scholarly journals Visual Decisions in the Presence of Measurement and Stimulus Correlations

2015 ◽  
Vol 27 (11) ◽  
pp. 2318-2353 ◽  
Author(s):  
Manisha Bhardwaj ◽  
Samuel Carroll ◽  
Wei Ji Ma ◽  
Krešimir Josić
Keyword(s):  
Decision Making ◽  
Sensory Input ◽  
Measurement Noise ◽  
Strong Impact ◽  
Strongly Correlated ◽  
Neural Responses ◽  
Multiple Targets ◽  
Statistical Structure ◽  
Perceptual Judgments ◽  
Sensory Noise

Humans and other animals base their decisions on noisy sensory input. Much work has been devoted to understanding the computations that underlie such decisions. The problem has been studied in a variety of tasks and with stimuli of differing complexity. However, how the statistical structure of stimuli, along with perceptual measurement noise, affects perceptual judgments is not well understood. Here we examine how correlations between the components of a stimulus—stimulus correlations—together with correlations in sensory noise, affect decision making. As an example, we consider the task of detecting the presence of a single or multiple targets among distractors. We assume that both the distractors and the observer’s measurements of the stimuli are correlated. The computations of an optimal observer in this task are nontrivial yet can be analyzed and understood intuitively. We find that when distractors are strongly correlated, measurement correlations can have a strong impact on performance. When distractor correlations are weak, measurement correlations have little impact unless the number of stimuli is large. Correlations in neural responses to structured stimuli can therefore have a strong impact on perceptual judgments.

scholarly journals Recurrent dynamics of prefrontal cortex during context-dependent decision-making

2020 ◽  
Author(s):  
Zach Cohen ◽  
Brian DePasquale ◽  
Mikio C. Aoi ◽  
Jonathan W. Pillow
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Irrelevant Stimulus ◽  
Neural Responses ◽  
One Dimensional ◽  
Systems Neuroscience ◽  
Neural Populations ◽  
Context Dependent ◽  
Monkey Prefrontal Cortex ◽  
Sensory Noise

AbstractA key problem in systems neuroscience is to understand how neural populations integrate relevant sensory inputs during decision-making. Here, we address this problem by training a structured recurrent neural network to reproduce both psychophysical behavior and neural responses recorded from monkey prefrontal cortex during a context-dependent per-ceptual decision-making task. Our approach yields a one-to-one mapping of model neurons to recorded neurons, and explicitly incorporates sensory noise governing the animal’s performance as a function of stimulus strength. We then analyze the dynamics of the resulting model in order to understand how the network computes context-dependent decisions. We find that network dynamics preserve both relevant and irrelevant stimulus information, and exhibit a grid of fixed points for different stimulus conditions as opposed to a one-dimensional line attractor. Our work provides new insights into context-dependent decision-making and offers a powerful framework for linking cognitive function with neural activity within an artificial model.

scholarly journals Neural Responses to Ambiguity Involve Domain-general and Domain-specific Emotion Processing Systems

2013 ◽  
Vol 25 (4) ◽  
pp. 547-557 ◽  
Author(s):  
Maital Neta ◽  
William M. Kelley ◽  
Paul J. Whalen
Keyword(s):  
Decision Making ◽  
Facial Expressions ◽  
Visual Processing ◽  
Neural Responses ◽  
Social Signals ◽  
Emotional Facial Expressions ◽  
Biologically Relevant ◽  
Domain Specific ◽  
Specific Emotion ◽  
On Line

Extant research has examined the process of decision making under uncertainty, specifically in situations of ambiguity. However, much of this work has been conducted in the context of semantic and low-level visual processing. An open question is whether ambiguity in social signals (e.g., emotional facial expressions) is processed similarly or whether a unique set of processors come on-line to resolve ambiguity in a social context. Our work has examined ambiguity using surprised facial expressions, as they have predicted both positive and negative outcomes in the past. Specifically, whereas some people tended to interpret surprise as negatively valenced, others tended toward a more positive interpretation. Here, we examined neural responses to social ambiguity using faces (surprise) and nonface emotional scenes (International Affective Picture System). Moreover, we examined whether these effects are specific to ambiguity resolution (i.e., judgments about the ambiguity) or whether similar effects would be demonstrated for incidental judgments (e.g., nonvalence judgments about ambiguously valenced stimuli). We found that a distinct task control (i.e., cingulo-opercular) network was more active when resolving ambiguity. We also found that activity in the ventral amygdala was greater to faces and scenes that were rated explicitly along the dimension of valence, consistent with findings that the ventral amygdala tracks valence. Taken together, there is a complex neural architecture that supports decision making in the presence of ambiguity: (a) a core set of cortical structures engaged for explicit ambiguity processing across stimulus boundaries and (b) other dedicated circuits for biologically relevant learning situations involving faces.

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 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.

Interpersonal relationship modulates the behavioral and neural responses during moral decision-making

2018 ◽  
Vol 672 ◽  
pp. 15-21 ◽  
Author(s):  
Youlong Zhan ◽  
Xiao Xiao ◽  
Jin Li ◽  
Lei Liu ◽  
Jie Chen ◽  
...  
Keyword(s):  
Decision Making ◽  
Interpersonal Relationship ◽  
Moral Decision ◽  
Neural Responses ◽  
Moral Decision Making

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.

Modeling Imprecision in Perception, Valuation, and Choice

2020 ◽  
Vol 12 (1) ◽  
pp. 579-601 ◽  
Author(s):  
Michael Woodford
Keyword(s):  
Decision Making ◽  
Experimental Psychology ◽  
Quantitative Relationship ◽  
Strategic Interaction ◽  
Economic Modeling ◽  
Stochastic Choice ◽  
Global Game ◽  
Perceptual Judgments ◽  
Economic Decision Making ◽  
Decoy Effects

Traditional decision theory assumes that people respond to the exact features of the options available to them, but observed behavior seems much less precise. This review considers ways of introducing imprecision into models of economic decision making and stresses the usefulness of analogies with the way that imprecise perceptual judgments are modeled in psychophysics—the branch of experimental psychology concerned with the quantitative relationship between objective features of an observer's environment and elicited reports about their subjective appearance. It reviews key ideas from psychophysics, provides examples of the kinds of data that motivate them, and proposes lessons for economic modeling. Applications include stochastic choice, choice under risk, decoy effects in marketing, global game models of strategic interaction, and delayed adjustment of prices in response to monetary disturbances.

scholarly journals Neural Activity Reveals Preferences without Choices

2014 ◽  
Vol 6 (2) ◽  
pp. 1-36 ◽  
Author(s):  
Alec Smith ◽  
B. Douglas Bernheim ◽  
Colin F. Camerer ◽  
Antonio Rangel
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Prediction Models ◽  
Neural Responses ◽  
Choice Data ◽  
Standard Methods ◽  
Potential Value ◽  
Out Of Sample ◽  
Actual Decision

We investigate the feasibility of inferring the choices people would make (if given the opportunity) based on their neural responses to the pertinent prospects when they are not engaged in actual decision making. The ability to make such inferences is of potential value when choice data are unavailable, or limited in ways that render standard methods of estimating choice mappings problematic. We formulate prediction models relating choices to “nonchoice” neural responses, and use them to predict out-of-sample choices for new items and for new groups of individuals. The predictions are sufficiently accurate to establish the feasibility of our approach. (JEL D12, D87)

scholarly journals Descending pathways generate perception of and neural responses to weak sensory input

PLoS Biology ◽  
2018 ◽  
Vol 16 (6) ◽  
pp. e2005239 ◽  
Author(s):  
Michael G. Metzen ◽  
Chengjie G. Huang ◽  
Maurice J. Chacron
Keyword(s):  
Sensory Input ◽  
Neural Responses ◽  
Descending Pathways

scholarly journals Performance-optimized hierarchical models only partially predict neural responses during perceptual decision making

2017 ◽  
Author(s):  
Laura Gwilliams ◽  
Jean-Rémi King
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Brain Activity ◽  
Hand Movement ◽  
Temporal Organization ◽  
Natural Image ◽  
Neural Responses ◽  
Perceptual Decision Making ◽  
Healthy Human ◽  
Perceptual Decision

AbstractModels of perceptual decision making have historically been designed to maximally explain behaviour and brain activity independently of their ability to actually perform tasks. More recently, performance-optimized models have been shown to correlate with brain responses to images and thus present a complementary approach to understand perceptual processes. In the present study, we compare how these approaches comparatively account for the spatio-temporal organization of neural responses elicited by ambiguous visual stimuli. Forty-six healthy human subjects performed perceptual decisions on briefly flashed stimuli constructed from ambiguous characters. The stimuli were designed to have 7 orthogonal properties, ranging from low-sensory levels (e.g. spatial location of the stimulus) to conceptual (whether stimulus is a letter or a digit) and task levels (i.e. required hand movement). Magneto-encephalography source and decoding analyses revealed that these 7 levels of representations are sequentially encoded by the cortical hierarchy, and actively maintained until the subject responds. This hierarchy appeared poorly correlated to normative, drift-diffusion, and 5-layer convolutional neural networks (CNN) optimized to accurately categorize alpha-numeric characters, but partially matched the sequence of activations of 3/6 state-of-the-art CNNs trained for natural image labeling (VGG-16, VGG-19, MobileNet). Additionally, we identify several systematic discrepancies between these CNNs and brain activity, revealing the importance of single-trial learning and recurrent processing. Overall, our results strengthen the notion that performance-optimized algorithms can converge towards the computational solution implemented by the human visual system, and open possible avenues to improve artificial perceptual decision making.

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