Imaging decision-related neural cascades in the human 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.

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Flexible categorization in perceptual decision making

10.1101/2020.05.23.110460 ◽

2020 ◽

Cited By ~ 2

Author(s):

Genís Prat-Ortega ◽

Klaus Wimmer ◽

Alex Roxin ◽

Jaime de la Rocha

Keyword(s):

Decision Making ◽

Large Body ◽

Network Models ◽

Perceptual Decision Making ◽

Drift Diffusion ◽

Perceptual Decision ◽

Attractor Dynamics ◽

Attractor Model ◽

Winner Take All ◽

The Brain

AbstractPerceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization, in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making.

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Interactions Among the Brain Default-Mode, Salience, and Central-Executive Networks During Perceptual Decision-Making of Moving Dots

Brain Connectivity ◽

10.1089/brain.2015.0379 ◽

2016 ◽

Vol 6 (3) ◽

pp. 249-254 ◽

Cited By ~ 26

Author(s):

Ganesh B. Chand ◽

Mukesh Dhamala

Keyword(s):

Decision Making ◽

Central Executive ◽

Perceptual Decision Making ◽

Default Mode ◽

Perceptual Decision ◽

The Brain

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Strategy-dependent effects of working-memory limitations on human perceptual decision-making

10.1101/2021.09.03.458917 ◽

2021 ◽

Author(s):

Kyra Schapiro ◽

Kresimir Josic ◽

Zachary Kilpatrick ◽

Joshua I Gold

Keyword(s):

Decision Making ◽

Working Memory ◽

Decision Variable ◽

Perceptual Decision Making ◽

Perceptual Decision ◽

Human Psychophysics ◽

Decision Variables ◽

The Impact ◽

The Brain ◽

Over Time

Deliberative decisions based on an accumulation of evidence over time depend on working memory, and working memory has limitations, but how these limitations affect deliberative decision-making is not understood. We used human psychophysics to assess the impact of working-memory limitations on the fidelity of a continuous decision variable. Participants decided the average location of multiple visual targets. This computed, continuous decision variable degraded with time and capacity in a manner that depended critically on the strategy used to form the decision variable. This dependence reflected whether the decision variable was computed either: 1) immediately upon observing the evidence, and thus stored as a single value in memory; or 2) at the time of the report, and thus stored as multiple values in memory. These results provide important constraints on how the brain computes and maintains temporally dynamic decision variables.

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A cerebellar role in evidence-guided decision-making

10.1101/343095 ◽

2018 ◽

Author(s):

Ben Deverett ◽

Sue Ann Koay ◽

Marlies Oostland ◽

Samuel S.-H. Wang

Keyword(s):

Decision Making ◽

Perceptual Decision Making ◽

Perceptual Decision ◽

Two Photon ◽

Related Information ◽

Distributed Computations ◽

Lateral Posterior ◽

Crus I ◽

The Brain ◽

Decision Errors

To make successful evidence-based decisions, the brain must rapidly and accurately transform sensory inputs into specific goal-directed behaviors. Most experimental work on this subject has focused on forebrain mechanisms. Here we show that during perceptual decision-making over a period of seconds, decision-, sensory-, and error-related information converge on the lateral posterior cerebellum in crus I, a structure that communicates bidirectionally with numerous forebrain regions. We trained mice on a novel evidence-accumulation task and demonstrated that cerebellar inactivation reduces behavioral accuracy without impairing motor parameters of action. Using two-photon calcium imaging, we found that Purkinje cell somatic activity encoded choice- and evidence-related variables. Decision errors were represented by dendritic calcium spikes, which are known to drive plasticity. We propose that cerebellar circuitry may contribute to the set of distributed computations in the brain that support accurate perceptual decision-making.

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

Cerebral Cortex ◽

10.1093/cercor/bhaa129 ◽

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.

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Representations of evidence for a perceptual decision in the human brain

10.1101/350876 ◽

2018 ◽

Author(s):

Sebastian Bitzer ◽

Hame Park ◽

Burkhard Maess ◽

Katharina von Kriegstein ◽

Stefan Kiebel

Keyword(s):

Decision Making ◽

Cingulate Cortex ◽

Posterior Cingulate Cortex ◽

Perceptual Decision Making ◽

Perceptual Decision ◽

Posterior Cingulate ◽

Brain Signals ◽

Brain Extracts ◽

The Brain ◽

Motor Areas

In perceptual decision making the brain extracts and accumulates decision evidence from a stimulus over time and eventually makes a decision based on the accumulated evidence. Several characteristics of this process have been observed in human electrophysiological experiments, especially an average build-up of motor-related signals supposedly reflecting accumulated evidence, when averaged across trials. Another recently established approach to investigate the representation of decision evidence in brain signals is to correlate the within-trial fluctuations of decision evidence with the measured signals. We here report results for a two-alternative forced choice reaction time experiment in which we applied this approach to human magnetoencephalographic (MEG) recordings. These results consolidate a range of previous findings. In addition, they show: 1) that decision evidence is most strongly represented in the MEG signals in three consecutive phases, 2) that motor areas contribute longer to these representations than parietal areas and 3) that posterior cingulate cortex is involved most consistently, among all brain areas, in all three of the identified phases. As most previous work on perceptual decision making in the brain has focused on parietal and motor areas, our findings therefore suggest that the role of the posterior cingulate cortex in perceptual decision making may be currently underestimated.

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How the brain prevents a second error in a perceptual decision-making task

Scientific Reports ◽

10.1038/srep32058 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 20

Author(s):

Rinaldo Livio Perri ◽

Marika Berchicci ◽

Giuliana Lucci ◽

Donatella Spinelli ◽

Francesco Di Russo

Keyword(s):

Decision Making ◽

Perceptual Decision Making ◽

Perceptual Decision ◽

The Brain

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Suboptimality in Perceptual Decision Making

10.1101/060194 ◽

2016 ◽

Cited By ~ 7

Author(s):

Dobromir Rahnev ◽

Rachel N. Denison

Keyword(s):

Decision Making ◽

Explanatory Power ◽

Extensive Literature ◽

Perceptual Decision Making ◽

Perceptual Decision ◽

Perceptual Illusions ◽

Wide Range ◽

False Sense ◽

Speed And Accuracy ◽

Suboptimal Behavior

Short AbstractHuman perceptual decisions are often described as optimal, but this view remains controversial. To elucidate the issue, we review the vast literature on suboptimalities in perceptual tasks and compile the proposed hypotheses about the origins of suboptimal behavior. Further, we argue that general claims about optimality are virtually meaningless and result in a false sense of progress. Instead, real progress can be achieved by building observer models that account for both optimal and suboptimal behavior. To achieve such progress, the field should focus on assessing the hypotheses about suboptimal behavior compiled here and stop chasing optimality.Long AbstractHuman perceptual decisions are often described as optimal. Critics of this view have argued that claims of optimality are overly flexible and lack explanatory power. Meanwhile, advocates for optimality have countered that such criticisms single out a few selected papers. To elucidate the issue of optimality in perceptual decision making, we review the extensive literature on suboptimal performance in perceptual tasks. We discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria, inadequate tradeoff between speed and accuracy, inappropriate confidence ratings, misweightings in cue combination, and findings related to various perceptual illusions and biases. In addition, we discuss conceptual shortcomings of a focus on optimality, such as definitional difficulties and the limited value of optimality claims in and of themselves. We therefore advocate that the field drop its emphasis on whether observed behavior is optimal and instead concentrate on building and testing detailed observer models that explain behavior across a wide range of tasks. To facilitate this transition, we compile the proposed hypotheses regarding the origins of suboptimal perceptual decisions reviewed here. We argue that verifying, rejecting, and expanding these explanations for suboptimal behavior – rather than assessing optimality per se – should be among the major goals of the science of perceptual decision making.

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Credo for optimality

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001346 ◽

2018 ◽

Vol 41 ◽

Cited By ~ 1

Author(s):

Alan A. Stocker

Keyword(s):

Decision Making ◽

Information Processing ◽

Sensory Information ◽

Perceptual Decision Making ◽

Perceptual Decision ◽

Sensory Information Processing ◽

The Brain ◽

Additional Constraints

AbstractOptimal or suboptimal, Rahnev & Denison (R&D) rightly argue that this ill-defined distinction is not useful when comparing models of perceptual decision making. However, what they miss is how valuable the focus on optimality has been in deriving these models in the first place. Rather than prematurely abandon the optimality assumption, we should refine this successful normative hypothesis with additional constraints that capture specific limitations of (sensory) information processing in the brain.

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