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Results: 1 to 10 of 125

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Accumulators, Neurons, and Response Time.
Schall JD
(2019) Trends Neurosci 42: 848-860
MeSH Terms: Animals, Brain, Decision Making, Humans, Mind-Body Relations, Metaphysical, Models, Neurological, Models, Psychological, Neurons, Psychophysiology, Reaction Time
Show Abstract · Added March 18, 2020
The marriage of cognitive neurophysiology and mathematical psychology to understand decision-making has been exceptionally productive. This interdisciplinary area is based on the proposition that particular neurons or circuits instantiate the accumulation of evidence specified by mathematical models of sequential sampling and stochastic accumulation. This linking proposition has earned widespread endorsement. Here, a brief survey of the history of the proposition precedes a review of multiple conundrums and paradoxes concerning the accuracy, precision, and transparency of that linking proposition. Correctly establishing how abstract models of decision-making are instantiated by particular neural circuits would represent a remarkable accomplishment in mapping mind to brain. Failing would reveal challenging limits for cognitive neuroscience. This is such a vigorous area of research because so much is at stake.
Copyright © 2019 Elsevier Ltd. All rights reserved.
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10 MeSH Terms
Pharmacological closure of the patent ductus arteriosus: when treatment still makes sense.
Gillam-Krakauer M, Hagadorn JI, Reese J
(2019) J Perinatol 39: 1439-1441
MeSH Terms: Acetaminophen, Cyclooxygenase Inhibitors, Decision Making, Ductus Arteriosus, Patent, Humans, Indomethacin, Infant, Infant, Low Birth Weight, Infant, Newborn, Infant, Premature
Added July 28, 2020
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MeSH Terms
A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task.
Verbruggen F, Aron AR, Band GP, Beste C, Bissett PG, Brockett AT, Brown JW, Chamberlain SR, Chambers CD, Colonius H, Colzato LS, Corneil BD, Coxon JP, Dupuis A, Eagle DM, Garavan H, Greenhouse I, Heathcote A, Huster RJ, Jahfari S, Kenemans JL, Leunissen I, Li CR, Logan GD, Matzke D, Morein-Zamir S, Murthy A, Paré M, Poldrack RA, Ridderinkhof KR, Robbins TW, Roesch M, Rubia K, Schachar RJ, Schall JD, Stock AK, Swann NC, Thakkar KN, van der Molen MW, Vermeylen L, Vink M, Wessel JR, Whelan R, Zandbelt BB, Boehler CN
(2019) Elife 8:
MeSH Terms: Animals, Consensus, Decision Making, Executive Function, Humans, Impulsive Behavior, Inhibition, Psychological, Models, Animal, Models, Psychological, Neuropsychological Tests, Psychomotor Performance, Reaction Time
Show Abstract · Added March 18, 2020
Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
© 2019, Verbruggen et al.
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Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence.
Servant M, Tillman G, Schall JD, Logan GD, Palmeri TJ
(2019) J Neurophysiol 121: 1300-1314
MeSH Terms: Animals, Decision Making, Macaca, Models, Neurological, Saccades, Sensorimotor Cortex, Sensory Gating, Stochastic Processes, Visual Fields, Visual Perception
Show Abstract · Added March 18, 2020
Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.
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Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs.
Keenan KE, Biller JR, Delfino JG, Boss MA, Does MD, Evelhoch JL, Griswold MA, Gunter JL, Hinks RS, Hoffman SW, Kim G, Lattanzi R, Li X, Marinelli L, Metzger GJ, Mukherjee P, Nordstrom RJ, Peskin AP, Perez E, Russek SE, Sahiner B, Serkova N, Shukla-Dave A, Steckner M, Stupic KF, Wilmes LJ, Wu HH, Zhang H, Jackson EF, Sullivan DC
(2019) J Magn Reson Imaging 49: e26-e39
MeSH Terms: Anthropometry, Breast, Decision Making, Deep Learning, Equipment Design, Female, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Phantoms, Imaging, Precision Medicine, Radiology, Interventional, Reference Standards, Reference Values, Reproducibility of Results, Robotics, Software
Show Abstract · Added March 5, 2020
LEVEL OF EVIDENCE - 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.
© 2019 International Society for Magnetic Resonance in Medicine.
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19 MeSH Terms
Neural mechanisms of speed-accuracy tradeoff of visual search: saccade vigor, the origin of targeting errors, and comparison of the superior colliculus and frontal eye field.
Reppert TR, Servant M, Heitz RP, Schall JD
(2018) J Neurophysiol 120: 372-384
MeSH Terms: Animals, Decision Making, Macaca radiata, Psychomotor Performance, Saccades, Superior Colliculi, Visual Fields
Show Abstract · Added March 18, 2020
Balancing the speed-accuracy tradeoff (SAT) is necessary for successful behavior. Using a visual search task with interleaved cues emphasizing speed or accuracy, we recently reported diverse contributions of frontal eye field (FEF) neurons instantiating salience evidence and response preparation. Here, we report replication of visual search SAT performance in two macaque monkeys, new information about variation of saccade dynamics with SAT, extension of the neurophysiological investigation to describe processes in the superior colliculus (SC), and a description of the origin of search errors in this task. Saccade vigor varied idiosyncratically across SAT conditions and monkeys but tended to decrease with response time. As observed in the FEF, speed-accuracy tradeoff was accomplished through several distinct adjustments in the superior colliculus. In "Accurate" relative to "Fast" trials, visually responsive neurons in SC as in FEF had lower baseline firing rates and later target selection. The magnitude of these adjustments in SC was indistinguishable from that in FEF. Search errors occurred when visual salience neurons in the FEF and the SC treated distractors as targets, even in the Accurate condition. Unlike FEF, the magnitude of visual responses in the SC did not vary across SAT conditions. Also unlike FEF, the activity of SC movement neurons when saccades were initiated was equivalent in Fast and Accurate trials. Saccade-related neural activity in SC, but not FEF, varied with saccade peak velocity. These results extend our understanding of the cortical and subcortical contributions to SAT. NEW & NOTEWORTHY Neurophysiological mechanisms of speed-accuracy tradeoff (SAT) have only recently been investigated. This article reports the first replication of SAT performance in nonhuman primates, the first report of variation of saccade dynamics with SAT, the first description of superior colliculus contributions to SAT, and the first description of the origin of errors during SAT. These results inform and constrain new models of distributed decision making.
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MeSH Terms
Subjective value representations during effort, probability and time discounting across adulthood.
Seaman KL, Brooks N, Karrer TM, Castrellon JJ, Perkins SF, Dang LC, Hsu M, Zald DH, Samanez-Larkin GR
(2018) Soc Cogn Affect Neurosci 13: 449-459
MeSH Terms: Adult, Aged, Aged, 80 and over, Aging, Brain, Choice Behavior, Cognition, Decision Making, Delay Discounting, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Motivation, Neuropsychological Tests, Physical Exertion, Probability, Psychomotor Performance, Reward, Young Adult
Show Abstract · Added April 15, 2019
Every day, humans make countless decisions that require the integration of information about potential benefits (i.e. rewards) with other decision features (i.e. effort required, probability of an outcome or time delays). Here, we examine the overlap and dissociation of behavioral preferences and neural representations of subjective value in the context of three different decision features (physical effort, probability and time delays) in a healthy adult life span sample. While undergoing functional neuroimaging, participants (N = 75) made incentive compatible choices between a smaller monetary reward with lower physical effort, higher probability, or a shorter time delay versus a larger monetary reward with higher physical effort, lower probability, or a longer time delay. Behavioral preferences were estimated from observed choices, and subjective values were computed using individual hyperbolic discount functions. We found that discount rates were uncorrelated across tasks. Despite this apparent behavioral dissociation between preferences, we found overlapping subjective value-related activity in the medial prefrontal cortex across all three tasks. We found no consistent evidence for age differences in either preferences or the neural representations of subjective value across adulthood. These results suggest that while the tolerance of decision features is behaviorally dissociable, subjective value signals share a common representation across adulthood.
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Perspective on the interpretation of research and translation to clinical care with therapy-associated metastatic breast cancer progression as an example.
Fingleton B, Lange K, Caldwell B, Bankaitis KV, Board of the Metastasis Research Society
(2017) Clin Exp Metastasis 34: 443-447
MeSH Terms: Biomedical Research, Breast Neoplasms, Decision Making, Disease Progression, Evidence-Based Medicine, Female, Humans, Translational Medical Research
Show Abstract · Added March 21, 2018
This commentary was written as a collaboration between the Board of the Metastasis Research Society and two patients with metastatic breast cancer. It was conceived in response to how preclinical scientific research is sometimes presented to non-scientists in a way that can cause stress and confusion. Translation of preclinical findings to the clinic requires overcoming multiple barriers. This is irrespective of whether the findings relate to exciting responses to new therapies or problematic effects of currently used therapies. It is important that these barriers are understood and acknowledged when research findings are summarized for mainstream reporting. To minimize confusion, patients should continue to rely on their oncology care team to help them interpret whether research findings presented in mainstream media have relevance for their individual care. Researchers, both bench and clinical, should work together where possible to increase options for patients with metastatic disease, which is still in desperate need of effective therapeutic approaches.
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8 MeSH Terms
When Enough Is Enough: Decision Criteria for Moving a Known Drug into Clinical Testing for a New Indication in the Absence of Preclinical Efficacy Data.
Pulley JM, Jerome RN, Zaleski NM, Shirey-Rice JK, Pruijssers AJ, Lavieri RR, Chettiar SN, Naylor HM, Aronoff DM, Edwards DA, Niswender CM, Dugan LL, Crofford LJ, Bernard GR, Holroyd KJ
(2017) Assay Drug Dev Technol 15: 354-361
MeSH Terms: Animals, Clinical Trials as Topic, Decision Making, Drug Evaluation, Preclinical, Drug Repositioning, Humans, Models, Animal
Show Abstract · Added March 26, 2019
Many animal models of disease are suboptimal in their representation of human diseases and lack of predictive power in the success of pivotal human trials. In the context of repurposing drugs with known human safety, it is sometimes appropriate to conduct the "last experiment first," that is, progressing directly to human investigations. However, there are not accepted criteria for when to proceed straight to humans to test a new indication. We propose a specific set of criteria to guide the decision-making around when to initiate human proof of principle without preclinical efficacy studies in animal models. This approach could accelerate the transition of novel therapeutic approaches to human applications.
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Bayesian statistical approaches to evaluating cognitive models.
Annis J, Palmeri TJ
(2018) Wiley Interdiscip Rev Cogn Sci 9:
MeSH Terms: Bayes Theorem, Cognition, Decision Making, Humans, Models, Psychological, Reaction Time
Show Abstract · Added April 3, 2018
Cognitive models aim to explain complex human behavior in terms of hypothesized mechanisms of the mind. These mechanisms can be formalized in terms of mathematical structures containing parameters that are theoretically meaningful. For example, in the case of perceptual decision making, model parameters might correspond to theoretical constructs like response bias, evidence quality, response caution, and the like. Formal cognitive models go beyond verbal models in that cognitive mechanisms are instantiated in terms of mathematics and they go beyond statistical models in that cognitive model parameters are psychologically interpretable. We explore three key elements used to formally evaluate cognitive models: parameter estimation, model prediction, and model selection. We compare and contrast traditional approaches with Bayesian statistical approaches to performing each of these three elements. Traditional approaches rely on an array of seemingly ad hoc techniques, whereas Bayesian statistical approaches rely on a single, principled, internally consistent system. We illustrate the Bayesian statistical approach to evaluating cognitive models using a running example of the Linear Ballistic Accumulator model of decision making (Brown SD, Heathcote A. The simplest complete model of choice response time: linear ballistic accumulation. Cogn Psychol 2008, 57:153-178). WIREs Cogn Sci 2018, 9:e1458. doi: 10.1002/wcs.1458 This article is categorized under: Neuroscience > Computation Psychology > Reasoning and Decision Making Psychology > Theory and Methods.
© 2017 Wiley Periodicals, Inc.
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6 MeSH Terms