scholarly journals An initial ‘snapshot’ of sensory information biases the likelihood and speed of subsequent changes of mind

2022 ◽  
Vol 18 (1) ◽  
pp. e1009738
Author(s):  
William Turner ◽  
Daniel Feuerriegel ◽  
Robert Hester ◽  
Stefan Bode
Keyword(s):  
Diffusion Model ◽  
Discrimination Task ◽  
Sensory Information ◽  
Reverse Correlation ◽  
Stimulus Luminance ◽  
Evidence Accumulation ◽  
Initial Decision ◽  
Unresolved Question ◽  
New Information ◽  
Luminance Discrimination

We often need to rapidly change our mind about perceptual decisions in order to account for new information and correct mistakes. One fundamental, unresolved question is whether information processed prior to a decision being made (‘pre-decisional information’) has any influence on the likelihood and speed with which that decision is reversed. We investigated this using a luminance discrimination task in which participants indicated which of two flickering greyscale squares was brightest. Following an initial decision, the stimuli briefly remained on screen, and participants could change their response. Using psychophysical reverse correlation, we examined how moment-to-moment fluctuations in stimulus luminance affected participants’ decisions. This revealed that the strength of even the very earliest (pre-decisional) evidence was associated with the likelihood and speed of later changes of mind. To account for this effect, we propose an extended diffusion model in which an initial ‘snapshot’ of sensory information biases ongoing evidence accumulation.

scholarly journals An initial ‘snapshot’ of sensory information biases the likelihood and speed of subsequent changes of mind

2020 ◽  
Author(s):  
William Turner ◽  
Daniel Feuerriegel ◽  
Robert Hester ◽  
Stefan Bode
Keyword(s):  
Diffusion Model ◽  
Discrimination Task ◽  
Sensory Information ◽  
Reverse Correlation ◽  
Stimulus Luminance ◽  
Evidence Accumulation ◽  
Initial Decision ◽  
Unresolved Question ◽  
New Information ◽  
Luminance Discrimination

AbstractWe often need to rapidly change our mind about perceptual decisions in order to account for new information and correct mistakes. One fundamental, unresolved question is whether information processed prior to a decision being made (‘pre-decisional information’) has any influence on the likelihood and speed with which that decision is reversed. We investigated this using a luminance discrimination task in which participants indicated which of two flickering greyscale squares was brightest. Following an initial decision, the stimuli briefly remained on screen, and participants could change their response. Using psychophysical reverse correlation, we examined how moment-to-moment fluctuations in stimulus luminance affected participants’ decisions. This revealed that the strength of even the very earliest (pre-decisional) evidence was associated with the likelihood and speed of later changes of mind. To account for this effect, we propose an extended diffusion model in which an initial ‘snapshot’ of sensory information biases ongoing evidence accumulation.

scholarly journals An initial ‘snapshot’ of sensory information biases the likelihood and speed of subsequent changes of mind

2020 ◽  
Author(s):  
William Turner ◽  
Daniel Feuerriegel ◽  
Robert Hester ◽  
Stefan Bode
Keyword(s):  
Diffusion Model ◽  
Discrimination Task ◽  
Sensory Information ◽  
Reverse Correlation ◽  
Stimulus Luminance ◽  
Evidence Accumulation ◽  
Initial Decision ◽  
Unresolved Question ◽  
New Information ◽  
Luminance Discrimination

We often need to rapidly change our mind about perceptual decisions in order to account for new information and correct mistakes. One fundamental, unresolved question is whether information processed prior to a decision being made (‘pre-decisional information’) has any influence on the likelihood and speed with which that decision is reversed. We investigated this using a luminance discrimination task in which participants indicated which of two flickering greyscale squares was brightest. Following an initial decision, the stimuli briefly remained on screen, and participants could change their response. Using psychophysical reverse correlation, we examined how moment-to-moment fluctuations in stimulus luminance affected participants’ decisions. This revealed that the strength of even the very earliest (pre-decisional) evidence was associated with the likelihood and speed of later changes of mind. To account for this effect, we propose an extended diffusion model in which an initial ‘snapshot’ of sensory information biases ongoing evidence accumulation.

Model Mediated Telemanipulation

2006 ◽  
Author(s):  
Probal Mitra ◽  
Gu¨nter Niemeyer
Keyword(s):  
Force Feedback ◽  
Sensory Information ◽  
Virtual Model ◽  
Basic Principles ◽  
Natural Motion ◽  
New Information ◽  
Remote Environment ◽  
The Stability ◽  
Master Device

A telemanipulation system allows a human user to manipulate a remote environment using a local interface (master robot) to control a remote (slave) robot. In doing so, it is desirable to provide users with appropriate sensory feedback, most often taking the form of visual and force information. In the presence of communication delays, however, a force feedback telemanipulation system must overcome detrimental effects caused by the delay, both on the quality of feedback to the user and the stability of the control system. For large delays, like those experienced in space telerobotics, the user's perceptive abilities are distorted and challenged by the lag between action and response. With this paper, a user-centered approach is proposed which seeks to simultaneously provide stable master-slave interaction as well as a natural user experience, tolerant of large delays. Rather than directly sending sensory information from the slave robot to the user, the goal is to use this information to create a real-time virtual model of the remote environment, which then serves as the user's interface. Maintaining a dynamic, virtual model locally at the master-side, the user is provided with immediate visual and haptic responses to his/her actions through the master device. At the remote site, the slave robot tracks the user's continuous and natural motion commands, while providing new information needed to update the virtual model. This method abstracts the data transmitted between the sites and creates greater delay tolerance. The basic principles of the approach are demonstrated on a simple one-degree of freedom telerobotic system, with a rigid, stationary slave environment.

scholarly journals Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

2020 ◽  
Author(s):  
Farshad Rafiei ◽  
Dobromir Rahnev
Keyword(s):  
Diffusion Model ◽  
Discrimination Task ◽  
Drift Rate ◽  
Response Times ◽  
Perceptual Discrimination ◽  
The Difference ◽  
Speed Accuracy ◽  
Selective Influence ◽  
Accuracy Tradeoff ◽  
Rt Distributions

It is often thought that the diffusion model explains all effects related to the speed-accuracy tradeoff (SAT) but this has previously been examined with only a few SAT conditions or only a few subjects. Here we collected data from 20 subjects who performed a perceptual discrimination task with five different difficulty levels and five different SAT conditions (5,000 trials/subject). We found that the five SAT conditions produced robustly U-shaped curves for (i) the difference between error and correct response times (RTs), (ii) the ratio of the standard deviation and mean of the RT distributions, and (iii) the skewness of the RT distributions. Critically, the diffusion model where only drift rate varies with contrast and only boundary varies with SAT could not account for any of the three U-shaped curves. Further, allowing all parameters to vary across conditions revealed that both the SAT and difficulty manipulations resulted in substantial modulations in every model parameter, while still providing imperfect fits to the data. These findings demonstrate that the diffusion model cannot fully explain the effects of SAT and establishes three robust but challenging effects that models of SAT should account for.

scholarly journals Choice-induced biases in perception

2016 ◽  
Author(s):  
Long Luu ◽  
Alan A Stocker
Keyword(s):  
Discrimination Task ◽  
Sensory Information ◽  
Motion Direction ◽  
Great Opportunity ◽  
Perceptual Illusion ◽  
Psychophysical Data ◽  
Observer Model ◽  
Discrimination Judgment ◽  
Perceived Motion ◽  
The Brain

AbstractIllusions provide a great opportunity to study how perception is affected by both the observer's expectations and the way sensory information is represented1,2,3,4,5,6. Recently, Jazayeri and Movshon7 reported a new and interesting perceptual illusion, demonstrating that the perceived motion direction of a dynamic random dot stimulus is systematically biased when preceded by a motion discrimination judgment. The authors hypothesized that these biases emerge because the brain predominantly relies on those neurons that are most informative for solving the discrimination task8, but then is using the same neural weighting profile for generating the percept. In other words, they argue that these biases are “mistakes” of the brain, resulting from using inappropriate neural read-out weights. While we were able to replicate the illusion for a different visual stimulus (orientation), our new psychophysical data suggest that the above interpretation is likely incorrect: Biases are not caused by a read-out profile optimized for solving the discrimination task but rather by the specific choices subjects make in the discrimination task on any given trial. We formulate this idea as a conditioned Bayesian observer model and show that it can explain the new as well as the original psychophysical data. In this framework, the biases are not caused by mistake but rather by the brain's attempt to remain ‘self-consistent’ in its inference process. Our model establishes a direct connection between the current perceptual illusion and the well-known phenomena of cognitive consistency and dissonance9,10.

scholarly journals Multi-sensory integration in the mouse cortical connectome using a network diffusion model

2019 ◽  
Author(s):  
Kamal Shadi ◽  
Eva Dyer ◽  
Constantine Dovrolis
Keyword(s):  
Diffusion Model ◽  
Temporal Cortex ◽  
Sensory Information ◽  
Brain Regions ◽  
Evoked Activity ◽  
Communication Dynamics ◽  
Activation Cascade ◽  
Alt Model ◽  
The Brain ◽  
Network Diffusion

AbstractHaving a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and information processing in the brain. In this work, we make steps towards understanding multi-sensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the Asynchronous Linear Threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). By comparing the model results to functional datasets based on Voltage Sensitive Dye (VSD) imaging, we find that in most cases the ALT model predicts the temporal ordering of an activation cascade correctly. Our results on the Mouse Connectivity Atlas from the Allen Institute for Brain Science show that a small number of brain regions are involved in many primary sensory streams – the claustrum and the parietal temporal cortex being at the top of the list. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multi-sensory information from multiple sensory regions, before utilizing that lower-dimensionality representation in higher-level association regions and more complex cognitive tasks.

scholarly journals Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making

2018 ◽  
Author(s):  
Kyle Dunovan ◽  
Catalina Vich ◽  
Matthew Clapp ◽  
Timothy Verstynen ◽  
Jonathan Rubin
Keyword(s):  
Decision Making ◽  
Basal Ganglia ◽  
Diffusion Model ◽  
Process Models ◽  
Decision Task ◽  
Drift Diffusion Model ◽  
Indirect Pathway ◽  
Drift Diffusion ◽  
Pathway Activity ◽  
Evidence Accumulation

AbstractCortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity.Author summaryCognitive process models such as reinforcement learning (RL) and the drift diffusion model (DDM) have helped to elucidate the basic algorithms underlying error-corrective learning and the evaluation of accumulating decision evidence leading up to a choice. While these relatively abstract models help to guide experimental and theoretical probes into associated phenomena, they remain uninformative about the actual physical mechanics by which learning and decision algorithms are carried out in a neurobiological substrate during adaptive choice behavior. Here we present an “upwards mapping” approach to bridging neural and cognitive models of value-based decision-making, showing how dopaminergic feedback alters the network-level dynamics of cortico-basal-ganglia-thalamic (CBGT) pathways during learning to bias behavioral choice towards more rewarding actions. By mapping “up” the levels of analysis, this approach yields specific predictions about aspects of neuronal activity that map to the quantities appearing in the cognitive decision-making framework.

scholarly journals Changes-of-mind in the absence of new post-decision evidence

2019 ◽  
Author(s):  
Nadim A. A. Atiya ◽  
Arkady Zgonnikov ◽  
Martin Schoemann ◽  
Stefan Scherbaum ◽  
Denis O’Hora ◽  
...  
Keyword(s):  
Discrimination Task ◽  
Neural Circuit ◽  
Response Times ◽  
Neural Population ◽  
Perceptual Decision Making ◽  
Top Down ◽  
Decision Uncertainty ◽  
Motion Discrimination ◽  
Initial Decision ◽  
Sensory Evidence

AbstractDecisions are occasionally accompanied by changes-of-mind. While considered a hallmark of cognitive flexibility, the mechanisms underlying changes-of-mind remain elusive. Previous studies on perceptual decision making have focused on changes-of-mind that are primarily driven by the accumulation of additional noisy sensory evidence after the initial decision. In a motion discrimination task, we demonstrate that changes-of-mind can occur even in the absence of additional evidence after the initial decision. Unlike previous studies of changes-of-mind, the majority of changes-of-mind in our experiment occurred in trials with prolonged initial response times. This suggests a distinct mechanism underlying such changes. Using a neural circuit model of decision uncertainty and change-of-mind behaviour, we demonstrate that this phenomenon is associated with top-down signals mediated by an uncertainty-monitoring neural population. Such a mechanism is consistent with recent neurophysiological evidence showing a link between changes-of-mind and elevated top-down neural activity. Our model explains the long response times associated with changes-of-mind through high decision uncertainty levels in such trials, and accounts for the observed motor response trajectories. Overall, our work provides a computational framework that explains changes-of-mind in the absence of new post-decision evidence.Authors SummaryWe used limited availability of sensory evidence during a standard motion discrimination task, and demonstrated that changes-of-mind could occur long after sensory information was no longer available. Unlike previous studies, our experiment further indicated that changes-of-mind were strongly linked to slow response time. We used a reduced version of a previously developed neural computational model of decision uncertainty and change-of-mind to account for these experimental observations. Importantly, our model showed that the replication of these experimental results required a strong link between change-of-mind and high decision uncertainty (i.e. low decision confidence), supporting the notion that change-of-mind are related to decision uncertainty or confidence.

scholarly journals Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Farshad Rafiei ◽  
Dobromir Rahnev
Keyword(s):  
Diffusion Model ◽  
Discrimination Task ◽  
Drift Rate ◽  
Response Times ◽  
Perceptual Discrimination ◽  
The Difference ◽  
Speed Accuracy ◽  
Selective Influence ◽  
Accuracy Tradeoff ◽  
Rt Distributions

AbstractIt is often thought that the diffusion model explains all effects related to the speed-accuracy tradeoff (SAT) but this has previously been examined with only a few SAT conditions or only a few subjects. Here we collected data from 20 subjects who performed a perceptual discrimination task with five different difficulty levels and five different SAT conditions (5000 trials/subject). We found that the five SAT conditions produced robustly U-shaped curves for (i) the difference between error and correct response times (RTs), (ii) the ratio of the standard deviation and mean of the RT distributions, and (iii) the skewness of the RT distributions. Critically, the diffusion model where only drift rate varies with contrast and only boundary varies with SAT could not account for any of the three U-shaped curves. Further, allowing all parameters to vary across conditions revealed that both the SAT and difficulty manipulations resulted in substantial modulations in every model parameter, while still providing imperfect fits to the data. These findings demonstrate that the diffusion model cannot fully explain the effects of SAT and establishes three robust but challenging effects that models of SAT should account for.

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.

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