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.

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

2020 ◽  
Vol 117 (15) ◽  
pp. 8382-8390 ◽  
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 ◽  
Neural Correlates ◽  
Anterior Insula ◽  
Evidence Accumulation ◽  
Perceptual Judgments ◽  
Anterior Prefrontal Cortex

The human capacity to compute the likelihood that a decision is correct—known as metacognition—has proven difficult to study in isolation as it usually cooccurs with decision making. Here, we isolated postdecisional from decisional contributions to metacognition by analyzing neural correlates of confidence 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 may improve 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 postdecisional accounts of confidence and propose a computational model of confidence in which metacognitive performance naturally improves when evidence accumulation is constrained upon committing a decision.

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.

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.

scholarly journals Pupil-linked arousal biases evidence accumulation towards desirable percepts during perceptual decision-making

2020 ◽  
Author(s):  
Yuan Chang Leong ◽  
Roma Dziembaj ◽  
Mark D’Esposito
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Visual Image ◽  
Perceptual Decision Making ◽  
Evidence Accumulation ◽  
Perceptual Judgments ◽  
Arousal System ◽  
Sensory Evidence ◽  
Arousal Response ◽  
Objective Representation

AbstractPeople are biased towards seeing outcomes 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 sensory perception. However, it remains unclear whether arousal would enhance or reduce motivational biases. Here we measured pupil dilation as a measure of arousal while participants performed a visual categorization task. We used monetary bonuses to motivate participants to see one category over another. Even though the reward-maximizing strategy was to perform the task accurately, participants were more likely to report seeing the motivationally desirable category. Furthermore, higher arousal levels were associated with making motivationally biased responses. Analyses using computational models indicated that arousal enhanced motivational effects by biasing evidence accumulation in favor of motivationally desirable percepts. These results suggest heightened arousal biases people towards what they want to see and away from an objective representation of the environment.Statement of RelevanceWhen confronted with an event of motivational significance (e.g., an opportunity to earn a huge reward), people often experience a strong arousal response that includes increased sweating, faster heart-rate and larger pupils. Does this arousal response help individuals make more accurate decisions, or does it instead bias and impair decision-making? This work examines the effects of arousal on how people decide what they see when they are motivated to see a particular outcome. We found that heightened arousal, as measured by larger pupils, was associated with a bias in how participants accumulated sensory evidence to make their decisions. As a result, participants became more likely to report seeing an ambiguous visual image as the interpretation they were motivated to see. Our results suggest that arousal biases perceptual judgments towards desirable percepts, and that modulating arousal levels could be a promising approach in reducing motivational biases in decision-making.

Hyper-responsivity to losses in the anterior insula during economic choice scales with depression severity

2017 ◽  
Vol 47 (16) ◽  
pp. 2879-2891 ◽  
Author(s):  
J. B. Engelmann ◽  
G. S. Berns ◽  
B. W. Dunlop
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Caudate Nucleus ◽  
Anterior Insula ◽  
Reward Processing ◽  
Ventromedial Prefrontal Cortex ◽  
Significant Shift ◽  
Core Network ◽  
Depression Severity ◽  
Economic Decision Making

BackgroundCommonly observed distortions in decision-making among patients with major depressive disorder (MDD) may emerge from impaired reward processing and cognitive biases toward negative events. There is substantial theoretical support for the hypothesis that MDD patients overweight potential losses compared with gains, though the neurobiological underpinnings of this bias are uncertain.MethodsTwenty-one unmedicated patients with MDD were compared with 25 healthy controls (HC) using functional magnetic resonance imaging (fMRI) together with an economic decision-making task over mixed lotteries involving probabilistic gains and losses. Region-of-interest analyses evaluated neural signatures of gain and loss coding within a core network of brain areas known to be involved in valuation (anterior insula, caudate nucleus, ventromedial prefrontal cortex).ResultsUsable fMRI data were available for 19 MDD and 23 HC subjects. Anterior insula signal showed negative coding of losses (gain > loss) in HC subjects consistent with previous findings, whereas MDD subjects demonstrated significant reversals in these associations (loss > gain). Moreover, depression severity further enhanced the positive coding of losses in anterior insula, ventromedial prefrontal cortex, and caudate nucleus. The hyper-responsivity to losses displayed by the anterior insula of MDD patients was paralleled by a reduced influence of gain, but not loss, stake size on choice latencies.ConclusionsPatients with MDD demonstrate a significant shift from negative to positive coding of losses in the anterior insula, revealing the importance of this structure in value-based decision-making in the context of emotional disturbances.

scholarly journals Context-invariant neural dynamics underlying the encoding of Bayesian uncertainty, but not confidence

2019 ◽  
Author(s):  
Vincenzo G. Fiore ◽  
Xiaosi Gu
Keyword(s):  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Effective Connectivity ◽  
Sensory Information ◽  
Anterior Insula ◽  
Anterior Cingulate ◽  
Neural Dynamics ◽  
Network Computing ◽  
Evidence Accumulation ◽  
Healthy Humans

AbstractBeliefs about action-outcomes contingencies are often updated in opaque environments where feedbacks might be inaccessible and agents might need to rely on other information for evidence accumulation. It remains unclear, however, whether and how the neural dynamics subserving confidence and uncertainty during belief updating might be context-dependent. Here, we applied a Bayesian model to estimate uncertainty and confidence in healthy humans (n=28) using two multi-option fMRI tasks, one with and one without feedbacks. We found that across both tasks, uncertainty was computed in the anterior insular, anterior cingulate, and dorsolateral prefrontal cortices, whereas confidence was encoded in anterior hippocampus, amygdala and medial prefrontal cortex. However, dynamic causal modelling (DCM) revealed a critical divergence between how effective connectivity in these networks was modulated by the available information. Specifically, there was directional influence from the anterior insula to other regions during uncertainty encoding, independent of outcome availability. Conversely, the network computing confidence was driven either by the anterior hippocampus when outcomes were not available, or by the medial prefrontal cortex and amygdala when feedbacks were immediately accessible. These findings indicate that confidence encoding might largely rely on evidence accumulation and therefore dynamically changes as a function of the available sensory information (i.e. symbolic sequences monitored by the hippocampus, and monetary feedbacks computed by amygdala and medial prefrontal cortex). In contrast, uncertainty could be triggered by any information that disputes existing beliefs (i.e. processed in the insula), independent of its content.Significance StatementOur choices are guided by our beliefs about action-outcome contingencies. In environments where only one action leads to a desired outcome, high estimated action-outcome probabilities result in confidence, whereas low probabilities distributed across multiple choices result in uncertainty. These estimations are continuously updated, sometimes based on feedbacks provided by the environment, but sometimes this update takes place in opaque environments where feedbacks are not readily available. Here, we show that uncertainty computations are driven by the anterior insula, independent of feedback availability. Conversely, confidence encoding dynamically adapts to the information available, as we found it was driven either by the anterior hippocampus, when feedback was absent, or by the medial prefrontal cortex and amygdala, otherwise.

scholarly journals Value generalization in human avoidance learning

2017 ◽  
Author(s):  
Agnes Norbury ◽  
Trevor W. Robbins ◽  
Ben Seymour
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Reinforcement Learning ◽  
Avoidance Learning ◽  
Anterior Insula ◽  
Ventromedial Prefrontal Cortex ◽  
Sensory Cortex ◽  
Primary Sensory Cortex ◽  
Human Volunteers ◽  
Mediate Generalization

SummaryGeneralization during aversive decision-making allows us to avoid a broad range of potential threats following experience with a limited set of exemplars. However, over-generalization, resulting in excessive and inappropriate avoidance, has been implicated in a variety of psychological disorders. Here, we use reinforcement learning modelling to dissect out different contributions to the generalization of instrumental avoidance in two groups of human volunteers (N=26, N=482). We found that generalization of avoidance could be parsed into perceptual and value-based processes, and further, that value-based generalization could be subdivided into that relating to aversive and neutral feedback - with corresponding circuits including primary sensory cortex, anterior insula, and ventromedial prefrontal cortex, respectively. Further, generalization from aversive, but not neutral, feedback was associated with self-reported anxiety and intrusive thoughts. These results reveal a set of distinct mechanisms that mediate generalization in avoidance learning, and show how specific individual differences within them can yield anxiety.

scholarly journals Anterior prefrontal cortex mediates implicit inferences

2018 ◽  
Author(s):  
Martijn E. Wokke ◽  
Tony Ro
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Decision Process ◽  
Explicit Knowledge ◽  
Real World Data ◽  
Predictive Information ◽  
Cueing Task ◽  
Eeg Recordings ◽  
Anterior Prefrontal Cortex ◽  
The Brain

AbstractFrequent experience with regularities in our environment allows us to use predictive information to guide our decision process. However, contingencies in our environment are not always explicitly present and sometimes need to be inferred. Heretofore, it remained unknown how predictive information guides decision-making when explicit knowledge is absent and how the brain shapes such implicit inferences. In the present experiment, participants performed a discrimination task in which a target stimulus was preceded by a predictive cue. Critically, participants had no explicit knowledge that some of the cues signaled an upcoming target, allowing us to investigate how implicit inferences emerge and guide decision-making. Despite unawareness of the cue-target contingencies, participants were able to use implicit information to improve performance. Concurrent EEG recordings demonstrate that implicit inferences rely upon interactions between internally and externally oriented networks, whereby anterior prefrontal regions inhibit right parietal cortex under internal implicit control.SignificanceRegularities in our environment can guide our behavior providing information about upcoming events. Interestingly, such predictive information does not need to be explicitly represented in order to effectively guide our decision process. Here, we show how the brain engages in such real-world ‘data mining’ and how implicit inferences emerge. We employed a contingency cueing task and demonstrate that implicit inferences influenced responses to subsequent targets despite a lack of awareness of cue-target contingencies. Further, we show that these implicit inferences emerge through interactions between internally- and externally-oriented neural networks. The current results highlight the importance of the anterior prefrontal cortex in transforming external events into predictive internalized models of the world.

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