scholarly journals Dataset from: Visual evidence accumulation guides decision-making in unrestrained mice

2020 ◽  
Author(s):  
Onyekachi Odoemene ◽  
Sashank Pisupati ◽  
Hien Nguyen ◽  
Anne Churchland
Keyword(s):  
Decision Making ◽  
Evidence Accumulation ◽  
Visual Evidence

scholarly journals Visual Evidence Accumulation Guides Decision-Making in Unrestrained Mice

2018 ◽  
Vol 38 (47) ◽  
pp. 10143-10155 ◽  
Author(s):  
Onyekachi Odoemene ◽  
Sashank Pisupati ◽  
Hien Nguyen ◽  
Anne K. Churchland
Keyword(s):  
Decision Making ◽  
Evidence Accumulation ◽  
Visual Evidence

scholarly journals Visual evidence accumulation guides decision-making in unrestrained mice

2017 ◽  
Author(s):  
Onyekachi Odoemene ◽  
Sashank Pisupati ◽  
Hien Nguyen ◽  
Anne K. Churchland
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Deep Understanding ◽  
Neural Mechanism ◽  
Neural Structure ◽  
Evidence Accumulation ◽  
Visual Evidence ◽  
Decision Making Behavior

AbstractThe ability to manipulate neural activity with precision is an asset in uncovering neural circuits for decision-making. Diverse tools for manipulating neurons are available for mice, but the feasibility of mice for decision-making studies remains unclear, especially when decisions require accumulating visual evidence. For example, whether mice’ decisions reflect leaky accumulation is not established, and the relevant and irrelevant factors that influence decisions are unknown. Further, causal circuits for visual evidence accumulation have not been established. To address these issues, we measured >500,000 decisions in 27 mice trained to judge the fluctuating rate of a sequence of flashes. Information throughout the 1000ms trial influenced choice, but early information was most influential. This suggests that information persists in neural circuits for ~1000ms with minimal accumulation leak. Further, while animals primarily based decisions on current stimulus rate, they were unable to entirely suppress additional factors: total stimulus brightness and the previous trial’s outcome. Next, we optogenetically inhibited anteromedial (AM) visual area using JAWS. Importantly, light activation biased choices in both injected and uninjected animals, demonstrating that light alone influences behavior. By varying stimulus-response contingency while holding stimulated hemisphere constant, we surmounted this obstacle to demonstrate that AM suppression biases decisions. By leveraging a large dataset to quantitatively characterize decision-making behavior, we establish mice as suitable for neural circuit manipulation studies, including the one here. Further, by demonstrating that mice accumulate visual evidence, we demonstrate that this strategy for reducing uncertainty in decision-making is employed by animals with diverse visual systems.Significance statementTo connect behaviors to their underlying neural mechanism, a deep understanding of the behavioral strategy is needed. This understanding is incomplete in mouse studies, in part because existing datasets have been too small to quantitatively characterize decision-making behavior. To surmount this, we measured the outcome of over 500,000 decisions made by 27 mice trained to judge visual stimuli. Our analyses offer new insights into mice’ decision-making strategies and compares them with those of other species. We then disrupted neural activity in a candidate neural structure and examined the effect on decisions. Our findings establish mice as a suitable organism for visual accumulation of evidence decisions. Further, the results highlight similarities in decision-making strategies across very different species.

A Neural Index of Inefficient Evidence Accumulation in Dyslexia Underlying Slow Perceptual Decision Making

Cortex ◽  
2021 ◽  
Author(s):  
Nicole R. Stefanac ◽  
Shou-Han Zhou ◽  
Megan M. Spencer-Smith ◽  
Redmond O’Connell ◽  
Mark A. Bellgrove
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Evidence Accumulation

scholarly journals Should I stay or should I go? Evidence accumulation drives decision making in human drivers

2020 ◽  
Author(s):  
Arkady Zgonnikov ◽  
David Abbink ◽  
Gustav Markkula
Keyword(s):  
Decision Making ◽  
Cognitive Processes ◽  
Cognitive Process ◽  
Response Times ◽  
Process Models ◽  
Cognitive Mechanisms ◽  
Naturalistic Decision Making ◽  
Dynamic Constraints ◽  
Evidence Accumulation ◽  
Out Of Sample

Laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. Yet it is unclear whether the cognitive processes implicated in simple, isolated decisions in the lab are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Here we aim to address the gap between modern cognitive models of decision making and studies of naturalistic decision making in drivers, which so far have provided only limited insight into the underlying cognitive processes. We investigate drivers' decision making during unprotected left turns, and model the cognitive process driving these decisions. Our model builds on the classical drift-diffusion model, and emphasizes, first, the drift rate linked to the relevant perceptual quantities dynamically sampled from the environment, and, second, collapsing decision boundaries reflecting the dynamic constraints imposed on the decision maker’s response by the environment. We show that the model explains the observed decision outcomes and response times, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions, effectively providing a way to predict human drivers’ behavior in real time. Our results reveal the cognitive mechanisms of gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help us to understand human behavior in complex real-world tasks.

scholarly journals An evidence accumulation model of perceptual discrimination with naturalistic stimuli

2018 ◽  
Author(s):  
Hector Palada ◽  
Rachel A Searston ◽  
Annabel Persson ◽  
Timothy Ballard ◽  
Matthew B Thompson
Keyword(s):  
Decision Making ◽  
Cognitive Processes ◽  
Decision Process ◽  
Dynamic Decision Making ◽  
Evidence Accumulation ◽  
Natural Stimuli ◽  
Perceptual Judgments ◽  
Linear Ballistic Accumulator ◽  
Decision Making Model ◽  
Speed Accuracy

Evidence accumulation models have been used to describe the cognitive processes underlying performance in tasks involving two-choice decisions about unidimensional stimuli, such as motion or orientation. Given the multidimensionality of natural stimuli, however, we might expect qualitatively different patterns of evidence accumulation in more applied perceptual tasks. One domain that relies heavily on human decisions about complex natural stimuli is fingerprint discrimination. We know little about the ability of evidence accumulation models to account for the dynamic decision process of a fingerprint examiner resolving if two different prints belong to the same finger or not. Here, we apply a dynamic decision-making model — the linear ballistic accumulator (LBA) — to fingerprint discrimination decisions in order to gain insight into the cognitive processes underlying these complex perceptual judgments. Across three experiments, we show that the LBA provides an accurate description of the fingerprint discrimination decision process with manipulations in visual noise, speed-accuracy emphasis, and training. Our results demonstrate that the LBA is a promising model for furthering our understanding of applied decision-making with naturally varying visual stimuli.

scholarly journals Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sean E Cavanagh ◽  
Joni D Wallis ◽  
Steven W Kennerley ◽  
Laurence T Hunt
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Orbitofrontal Cortex ◽  
Receptive Fields ◽  
Neural Mechanism ◽  
Spike Rate ◽  
Accumulation Process ◽  
Single Neurons ◽  
Evidence Accumulation ◽  
Autocorrelation Structure

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.

scholarly journals Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging

2018 ◽  
Author(s):  
Michael Pereira ◽  
Nathan Faivre ◽  
Iñaki Iturrate ◽  
Marco Wirthlin ◽  
Luana Serafini ◽  
...  
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Healthy Volunteers ◽  
Multimodal Imaging ◽  
Inferior Frontal Gyrus ◽  
Anterior Insula ◽  
Generative Model ◽  
Evidence Accumulation ◽  
Perceptual Judgments ◽  
Anterior Prefrontal Cortex

AbstractThe human capacity to compute the likelihood that a decision is correct - known as metacognition - has proven difficult to study in isolation as it usually co-occurs with decision-making. Here, we isolated post-decisional from decisional contributions to metacognition by combining a novel paradigm with multimodal imaging. Healthy volunteers reported their confidence in the accuracy of decisions they made or decisions they observed. We found better metacognitive performance for committed vs. observed decisions, indicating that committing to a decision informs confidence. Relying on concurrent electroencephalography and hemodynamic recordings, we found a common correlate of confidence following committed and observed decisions in the inferior frontal gyrus, and a dissociation in the anterior prefrontal cortex and anterior insula. We discuss these results in light of decisional and post-decisional accounts of confidence, and propose a generative model of confidence in which metacognitive performance naturally improves when evidence accumulation is constrained upon committing a decision.

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.

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