scholarly journals The geometry of decision-making in individuals and collectives

2021 ◽  
Vol 118 (50) ◽  
pp. e2102157118
Author(s):  
Vivek H. Sridhar ◽  
Liang Li ◽  
Dan Gorbonos ◽  
Máté Nagy ◽  
Bianca R. Schell ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Food Sources ◽  
Larval Zebrafish ◽  
Critical Transitions ◽  
Spatially Distributed ◽  
The One ◽  
Potential Food ◽  
The Brain ◽  
Bifurcation Process

Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process, we reveal the occurrence of spontaneous and abrupt “critical” transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one among, the remaining options. This bifurcation process repeats until only one option—the one ultimately selected—remains. Thus, we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space–time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological contexts, there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.

scholarly journals The geometry of decision-making

2021 ◽  
Author(s):  
Vivek Hari Sridhar ◽  
Liang Li ◽  
Dan Gorbonos ◽  
Mate Nagy ◽  
Bianca R Schell ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Food Sources ◽  
Larval Zebrafish ◽  
Critical Transitions ◽  
Spatially Distributed ◽  
The One ◽  
Potential Food ◽  
The Brain ◽  
Bifurcation Process

Choosing among spatially-distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges, to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process we reveal the occurrence of spontaneous and abrupt "critical" transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one, among the remaining options. This bifurcation process repeats until only one option---the one ultimately selected---remains. Thus we predict that the brain repeatedly breaks multi-choice decisions into a series of binary decisions in space-time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological context, we show that there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.

scholarly journals Neural Correlates of Evidence and Urgency During Human Perceptual Decision-Making in Dynamically Changing Conditions

2020 ◽  
Vol 30 (10) ◽  
pp. 5471-5483
Author(s):  
Y Yau ◽  
M Dadar ◽  
M Taylor ◽  
Y Zeighami ◽  
L K Fellows ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Sensory Information ◽  
Neural Correlates ◽  
Primate Research ◽  
Perceptual Decision Making ◽  
Emotional Information ◽  
Perceptual Decision ◽  
The Face ◽  
The Brain

Abstract Current models of decision-making assume that the brain gradually accumulates evidence and drifts toward a threshold that, once crossed, results in a choice selection. These models have been especially successful in primate research; however, transposing them to human fMRI paradigms has proved it to be challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-by-trial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.

Choice, optimal foraging, and the delay-reduction hypothesis

1985 ◽  
Vol 8 (2) ◽  
pp. 315-330 ◽  
Author(s):  
Edmund Fantino ◽  
Nureya Abarca
Keyword(s):  
Decision Making ◽  
Operant Conditioning ◽  
Optimal Foraging ◽  
Interdisciplinary Approach ◽  
Optimal Foraging Theory ◽  
Food Presentation ◽  
Food Sources ◽  
Delay Reduction ◽  
Closed Economies ◽  
Potential Food

AbstractBehaving organisms are continually choosing. Recently the theoretical and empirical study of decision making by behavioral ecologists and experimental psychologists have converged in the area of foraging, particularly food acquisition. This convergence has raised the interdisciplinary question of whether principles that have emerged from the study of decision making in the operant conditioning laboratory are consistent with decision making in naturally occurring foraging. One such principle, the “parameter-free delay-reduction hypothesis, ” developed in studies of choice in the operant conditioning laboratory, states that the effectiveness of a stimulus as a reinforcer may be predicted most accurately by calculating the decrease in time to food presentation correlated with the onset of the stimulus, relative to the length of time to food presentation measured from the onset of the preceding stimulus. Since foraging involves choice, the delay-reduction hypothesis may be extended to predict aspects of foraging. We discuss the strategy of assessing parameters of foraging with operant laboratory analogues to foraging. We then compare the predictions of the delay-reduction hypothesis with those of optimal foraging theory, developed by behavioral ecologists, showing that, with two exceptions, the two positions make comparable predictions. The delay-reduction hypothesis is also compared to several contemporary pscyhological accounts of choice. Results from several of our experiments with pigeons, designed as operant conditioning simulations of foraging, have shown the following: The more time subjects spend searching for or traveling between potential food sources, the less selective they become, that is, the more likely they are to accept the less preferred outcome; increasing time spent procuring (“handling”) food increases selectivity; how often the preferred outcome is available has a greater effect on choice then how often the less preferred outcome is available; subjects maximize reinforcement whether it is the rate, amount, or probability of reinforcement that is varied; there are no significant differences between subjects performing under different types of deprivation (open vs. closed economies). These results are all consistent with the delay-reduction hypothesis. Moreover, they suggest that the technology of the operant conditioning laboratory may have fruitful application in the study of foraging, and, in doing so, they underscore the importance of an interdisciplinary approach to behavior.

Decision-Making Theoretical Models and Cognitive Biases

2018 ◽  
pp. 1-26
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Cognitive Biases ◽  
Selection Process ◽  
Theoretical Models ◽  
The Other ◽  
Decision Making Process ◽  
Somatic Marker ◽  
Decision Making Models ◽  
The One

Decision making is a cognitive evaluation and selection process on a set of options in order to get to a series of objectives, so the decision-making process is complex. For that, this chapter will talk about the most important decision-making models found in the scientific literature. On the one hand, it will explain the computational models of decision making: connectionist, probabilistic, and qualitative. On the other hand, it will describe the somatic marker model of Damasio and the model of decision making based on heuristics of Kanheman and Tversky. Note that all decision-making models are valid and will depend on the decision in particular that a model will be explanatory of or not. Moreover, some of the models can also act in a complementary way.

scholarly journals Imaging decision-related neural cascades in the human brain

2016 ◽  
Author(s):  
Jordan Muraskin ◽  
Truman R. Brown ◽  
Jennifer M. Walz ◽  
Bryan Conroy ◽  
Robin I. Goldman ◽  
...  
Keyword(s):  
Decision Making ◽  
Brain Dynamics ◽  
Perceptual Decision Making ◽  
Spatiotemporal Resolution ◽  
Perceptual Decision ◽  
Response Network ◽  
Wide Range ◽  
Spatially Distributed ◽  
Methodological Advance ◽  
The Brain

AbstractPerceptual decisions depend on coordinated patterns of neural activity cascading across the brain, running in time from stimulus to response and in space from primary sensory regions to the frontal lobe. Measuring this cascade and how it flows through the brain is key to developing an understanding of how our brains function. However observing, let alone understanding, this cascade, particularly in humans, is challenging. Here, we report a significant methodological advance allowing this observation in humans at unprecedented spatiotemporal resolution. We use a novel encoding model to link simultaneously measured electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals to infer the high-resolution spatiotemporal brain dynamics taking place during rapid visual perceptual decision-making. After demonstrating the methodology replicates past results, we show that it uncovers a previously unobserved sequential reactivation of a substantial fraction of the pre-response network whose magnitude correlates with decision confidence. Our results illustrate that a temporally coordinated and spatially distributed neural cascade underlies perceptual decision-making, with our methodology illuminating complex brain dynamics that would otherwise be unobservable using conventional fMRI or EEG separately. We expect this methodology to be useful in observing brain dynamics in a wide range of other mental processes.

scholarly journals Neural correlates of evidence and urgency during human perceptual decision-making in dynamically changing conditions

2019 ◽  
Author(s):  
Y. Yau ◽  
M. Dadar ◽  
M. Taylor ◽  
Y. Zeighami ◽  
L.K. Fellows ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Sensory Information ◽  
Neural Correlates ◽  
Primate Research ◽  
Perceptual Decision Making ◽  
Emotional Information ◽  
Perceptual Decision ◽  
The Face ◽  
The Brain

AbstractCurrent models of decision-making assume that the brain gradually accumulates evidence and drifts towards a threshold which, once crossed, results in a choice selection. These models have been especially successful in primate research, however transposing them to human fMRI paradigms has proved challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-by-trial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.

scholarly journals Identifying the neural dynamics of category decisions with computational model-based fMRI

2020 ◽  
Author(s):  
Emily Heffernan ◽  
Juliana Daphne Adema ◽  
Michael Louis Mack
Keyword(s):  
Decision Making ◽  
Category Learning ◽  
Computational Models ◽  
Brain Regions ◽  
Neural Dynamics ◽  
Neural Activation ◽  
Neural Signals ◽  
Neural Representations ◽  
Model Predictions ◽  
The Brain

Successful categorization requires a careful coordination of attention, representation, and decision making. Comprehensive theories that span levels of analysis are key to understanding the computational and neural dynamics of categorization. Here, we build on recent work linking neural representations of category learning to computational models to investigate how category decision making is driven by neural signals across the brain. We uniquely combine functional magnetic resonance imaging with drift diffusion and exemplar-based categorization models to show that trial-by-trial fluctuations in neural activation from regions of occipital, cingulate, and lateral prefrontal cortices are linked to category decisions. Notably, only lateral prefrontal cortex activation was associated with exemplar-based model predictions of trial-by-trial category evidence. We propose that these brain regions underlie distinct functions that contribute to successful category learning.

Central Nerve System Impairment, AMA Guides, Sixth Edition: An Overview

2018 ◽  
Vol 23 (1) ◽  
pp. 10-13
Author(s):  
James B. Talmage ◽  
Jay Blaisdell
Keyword(s):  
Spinal Cord ◽  
Central Nervous System ◽  
Nervous System ◽  
Neurological Impairment ◽  
Sixth Edition ◽  
Central Nerve System ◽  
The Central Nervous System ◽  
The One ◽  
Nerve System ◽  
The Brain

Abstract Injuries that affect the central nervous system (CNS) can be catastrophic because they involve the brain or spinal cord, and determining the underlying clinical cause of impairment is essential in using the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), in part because the AMA Guides addresses neurological impairment in several chapters. Unlike the musculoskeletal chapters, Chapter 13, The Central and Peripheral Nervous System, does not use grades, grade modifiers, and a net adjustment formula; rather the chapter uses an approach that is similar to that in prior editions of the AMA Guides. The following steps can be used to perform a CNS rating: 1) evaluate all four major categories of cerebral impairment, and choose the one that is most severe; 2) rate the single most severe cerebral impairment of the four major categories; 3) rate all other impairments that are due to neurogenic problems; and 4) combine the rating of the single most severe category of cerebral impairment with the ratings of all other impairments. Because some neurological dysfunctions are rated elsewhere in the AMA Guides, Sixth Edition, the evaluator may consult Table 13-1 to verify the appropriate chapter to use.

scholarly journals Shifting uncertainty intolerance: methylphenidate and attention-deficit hyperactivity disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alekhya Mandali ◽  
Arjun Sethi ◽  
Mara Cercignani ◽  
Neil A. Harrison ◽  
Valerie Voon
Keyword(s):  
Decision Making ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Computational Models ◽  
Risky Choice ◽  
Risk Tolerance ◽  
Healthy Population ◽  
Hyperactivity Disorder ◽  
Risky Choices ◽  
Cardinal Symptom

AbstractRisk evaluation is a critical component of decision making. Risk tolerance is relevant in both daily decisions and pathological disorders such as attention-deficit hyperactivity disorder (ADHD), where impulsivity is a cardinal symptom. Methylphenidate, a commonly prescribed drug in ADHD, improves attention but has mixed reports on risk-based decision making. Using a double-blinded placebo protocol, we studied the risk attitudes of ADHD patients and age-matched healthy volunteers while performing the 2-step sequential learning task and examined the effect of methylphenidate on their choices. We then applied a novel computational analysis using the hierarchical drift–diffusion model to extract parameters such as threshold (‘a’—amount of evidence accumulated before making a decision), drift rate (‘v’—information processing speed) and response bias (‘z’ apriori bias towards a specific choice) focusing specifically on risky choice preference. Critically, we show that ADHD patients on placebo have an apriori bias towards risky choices compared to controls. Furthermore, methylphenidate enhanced preference towards risky choices (higher apriori bias) in both groups but had a significantly greater effect in the patient population independent of clinical scores. Thus, methylphenidate appears to shift tolerance towards risky uncertain choices possibly mediated by prefrontal dopaminergic and noradrenergic modulation. We emphasise the utility of computational models in detecting underlying processes. Our findings have implications for subtle yet differential effects of methylphenidate on ADHD compared to healthy population.

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