Decision making by urgency gating: theory and experimental support

2012 ◽  
Vol 108 (11) ◽  
pp. 2912-2930 ◽  
Author(s):  
David Thura ◽  
Julie Beauregard-Racine ◽  
Charles-William Fradet ◽  
Paul Cisek
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Time Window ◽  
Sensory Information ◽  
Natural Conditions ◽  
Accuracy Criterion ◽  
Sensory Evidence ◽  
Dot Motion ◽  
The Brain ◽  
Random Dot Motion

It is often suggested that decisions are made when accumulated sensory information reaches a fixed accuracy criterion. This is supported by many studies showing a gradual build up of neural activity to a threshold. However, the proposal that this build up is caused by sensory accumulation is challenged by findings that decisions are based on information from a time window much shorter than the build-up process. Here, we propose that in natural conditions where the environment can suddenly change, the policy that maximizes reward rate is to estimate evidence by accumulating only novel information and then compare the result to a decreasing accuracy criterion. We suggest that the brain approximates this policy by multiplying an estimate of sensory evidence with a motor-related urgency signal and that the latter is primarily responsible for neural activity build up. We support this hypothesis using human behavioral data from a modified random-dot motion task in which motion coherence changes during each trial.

scholarly journals Transient neuronal suppression for exploitation of new sensory evidence

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Maxwell Shinn ◽  
Daeyeol Lee ◽  
John D. Murray ◽  
Hyojung Seo
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Temporal Integration ◽  
Motor Preparation ◽  
Stimulus Onset ◽  
Frontal Eye Field ◽  
Perceptual Decision Making ◽  
Behavioral Studies ◽  
The Face ◽  
Sensory Evidence

AbstractIn noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify and respond via saccade to the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals’ behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset but sensitive to stimulus strength. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is important for dynamic perceptual decision making.

The Emerging Standard Neurobiological Model of Decision Making

2018 ◽  
Author(s):  
Shih-Wei Wu ◽  
Paul W. Glimcher
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Neural Activity ◽  
First Century ◽  
The Other ◽  
Neoclassical Economic ◽  
Internal Representations ◽  
Neurobiological Model ◽  
Neoclassical Economic Theory ◽  
The Brain

The standard neurobiological model of decision making has evolved, since the turn of the twenty-first century, from a confluence of economic, psychological, and neurosci- entific studies of how humans make choices. Two fundamental insights have guided the development of this model during this period, one drawn from economics and the other from neuroscience. The first derives from neoclassical economic theory, which unambiguously demonstrated that logically consistent choosers behave “as if” they had some internal, continuous, and monotonic representation of the values of any choice objects under consideration. The second insight derives from neurobiological studies suggesting that the brain can both represent, in patterns of local neural activity, and compare, by a process of interneuronal competition, internal representations of value associated with different choices.

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 Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

2020 ◽  
Author(s):  
Benjamin R. Cowley ◽  
Adam C. Snyder ◽  
Katerina Acar ◽  
Ryan C. Williamson ◽  
Byron M. Yu ◽  
...  
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Neural Activity ◽  
Internal State ◽  
Sensory Stimulus ◽  
Perceptual Decision Making ◽  
Population Activity ◽  
Perceptual Decision ◽  
Slow Drift ◽  
Sensory Evidence

AbstractAn animal’s decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This internal state is a product of cognitive factors, such as fatigue, motivation, and arousal, but it is unclear how these factors influence the neural processes that encode the sensory stimulus and form a decision. We discovered that, over the timescale of tens of minutes during a perceptual decision-making task, animals slowly shifted their likelihood of reporting stimulus changes. They did this unprompted by task conditions. We recorded neural population activity from visual area V4 as well as prefrontal cortex, and found that the activity of both areas slowly drifted together with the behavioral fluctuations. We reasoned that such slow fluctuations in behavior could either be due to slow changes in how the sensory stimulus is processed or due to a process that acts independently of sensory processing. By analyzing the recorded activity in conjunction with models of perceptual decision-making, we found evidence for the slow drift in neural activity acting as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in the population activity across multiple brain areas, hidden from typical trial-averaged analyses and revealed only when considering the passage of time within each experimental session. Knowledge of this cognitive factor was critical in elucidating how sensory signals and the internal state together contribute to the decision-making process.

Effects of Selective and Divided Attention on Audiovisual Interaction

2013 ◽  
pp. 311-319
Author(s):  
Weiping Yang ◽  
Yulin Gao ◽  
Jinglong Wu
Keyword(s):  
Everyday Life ◽  
Neural Activity ◽  
Divided Attention ◽  
Sensory Information ◽  
Audiovisual Integration ◽  
Neural Activities ◽  
Attention System ◽  
The Brain ◽  
Audiovisual Interaction

In everyday life, visual and auditory are the most common forms of sensory information. Therefore, audiovisual interaction in the brain plays an important role in performance and perception. In addition, our attention system allows us to dynamically select and enhance the processing of objects and events that are the most relevant at each moment. Some studies suggest that attention can modulate audiovisual integration. However, different neural activity of multimodal audiovisual integration can be seen in different attention conditions. This review focuses on the question of what affects selective and divided attention in audiovisual interaction. Neural activities of audiovisual under selective and divided attention conditions are also discussed. This review aims to bring together and summarize previous studies on the interactions between attention and audiovisual integration.

Spontaneous Neural Fluctuations Predict Decisions to Attend

2014 ◽  
Vol 26 (11) ◽  
pp. 2578-2584 ◽  
Author(s):  
Jesse J. Bengson ◽  
Todd A. Kelley ◽  
Xiaoke Zhang ◽  
Jane-Ling Wang ◽  
George R. Mangun
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Intrinsic Property ◽  
Alternative View ◽  
Alpha Band ◽  
Band Power ◽  
Trial Basis ◽  
Human Participants ◽  
Neural Signaling ◽  
The Brain

Ongoing variability in neural signaling is an intrinsic property of the brain. Often this variability is considered to be noise and ignored. However, an alternative view is that this variability is fundamental to perception and cognition and may be particularly important in decision-making. Here, we show that a momentary measure of occipital alpha-band power (8–13 Hz) predicts choices about where human participants will focus spatial attention on a trial-by-trial basis. This finding provides evidence for a mechanistic account of decision-making by demonstrating that ongoing neural activity biases voluntary decisions about where to attend within a given moment.

scholarly journals Evidence against perfect integration of sensory information during perceptual decision making

2016 ◽  
Vol 115 (2) ◽  
pp. 915-930 ◽  
Author(s):  
Matthew A. Carland ◽  
Encarni Marcos ◽  
David Thura ◽  
Paul Cisek
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Temporal Integration ◽  
Sensory Information ◽  
Reaction Times ◽  
Perceptual Decision Making ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Perceptual Decision ◽  
Low Pass

Perceptual decision making is often modeled as perfect integration of sequential sensory samples until the accumulated total reaches a fixed decision bound. In that view, the buildup of neural activity during perceptual decision making is attributed to temporal integration. However, an alternative explanation is that sensory estimates are computed quickly with a low-pass filter and combined with a growing signal reflecting the urgency to respond and it is the latter that is primarily responsible for neural activity buildup. These models are difficult to distinguish empirically because they make similar predictions for tasks in which sensory information is constant within a trial, as in most previous studies. Here we presented subjects with a variant of the classic constant-coherence motion discrimination (CMD) task in which we inserted brief motion pulses. We examined the effect of these pulses on reaction times (RTs) in two conditions: 1) when the CMD trials were blocked and subjects responded quickly and 2) when the same CMD trials were interleaved among trials of a variable-motion coherence task that motivated slower decisions. In the blocked condition, early pulses had a strong effect on RTs but late pulses did not, consistent with both models. However, when subjects slowed their decision policy in the interleaved condition, later pulses now became effective while early pulses lost their efficacy. This last result contradicts models based on perfect integration of sensory evidence and implies that motion signals are processed with a strong leak, equivalent to a low-pass filter with a short time constant.

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.

scholarly journals Illusory sound texture reveals multi-second statistical completion in auditory scene analysis

2019 ◽  
Author(s):  
Richard McWalter ◽  
Josh H. McDermott
Keyword(s):  
Sensory Information ◽  
Auditory Scene Analysis ◽  
Scene Analysis ◽  
Sound Sources ◽  
Stochastic Nature ◽  
Perceptual Completion ◽  
Auditory Scene ◽  
Stable Representation ◽  
Sensory Evidence ◽  
The Brain

AbstractSound sources in the world are experienced as stable even when intermittently obscured, implying perceptual completion mechanisms that “fill in” missing sensory information. We demonstrate a filling-in phenomenon in which the brain extrapolates the statistics of background sounds (textures) over periods of several seconds when they are interrupted by another sound, producing vivid percepts of illusory texture. The effect differs from previously described completion effects in that 1) the extrapolated sound must be defined statistically given the stochastic nature of texture, and 2) in lasting much longer, enabling introspection and facilitating assessment of the underlying representation. Illusory texture appeared to be integrated into texture statistic estimates indistinguishably from actual texture, suggesting that it is represented similarly to actual texture. The illusion appears to represent an inference about whether the background is likely to continue during concurrent sounds, providing a stable representation of the environment despite unstable sensory evidence.

scholarly journals Illusory sound texture reveals multi-second statistical completion in auditory scene analysis

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Richard McWalter ◽  
Josh H. McDermott
Keyword(s):  
Sensory Information ◽  
Auditory Scene Analysis ◽  
Scene Analysis ◽  
Sound Sources ◽  
Stochastic Nature ◽  
Statistical Representation ◽  
Perceptual Completion ◽  
Auditory Scene ◽  
Sensory Evidence ◽  
The Brain

Abstract Sound sources in the world are experienced as stable even when intermittently obscured, implying perceptual completion mechanisms that “fill in” missing sensory information. We demonstrate a filling-in phenomenon in which the brain extrapolates the statistics of background sounds (textures) over periods of several seconds when they are interrupted by another sound, producing vivid percepts of illusory texture. The effect differs from previously described completion effects in that 1) the extrapolated sound must be defined statistically given the stochastic nature of texture, and 2) the effect lasts much longer, enabling introspection and facilitating assessment of the underlying representation. Illusory texture biases subsequent texture statistic estimates indistinguishably from actual texture, suggesting that it is represented similarly to actual texture. The illusion appears to represent an inference about whether the background is likely to continue during concurrent sounds, providing a stable statistical representation of the ongoing environment despite unstable sensory evidence.

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