EXPRESS: Modeling the Impact of Single vs Dual Presentation on Visual Discrimination Across Resolutions

Visual categorisation relies on our ability to extract useful diagnostic information from complex stimuli. To do this, we can utilise both the 'high-level' and 'low-level' information in a stimulus, however the extent to which changes in these properties impact the decision-making process is less clear. We manipulated participants' access to high-level category features via gradated reductions to image resolution while exploring the impact of access to additional category features through a dual stimulus presentation when compared to single stimulus presentation. Results showed that while increasing image resolution consistently resulted in better choice performance, no benefit was found for dual presentation over single presentation, despite responses for dual presentation being slower compared to single presentation. Applying the diffusion decision model revealed increases in drift rate as a function of resolution, but no change in drift rate for single versus dual presentation. The increase in response time for dual presentation was instead accounted for by an increase in response caution for dual presentations. These findings suggest that while increasing access to high-level features (via increased resolution) can improve participants' categorisation performance, increasing access to both high- and low-level features (via an additional stimulus) does not.

Applying Generative Artificial Intelligence to cognitive models of decision making

Introduction

Generative Artificial Intelligence has made significant impacts in many fields, including computational cognitive modeling of decision making, although these applications have not yet been theoretically related to each other. This work introduces a categorization of applications of Generative Artificial Intelligence to cognitive models of decision making.

Methods

This categorization is used to compare the existing literature and to provide insight into the design of an ablation study to evaluate our proposed model in three experimental paradigms. These experiments used for model comparison involve modeling human learning and decision making based on both visual information and natural language, in tasks that vary in realism and complexity. This comparison of applications takes as its basis Instance-Based Learning Theory, a theory of experiential decision making from which many models have emerged and been applied to a variety of domains and applications.

Results

The best performing model from the ablation we performed used a generative model to both create memory representations as well as predict participant actions. The results of this comparison demonstrates the importance of generative models in both forming memories and predicting actions in decision-modeling research.

Discussion

In this work, we present a model that integrates generative and cognitive models, using a variety of stimuli, applications, and training methods. These results can provide guidelines for cognitive modelers and decision making researchers interested in integrating Generative AI into their methods.

Multitasking While Driving: Central Bottleneck or Problem State Interference?

OBJECTIVE

The objective of this work was to investigate if visuospatial attention and working memory load interact at a central control resource or at a task-specific, information processing resource during driving.

BACKGROUND

In previous multitasking driving experiments, interactions between different cognitive concepts (e.g., attention and working memory) have been found. These interactions have been attributed to a central bottleneck or to the so-called problem-state bottleneck, related to working memory usage.

METHOD

We developed two different cognitive models in the cognitive architecture ACT-R, which implement the central vs. problem-state bottleneck. The models performed a driving task, during which we varied visuospatial attention and working memory load. We evaluated the model by conducting an experiment with human participants and compared the behavioral data to the model's behavior.

RESULTS

The problem-state-bottleneck model could account for decreased driving performance due to working memory load as well as increased visuospatial attentional demands as compared to the central-bottleneck model, which could not account for effects of increased working memory load.

CONCLUSION

The interaction between working memory and visuospatial attention in our dual tasking experiment can be best characterized by a bottleneck in the working memory. The model results suggest that as working memory load becomes higher, drivers manage to perform fewer control actions, which leads to decreasing driving performance.

APPLICATION

Predictions about the effect of different mental loads can be used to quantify the contribution of each subtask allowing for precise assessments of the current overall mental load, which automated driving systems may adapt to.

Modeling Human Steering Behavior in Haptic Shared Control of Autonomy-Enabled Unmanned Ground Vehicles

OBJECTIVE

A human steering model for teleoperated driving is extended to capture the human steering behavior in haptic shared control of autonomy-enabled Unmanned Ground Vehicles (UGVs).

BACKGROUND

Prior studies presented human steering models for teleoperation of a passenger-sized Unmanned Ground Vehicle, where a human is fully in charge of driving. However, these models are not applicable when a human needs to interact with autonomy in haptic shared control of autonomy-enabled UGVs. How a human operator reacts to the presence of autonomy needs to be studied and mathematically encapsulated in a module to capture the collaboration between human and autonomy.

METHOD

Human subject tests are conducted to collect data in haptic shared control for model development and validation. The ACT-R architecture and two-point steering model used in the previous literature are adopted to predict the operator's desired steering angle. A torque conversion module is developed to convert the steering command from the ACT-R model to human torque input, thus enabling haptic shared control with autonomy. A parameterization strategy is described to find the set of model parameters that optimize the haptic shared control performance in terms of minimum average lane keeping error (ALKE).

RESULTS

The model predicts the minimum ALKE human subjects achieve in shared control.

CONCLUSIONS

The extended model can successfully predict the best haptic shared control performance as measured by ALKE.

APPLICATION

This model can be used in place of human operators, enabling fully simulation-based engineering, in the development and evaluation of haptic shared control technologies for autonomy-enabled UGVs, including control negotiation strategies and autonomy capabilities.

A Cognitive Computational Approach to Social and Collective Decision-Making

Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.

Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making

Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the ϵ -softmax policy). However, this assumption is not guaranteed to hold - for example, a subject might disengage and lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Here, we introduce a new, computationally inexpensive method to dynamically infer the levels of noise in choice behavior, under a model assumption that agents can transition between two discrete latent states (e.g., fully engaged and random). Using simulations, we show that modeling noise levels dynamically instead of statically can substantially improve model fit and parameter estimation, especially in the presence of long periods of noisy behavior, such as prolonged attentional lapses. We further demonstrate the empirical benefits of dynamic noise estimation at the individual and group levels by validating it on four published datasets featuring diverse populations, tasks, and models. Based on the theoretical and empirical evaluation of the method reported in the current work, we expect that dynamic noise estimation will improve modeling in many decision-making paradigms over the static noise estimation method currently used in the modeling literature, while keeping additional model complexity and assumptions minimal.

Driver behavior and mental workload for takeover safety in automated driving: ACT-R prediction modeling approach

OBJECTIVE

Conditional automated driving (SAE level 3) requires the driver to take over the vehicle if the automated system fails. The mental workload that can occur in these takeover situations is an important human factor that can directly affect driver behavior and safety, so it is important to predict it. Therefore, this study introduces a method to predict mental workload during takeover situations in automated driving, using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. The mental workload prediction model proposed in this study is a computational model that can become the basis for emerging crash avoidance technologies in future autonomous driving situations.

METHODS

The methodology incorporates the ACT-R cognitive architecture, known for its robustness in modeling cognitive processes and predicting performance. The proposed takeover cognitive model includes the symbolic structure for repeatedly checking the driving situation and performing decision-making for takeover as well as Non-Driving-Related Tasks (NDRT). We employed the ACT-R cognitive model to predict mental workload during takeover in automated driving scenarios. The model's predictions are validated against physiological data and performance data from the validation test.

RESULTS

The model demonstrated high accuracy, with an r-square value of 0.97, indicating a strong correlation between the predicted and actual mental workload. It successfully captured the nuances of multitasking in driving scenarios, showcasing the model's adaptability in representing diverse cognitive demands during takeover.

CONCLUSIONS

The study confirms the efficacy of the ACT-R model in predicting mental workload for takeover scenarios in automated driving. It underscores the model's potential in improving driver-assistance systems, enhancing vehicle safety, and ensuring the efficient integration of human-machine roles. The research contributes significantly to the field of cognitive modeling, providing robust predictions and insights into human behavior in automated driving tasks.

Flood Risk and Preventive Choices: A Framework for Studying Human Behaviors

The topic of flood phenomena has always been of considerable importance due to the high risks it entails, both in terms of potential economic and social damage and the jeopardizing of human lives themselves. The spread of climate change is making this topic even more relevant. This work aims to contribute to evaluating the role that human factors can play in responding to critical hydrogeological phenomena. In particular, we introduce an agent-based platform for analyzing social behaviors in these critical situations. In our experiments, we simulate a population that is faced with the risk of a potentially catastrophic event. In this scenario, citizens (modeled through cognitive agents) must assess the risk they face by relying on their sources of information and mutual trust, enabling them to respond effectively. Specifically, our contributions include (1) an analysis of some behavioral profiles of citizens and authorities; (2) the identification of the "dissonance between evaluation and action" effect, wherein an individual may behave differently from what their information sources suggest, despite having full trust in them in situations of particular risk; (3) the possibility of using the social structure as a "social risk absorber", enabling support for a higher level of risk. While the results obtained at this level of abstraction are not exhaustive, they identify phenomena that can occur in real-world scenarios and can be useful in defining general guidelines.

A hybrid computational approach to anticipate individuals in sequential problem solving

Human-awareness is an ever more important requirement for AI systems that are designed to assist humans with daily physical interactions and problem solving. This is especially true for patients that need support to stay as independent as possible. To be human-aware, an AI should be able to anticipate the intentions of the individual humans it interacts with, in order to understand the difficulties and limitations they are facing and to adapt accordingly. While data-driven AI approaches have recently gained a lot of attention, more research is needed on assistive AI systems that can develop models of their partners' goals to offer proactive support without needing a lot of training trials for new problems. We propose an integrated AI system that can anticipate actions of individual humans to contribute to the foundations of trustworthy human-robot interaction. We test this in Tangram, which is an exemplary sequential problem solving task that requires dynamic decision making. In this task the sequences of steps to the goal might be variable and not known by the system. These are aspects that are also recognized as real world challenges for robotic systems. A hybrid approach based on the cognitive architecture ACT-R is presented that is not purely data-driven but includes cognitive principles, meaning heuristics that guide human decisions. Core of this Cognitive Tangram Solver (CTS) framework is an ACT-R cognitive model that simulates human problem solving behavior in action, recognizes possible dead ends and identifies ways forward. Based on this model, the CTS anticipates and adapts its predictions about the next action to take in any given situation. We executed an empirical study and collected data from 40 participants. The predictions made by CTS were evaluated with the participants' behavior, including comparative statistics as well as prediction accuracy. The model's anticipations compared to the human test data provide support for justifying further steps built upon our conceptual approach.

Meta-Learned Models of Cognition

Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. While the former requires the specification of a fixed set of computational structures and a definition of how these structures interact with each other, the latter necessitates the commitment to a particular prior and a likelihood function which - in combination with Bayes' rule - determine the model's behavior. In recent years, a new framework has established itself as a promising tool for building models of human cognition: the framework of meta-learning. In contrast to the previously mentioned model classes, meta-learned models acquire their inductive biases from experience, i.e., by repeatedly interacting with an environment. However, a coherent research program around meta-learned models of cognition is still missing to this day. The purpose of this article is to synthesize previous work in this field and establish such a research program. We accomplish this by pointing out that meta-learning can be used to construct Bayes-optimal learning algorithms, allowing us to draw strong connections to the rational analysis of cognition. We then discuss several advantages of the meta-learning framework over traditional methods and reexamine prior work in the context of these new insights.

An individual differences perspective on pragmatic abilities in the preschool years

Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N = 228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities. RESEARCH HIGHLIGHTS: Individual differences in pragmatic abilities are important to understanding variation in language development. Research in this domain lacks a precise theoretical framework and psychometrically high-quality measures. We present six tasks capturing a wide range of pragmatic abilities with excellent re-test reliability. We use a computational cognitive model to provide a substantive theory of individual differences in pragmatic abilities.

Locus Coeruleus Integrity Is Linked to Response Inhibition Deficits in Parkinson's Disease and Progressive Supranuclear Palsy

Parkinson's disease (PD) and progressive supranuclear palsy (PSP) both impair response inhibition, exacerbating impulsivity. Inhibitory control deficits vary across individuals and are linked with worse prognosis, and lack improvement on dopaminergic therapy. Motor and cognitive control are associated with noradrenergic innervation of the cortex, arising from the locus coeruleus (LC) noradrenergic system. Here we test the hypothesis that structural variation of the LC explains response inhibition deficits in PSP and PD. Twenty-four people with idiopathic PD, 14 with PSP-Richardson's syndrome, and 24 age- and sex-matched controls undertook a stop-signal task and ultrahigh field 7T magnetization-transfer-weighted imaging of the LC. Parameters of "race models" of go- versus stop-decisions were estimated using hierarchical Bayesian methods to quantify the cognitive processes of response inhibition. We tested the multivariate relationship between LC integrity and model parameters using partial least squares. Both disorders impaired response inhibition at the group level. PSP caused a distinct pattern of abnormalities in inhibitory control with a paradoxically reduced threshold for go responses, but longer nondecision times, and more lapses of attention. The variation in response inhibition correlated with the variability of LC integrity across participants in both clinical groups. Structural imaging of the LC, coupled with behavioral modeling in parkinsonian disorders, confirms that LC integrity is associated with response inhibition and LC degeneration contributes to neurobehavioral changes. The noradrenergic system is therefore a promising target to treat impulsivity in these conditions. The optimization of noradrenergic treatment is likely to benefit from stratification according to LC integrity.SIGNIFICANCE STATEMENT Response inhibition deficits contribute to clinical symptoms and poor outcomes in people with Parkinson's disease and progressive supranuclear palsy. We used cognitive modeling of performance of a response inhibition task to identify disease-specific mechanisms of abnormal inhibitory control. Response inhibition in both patient groups was associated with the integrity of the noradrenergic locus coeruleus, which we measured in vivo using ultra-high field MRI. We propose that the imaging biomarker of locus coeruleus integrity provides a trans-diagnostic tool to explain individual differences in response inhibition ability beyond the classic nosological borders and diagnostic criteria. Our data suggest a potential new stratified treatment approach for Parkinson's disease and progressive supranuclear palsy.

A study on surprisal and semantic relatedness for eye-tracking data prediction

Previous research in computational linguistics dedicated a lot of effort to using language modeling and/or distributional semantic models to predict metrics extracted from eye-tracking data. However, it is not clear whether the two components have a distinct contribution, with recent studies claiming that surprisal scores estimated with large-scale, deep learning-based language models subsume the semantic relatedness component. In our study, we propose a regression experiment for estimating different eye-tracking metrics on two English corpora, contrasting the quality of the predictions with and without the surprisal and the relatedness components. Different types of relatedness scores derived from both static and contextual models have also been tested. Our results suggest that both components play a role in the prediction, with semantic relatedness surprisingly contributing also to the prediction of function words. Moreover, they show that when the metric is computed with the contextual embeddings of the BERT model, it is able to explain a higher amount of variance.

Three levels at which the user's cognition can be represented in artificial intelligence

Artificial intelligence (AI) plays an important role in modern society. AI applications are omnipresent and assist many decisions we make in daily life. A common and important feature of such AI applications are user models. These models allow an AI application to adapt to a specific user. Here, we argue that user models in AI can be optimized by modeling these user models more closely to models of human cognition. We identify three levels at which insights from human cognition can be-and have been-integrated in user models. Such integration can be very loose with user models only being inspired by general knowledge of human cognition or very tight with user models implementing specific cognitive processes. Using AI-based applications in the context of education as a case study, we demonstrate that user models that are more deeply rooted in models of cognition offer more valid and more fine-grained adaptations to an individual user. We propose that such user models can also advance the development of explainable AI.

Grammatical Perspective-Taking in Comprehension and Production

Language use in conversation requires conversation partners to consider each other's points-of-view, or perspectives. A large body of work has explored how conversation partners take into account differences in knowledge states when choosing referring expressions. This paper explores how well findings from perspective-taking in reference generalize to a relatively understudied domain of perspective: the processing of grammatical perspectival expressions like the motion verbs come and go in English. We re-visit findings from perspective-taking in reference that conversation participants are subject to egocentric biases: they are biased towards their own perspectives. Drawing on theoretical proposals for grammatical perspective-taking and prior experimental studies of perspective-taking in reference, we compare two models of grammatical perspective-taking: a serial anchoring-and-adjustment model, and a simultaneous integration model. We test their differing predictions in a series of comprehension and production experiments using the perspectival motion verbs come and go as a case study. While our comprehension studies suggest that listeners reason simultaneously over multiple perspectives, as in the simultaneous integration model, our production findings are more mixed: we find support for only one of the simultaneous integration model's two key predictions. More generally, our findings suggest a role for egocentric bias in production for grammatical perspective-taking as well as when choosing referring expressions.

Decision strategies while intoxicated relate to alcohol-impaired driving attitudes and intentions

OBJECTIVE

Approximately 28 million individuals engage in alcohol-impaired driving (AID) every year. This study investigated individuals' AID decision making strategies under intoxication, their variability across the breath alcohol concentration (BrAC) curve, and the association between strategy and AID attitudes, intentions, and behavior.

METHOD

Seventy-nine adults (mean 23.9 years, 57% female) who drank alcohol ≥2 days per week and lived >2 miles away from their typical drinking locations completed an alcohol administration protocol and AID decision making task. AID attitudes, intentions, and behaviors were assessed repeatedly across the BrAC curve. Bayesian cognitive modeling identified decision strategies used by individuals on the AID decision making task, revealing whether alcohol consumption level and/or ride service cost factored into individuals' decisions to drive while impaired or obtain a ride. Additional analyses tested whether AID attitudes and intentions were related to individuals' decision strategies.

RESULTS

Two decision strategies were examined on the ascending and descending limbs of the BrAC curve: compensatory (both consumption level and ride service cost factored into AID decisions) and non-compensatory (only consumption level factored into AID decisions). Switching to a compensatory strategy on the descending limb was associated with lower perceived intoxication, perceiving AID as less dangerous, and being willing to drive above the legal BrAC limit.

CONCLUSIONS

Results suggest that risk for engaging in AID is higher for those using a cost-sensitive, compensatory strategy when making AID decisions under intoxication. Future research is needed to test whether AID countermeasures (e.g., subsidized ride services) are differentially effective according to decision strategy type. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Can neuropsychological testing be improved with model-based approaches?

There has been little impact of cognitive psychology and modeling on neuropsychological testing for over 50 years. There is also a disconnect between those tests and the constructs they are said to measure. We discuss studies at the interface between testing and modeling that illustrate the opportunity for advances.

Sensorimotor Self-organization via Circular-Reactions

Newborns demonstrate innate abilities in coordinating their sensory and motor systems through reflexes. One notable characteristic is circular reactions consisting of self-generated motor actions that lead to correlated sensory and motor activities. This paper describes a model for goal-directed reaching based on circular reactions and exocentric reference-frames. The model is built using physiologically plausible visual processing modules and arm-control neural networks. The model incorporates map representations with ego- and exo-centric reference frames for sensory inputs, vector representations for motor systems, as well as local associative learning that result from arm explorations. The integration of these modules is simulated and tested in a three-dimensional spatial environment using Unity3D. The results show that, through self-generated activities, the model self-organizes to generate accurate arm movements that are tolerant with respect to various sources of noise.

Optimizing Cognitive Assessment Outcome Measures for Alzheimer's Disease by Matching Wordlist Memory Test Features to Scoring Methodology

Cognitive assessment with wordlist memory tests is a cost-effective and non-invasive method of identifying cognitive changes due to Alzheimer's disease and measuring clinical outcomes. However, with a rising need for more precise and granular measures of cognitive changes, especially in earlier or preclinical stages of Alzheimer's disease, traditional scoring methods have failed to provide adequate accuracy and information. Well-validated and widely adopted wordlist memory tests vary in many ways, including list length, number of learning trials, order of word presentation across trials, and inclusion of semantic categories, and these differences meaningfully impact cognition. While many simple scoring methods fail to account for the information that these features provide, extensive effort has been made to develop scoring methodologies, including the use of latent models that enable capture of this information for preclinical differentiation and prediction of cognitive changes. In this perspective article, we discuss prominent wordlist memory tests in use, their features, how different scoring methods fail or successfully capture the information these features provide, and recommendations for emerging cognitive models that optimally account for wordlist memory test features. Matching the use of such scoring methods to wordlist memory tests with appropriate features is key to obtaining precise measurement of subtle cognitive changes.

COMPASS: Computations for Orientation and Motion Perception in Altered Sensorimotor States

Reliable perception of self-motion and orientation requires the central nervous system (CNS) to adapt to changing environments, stimuli, and sensory organ function. The proposed computations required of neural systems for this adaptation process remain conceptual, limiting our understanding and ability to quantitatively predict adaptation and mitigate any resulting impairment prior to completing adaptation. Here, we have implemented a computational model of the internal calculations involved in the orientation perception system’s adaptation to changes in the magnitude of gravity. In summary, we propose that the CNS considers parallel, alternative hypotheses of the parameter of interest (in this case, the CNS’s internal estimate of the magnitude of gravity) and uses the associated sensory conflict signals (i.e., difference between sensory measurements and the expectation of them) to sequentially update the posterior probability of each hypothesis using Bayes rule. Over time, an updated central estimate of the internal magnitude of gravity emerges from the posterior probability distribution, which is then used to process sensory information and produce perceptions of self-motion and orientation. We have implemented these hypotheses in a computational model and performed various simulations to demonstrate quantitative model predictions of adaptation of the orientation perception system to changes in the magnitude of gravity, similar to those experienced by astronauts during space exploration missions. These model predictions serve as quantitative hypotheses to inspire future experimental assessments.

Trust Miscalibration Is Sometimes Necessary: An Empirical Study and a Computational Model

The literature on trust seems to have reached a consensus that appropriately calibrated trust in humans or machines is highly desirable; miscalibrated (i.e., over- or under-) trust has been thought to only have negative consequences (i.e., over-reliance or under-utilization). While not invalidating the general idea of trust calibration, a published computational cognitive model of trust in strategic interaction predicts that some local and temporary violations of the trust calibration principle are critical for sustained success in strategic situations characterized by interdependence and uncertainty (e.g., trust game, prisoner’s dilemma, and Hawk-dove). This paper presents empirical and computational modeling work aimed at testing the predictions of under- and over-trust in an extension of the trust game, the multi-arm trust game, that captures some important characteristics of real-world interpersonal and human-machine interactions, such as the ability to choose when and with whom to interact among multiple agents. As predicted by our previous model, we found that, under conditions of increased trust necessity, participants actively reconstructed their trust-investment portfolios by discounting their trust in their previously trusted counterparts and attempting to develop trust with the counterparts that they previously distrusted. We argue that studying these exceptions of the principle of trust calibration might be critical for understanding long-term trust calibration in dynamic environments.

Learning Numerosity Representations with Transformers: Number Generation Tasks and Out-of-Distribution Generalization

One of the most rapidly advancing areas of deep learning research aims at creating models that learn to disentangle the latent factors of variation from a data distribution. However, modeling joint probability mass functions is usually prohibitive, which motivates the use of conditional models assuming that some information is given as input. In the domain of numerical cognition, deep learning architectures have successfully demonstrated that approximate numerosity representations can emerge in multi-layer networks that build latent representations of a set of images with a varying number of items. However, existing models have focused on tasks requiring to conditionally estimate numerosity information from a given image. Here, we focus on a set of much more challenging tasks, which require to conditionally generate synthetic images containing a given number of items. We show that attention-based architectures operating at the pixel level can learn to produce well-formed images approximately containing a specific number of items, even when the target numerosity was not present in the training distribution.

Development of a novel hybrid cognitive model validation framework for implementation under COVID-19 restrictions

The purpose of this study was to develop a method for validation of cognitive models consistent with the remote working situation arising from COVID-19 restrictions in place in Spring 2020. We propose a framework for structuring validation tasks and applying a scoring system to determine initial model validity. We infer an objective validity level for cognitive models requiring no in-person observations, and minimal reliance on remote usability and observational studies. This approach has been derived from the necessity of the COVID-19 response, however, we believe this approach can lower costs and reduce timelines to initial validation in post-Covid-19 studies, enabling faster progress in the development of cognitive engineering systems. A three-stage hybrid validation framework was developed based on existing validation methods and was adapted to enable compliance with the specific limitations derived from COVID-19 response restrictions. This validation method includes elements of argument-based validation combined with a cognitive walkthrough analysis, and reflexivity assessments. We conducted a case study of the proposed framework on a developmental cognitive model of cardiovascular surgery to demonstrate application of a real-world validation task. This framework can be easily and quickly implemented by a small research team and provides a structured validation method to increase confidence in assumptions as well as to provide evidence to support validity claims in the early stages of model development.

Applying Bayesian cognitive models to decisions to drive after drinking

BACKGROUND AND AIMS

Despite widespread negative perceptions, the prevalence of alcohol-impaired driving (AID) in the United States remains unacceptably high. This study used a novel decision task to evaluate whether individuals considered both ride service cost and alcohol consumption level when deciding whether or not to drive, and whether the resulting strategy was associated with engagement in AID.

DESIGN

A two-sample study, where sample 1 developed a novel AID decision task to classify participants by decision strategy. Sample 2 was used to cross-validate the task and examine whether decision strategy classifications were predictive of prior reported AID behavior.

SETTING

A laboratory setting at the University of Missouri, USA.

PARTICIPANTS

Sample 1 included 38 student participants from introductory psychology classes at the University of Missouri. Sample 2 included 67 young adult participants recruited from the local community.

MEASUREMENTS

We developed a decision task that presented hypothetical drinking scenarios that varied in quantity of alcohol consumption (one to six drinks) and the cost of a ride service ($5-25). We applied a Bayesian computational model to classify choices as consistent with either: integrating both ride cost and consumption level (compensatory) or considering only consumption level (non-compensatory) when making hypothetical AID decisions. In sample 2, we assessed established AID risk factors (sex, recent alcohol consumption, perceived safe limit) and recent (past 3 months) engagement in AID.

FINDINGS

In sample 1, the majority of participants were classified as using decision strategies consistent with either a compensatory or non-compensatory process. Results from sample 2 replicated the overall classification rate and demonstrated that participants who used a compensatory strategy were more likely to report recent AID, even after accounting for study covariates.

CONCLUSIONS

In a hypothetical alcohol-impaired driving (AID) decision task, individuals who considered both consumption level and ride service cost were more likely to report recent AID than those who made decisions based entirely on consumption level.

Neurophysiology of Human Perceptual Decision-Making

The discovery of neural signals that reflect the dynamics of perceptual decision formation has had a considerable impact. Not only do such signals enable detailed investigations of the neural implementation of the decision-making process but they also can expose key elements of the brain's decision algorithms. For a long time, such signals were only accessible through direct animal brain recordings, and progress in human neuroscience was hampered by the limitations of noninvasive recording techniques. However, recent methodological advances are increasingly enabling the study of human brain signals that finely trace the dynamics of the unfolding decision process. In this review, we highlight how human neurophysiological data are now being leveraged to furnish new insights into the multiple processing levels involved in forming decisions, to inform the construction and evaluation of mathematical models that can explain intra- and interindividual differences, and to examine how key ancillary processes interact with core decision circuits.

From convolutional neural networks to models of higher-level cognition (and back again)

The remarkable successes of convolutional neural networks (CNNs) in modern computer vision are by now well known, and they are increasingly being explored as computational models of the human visual system. In this paper, we ask whether CNNs might also provide a basis for modeling higher-level cognition, focusing on the core phenomena of similarity and categorization. The most important advance comes from the ability of CNNs to learn high-dimensional representations of complex naturalistic images, substantially extending the scope of traditional cognitive models that were previously only evaluated with simple artificial stimuli. In all cases, the most successful combinations arise when CNN representations are used with cognitive models that have the capacity to transform them to better fit human behavior. One consequence of these insights is a toolkit for the integration of cognitively motivated constraints back into CNN training paradigms in computer vision and machine learning, and we review cases where this leads to improved performance. A second consequence is a roadmap for how CNNs and cognitive models can be more fully integrated in the future, allowing for flexible end-to-end algorithms that can learn representations from data while still retaining the structured behavior characteristic of human cognition.

If Mathematical Psychology Did Not Exist We Might Need to Invent It: A Comment on Theory Building in Psychology

It is commonplace, when discussing the subject of psychological theory, to write articles from the assumption that psychology differs from the physical sciences in that we have no theories that would support cumulative, incremental science. In this brief article I discuss one counterexample: Shepard's law of generalization and the various Bayesian extensions that it inspired over the past 3 decades. Using Shepard's law as a running example, I argue that psychological theory building is not a statistical problem, mathematical formalism is beneficial to theory, measurement and theory have a complex relationship, rewriting old theory can yield new insights, and theory growth can drive empirical work. Although I generally suggest that the tools of mathematical psychology are valuable to psychological theorists, I also comment on some limitations to this approach.

A Review of Individual Operational Cognitive Readiness: Theory Development and Future Directions

OBJECTIVE

The aim of this paper is to provide a comprehensive and original review of the theoretical development of the individual operational cognitive readiness (OCR) theory.

BACKGROUND

Cognitive readiness (CR) is a concept that has the potential to predict the performance of human individuals and teams prior to engaging in complex, dynamic, and resource-limited task environments. However, the current state of the literature is confusing and laborious, with heterogeneous views regarding the theoretical frameworks among leading researchers.

METHOD

This review (1) undertakes a systematic approach toward categorizing published CR literature into theoretical and measurement contributions across the different levels of CR, (2) carries a critical evaluation of the CR and OCR theoretical frameworks, and (3) provides directions for future research guided by gaps identified during the review process and other published literatures.

RESULTS

Results from the categorization of published CR literature provide a new, valuable, synthesized CR library for researchers to consult to streamline their CR literature review process. Critical examination of the CR and OCR theoretical frameworks leads to positing that new components should be explored for OCR.

CONCLUSION

There are many possible directions for future research including evaluating domain-independent components of OCR and evaluating the relationship between biofeedback measures and performance in CR models.

APPLICATION

The Defense domain continues to be the focal application of CR. However, CR could be used by other application domains, such as sports and emergency services, that require their working personnel to engage in complex, uncertain, and dynamic task environments.

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient design of experiments. Importantly, parameter reviews provide crucial-if not indispensable-information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters (Lee, 2018). In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model (DDM; Ratcliff and McKoon, 2008), the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process.

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

In recent years collaborative robots have become major market drivers in industry 5.0, which aims to incorporate them alongside humans in a wide array of settings ranging from welding to rehabilitation. Improving human–machine collaboration entails using computational algorithms that will save processing as well as communication cost. In this study we have constructed an agent that can choose when to cooperate using an optimal strategy. The agent was designed to operate in the context of divergent interest tacit coordination games in which communication between the players is not possible and the payoff is not symmetric. The agent’s model was based on a behavioral model that can predict the probability of a player converging on prominent solutions with salient features (e.g., focal points) based on the player’s Social Value Orientation (SVO) and the specific game features. The SVO theory pertains to the preferences of decision makers when allocating joint resources between themselves and another player in the context of behavioral game theory. The agent selected stochastically between one of two possible policies, a greedy or a cooperative policy, based on the probability of a player to converge on a focal point. The distribution of the number of points obtained by the autonomous agent incorporating the SVO in the model was better than the results obtained by the human players who played against each other (i.e., the distribution associated with the agent had a higher mean value). Moreover, the distribution of points gained by the agent was better than any of the separate strategies the agent could choose from, namely, always choosing a greedy or a focal point solution. To the best of our knowledge, this is the first attempt to construct an intelligent agent that maximizes its utility by incorporating the belief system of the player in the context of tacit bargaining. This reward-maximizing strategy selection process based on the SVO can also be potentially applied in other human–machine contexts, including multiagent systems.

Personalizing Human-Agent Interaction Through Cognitive Models

Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI.

Cognitive Models in Cybersecurity: Learning From Expert Analysts and Predicting Attacker Behavior

Cybersecurity stands to benefit greatly from models able to generate predictions of attacker and defender behavior. On the defender side, there is promising research suggesting that Symbolic Deep Learning (SDL) may be employed to automatically construct cognitive models of expert behavior based on small samples of expert decisions. Such models could then be employed to provide decision support for non-expert users in the form of explainable expert-based suggestions. On the attacker side, there is promising research suggesting that model-tracing with dynamic parameter fitting may be used to automatically construct models during live attack scenarios, and to predict individual attacker preferences. Predicted attacker preferences could then be exploited for mitigating risk of successful attacks. In this paper we examine how these two cognitive modeling approaches may be useful for cybersecurity professionals via two human experiments. In the first experiment participants play the role of cyber analysts performing a task based on Intrusion Detection System alert elevation. Experiment results and analysis reveal that SDL can help to reduce missed threats by 25%. In the second experiment participants play the role of attackers picking among four attack strategies. Experiment results and analysis reveal that model-tracing with dynamic parameter fitting can be used to predict (and exploit) most attackers' preferences 40-70% of the time. We conclude that studies and models of human cognition are highly valuable for advancing cybersecurity.

A linear threshold model for optimal stopping behavior

In many real-life decisions, options are distributed in space and time, making it necessary to search sequentially through them, often without a chance to return to a rejected option. The optimal strategy in these tasks is to choose the first option that is above a threshold that depends on the current position in the sequence. The implicit decision-making strategies by humans vary but largely diverge from this optimal strategy. The reasons for this divergence remain unknown. We present a model of human stopping decisions in sequential decision-making tasks based on a linear threshold heuristic. The first two studies demonstrate that the linear threshold model accounts better for sequential decision making than existing models. Moreover, we show that the model accurately predicts participants' search behavior in different environments. In the third study, we confirm that the model generalizes to a real-world problem, thus providing an important step toward understanding human sequential decision making.

Causal Structure Learning in Continuous Systems

Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e., those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss-Markov ("Ornstein-Uhlenbeck") processes and show how such OU networks can express complex dynamic phenomena, such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world.

Commentary: A View From the Inside-A Perspective on How the American Board of Internal Medicine (ABIM) Is Innovating in Response to Feedback

The American Board of Internal Medicine (ABIM) is implementing new methods for the development of examination content in response to feedback from the internal medicine community and in recognition that there is always room for improvement in the assessment of the skills and knowledge of practicing physicians. First, ABIM is exploring a new cognitive model-based approach to content development in efforts to improve exam relevancy. Second, ABIM has created a new Item-Writing Task Force in an effort to ensure a broad representation of internists from across the country who are engaged in all aspects of clinical practice. Through these mechanisms, the goal is the improved fairness and validity evidence of examinations that are relevant to how medicine is practiced today.

Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter Estimation, and Model-Based Control

OBJECTIVE

The objective is to propose three quantitative models of trust in automation.

BACKGROUND

Current trust-in-automation literature includes various definitions and frameworks, which are reviewed.

METHOD

This research shows how three existing models, namely those for signal detection, statistical parameter estimation calibration, and internal model-based control, can be revised and reinterpreted to apply to trust in automation useful for human-system interaction design.

RESULTS

The resulting reinterpretation is presented quantitatively and graphically, and the measures for trust and trust calibration are discussed, along with examples of application.

CONCLUSION

The resulting models can be applied to provide quantitative trust measures in future experiments or system designs.

APPLICATIONS

Simple examples are provided to explain how model application works for the three trust contexts that correspond to signal detection, parameter estimation calibration, and model-based open-loop control.

Building Memory Representations for Exemplar-Based Judgment: A Role for Ventral Precuneus

The brain networks underlying human multiple-cue judgment, the judgment of a continuous criterion based on multiple cues, have been examined in a few recent studies, and the ventral precuneus has been found to be a key region. Specifically, activation differences in ventral precuneus (as measured with functional magnetic resonance imaging, fMRI) has been linked to an exemplar-based judgment process, where judgments are based on memory for previous similar cases. Ventral precuneus is implicated in various episodic memory processes, notably such that increased activity during learning in this region as well as in the ventromedial prefrontal cortex (vmPFC) and the medial temporal lobes (MTL) have been linked to retrieval success. The present study used fMRI during a multiple-cue judgment task to gain novel neurocognitive evidence informative for the link between learning-related activity changes in ventral precuneus and exemplar-based judgment. Participants (N = 27) spontaneously learned to make judgments during fMRI, in a multiple-cue judgment task specifically designed to induce exemplar-based processing. Contrasting brain activity during late learning to early learning revealed higher activity in ventral precuneus, the bilateral MTL, and the vmPFC. Activity in the ventral precuneus and the vmPFC was found to parametrically increase between each judgment event, and activity levels in the ventral precuneus predicted performance after learning. These results are interpreted such that the ventral precuneus supports the aspects of exemplar-based processes that are related to episodic memory, tentatively by building, storing, and being implicated in retrieving memory representations for judgment.

Relation-Based Categorization and Category Learning as a Result From Structural Alignment. The RoleMap Model

Relational categories are structure-based categories, defined not only by their internal properties but also by their extrinsic relations with other categories. For example, predator could not be defined without referring to hunt and prey. Even though they are commonly used, there are few models taking into account any relational information. A category learning and categorization model aiming to fill this gap is presented. Previous research addresses the hypothesis that the acquisition and the use of relational categories are underlined by structural alignment. That is why the proposed RoleMap model is based on mechanisms often studied as the analogy-making sub-processes, developed on a suitable for this cognitive architecture. RoleMap is conceived in such a way that relation-based category learning and categorization emerge while other tasks are performed. The assumption it steps on is that people constantly make structural alignments between what they experience and what they know. During these alignments various mappings and anticipations emerge. The mappings capture commonalities between the target (the representation of the current situation) and the memory, while the anticipations try to fill the missing information in the target, based on the conceptual system. Because some of the mappings are highly important, they are transformed into a distributed representation of a new concept for further use, which denotes the category learning. When some knowledge is missing in the target, meaning it is uncategorized, that knowledge is transferred from memory in the form of anticipations. The wining anticipation is transformed into a category member, denoting the act of categorization. The model’s behavior emerges from the competition between these two pressures – to categorize and to create new categories. Several groups of simulations demonstrate that the model can deal with relational categories in a context-dependent manner and to account for single-shot learning, challenging most of the existing approaches to category learning. The model also simulates previous empirical data pointing to the thematic categories and to the puzzling inverse base-rate effect. Finally, the model’s strengths and limitations are evaluated.

Some Evidence for an Association Between Early Life Adversity and Decision Urgency

The relationship between early life adversity and adult outcomes is traditionally investigated relative to risk and protective factors (e.g., resilience, cognitive appraisal), and poor self-control or decision-making. However, life history theory suggests this relationship may be adaptive-underpinned by mechanisms that use early environmental cues to alter the developmental trajectory toward more short-term strategies. These short-term strategies have some theoretical overlap with the most common process models of decision-making-evidence accumulation models-which model decision urgency as a decision threshold. The current study examined the relationship between decision urgency (through the linear ballistic accumulator) and early life adversity. A mixture of analysis methods, including a joint model analysis designed to explicitly account for uncertainty in estimated decision urgency values, revealed weak-to-strong evidence in favor of a relationship between decision urgency and early life adversity, suggesting a possible effect of life history strategy on even the most basic decisions.

Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data

A key goal of model-based cognitive neuroscience is to estimate the trial-by-trial fluctuations of cognitive model parameters in order to link these fluctuations to brain signals. However, previously developed methods are limited by being difficult to implement, time-consuming, or model-specific. Here, we propose an easy, efficient and general approach to estimating trial-wise changes in parameters: Leave-One-Trial-Out (LOTO). The rationale behind LOTO is that the difference between parameter estimates for the complete dataset and for the dataset with one omitted trial reflects the parameter value in the omitted trial. We show that LOTO is superior to estimating parameter values from single trials and compare it to previously proposed approaches. Furthermore, the method makes it possible to distinguish true variability in a parameter from noise and from other sources of variability. In our view, the practicability and generality of LOTO will advance research on tracking fluctuations in latent cognitive variables and linking them to neural data.

Neural Correlates of Workload Transition in Multitasking: An ACT-R Model of Hysteresis Effect

This study investigated the effect of task demand transitions at multiple levels of analysis including behavioral performance, subjective rating, and brain effective connectivity, while comparing human data to Adaptive Control of Thought-Rational (ACT-R) simulated data. Three stages of task demand were designed and performed sequentially (Low-High-Low) during AF-MATB tasks, and the differences in neural connectivity during workload transition were identified. The NASA Task Load Index (NASA-TLX) and the Instantaneous Self-Assessment (ISA) were used to measure the subjective mental workload that accompanies the hysteresis effect in the task demand transitions. The results found significant hysteresis effects on performance and various brain network measures such as outflow of the prefrontal cortex and connectivity magnitude. These findings would assist in clarifying the direction and strength of the Granger Causality under demand transitions. As a result, these findings involving the neural mechanisms of hysteresis effects in multitasking environments may be utilized in applications of neuroergonomics research. The ability to compare data derived from human participants to data gathered by the ACT-R model allows researchers to better account for hysteresis effects in neuro-cognitive models in the future.

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables

Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.

Value-based attentional capture affects multi-alternative decision making

Humans and other animals often violate economic principles when choosing between multiple alternatives, but the underlying neurocognitive mechanisms remain elusive. A robust finding is that adding a third option can alter the relative preference for the original alternatives, but studies disagree on whether the third option's value decreases or increases accuracy. To shed light on this controversy, we used and extended the paradigm of one study reporting a positive effect. However, our four experiments with 147 human participants and a reanalysis of the original data revealed that the positive effect is neither replicable nor reproducible. In contrast, our behavioral and eye-tracking results are best explained by assuming that the third option's value captures attention and thereby impedes accuracy. We propose a computational model that accounts for the complex interplay of value, attention, and choice. Our theory explains how choice sets and environments influence the neurocognitive processes of multi-alternative decision making.

Approximating Bayesian Inference through Model Simulation

The ultimate test of the validity of a cognitive theory is its ability to predict patterns of empirical data. Cognitive models formalize this test by making specific processing assumptions that yield mathematical predictions, and the mathematics allow the models to be fitted to data. As the field of cognitive science has grown to address increasingly complex problems, so too has the complexity of models increased. Some models have become so complex that the mathematics detailing their predictions are intractable, meaning that the model can only be simulated. Recently, new Bayesian techniques have made it possible to fit these simulation-based models to data. These techniques have even allowed simulation-based models to transition into neuroscience, where tests of cognitive theories can be biologically substantiated.

Cognitive Models in Intelligence Research: Advantages and Recommendations for Their Application

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.

Simulations in Cyber-Security: A Review of Cognitive Modeling of Network Attackers, Defenders, and Users

Computational models of cognitive processes may be employed in cyber-security tools, experiments, and simulations to address human agency and effective decision-making in keeping computational networks secure. Cognitive modeling can addresses multi-disciplinary cyber-security challenges requiring cross-cutting approaches over the human and computational sciences such as the following: (a) adversarial reasoning and behavioral game theory to predict attacker subjective utilities and decision likelihood distributions, (b) human factors of cyber tools to address human system integration challenges, estimation of defender cognitive states, and opportunities for automation, (c) dynamic simulations involving attacker, defender, and user models to enhance studies of cyber epidemiology and cyber hygiene, and (d) training effectiveness research and training scenarios to address human cyber-security performance, maturation of cyber-security skill sets, and effective decision-making. Models may be initially constructed at the group-level based on mean tendencies of each subject's subgroup, based on known statistics such as specific skill proficiencies, demographic characteristics, and cultural factors. For more precise and accurate predictions, cognitive models may be fine-tuned to each individual attacker, defender, or user profile, and updated over time (based on recorded behavior) via techniques such as model tracing and dynamic parameter fitting.

Using Cognitive Agents to Train Negotiation Skills

Training negotiation is difficult because it is a complex, dynamic activity that involves multiple parties. It is often not clear how to create situations in which students can practice negotiation or how to measure students' progress. Some have begun to address these issues by creating artificial software agents with which students can train. These agents have the advantage that they can be "reset," and played against multiple times. This allows students to learn from their mistakes and try different strategies. However, these agents are often based on normative theories of how negotiators should conduct themselves, not necessarily how people actually behave in negotiations. Here, we take a step toward addressing this gap by developing an agent grounded in a cognitive architecture, ACT-R. This agent contains a model of theory-of-mind, the ability of humans to reason about the mental states of others. It uses this model to try to infer the strategy of the opponent and respond accordingly. In a series of experiments, we show that this agent replicates some aspects of human performance, is plausible to human negotiators, and can lead to learning gains in a small-scale negotiation task.

Approaches to Cognitive Modeling in Dynamic Systems Control

Much of human decision making occurs in dynamic situations where decision makers have to control a number of interrelated elements (dynamic systems control). Although in recent years progress has been made toward assessing individual differences in control performance, the cognitive processes underlying exploration and control of dynamic systems are not yet well understood. In this perspectives article we examine the contribution of different approaches to modeling cognition in dynamic systems control, including instance-based learning, heuristic models, complex knowledge-based models and models of causal learning. We conclude that each approach has particular strengths in modeling certain aspects of cognition in dynamic systems control. In particular, Bayesian models of causal learning and hybrid models combining heuristic strategies with reinforcement learning appear to be promising avenues for further work in this field.

Impact of Expert Commentary and Student Reflection on Veterinary Clinical Decision-Making Skills in an Innovative Electronic-Learning Case-Based Platform

One challenge in veterinary education is bridging the divide between the nature of classroom examples (well-defined problem solving) and real world situations (ill-defined problem solving). Solving the latter often relies on experiential knowledge, which is difficult to impart to inexperienced students. A multidisciplinary team including veterinary specialists and learning scientists developed an interactive, e-learning case-based module in which students made critical decisions at five specific points (Decision Points [DPs]). After committing to each decision (Original Answers), students reflected on the thought processes of experts making similar decisions, and were allowed to revise their decisions (Revised Answers); both sets of answers were scored. In Phase I, performance of students trained using the module (E-Learning Group) and by lecture (Traditional Group) was compared on the course final examination. There was no difference in performance between the groups, suggesting that the e-learning module was as effective as traditional lecture for content delivery. In Phase II, differences between Original Answers and Revised Answers were evaluated for a larger group of students, all of whom used the module as the sole method of instruction. There was a significant improvement in scores between Original and Revised Answers for four out of five DPs (DP1, p =.004; DP2, p =.04; DP4, p <.001; DP5, p <.001). The authors conclude that the ability to rehearse clinical decision making through this tool, without direct individual feedback from an instructor, may facilitate students' transition from problem solving in a well-structured classroom setting to an ill-structured clinical setting.

Developmental Changes in Learning: Computational Mechanisms and Social Influences

Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.