scholarly journals Response-locked classification image analysis of perceptual decision making in contrast detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hironori Maruyama ◽  
Natsuki Ueno ◽  
Isamu Motoyoshi
Keyword(s):  
Decision Making ◽  
Detection Task ◽  
Dynamic Decision Making ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Classification Image ◽  
Dynamic Noise ◽  
Decision Mechanism ◽  
Simple Detection ◽  
Positive Weights

AbstractIn many situations, humans make decisions based on serially sampled information through the observation of visual stimuli. To quantify the critical information used by the observer in such dynamic decision making, we here applied a classification image (CI) analysis locked to the observer's reaction time (RT) in a simple detection task for a luminance target that gradually appeared in dynamic noise. We found that the response-locked CI shows a spatiotemporally biphasic weighting profile that peaked about 300 ms before the response, but this profile substantially varied depending on RT; positive weights dominated at short RTs and negative weights at long RTs. We show that these diverse results are explained by a simple perceptual decision mechanism that accumulates the output of the perceptual process as modelled by a spatiotemporal contrast detector. We discuss possible applications and the limitations of the response-locked CI analysis.

scholarly journals A response-locked classification image analysis of the perceptual decision making : contrast detection

2021 ◽  
Author(s):  
Hironori Maruyama ◽  
Natsuki Ueno ◽  
Isamu Motoyoshi
Keyword(s):  
Decision Making ◽  
Detection Task ◽  
Dynamic Decision Making ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Classification Image ◽  
Dynamic Noise ◽  
Decision Mechanism ◽  
Simple Detection ◽  
Positive Weights

In many situations, humans make decisions based on serially sampled information through the observation of visual stimuli. To quantify the critical information used by the observer in such dynamic decision making, we here applied a classification image (CI) analysis locked to the observer's reaction time (RT) in a simple detection task for a luminance target that gradually appeared in dynamic noise. We found that the response-locked CI shows a spatiotemporally biphasic weighting profile that peaked about 300 ms before the response, but this profile substantially varied depending on RT; positive weights dominated at short RTs and negative weights at long RTs. We show that these diverse results are explained by a simple perceptual decision mechanism that accumulates the output of the perceptual process as modelled by a spatiotemporal contrast detector. We discuss possible applications and the limitations of the response-locked CI analysis.

scholarly journals Low-level image statistics in natural scenes influence perceptual decision-making

2018 ◽  
Author(s):  
Noor Seijdel ◽  
Sara Jahfari ◽  
Iris I.A. Groen ◽  
H. Steven Scholte
Keyword(s):  
Decision Making ◽  
Real World ◽  
Detection Task ◽  
Natural Scenes ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Low Level ◽  
Animal Detection

A fundamental component of interacting with our environment is gathering and interpretation of sensory information. When investigating how perceptual information shapes the mechanisms of decision-making, most researchers have relied on the use of manipulated or unnatural information as perceptual input, resulting in findings that may not generalize to real-world scenes. Unlike simplified, artificial stimuli, real-world scenes contain low-level regularities (natural scene statistics) that are informative about the structural complexity of a scene, which the brain could exploit during perceptual decision-making. In this study, participants performed an animal detection task on low, medium or high complexity scenes as determined by two biologically plausible natural scene statistics, contrast energy (CE) or spatial coherence (SC). In experiment 1, stimuli were sampled such that CE and SC both influenced scene complexity. Diffusion modeling showed that both the speed of information processing and the required evidence were affected by the low-level scene complexity. Experiment 2a/b refined these observations by showing how the isolated manipulation of SC alone resulted in weaker but comparable effects on decision-making, whereas the manipulation of only CE had no effect. Overall, performance was best for scenes with intermediate complexity. Our systematic definition of natural scene statistics quantifies how complexity of natural scenes interacts with decision-making in an animal detection task. We speculate that the computation of CE and SC could serve as an indication to adjust perceptual decision-making based on the complexity of the input.

scholarly journals A causal role for mouse superior colliculus in visual perceptual decision-making

2019 ◽  
Author(s):  
Lupeng Wang ◽  
Kerry McAlonan ◽  
Sheridan Goldstein ◽  
Charles R. Gerfen ◽  
Richard J. Krauzlis
Keyword(s):  
Decision Making ◽  
Superior Colliculus ◽  
Visual Target ◽  
Target Selection ◽  
Detection Task ◽  
Change Detection Task ◽  
Perceptual Decision Making ◽  
Detection Thresholds ◽  
Perceptual Decision ◽  
Orientation Change

AbstractThe superior colliculus (SC) is arguably the most important visual structure in the mouse brain and is well-known for its involvement in innate responses to visual threats and prey items. In other species, the SC plays a central role in voluntary as well as innate visual functions, including crucial contributions to selective attention and perceptual decision-making. In the mouse, the possible role of the SC in voluntary visual choice behaviors has not been established. Here, we demonstrate that the mouse SC plays a causal role in visual perceptual decision-making by transiently inhibiting SC activity during an orientation-change detection task. First, unilateral SC inhibition induced spatially specific deficits in detection. Hit rates were reduced and reaction times increased for orientation changes in the contralateral but not ipsilateral visual field. Second, the deficits caused by SC inhibition were specific to a temporal epoch coincident with early visual burst responses in the SC. Inhibiting SC during this 100-ms period caused a contralateral detection deficit, whereas inhibition immediately before or after did not. Third, SC inhibition reduced visual detection sensitivity. Psychometric analysis revealed that inhibiting SC visual activity significantly increased detection thresholds for contralateral orientation changes. In addition, effects on detection thresholds and lapse rates caused by SC inhibition were larger in the presence of a competing visual stimulus, indicating a role for the mouse SC in visual target selection. Together, our results demonstrate that the mouse SC plays a crucial role in voluntary visual choice behaviors.Significance statementThe mouse superior colliculus has become a popular model for studying the circuit organization and development of the visual system. Although the SC is a fundamental component of the visual pathways in mice, its role in visual perceptual decision-making is not clear. By investigating how temporally precise SC inhibition influenced behavioral performance during a visually guided orientation change detection task, we identified a 100-ms temporal epoch of SC visual activity that is crucial for the ability of mice to detect behaviorally relevant visual changes. In addition, we found that SC inhibition also caused deficits in visual target selection. Thus, our findings highlight the importance of the SC for visual perceptual choice behavior in the mouse.

scholarly journals Bayesian confidence in optimal decisions

2020 ◽  
Author(s):  
Joshua Michael Calder-Travis ◽  
Lucie Charles ◽  
Rafal Bogacz ◽  
Nick Yeung
Keyword(s):  
Decision Making ◽  
Response Times ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Decision Mechanism ◽  
Trial Basis ◽  
The Difference ◽  
Normative Models ◽  
The Brain ◽  
Quantitative Account

The drift diffusion model (DDM) provides an excellent account of decisions and response times. It also features the optimal property of tracking the difference in evidence between two options. However, the DDM struggles to account for human confidence reports, because responses are triggered when the difference in evidence reaches a set value, suggesting confidence in all decisions should be equal. Previously considered extensions to the DDM fall short of providing an adequate quantitative account of confidence. Possibly because of this, much confidence research has used non-normative models of the decision mechanism. Motivated by the idea that perceptual decision-making will reflect optimal computation, we consider 9 variants of the DDM. Motivated by the idea that the brain will not duplicate the representation of evidence, in all model variants confidence is read out from the decision mechanism. We compare the models to benchmark results, and make 4 qualitative predictions which we verify in a preregistered study. Modelling confidence on a trial-by-trial basis, we find that a subset of model variants provide an excellent account of the precise quantitative effects observed in confidence data. Specifically, models in which confidence reflects a miscalibrated Bayesian readout perform best. These results support the claim that confidence is based on the decision mechanism, which is itself optimal. Therefore, there is no need to abandon the idea that the implementation of perceptual decision-making will reflect optimal computation.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

Effects of a Brief Mindfulness-Based Attentional Intervention on Threat-Related Perceptual Decision-Making

Mindfulness ◽  
2021 ◽  
Author(s):  
Sungjin Im ◽  
Maya A. Marder ◽  
Gabriella Imbriano ◽  
Tamara J. Sussman ◽  
Aprajita Mohanty
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Perceptual Decision

scholarly journals Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2461
Author(s):  
Alexander Kuc ◽  
Vadim V. Grubov ◽  
Vladimir A. Maksimenko ◽  
Natalia Shusharina ◽  
Alexander N. Pisarchik ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensory Information ◽  
Stimulus Onset ◽  
Perceptual Process ◽  
Perceptual Decision Making ◽  
Beta Band ◽  
Perceptual Decision ◽  
Time Frequency ◽  
Band Power ◽  
Sensory Features

Perceptual decision-making requires transforming sensory information into decisions. An ambiguity of sensory input affects perceptual decisions inducing specific time-frequency patterns on EEG (electroencephalogram) signals. This paper uses a wavelet-based method to analyze how ambiguity affects EEG features during a perceptual decision-making task. We observe that parietal and temporal beta-band wavelet power monotonically increases throughout the perceptual process. Ambiguity induces high frontal beta-band power at 0.3–0.6 s post-stimulus onset. It may reflect the increasing reliance on the top-down mechanisms to facilitate accumulating decision-relevant sensory features. Finally, this study analyzes the perceptual process using mixed within-trial and within-subject design. First, we found significant percept-related changes in each subject and then test their significance at the group level. Thus, observed beta-band biomarkers are pronounced in single EEG trials and may serve as control commands for brain-computer interface (BCI).

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
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