Visual Search and Decluttering in Tactical Situation Displays: A Computational Modeling Approach

2021 ◽  
Vol 65 (1) ◽  
pp. 1425-1431
Author(s):  
May Jorella Lazaro ◽  
Sungho Kim ◽  
Yohan Kang ◽  
Myung Hwan Yun
Keyword(s):  
Visual Search ◽  
Computational Modeling ◽  
Air Force ◽  
Search Behavior ◽  
Model Parameters ◽  
Search Performance ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Persistent Problem ◽  
Modeling Data

Clutter in tactical situation displays (TSD) is a persistent problem that affects pilots’ performance. Decluttering methods such as dimming, dotting, small-sizing and removal have been used in several display types to reduce clutter. This study aims to investigate the effects of different decluttering methods applied in TSD on pilots’ visual search performance. It also aims to uncover the basic psychophysical processes underlying the pilots’ visual search behavior through computational modeling. Data from fifteen Air-Force pilots showed that accuracy is higher and response time is faster when the TSD is decluttered, regardless of the technique. However, when the data was fitted into the hierarchical drift-diffusion model, it was revealed that among the techniques tested, dimming yielded the best search performance based on the model parameters. This study suggests that analyzing behavioral data through computational modeling may lead to better insights that are more practical and applicable in solving the issues in visual search in TSDs.

scholarly journals Computational modeling of the N-Back task in the ABCD study: associations of drift diffusion model parameters to polygenic scores of mental disorders and cardiometabolic diseases

2021 ◽  
Author(s):  
Mads Lund Pedersen ◽  
Dag Alnæs ◽  
Dennis van der Meer ◽  
Sara Fernandez ◽  
Pierre Berthet ◽  
...  
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Mental Disorders ◽  
Computational Modeling ◽  
Decision Time ◽  
Decision Threshold ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Cardiometabolic Diseases ◽  
Back Task

Background. Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with PRS for mental disorders and comorbid cardiometabolic diseases. Methods. We used the drift diffusion model to infer latent computational processes underlying decision-making and working memory during the N-back task in 3707 children aged 9-10 from the ABCD Study. SNP-based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated N-back task performance and neurocognitive assessments. PRS was calculated for Alzheimer’s disease (AD), bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia and type 2 diabetes. Results. Heritability estimates of cognitive phenotypes ranged from 12 to 39%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRS for CAD and schizophrenia. Longer non-decision time was associated with higher PRS for AD and lower PRS for CAD. Narrower decision threshold was associated with higher PRS for CAD. Load-dependent effects on non-decision time and decision threshold were associated with PRS for AD and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRS for any of the mental or cardiometabolic phenotypes.Conclusions. We identified distinct associations between computational cognitive processes to genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.

scholarly journals The drift diffusion model as the choice rule in inter-temporal and risky choice: a case study in medial orbitofrontal cortex lesion patients and controls

2019 ◽  
Author(s):  
Jan Peters ◽  
Mark D’Esposito
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Choice Model ◽  
Sequential Sampling ◽  
Temporal Discounting ◽  
Risky Choice ◽  
Choice Rule ◽  
Model Parameters ◽  
Drift Diffusion Model ◽  
Drift Diffusion

AbstractSequential sampling models such as the drift diffusion model have a long tradition in research on perceptual decision-making, but mounting evidence suggests that these models can account for response time distributions that arise during reinforcement learning and value-based decision-making. Building on this previous work, we implemented the drift diffusion model as the choice rule in inter-temporal choice (temporal discounting) and risky choice (probability discounting) using a hierarchical Bayesian estimation scheme. We validated our approach in data from nine patients with focal lesions to the ventromedial prefrontal cortex / medial orbitofrontal cortex (vmPFC/mOFC) and nineteen age- and education-matched controls. Choice model parameters estimated via standard softmax action selection were reliably reproduced using the drift diffusion model as the choice rule, both for temporal discounting and risky choice. Model comparison revealed that, for both tasks, the data were best accounted for by a variant of the drift diffusion model including a non-linear mapping from value-differences to trial-wise drift rates. Posterior predictive checks of the winning models revealed a reasonably good fit to individual participants reaction time distributions. We then applied this modeling framework and 1) reproduced our previous results regarding temporal discounting in vmPFC/mOFC patients and 2) showed in a previously unpublished data set on risky choice that vmPFC/mOFC patients exhibit increased risk-taking relative to controls. Analyses of diffusion model parameters revealed that vmPFC/mOFC damage abolished neither value sensitivity nor asymptote of the drift rate. Rather, it substantially increased non-decision times and reduced response caution during risky choice. Our results highlight that novel insights can be gained from applying sequential sampling models in studies of inter-temporal and risky decision-making in cognitive neuroscience.

Identifying Feigned Cognitive Impairment: Investigating the Utility of Diffusion Model Analyses

Assessment ◽  
2020 ◽  
pp. 107319112096231
Author(s):  
Elad Omer ◽  
Tomer Elbaum ◽  
Yoram Braw
Keyword(s):  
Cognitive Impairment ◽  
Diffusion Model ◽  
Cognitive Processes ◽  
Forced Choice ◽  
Model Parameters ◽  
Performance Validity ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Choice Performance ◽  
Feigned Cognitive Impairment

Forced-choice performance validity tests are routinely used for the detection of feigned cognitive impairment. The drift diffusion model deconstructs performance into distinct cognitive processes using accuracy and response time measures. It thereby offers a unique approach for gaining insight into examinees’ speed-accuracy trade-offs and the cognitive processes that underlie their performance. The current study is the first to perform such analyses using a well-established forced-choice performance validity test. To achieve this aim, archival data of healthy participants, either simulating cognitive impairment in the Word Memory Test or performing it to the best of their ability, were analyzed using the EZ-diffusion model ( N = 198). The groups differed in the three model parameters, with drift rate emerging as the best predictor of group membership. These findings provide initial evidence for the usefulness of the drift diffusion model in clarifying the cognitive processes underlying feigned cognitive impairment and encourage further research.

scholarly journals Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data

2020 ◽  
Vol 3 (4) ◽  
pp. 458-471 ◽  
Author(s):  
Mads L. Pedersen ◽  
Michael J. Frank
Keyword(s):  
Parameter Estimation ◽  
Reinforcement Learning ◽  
Diffusion Model ◽  
Simultaneous Estimation ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Bayesian Parameter Estimation ◽  
The Impact

AbstractCognitive models have been instrumental for generating insights into the brain processes underlying learning and decision making. In reinforcement learning it has recently been shown that not only choice proportions but also their latency distributions can be well captured when the choice function is replaced with a sequential sampling model such as the drift diffusion model. Hierarchical Bayesian parameter estimation further enhances the identifiability of distinct learning and choice parameters. One caveat is that these models can be time-consuming to build, sample from, and validate, especially when models include links between neural activations and model parameters. Here we describe a novel extension to the widely used hierarchical drift diffusion model (HDDM) toolbox, which facilitates flexible construction, estimation, and evaluation of the reinforcement learning drift diffusion model (RLDDM) using hierarchical Bayesian methods. We describe the types of experiments most applicable to the model and provide a tutorial to illustrate how to perform quantitative data analysis and model evaluation. Parameter recovery confirmed that the method can reliably estimate parameters with varying numbers of synthetic subjects and trials. We also show that the simultaneous estimation of learning and choice parameters can improve the sensitivity to detect brain–behavioral relationships, including the impact of learned values and fronto-basal ganglia activity patterns on dynamic decision parameters.

scholarly journals Individual differences in visual search performance extend from artificial arrays to naturalistic environments.

2021 ◽  
Author(s):  
Thomas L. Botch ◽  
Brenda D. Garcia ◽  
Yeo Bi Choi ◽  
Caroline E. Robertson
Keyword(s):  
Individual Differences ◽  
Visual Search ◽  
Eye Movements ◽  
Real World ◽  
Search Task ◽  
Visual Search Task ◽  
Search Behavior ◽  
Search Performance ◽  
Set Size ◽  
Visual Clutter

Visual search is a universal human activity in naturalistic environments. Traditionally, visual search is investigated under tightly controlled conditions, where head-restricted participants locate a minimalistic target in a cluttered array presented on a computer screen. Do classic findings of visual search extend to naturalistic settings, where participants actively explore complex, real-world scenes? Here, we leverage advances in virtual reality (VR) technology to relate individual differences in classic visual search paradigms to naturalistic search behavior. In a naturalistic visual search task, participants looked for an object within their environment via a combination of head-turns and eye-movements using a head-mounted display. Then, in a classic visual search task, participants searched for a target within a simple array of colored letters using only eye-movements. We tested how set size, a property known to limit visual search within computer displays, predicts the efficiency of search behavior inside immersive, real-world scenes that vary in levels of visual clutter. We found that participants' search performance was impacted by the level of visual clutter within real-world scenes. Critically, we also observed that individual differences in visual search efficiency in classic search predicted efficiency in real-world search, but only when the comparison was limited to the forward-facing field of view for real-world search. These results demonstrate that set size is a reliable predictor of individual performance across computer-based and active, real-world visual search behavior.

scholarly journals Principles of tactile search over the body

2019 ◽  
Author(s):  
Elizabeth J. Halfen ◽  
John F. Magnotti ◽  
Md. Shoaibur Rahman ◽  
Jeffrey M. Yau
Keyword(s):  
Visual Search ◽  
Target Stimulus ◽  
Response Times ◽  
Search Behavior ◽  
The Body ◽  
Search Process ◽  
Entire Body ◽  
Search Performance ◽  
Serial Search ◽  
The Impact

AbstractAlthough we experience complex patterns over our entire body, how we selectively perceive multi-site touch over our bodies remains poorly understood. Here, we characterized tactile search behavior over the body using a tactile analog of the classic visual search task. Participants judged whether a target stimulus (e.g., 10-Hz vibration) was present or absent on the upper or lower limbs. When present, the target stimulus could occur alone or with distractor stimuli (e.g., 30-Hz vibrations) on other body locations. We varied the number and spatial configurations of the distractors as well as the target and distractor frequencies and measured the impact of these factors on search response times. First, we found that response times were faster on target-present trials compared to target-absent trials. Second, response times increased with the number of stimulated sites, suggesting a serial search process. Third, search performance differed depending on stimulus frequencies. This frequency-dependent behavior may be related to perceptual grouping effects based on timing cues. We constructed models to explore how the locations of the tactile cues influenced search behavior. Our modeling results reveal that, in isolation, cues on the index fingers make relatively greater contributions to search performance compared to stimulation experienced on other body sites. Additionally, co-stimulation of sites within the same limb or simply on the same body side preferentially influence search behavior. Our collective findings identify some principles of attentional search that are common to vision and touch, but others that highlight key differences that may be unique to body-based spatial perception.New & NoteworthyLittle is known about how we selectively experience multi-site touch over the body. Using a tactile analog of the classic visual search paradigm, we show that tactile search behavior for flutter cues is generally consistent with a serial search process. Modeling results reveal the preferential contributions of index finger stimulation and two-site interactions involving ipsilateral and within-limb patterns. Our results offer initial evidence for spatial and temporal principles underlying tactile search behavior over the body.

scholarly journals Lexical-semantic and executive deficits revealed by computational modelling: a drift diffusion model perspective

2019 ◽  
Author(s):  
Lara Todorova ◽  
David Neville ◽  
Vitória Piai
Keyword(s):  
Semantic Memory ◽  
Diffusion Model ◽  
Drift Rate ◽  
Related Word ◽  
Model Parameters ◽  
Decision Threshold ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Lexical Semantic ◽  
Two Systems

Flexible language use requires coordinated functioning of two systems: conceptual representations and control. The interaction between two systems can be observed when people are asked to match a word to a picture. Participants are slower and less accurate for related word-picture pairs (word: banana, picture: apple) relative to unrelated pairs (word: banjo, picture: apple). The mechanism underlying interference however is still unclear. We analyzed word-picture verification (WPM) performance of patients with stroke-induced lesions to the left-temporal (N = 5) or left-frontal cortex (N = 5) and matched controls (N = 12) using the drift diffusion model (DDM). In DDM the process of making a decision is described as the stochastic accumulation of evidence towards a response. The parameters of the DDM model that characterize this process are decision threshold, drift rate, starting point and non-decision time. Each of them bears cognitive interpretability and we compared the estimated model parameters from controls and patients to investigate the mechanisms of WPM interference. WPM performance in controls was explained by the amount of information needed to make a decision (decision threshold): a higher threshold was associated with related word-picture pairs relative to unrelated ones. No difference was found in the quality of the evidence (drift rate). This suggests an executive rather than semantic mechanism underlying WPM interference. Both patients with temporal and frontal lesions exhibited both increased drift rate and decision threshold for unrelated pairs relative to related ones. Left-frontal and temporal damage affected the computations required by WPM similarly, resulting in systematic deficits across lexical-semantic memory and executive functions. These results support a diverse but interactive role of lexical-semantic memory and semantic control mechanisms.

Sign in / Sign up

Export Citation Format

Share Document