threat out of eight total) and risk-seeking (model declares a cyber attack after perceiving seven threats out of eight total). Adversarial behavior is simulated by considering different attack strategies: patient (threats occur late) and impatient (threats occur early).Results: For an impatient strategy, risk-averse models with threat-prone experiences show improved detection compared with risk-seeking models with nonthreat-prone experiences; however, the same is not true for a patient strategy.Conclusions: Based upon model predictions, a defender's prior threat experiences and his or her tolerance to threats are likely to predict detection accuracy; but considering the nature of adversarial behavior is also important.Application: Decision-support tools that consider the role of a defender's experience and tolerance to threats along with the nature of adversarial behavior are likely to improve a defender's overall threat detection. [ABSTRACT FROM AUTHOR]

Objective: To determine the effects of an adversary’s behavior on the defender’s accurate and timely detection of network threats. Background: Cyber attacks cause major work disruption. It is important to understand how a defender’s behavior (experience and tolerance to threats), as well as adversarial behavior (attack strategy), might impact the detection of threats. In this article, we use cognitive modeling to make predictions regarding these factors. Method: Different model types representing a defender, based on Instance-Based Learning Theory (IBLT), faced different adversarial behaviors. A defender’s model was defined by experience of threats: threat-prone (90% threats and 10% nonthreats) and nonthreat-prone (10% threats and 90% nonthreats); and different tolerance levels to threats: risk-averse (model declares a cyber attack after perceiving one threat out of eight total) and riskseeking (model declares a cyber attack after perceiving seven threats out of eight total). Adversarial behavior is simulated by considering different attack strategies: patient (threats occur late) and impatient (threats occur early). Results: For an impatient strategy, risk-averse models with threat-prone experiences show improved detection compared with risk-seeking models with nonthreat-prone experiences; however, the same is not true for a patient strategy. Conclusions: Based upon model predictions, a defender’s prior threat experiences and his or her tolerance to threats are likely to predict detection accuracy; but considering the nature of adversarial behavior is also important. Application: Decision-support tools that consider the role of a defender’s experience and tolerance to threats along with the nature of adversarial behavior are likely to improve a defender’s overall threat detection. [ABSTRACT FROM AUTHOR]

Hills and Hertwig (2012) challenge the proposed similarity of the exploration-exploitation transitions found in Gonzalez and Dutt (2011) between the 2 experimental paradigms of decisions from experience (sampling and repeated-choice), which was predicted by an instance-based learning (IBL) model. The heart of their argument is that in the sampling paradigm, an impression of reduced exploration over time (alternation rate, A-rate) is produced by an inverse relationship between the sample size and the A-rate, and the aggregation of participants with different sample sizes. They suggest a normalization of the A-rate, which produces constant A-rate curves during sampling, and conclude with certain "ensuing problems for the IBL model." We show that the reduction of A-rate during sampling occurs even when sample length is controlled for; that regardless of the sampling length, the maximization behavior during sampling predicts the final choice; and that the IBL model accounts for a negative correlation between sample size and the predicted A-rate. Furthermore, when the IBL model's A-rates are normalized they result in a flattened exploration curve similar to that found in human data. These results indicate that the transition from exploration to exploitation in the sampling paradigm (which has also been found in the repeated-choice paradigm) is not an illusion resulting from data aggregation; and furthermore, the same data with or without normalization may be interpreted differently, but such interpretations do not invalidate the mechanisms of the IBL model. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool. [Copyright &y& Elsevier]

The use of analogies and repeated feedback might help people learn about the dynamics of climate change. In this paper, we study the influence of repeated feedback on the control of a carbon-dioxide (CO) concentration to a goal level in a Dynamic Climate Change Simulator (DCCS) using the 'bathtub' analogy. DCCS is a simplification of the complex climate system into its essential elements: CO concentration (stock); man-made CO emissions (inflow); and natural CO removal or absorption in the atmosphere (outflow). In a laboratory experiment involving DCCS, we manipulated feedback delays in two ways: the frequency of emission decisions and the rate of CO absorption from the atmosphere (climate dynamics). Our results revealed that participants' ability to control the CO concentration generally remained poor even in conditions where they were allowed to revise their emission decisions frequently (i.e., every 2 years) and where the climate dynamics were rapid (i.e., 1.6% of CO concentration was removed every year). Participants' control of the concentration only improved with repeated feedback in conditions of lesser feedback delay. Moreover, the delay due to climate dynamics had a greater effect on participants' control than the delay due to emission decisions frequency. We provide future research directions and highlight the potential of using simulations like DCCS to help people learn about dynamics of Earth's climate. [ABSTRACT FROM AUTHOR]

In decisions from experience, there are 2 experimental paradigms: sampling and repeated-choice. In the sampling paradigm, participants sample between 2 options as many times as they want (i.e., the stopping point is variable), observe the outcome with no real consequences each time, and finally select 1 of the 2 options that cause them to earn or lose money. In the repeated-choice paradigm, participants select 1 of the 2 options for a fixed number of times and receive immediate outcome feedback that affects their earnings. These 2 experimental paradigms have been studied independently, and different cognitive processes have often been assumed to take place in each, as represented in widely diverse computational models. We demonstrate that behavior in these 2 paradigms relies upon common cognitive processes proposed by the instance-based learning theory (IBLT; Gonzalez, Lerch, & Lebiere, 2003) and that the stopping point is the only difference between the 2 paradigms. A single cognitive model based on IBLT (with an added stopping point rule in the sampling paradigm) captures human choices and predicts the sequence of choice selections across both paradigms. We integrate the paradigms through quantitative model comparison, where IBLT outperforms the best models created for each paradigm separately. We discuss the implications for the psychology of decision making. [ABSTRACT FROM AUTHOR]

Abstract: Recent research in behavioral sciences presents strong evidence of poor human understanding for dynamic systems. Computer-based dynamic control tasks have an important potential for helping behavioral scientists advance research that investigates reasons for poor understanding and for helping students understand how dynamic systems work. In this paper, we introduce a simulation called Dynamic Stocks and Flows (DSF) that portrays the basic building blocks of dynamic systems: an accumulation; an inflow and outflow determined by an environment; and an inflow and outflow determined by a decision maker. In DSF, decision makers control the accumulation to a goal level by making repeated inflow and outflow decisions. We provide details of an experiment conducted with DSF that highlight some problems people face in controlling a dynamic system with different kinds of environmental inflow and outflow functions. DSF is flexible enough to represent dynamic systems with continuous or discrete accumulations, and with real-time or event-driven decision-making. We suggest that these and other features in DSF make it a good research and educational tool. [Copyright &y& Elsevier]

This paper presents a case of parsimony and generalization in model comparisons. We submitted two versions of the same cognitive model to the Market Entry Competition (MEC), which involved four-person and two-alternative (enter or stay out) games. Our model was designed according to the Instance-Based Learning Theory (IBLT). The two versions of the model assumed the same cognitive principles of decision making and learning in the MEC. The only difference between the two models was the assumption of homogeneity among the four participants: one model assumed homogeneous participants (IBL-same) while the other model assumed heterogeneous participants (IBL-different). The IBL-same model involved three free parameters in total while the IBL-different involved 12 free parameters, i.e., three free parameters for each of the four participants. The IBL-different model outperformed the IBL-same model in the competition, but after exposing the models to a more challenging generalization test (the Technion Prediction Tournament), the IBL-same model outperformed the IBL-different model. Thus, a loser can be a winner depending on the generalization conditions used to compare models. We describe the models and the process by which we reach these conclusions. [ABSTRACT FROM AUTHOR]

Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between‐subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk‐seeking behavior among participants. We developed cognitive and heuristics models using instance‐based learning theory and natural mean heuristic. Results reveal models' dependency on recent and frequent observations during information sampling. These experience‐based cognitive models could help build artificial intelligence models with fewer preference reversals. This paper focuses on investigating the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Furthermore, Uttrani et al. develop cognitive models using instance‐based learning theory and natural mean heuristic to account for human choices across different problem frames and types in the experience format. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring. [ABSTRACT FROM AUTHOR]

Background: To assess the effect of health information on immunisation uptake in rural India, we conducted an individually randomised controlled trial of health information messages targeting the mothers of unvaccinated or incompletely vaccinated children through home visits in rural Uttar Pradesh, India.Methods and Findings: The study tested a brief intervention that provided mothers face-to-face with information on the benefits of the tetanus vaccine. Participants were 722 mothers of children aged 0-36 months who had not received 3 doses of diphtheria-pertussis-tetanus (DPT) vaccine (DPT3). Mothers were randomly assigned in a ratio of 1:1:1 to 1 of 3 study arms: mothers in the first treatment group received information framed as a gain (e.g., the child is less likely to get tetanus and more likely to be healthy if vaccinated), mothers in the second treatment group received information framed in terms of a loss (e.g., the child is more likely to get tetanus and suffer ill health if not vaccinated), and the third arm acted as a control group, with no information given to the mother. Surveys were conducted at baseline (September 2015) and after the intervention (April 2016). The primary outcome was the proportion of children who had received DPT3 measured after 7 months of follow-up. The analysis was by intention to treat. A total of 16 (2.2%) participants were lost to follow-up. The coverage of DPT3 was 28% in the control group and 43% in the pooled information groups, giving a risk difference of 15 percentage points (95% CI: 7% to 22%, p < 0.001) and a relative risk of 1.52 (95% CI: 1.2 to 1.9, p < 0.001). The information intervention increased the rate of measles vaccination by 22 percentage points (risk difference: 22%, 95% CI: 14% to 30%, p < 0.001; relative risk: 1.53, 95% CI: 1.29 to 1.80) and the rate of full immunisation by 14 percentage points (risk difference: 14%, 95% CI: 8% to 21%, p < 0.001; relative risk: 1.72, 95% CI: 1.29 to 2.29). It had a large positive effect on knowledge of the causes, symptoms, and prevention of tetanus but no effect on perceptions of vaccine efficacy. There was no difference in the proportion of children with DPT3 between the group that received information framed as a loss and the group that received information framed as a gain (risk difference: 4%, 95% CI: -5% to 13%; p = 0.352; relative risk: 1.11, 95% CI: 0.90 to 1.36). The cost per disability-adjusted life year averted of providing information was US$186, making the intervention highly cost-effective with respect to the WHO-recommended threshold of once the gross domestic product per capita (US$793 in the case of Uttar Pradesh). Key study limitations include the modest sample size for this trial, limiting power to detect small differences in the framing of information, and the potential for contamination among households.Conclusions: Providing mothers of unvaccinated/incompletely vaccinated children with information on tetanus and the benefits of DPT vaccination substantially increased immunisation coverage and was highly cost-effective. The framing of the health information message did not appear to matter.Trial Registration: The trial is registered with ISRCTN, number ISRCTN84560580. [ABSTRACT FROM AUTHOR]

Maternal depression is an important public health concern. We investigated whether a national-scale initiative that provides cash transfers to women giving birth in government health facilities, the Janani Suraksha Yojana (JSY), reduced maternal depression in India’s largest state, Uttar Pradesh. Using primary data on 1695 women collected in early 2015, our quasi-experimental design exploited the fact that some women did not receive the JSY cash due to administrative problems in its disbursement – reasons that are unlikely to be correlated with determinants of maternal depression. We found that receipt of the cash was associated with an 8.5% reduction in the continuous measure of maternal depression and a 36% reduction in moderate depression. There was no evidence of an association with measures of emotional well-being, namely happiness and worry. The results suggest that the JSY had a clinically meaningful effect in reducing the burden of maternal depression, possibly by lessening the financial strain of delivery care. They contribute to the evidence that financial incentive schemes may have public health benefits beyond improving uptake of targeted health services. [ABSTRACT FROM AUTHOR]

Childhood vaccinations are among the most cost-effective health interventions. Yet, in India, where immunisation services are widely available free of charge, a substantial proportion of children remain unvaccinated. We revisit households 30 months after a randomised experiment of a health information intervention designed to educate mothers on the benefits of child vaccination in Uttar Pradesh, India. We find that the large short-term effects on the uptake of diphtheria–pertussis–tetanus and measles vaccination were sustained at 30 months, suggesting the intervention did not simply bring forward vaccinations. We apply causal forests and find that the intervention increased vaccination uptake, but that there was substantial variation in the magnitude of the estimated effects. We conclude that characterising those who benefited most and conversely those who benefited least provides policy-makers with insights on how the intervention worked, and how the targeting of households could be improved. [ABSTRACT FROM AUTHOR]

Background: Social franchising is the fastest growing market-based approach to organising and improving the quality of care in the private sector of low- and middle-income countries, but there is limited evidence on its impact and cost-effectiveness. The "Sky" social franchise model was introduced in the Indian state of Uttar Pradesh in late 2013. Methods/design: Difference-in-difference methods will be used to estimate the impact of the social franchise programme on the quality and coverage of health services along the continuum of care for reproductive, maternal and newborn health. Comparison clusters will be selected to be as similar as possible to intervention clusters using nearest neighbour matching methods. Two rounds of data will be collected from a household survey of 3600 women with a birth in the last 2 years and a survey of 450 health providers in the same localities. To capture the full range of effects, 59 study outcomes have been specified and then grouped into conceptually similar domains. Methods to account for multiple inferences will be used based on the pre-specified grouping of outcomes. A process evaluation will seek to understand the scale of the social franchise network, the extent to which various components of the programme are implemented and how impacts are achieved. An economic evaluation will measure the costs of setting up, maintaining and running the social franchise as well as the cost-effectiveness and financial sustainability of the programme. Discussion: There is a dearth of evidence demonstrating whether market-based approaches such as social franchising can improve care in the private sector. This evaluation will provide rigorous evidence on whether an innovative model of social franchising can contribute to better population health in a low-income setting. [ABSTRACT FROM AUTHOR]

Objective: We aim to learn about the cognitive mechanisms governing the decisions of attackers and defenders in cybersecurity involving intrusion detection systems (IDSs). Background: Prior research has experimentally studied the role of the presence and accuracy of IDS alerts on attacker's and defender's decisions using a game-theoretic approach. However, little is known about the cognitive mechanisms that govern these decisions. Method: To investigate the cognitive mechanisms governing the attacker's and defender's decisions in the presence of IDSs of different accuracies, instance-based learning (IBL) models were developed. One model (NIDS) disregarded the IDS alerts and one model (IDS) considered them in the instance structure. Both the IDS and NIDS models were trained in an existing dataset where IDSs were either absent or present and they possessed different accuracies. The calibrated IDS model was tested in a newly collected test dataset where IDSs were present 50% of the time and they possessed different accuracies. Results: Both the IDS and NIDS models were able to account for human decisions in the training dataset, where IDS was absent or present and it possessed different accuracies. However, the IDS model could accurately predict the decision-making in only one of the several IDS accuracy conditions in the test dataset. Conclusions: Cognitive models like IBL may provide some insights regarding the cognitive mechanisms governing the decisions of attackers and defenders in conditions not involving IDSs or IDSs of different accuracies. Application: IBL models may be helpful for penetration testing exercises in scenarios involving IDSs of different accuracies. [ABSTRACT FROM AUTHOR]

Binary-choice reactions are typically faster when the stimulus location corresponds with that of the response than when it does not. This advantage of spatial correspondence is known as the stimulusresponse compatibility (SRC) effect when the mapping of stimulus location, as the relevant stimulus dimension, is varied to be compatible or incompatible with response location. It is called the Simon effect when stimulus location is task-irrelevant. The SRC effect is eliminated when compatible and incompatible spatial mappings are mixed within a trial block, and the Simon effect is eliminated when the Simon task is mixed with the SRC task with incompatible spatial mapping. Eliminations of both types have been attributed to suppression of an automatic response-activation route. We tested predictions of this suppression hypothesis for conditions in which the SRC and Simon tasks were intermixed and the spatial mappings on the SRC trials could be compatible or incompatible. In Experiment 1, the two tasks were equally likely, as were compatible and incompatible spatial mappings on SRC trials; in Experiment 2, the SRC or Simon task was more frequent; and, in Experiment 3, the compatible or incompatible location mapping for the SRC task was more frequent. The SRC effect was absent overall in all experiments, whereas the Simon effect was robust to the manipulations and showed the characteristic decrease across the reaction time (RT) distribution. This dissociation of effects implies that the automatic responseactivation route is not suppressed in mixed conditions and suggests that mixing influences the SRC and Simon effects by different means. [ABSTRACT FROM AUTHOR]

Duchenne Muscular Dystrophy has emerged as a model to assess cognitive domains. The DMD gene variant location and its association with variable degrees of cognitive impairment necessitate identification of a common denominator. Computer architectures provide a framework to delineate the mechanisms involved in the cognitive functioning of the human brain. Copy number variations in the 79 exons of DMD gene were screened in 84 DMD subjects by Multiplex Ligation-dependent Probe Amplification (MLPA). DMD subjects were categorized based on the presence or absence of DP140 isoform. The cognitive and neuropsychological assessments were carried out as per inclusion criteria using standard scales. Instance-based learning theory (IBLT) based on the partial matching process was developed to mimic Stroop Color and Word Task (SCWT) performance on Adaptive Control of Thought-Rational (ACT-R) cognitive architecture based on IBLT. Genotype–phenotype correlation was conducted based on the mutation location in DMD gene. Assessment of specific cognitive domains in DP140 − ve group corresponded to the involvement of multiple brain lobes including temporal (verbal and visual learning and memory), parietal (visuo-conceptual and visuo-constructive abilities) and frontal (sustained and focused attention, verbal fluency, cognitive control). Working memory axis was found to be the central domain through tasks including RAVLT trial 1, recency effect, digit span backward, working memory index, arithmetic subtests in the Dp140 − ve group. IBLT validated the non-reliance of DMD subjects on recency indicating affected working memory domain. Modeling strategy revealed altered working memory processes in DMD cases with affected Dp140 isoform. DMD brain was observed to rely on primacy than the recency suggesting alterations in working memory capacity. Modeling revealed lowered activation of DMD brain with Dp140 − ve in order to retrieve the instances. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to determine how monetary motivations influence decision making of humans performing as security analysts and hackers in a cybersecurity game.Background: Cyberattacks are increasing at an alarming rate. As cyberattacks often cause damage to existing cyber infrastructures, it is important to understand how monetary rewards may influence decision making of hackers and analysts in the cyber world. Currently, only limited attention has been given to this area.Method: In an experiment, participants were randomly assigned to three between-subjects conditions ( n = 26 for each condition): equal payoff, where the magnitude of monetary rewards for hackers and defenders was the same; rewarding hacker, where the magnitude of monetary reward for hacker's successful attack was 10 times the reward for analyst's successful defense; and rewarding analyst, where the magnitude of monetary reward for analyst's successful defense was 10 times the reward for hacker's successful attack. In all conditions, half of the participants were human hackers playing against Nash analysts and half were human analysts playing against Nash hackers.Results: Results revealed that monetary rewards for human hackers and analysts caused a decrease in attack and defend actions compared with the baseline. Furthermore, rewarding human hackers for undetected attacks made analysts deviate significantly from their optimal behavior.Conclusions: If hackers are rewarded for their undetected attack actions, then this causes analysts to deviate from optimal defend proportions. Thus, analysts need to be trained not become overenthusiastic in defending networks.Application: Applications of our results are to networks where the influence of monetary rewards may cause information theft and system damage. [ABSTRACT FROM AUTHOR]

Background: Social franchising is the fastest growing market-based approach to organising and improving the quality of care in the private sector of low- and middle-income countries, but there is limited evidence on its impact and cost-effectiveness. The "Sky" social franchise model was introduced in the Indian state of Uttar Pradesh in late 2013.Methods/design: Difference-in-difference methods will be used to estimate the impact of the social franchise programme on the quality and coverage of health services along the continuum of care for reproductive, maternal and newborn health. Comparison clusters will be selected to be as similar as possible to intervention clusters using nearest neighbour matching methods. Two rounds of data will be collected from a household survey of 3600 women with a birth in the last 2 years and a survey of 450 health providers in the same localities. To capture the full range of effects, 59 study outcomes have been specified and then grouped into conceptually similar domains. Methods to account for multiple inferences will be used based on the pre-specified grouping of outcomes. A process evaluation will seek to understand the scale of the social franchise network, the extent to which various components of the programme are implemented and how impacts are achieved. An economic evaluation will measure the costs of setting up, maintaining and running the social franchise as well as the cost-effectiveness and financial sustainability of the programme.Discussion: There is a dearth of evidence demonstrating whether market-based approaches such as social franchising can improve care in the private sector. This evaluation will provide rigorous evidence on whether an innovative model of social franchising can contribute to better population health in a low-income setting. [ABSTRACT FROM AUTHOR]

We analyze the dynamics of repeated interaction of two players in the Prisoner's Dilemma ( PD) under various levels of interdependency information and propose an instance-based learning cognitive model ( IBL- PD) to explain how cooperation emerges over time. Six hypotheses are tested regarding how a player accounts for an opponent's outcomes: the selfish hypothesis suggests ignoring information about the opponent and utilizing only the player's own outcomes; the extreme fairness hypothesis weighs the player's own and the opponent's outcomes equally; the moderate fairness hypothesis weighs the opponent's outcomes less than the player's own outcomes to various extents; the linear increasing hypothesis increasingly weighs the opponent's outcomes at a constant rate with repeated interactions; the hyperbolic discounting hypothesis increasingly and nonlinearly weighs the opponent's outcomes over time; and the dynamic expectations hypothesis dynamically adjusts the weight a player gives to the opponent's outcomes, according to the gap between the expected and the actual outcomes in each interaction. When players lack explicit feedback about their opponent's choices and outcomes, results are consistent with the selfish hypothesis; however, when this information is made explicit, the best predictions result from the dynamic expectations hypothesis. [ABSTRACT FROM AUTHOR]

Three experiments simulating military RADAR detection addressed a training difficulty hypothesis (training with difficulty promotes superior later testing performance) and a procedural reinstatement hypothesis (test performance improves when training conditions match test conditions). Training and testing were separated by 1 week. Participants detected targets (either alphanumeric characters or vehicle pictures) occurring among distractors. Two secondary tasks were used to increase difficulty (a concurrent, irrelevant tone-counting task and a sequential, relevant action-firing response). In Experiment 1, involving alphanumeric targets with rapid displays, tone counting during training degraded test performance. In Experiment 2, involving vehicle targets with both sources of difficulty and slower presentation times, training under relevant difficulty aided test accuracy. In Experiment 3, involving vehicle targets and action firing with slow presentation times, test accuracy tended to be worst when neither training nor testing involved difficult conditions. These results show boundary conditions for the training difficulty and procedural reinstatement hypotheses. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: A prominent strategy to engage private sector health providers in low- and middle-income countries is clinical social franchising, an organisational model that applies the principles of commercial franchising for socially beneficial goals. The Matrika programme, a multi-faceted social franchise model to improve maternal health, was implemented in three districts of Uttar Pradesh, India, between 2013 and 2016. Previous research indicates that the intervention was not effective in improving the quality and coverage of maternal health services at the population level. This paper reports findings from an independent external process evaluation, conducted alongside the impact evaluation, with the aim of explaining the impact findings. It focuses on the main component of the programme, the "Sky" social franchise.Methods: We first developed a theory of change, mapping the key mechanisms through which the programme was hypothesised to have impact. We then undertook a multi-methods study, drawing on both quantitative and qualitative primary data from a wide range of sources to assess the extent of implementation and to understand mechanisms of impact and the role of contextual factors. We analysed the quantitative data descriptively to generate indicators of implementation. We undertook a thematic analysis of the qualitative data before holding reflective meetings to triangulate across data sources, synthesise evidence, and identify the main findings. Finally, we used the framework provided by the theory of change to organise and interpret our findings.Results: We report six key findings. First, despite the franchisor achieving its recruitment targets, the competitive nature of the market for antenatal care meant social franchise providers achieved very low market share. Second, all Sky health providers were branded but community awareness of the franchise remained low. Third, using lower-level providers and community health volunteers to encourage women to attend franchised antenatal care services was ineffective. Fourth, referral linkages were not sufficiently strong between antenatal care providers in the franchise network and delivery care providers. Fifth, Sky health providers had better knowledge and self-reported practice than comparable health providers, but overall, the evidence pointed to poor quality of care across the board. Finally, telemedicine was perceived by clients as an attractive feature, but problems in the implementation of the technology meant its effect on quality of antenatal care was likely limited.Conclusions: These findings point towards the importance of designing programmes based on a strong theory of change, understanding market conditions and what patients value, and rigorously testing new technologies. The design of future social franchising programmes should take account of the challenges documented in this and other evaluations. [ABSTRACT FROM AUTHOR]