scholarly journals The interplay between multisensory integration and perceptual decision making

2019 ◽  
Author(s):  
Manuel R. Mercier ◽  
Celine Cappe
Keyword(s):  
Decision Making ◽  
Multisensory Integration ◽  
Multisensory Processing ◽  
Optimal Decision ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Human Participants ◽  
Sensory Encoding ◽  
Optimal Decision Making ◽  
The Brain

AbstractFacing perceptual uncertainty, the brain combines information from different senses to shape optimal decision making and to guide behavior. Despite overlapping neural networks underlying multisensory integration and perceptual decision making, the process chain of decision formation has been studied mostly in unimodal contexts and is thought to be supramodal. To reveal whether and how multisensory processing interplay with perceptual decision making, we devised a paradigm mimicking naturalistic situations where human participants were exposed to continuous cacophonous audiovisual inputs containing an unpredictable relevant signal cue in one or two modalities. Using multivariate pattern analysis on concurrently recorded EEG, we decoded the neural signatures of sensory encoding and decision formation stages. Generalization analyses across conditions and time revealed that multisensory signal cues were processed faster during both processing stages. We further established that acceleration of neural dynamics was directly linked to two distinct multisensory integration processes and associated with multisensory benefit. Our results, substantiated in both detection and categorization tasks, provide evidence that the brain integrates signals from different modalities at both the sensory encoding and the decision formation stages.

scholarly journals Flexible categorization in perceptual decision making

2020 ◽  
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.

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

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.

scholarly journals A cerebellar role in evidence-guided decision-making

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.

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.

Optimal Decision Making on the Basis of Evidence Represented in Spike Trains

2010 ◽  
Vol 22 (5) ◽  
pp. 1113-1148 ◽  
Author(s):  
Jiaxiang Zhang ◽  
Rafal Bogacz
Keyword(s):  
Decision Making ◽  
Spike Trains ◽  
Decision Procedures ◽  
Optimal Decision ◽  
Ratio Test ◽  
Perceptual Decision Making ◽  
Sensory Evidence ◽  
Optimal Decision Making ◽  
Alternative Choice ◽  
Choice Tasks

Experimental data indicate that perceptual decision making involves integration of sensory evidence in certain cortical areas. Theoretical studies have proposed that the computation in neural decision circuits approximates statistically optimal decision procedures (e.g., sequential probability ratio test) that maximize the reward rate in sequential choice tasks. However, these previous studies assumed that the sensory evidence was represented by continuous values from gaussian distributions with the same variance across alternatives. In this article, we make a more realistic assumption that sensory evidence is represented in spike trains described by the Poisson processes, which naturally satisfy the mean-variance relationship observed in sensory neurons. We show that for such a representation, the neural circuits involving cortical integrators and basal ganglia can approximate the optimal decision procedures for two and multiple alternative choice tasks.

scholarly journals Representations of evidence for a perceptual decision in the human brain

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.

scholarly journals Alertness modulates perceptual decision-making

2020 ◽  
Author(s):  
Sridhar R. Jagannathan ◽  
Corinne A. Bareham ◽  
Tristan A. Bekinschtein
Keyword(s):  
Decision Making ◽  
Neural Dynamics ◽  
External Information ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Diffusion Modelling ◽  
Human Participants ◽  
Multivariate Pattern ◽  
Work Done

ABSTRACTThe ability to make decisions based on external information, prior knowledge and context is a crucial aspect of cognition and it may determine the success and survival of an organism. Despite extensive and detailed work done on the decision making mechanisms, the understanding of the effects of arousal remain limited. Here we characterise behavioural and neural dynamics of decision making in awake and low alertness periods to characterise the compensatory signatures of the cognitive system when arousal decreases. We used an auditory tone-localisation task in human participants under conditions of fully awake and low arousal. Behavioural dynamics analyses using psychophysics, signal detection theory and drift-diffusion modelling showed slower responses, decreased performance and a lower rate of evidence accumulation due to alertness fluctuations. To understand the modulation in neural dynamics we used multivariate pattern analysis (decoding), identifying a shift in the temporal and spatial signatures involved. Finally, we connected the computational parameters identified in the drift diffusion modelling with neural signatures, capturing the effective lag exerted by alertness in the neurocognitive system underlying decision making. These results define the reconfiguration of the brain networks, regions and dynamics needed for the implementation of perceptual decision making, revealing mechanisms of resilience of cognition when challenged by decreases in arousal.

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