scholarly journals Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence

2017 ◽  
Author(s):  
Rachel N. Denison ◽  
William T. Adler ◽  
Marisa Carrasco ◽  
Wei Ji Ma
Keyword(s):  
Sensory Input ◽  
Model Comparison ◽  
Formal Model ◽  
Decision Rules ◽  
Perceptual Decision Making ◽  
Categorization Task ◽  
Natural Vision ◽  
Human Categorization ◽  
Decision Boundaries ◽  
Different Levels

AbstractPerceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision making. Here we show that human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observer’s attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observer’s decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.

scholarly journals Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence

2018 ◽  
Vol 115 (43) ◽  
pp. 11090-11095 ◽  
Author(s):  
Rachel N. Denison ◽  
William T. Adler ◽  
Marisa Carrasco ◽  
Wei Ji Ma
Keyword(s):  
Sensory Input ◽  
Model Comparison ◽  
Formal Model ◽  
Decision Rules ◽  
Perceptual Decision Making ◽  
Categorization Task ◽  
Natural Vision ◽  
Human Categorization ◽  
Decision Boundaries ◽  
Different Levels

Perceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision-making. Here we show that, in a task in which uncertainty is relevant for performance, human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observer’s attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observer’s decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.

scholarly journals Taming the Tiger: Voting Rights and Political Instability in Latin America

2004 ◽  
Vol 46 (2) ◽  
pp. 29-58 ◽  
Author(s):  
Josep M. Colomer
Keyword(s):  
Latin America ◽  
Latin American ◽  
Empirical Data ◽  
Formal Model ◽  
Political Instability ◽  
Voting Rights ◽  
Historical Survey ◽  
Electoral Rules ◽  
The Relationship ◽  
Different Levels

AbstractThis article discusses the relationship between certain institutional regulations of voting rights and elections, different levels of electoral participation, and the degree of political instability in several Latin American political experiences. A formal model specifies the hypotheses that sudden enlargements of the electorate may provoke high levels of political instability, especially under plurality and other restrictive electoral rules, while gradual enlargements of the electorate may prevent much electoral and political innovation and help stability. Empirical data illustrate these hypotheses. A historical survey identifies different patterns of political instability and stability in different countries and periods, which can be compared with the adoption of different voting rights regulations and electoral rules either encouraging or depressing turnout.

scholarly journals Bureaucratic Corruption, Democracy and Judicial Independence

2016 ◽  
Vol 1 (1) ◽  
pp. 7
Author(s):  
Gang Wang
Keyword(s):  
Political Institutions ◽  
Instrumental Variable ◽  
Formal Model ◽  
Judicial Independence ◽  
Bureaucratic Corruption ◽  
Institutional Constraint ◽  
Determinants Of Corruption ◽  
The Government ◽  
Different Levels

<p>The theoretical literature in economics and political science has made numerous efforts in understanding the determinants of corruption and stressed the importance of political institutions in shaping the patterns of government corruption. Nevertheless, very few researches focus on the role of judicial system. Employing a formal model with empirical analyses, I incorporate economic factors with political constraints to investigate the different roles of democracy and judicial independence in determining the level of bureaucrats’ corruption across countries. Empirically, the instrumental variable (IV) approach is applied to resolve the endogeneity problems. The evidence indicates that different levels of corruption across countries are significantly influenced by the degrees of judicial independence. To fight corruption successfully, I contend that the judiciary, as a hard institutional constraint to resist bureaucratic corruption, has to be independent from the government. </p>

scholarly journals Neurophysiological dynamics of phrase-structure building during sentence processing

2017 ◽  
Vol 114 (18) ◽  
pp. E3669-E3678 ◽  
Author(s):  
Matthew J. Nelson ◽  
Imen El Karoui ◽  
Kristof Giber ◽  
Xiaofang Yang ◽  
Laurent Cohen ◽  
...  
Keyword(s):  
Sentence Processing ◽  
Model Comparison ◽  
Formal Model ◽  
Syntactic Structure ◽  
Brain Activity ◽  
Transition Probability ◽  
Middle Temporal Gyrus ◽  
Similar Amount ◽  
Gamma Power ◽  
The Brain

Although sentences unfold sequentially, one word at a time, most linguistic theories propose that their underlying syntactic structure involves a tree of nested phrases rather than a linear sequence of words. Whether and how the brain builds such structures, however, remains largely unknown. Here, we used human intracranial recordings and visual word-by-word presentation of sentences and word lists to investigate how left-hemispheric brain activity varies during the formation of phrase structures. In a broad set of language-related areas, comprising multiple superior temporal and inferior frontal sites, high-gamma power increased with each successive word in a sentence but decreased suddenly whenever words could be merged into a phrase. Regression analyses showed that each additional word or multiword phrase contributed a similar amount of additional brain activity, providing evidence for a merge operation that applies equally to linguistic objects of arbitrary complexity. More superficial models of language, based solely on sequential transition probability over lexical and syntactic categories, only captured activity in the posterior middle temporal gyrus. Formal model comparison indicated that the model of multiword phrase construction provided a better fit than probability-based models at most sites in superior temporal and inferior frontal cortices. Activity in those regions was consistent with a neural implementation of a bottom-up or left-corner parser of the incoming language stream. Our results provide initial intracranial evidence for the neurophysiological reality of the merge operation postulated by linguists and suggest that the brain compresses syntactically well-formed sequences of words into a hierarchy of nested phrases.

Pupil-Linked Arousal Biases Evidence Accumulation Toward Desirable Percepts During Perceptual Decision-Making

2021 ◽  
Vol 32 (9) ◽  
pp. 1494-1509
Author(s):  
Yuan Chang Leong ◽  
Roma Dziembaj ◽  
Mark D’Esposito
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Perceptual Decision Making ◽  
Categorization Task ◽  
Perceptual Decision ◽  
Evidence Accumulation ◽  
Perceptual Judgments ◽  
Significant Events ◽  
Arousal System ◽  
Objective Representation

People’s perceptual reports are biased toward percepts they are motivated to see. The arousal system coordinates the body’s response to motivationally significant events and is well positioned to regulate motivational effects on perceptual judgments. However, it remains unclear whether arousal would enhance or reduce motivational biases. Here, we measured pupil dilation as a measure of arousal while participants ( N = 38) performed a visual categorization task. We used monetary bonuses to motivate participants to perceive one category over another. Even though the reward-maximizing strategy was to perform the task accurately, participants were more likely to report seeing the desirable category. Furthermore, higher arousal levels were associated with making motivationally biased responses. Analyses using computational models suggested that arousal enhanced motivational effects by biasing evidence accumulation in favor of desirable percepts. These results suggest that heightened arousal biases people toward what they want to see and away from an objective representation of the environment.

Representation of tactile signals in primate supplementary motor area

1993 ◽  
Vol 70 (6) ◽  
pp. 2690-2694 ◽  
Author(s):  
R. Romo ◽  
S. Ruiz ◽  
P. Crespo ◽  
A. Zainos ◽  
H. Merchant
Keyword(s):  
Reaction Time ◽  
Decision Process ◽  
Sensory Input ◽  
Supplementary Motor Area ◽  
Motor Area ◽  
Control Task ◽  
Categorization Task ◽  
Tactile Stimuli ◽  
Time Period ◽  
All Speeds

1. We have studied the neuronal activity in the supplementary motor area (SMA) of two monkeys who categorized the speed of moving tactile stimuli delivered to the glabrous skin of the hand ipsilateral to the site of cortical recording and contralateral to the responding arm. 2. A large number of SMA neurons responded to the stimuli of all speeds (176 of 522) but only when those stimuli controlled behavior. 3. A second class of SMA neurons responded differentially in the categorization task (35 during the stimuli and 51 during the reaction time period) and predicted its outcome. 4. To dissociate the interrupt target switches presses from the tactile categorization responses, sixteen neurons, which responded to the stimuli in all speeds, and 11 neurons, which discharged differentially, were tested in a visual control task. None of these two classes of neurons responded in this situation. 5. It is concluded that the SMA ipsilateral to sensory input and contralateral to the responding arm is involved in the sensory decision process in this somesthetic categorization task.

scholarly journals Neural computations underlying inverse reinforcement learning in the human brain

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Sven Collette ◽  
Wolfgang M Pauli ◽  
Peter Bossaerts ◽  
John O'Doherty
Keyword(s):  
Reinforcement Learning ◽  
Human Brain ◽  
Model Comparison ◽  
Formal Model ◽  
Food Preferences ◽  
Slot Machines ◽  
Inverse Reinforcement Learning ◽  
Neural Computations ◽  
Fmri Analysis ◽  
Imitation Strategy

In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred food outcomes with varying probabilities, through observing the repeated slot choices of agents with similar and dissimilar food preferences. Using formal model comparison, we found that participants implemented inverse RL as opposed to a simple imitation strategy, in which the actions of the other agent are copied instead of inferring the underlying reward structure of the decision problem. Our computational fMRI analysis revealed that anterior dorsomedial prefrontal cortex encoded inferences about action-values within the value space of the agent as opposed to that of the observer, demonstrating that inverse RL is an abstract cognitive process divorceable from the values and concerns of the observer him/herself.

scholarly journals A confirmation bias in perceptual decision-making due to hierarchical approximate inference

2018 ◽  
Author(s):  
Richard D. Lange ◽  
Ankani Chattoraj ◽  
Jeffrey M. Beck ◽  
Jacob L. Yates ◽  
Ralf M. Haefner
Keyword(s):  
Model Comparison ◽  
Empirical Studies ◽  
Temporal Integration ◽  
Sensory Information ◽  
Quantitative Model ◽  
Perceptual Decision Making ◽  
Sensory Evidence ◽  
Hierarchical Nature ◽  
Integration Strategies ◽  
The Brain

AbstractHuman decisions are known to be systematically biased. A prominent example of such a bias occurs when integrating a sequence of sensory evidence over time. Previous empirical studies differ in the nature of the bias they observe, ranging from favoring early evidence (primacy), to favoring late evidence (recency). Here, we present a unifying framework that explains these biases and makes novel psychophysical and neurophysiological predictions. By explicitly modeling both the approximate and the hierarchical nature of inference in the brain, we show that temporal biases depend on the balance between “sensory information” and “category information” in the stimulus. Finally, we present new data from a human psychophysics task that confirms a critical prediction of our framework showing that effective temporal integration strategies can be robustly changed within each subject, and that allows us to exclude alternate explanations through quantitative model comparison.

scholarly journals Confidence-Controlled Hebbian Learning Efficiently Extracts Category Membership From Stimuli Encoded in View of a Categorization Task

2021 ◽  
pp. 1-33
Author(s):  
Kevin Berlemont ◽  
Jean-Pierre Nadal
Keyword(s):  
Decision Making ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Descent Method ◽  
Category Membership ◽  
Gradient Descent Method ◽  
Perceptual Decision Making ◽  
Categorization Task ◽  
Attractor Network ◽  
Categorical Information

Abstract In experiments on perceptual decision making, individuals learn a categorization task through trial-and-error protocols. We explore the capacity of a decision-making attractor network to learn a categorization task through reward-based, Hebbian-type modifications of the weights incoming from the stimulus encoding layer. For the latter, we assume a standard layer of a large number of stimu lus-specific neurons. Within the general framework of Hebbian learning, we have hypothesized that the learning rate is modulated by the reward at each trial. Surprisingly, we find that when the coding layer has been optimized in view of the categorization task, such reward-modulated Hebbian learning (RMHL) fails to extract efficiently the category membership. In previous work, we showed that the attractor neural networks' nonlinear dynamics accounts for behavioral confidence in sequences of decision trials. Taking advantage of these findings, we propose that learning is controlled by confidence, as computed from the neural activity of the decision-making attractor network. Here we show that this confidence-controlled, reward-based Hebbian learning efficiently extracts categorical information from the optimized coding layer. The proposed learning rule is local and, in contrast to RMHL, does not require storing the average rewards obtained on previous trials. In addition, we find that the confidence-controlled learning rule achieves near-optimal performance. In accordance with this result, we show that the learning rule approximates a gradient descent method on a maximizing reward cost function.

scholarly journals Modeling dynamic allocation of effort in a sequential task using discounting models

2019 ◽  
Author(s):  
Cuevas Rivera Darío ◽  
Strobel Alexander ◽  
Goschke Thomas ◽  
Stefan J. Kiebel
Keyword(s):  
Model Comparison ◽  
Formal Model ◽  
Model Parameters ◽  
Sequential Decision ◽  
Dynamic Allocation ◽  
Planning Strategy ◽  
Effort Discounting ◽  
Binary Choices ◽  
Minimum Number ◽  
Neural Underpinnings

Most rewards in our lives require effort to obtain them. It is known that effort is seen by humans as carrying an intrinsic disutility which devalues the obtainable reward. Established models for effort discounting account for this by using participant-specific discounting parameters inferred from experiments. These parameters offer only a static glance into the bigger picture of effort exertion. The mechanism underlying the dynamic changes in a participant’s willingness to exert effort is still unclear and an active topic of research. Here, we modeled dynamic effort exertion as a consequence of effort- and probability-discounting mechanisms during goal reaching, sequential behavior. To do this, we developed a novel sequential decision-making task in which participants make binary choices to reach a minimum number of points. Importantly, the time points and circumstances of effort allocation are decided by participants according to their own preferences and not imposed directly by the task. Using the computational model to analyze participants’ choices, we show that the dynamics of effort exertion arise from a combination of changing task needs and forward planning. In other words, the interplay between a participant’s inferred discounting parameters is sufficient to explain the dynamic allocation of effort during goal reaching. Using formal model comparison, we also infer the forward-planning strategy used by participants. The model allows us to characterize a participant’s effort exertion in terms of only a few parameters. Moreover, the model can be adapted to a number of tasks used in establishing the neural underpinnings of forward-planning behavior and meta-control, allowing for the characterization of behavior in terms of model parameters.

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