Neural Dynamics of Serial Dependence in Numerosity Perception

2020 ◽  
Vol 32 (1) ◽  
pp. 141-154 ◽  
Author(s):  
Michele Fornaciai ◽  
Joonkoo Park
Keyword(s):  
Time Course ◽  
Irrelevant Stimulus ◽  
Perceptual Bias ◽  
Neural Representation ◽  
Neural Dynamics ◽  
Serial Dependence ◽  
Initial Stimulus ◽  
Numerosity Discrimination ◽  
Neural Signature ◽  
Numerosity Perception

Serial dependence—an attractive perceptual bias whereby a current stimulus is perceived to be similar to previously seen ones—is thought to represent the process that facilitates the stability and continuity of visual perception. Recent results demonstrate a neural signature of serial dependence in numerosity perception, emerging very early in the time course during perceptual processing. However, whether such a perceptual signature is retained after the initial processing remains unknown. Here, we address this question by investigating the neural dynamics of serial dependence using a recently developed technique that allowed a reactivation of hidden memory states. Participants performed a numerosity discrimination task during EEG recording, with task-relevant dot array stimuli preceded by a task-irrelevant stimulus inducing serial dependence. Importantly, the neural network storing the representation of the numerosity stimulus was perturbed (or pinged) so that the hidden states of that representation can be explicitly quantified. The results first show that a neural signature of serial dependence emerges early in the brain signals, starting soon after stimulus onset. Critical to the central question, the pings at a later latency could successfully reactivate the biased representation of the initial stimulus carrying the signature of serial dependence. These results provide one of the first pieces of empirical evidence that the biased neural representation of a stimulus initially induced by serial dependence is preserved throughout a relatively long period.

scholarly journals Attractive Serial Dependence in the Absence of an Explicit Task

2018 ◽  
Vol 29 (3) ◽  
pp. 437-446 ◽  
Author(s):  
Michele Fornaciai ◽  
Joonkoo Park
Keyword(s):  
Visual Processing ◽  
Sensory Information ◽  
Memory Representation ◽  
Psychophysical Experiment ◽  
Serial Dependence ◽  
Neural Signals ◽  
Perceptual Representations ◽  
Neural Signature ◽  
Numerosity Perception ◽  
A Current

Attractive serial dependence refers to an adaptive change in the representation of sensory information, whereby a current stimulus appears to be similar to a previous one. The nature of this phenomenon is controversial, however, as serial dependence could arise from biased perceptual representations or from biased traces of working memory representation at a decisional stage. Here, we demonstrated a neural signature of serial dependence in numerosity perception emerging early in the visual processing stream even in the absence of an explicit task. Furthermore, a psychophysical experiment revealed that numerosity perception is biased by a previously presented stimulus in an attractive way, not by repulsive adaptation. These results suggest that serial dependence is a perceptual phenomenon starting from early levels of visual processing and occurring independently from a decision process, which is consistent with the view that these biases smooth out noise from neural signals to establish perceptual continuity.

scholarly journals Perceptual decisions are biased toward relevant prior choices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Helen Feigin ◽  
Shira Baror ◽  
Moshe Bar ◽  
Adam Zaidel
Keyword(s):  
Sensory Information ◽  
Irrelevant Stimulus ◽  
Stimulus Feature ◽  
Serial Dependence ◽  
Low Level ◽  
Subsequent Test ◽  
Key Factor ◽  
High Level ◽  
Motor Actions ◽  
The Brain

AbstractPerceptual decisions are biased by recent perceptual history—a phenomenon termed 'serial dependence.' Here, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor actions. Participants discriminated whether a brief visual stimulus lay to left/right of the screen center. Following a series of biased ‘prior’ location discriminations, subsequent ‘test’ location discriminations were biased toward the prior choices, even when these were reported via different motor actions (using different keys), and when the prior and test stimuli differed in color. By contrast, prior discriminations about an irrelevant stimulus feature (color) did not substantially influence subsequent location discriminations, even though these were reported via the same motor actions. Additionally, when color (not location) was discriminated, a bias in prior stimulus locations no longer influenced subsequent location discriminations. Although low-level stimuli and motor actions did not trigger serial-dependence on their own, similarity of these features across discriminations boosted the effect. These findings suggest that relevance across perceptual decisions is a key factor for serial dependence. Accordingly, serial dependence likely reflects a high-level mechanism by which the brain predicts and interprets new incoming sensory information in accordance with relevant prior choices.

scholarly journals Serial dependence in numerosity perception

2018 ◽  
Vol 18 (9) ◽  
pp. 15 ◽  
Author(s):  
Michele Fornaciai ◽  
Joonkoo Park
Keyword(s):  
Serial Dependence ◽  
Numerosity Perception

scholarly journals Early Visual Cortex Dynamics during Top–Down Modulated Shifts of Feature-Selective Attention

2016 ◽  
Vol 28 (4) ◽  
pp. 643-655 ◽  
Author(s):  
Matthias M. Müller ◽  
Mireille Trautmann ◽  
Christian Keitel
Keyword(s):  
Visual Cortex ◽  
Selective Attention ◽  
Time Course ◽  
Temporal Dynamics ◽  
Behavioral Data ◽  
Coherent Motion ◽  
Neural Dynamics ◽  
Early Visual Cortex ◽  
Time Courses ◽  
Feature Dimension

Shifting attention from one color to another color or from color to another feature dimension such as shape or orientation is imperative when searching for a certain object in a cluttered scene. Most attention models that emphasize feature-based selection implicitly assume that all shifts in feature-selective attention underlie identical temporal dynamics. Here, we recorded time courses of behavioral data and steady-state visual evoked potentials (SSVEPs), an objective electrophysiological measure of neural dynamics in early visual cortex to investigate temporal dynamics when participants shifted attention from color or orientation toward color or orientation, respectively. SSVEPs were elicited by four random dot kinematograms that flickered at different frequencies. Each random dot kinematogram was composed of dashes that uniquely combined two features from the dimensions color (red or blue) and orientation (slash or backslash). Participants were cued to attend to one feature (such as color or orientation) and respond to coherent motion targets of the to-be-attended feature. We found that shifts toward color occurred earlier after the shifting cue compared with shifts toward orientation, regardless of the original feature (i.e., color or orientation). This was paralleled in SSVEP amplitude modulations as well as in the time course of behavioral data. Overall, our results suggest different neural dynamics during shifts of attention from color and orientation and the respective shifting destinations, namely, either toward color or toward orientation.

The Time Course of Stimulus Expectation in a Saccadic Decision Task

2007 ◽  
Vol 97 (4) ◽  
pp. 2722-2730 ◽  
Author(s):  
A. Oswal ◽  
Miriam Ogden ◽  
R.H.S. Carpenter
Keyword(s):  
Reaction Time ◽  
Time Course ◽  
Prior Probability ◽  
Quantitative Relationship ◽  
Neural Representation ◽  
Decision Task ◽  
Short Term ◽  
Low Pass ◽  
Quantitative Explanation

Because the time to respond to a stimulus depend markedly on expectation, measurements of reaction time can, conversely, provide information about the brain's estimate of the probability of a stimulus. Previous studies have shown that the quantitative relationship between reaction time and static, long-term expectation or prior probability can be explained economically by the LATER model of decision reaction time. What is not known, however, is how the neural representation of expectation changes in the short term, as a result of immediate cues. Here, we manipulate the foreperiod—the delay between the start of a trial and the appearance of the stimulus—to see how saccadic latency, and thus expectation, varies with different delays. It appears that LATER can provide a quantitative explanation of this relationship, in terms both of average latencies and of their statistical distribution. We also show that expectancy appears to be subject to a process of low-pass filtering, analogous to the spatial blur that degrades visual acuity.

Optimal Inputs for Phase Models of Spiking Neurons

2006 ◽  
Vol 1 (4) ◽  
pp. 358-367 ◽  
Author(s):  
Jeff Moehlis ◽  
Eric Shea-Brown ◽  
Herschel Rabitz
Keyword(s):  
Time Course ◽  
Phase Response Curve ◽  
Spiking Neurons ◽  
Neural Dynamics ◽  
Neural Oscillator ◽  
Neural Models ◽  
Optimal Inputs ◽  
Integral Measure ◽  
Phase Models

Variational methods are used to determine the optimal currents that elicit spikes in various phase reductions of neural oscillator models. We show that, for a given reduced neuron model and target spike time, there is a unique current that minimizes a square-integral measure of its amplitude. For intrinsically oscillatory models, we further demonstrate that the form and scaling of this current is determined by the model’s phase response curve. These results reflect the role of intrinsic neural dynamics in determining the time course of synaptic inputs to which a neuron is optimally tuned to respond, and are illustrated using phase reductions of neural models valid near typical bifurcations to periodic firing, as well as the Hodgkin-Huxley equations.

scholarly journals A geometric representation unveils learning dynamics in primate neurons

2019 ◽  
Author(s):  
Yarden Cohen ◽  
Elad Schneidman ◽  
Rony Paz
Keyword(s):  
De Novo ◽  
Large Fraction ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Neural Representation ◽  
Neural Dynamics ◽  
Geometric Representation ◽  
Dorsal Anterior Cingulate Cortex ◽  
Magnitude Increase ◽  
Vector Magnitude

AbstractPrimates can quickly and advantageously adopt new behaviors based on changing stimuli relationships. We studied acquisition of a classification task while recording single neurons in the dorsal-anterior-cingulate-cortex (dACC) and the Striatum. Monkeys performed trial-by-trial classification on a rich set of multi-cue patterns, allowing de-novo learning every few days. To examine neural dynamics during the learning itself, we represent each rule with a spanning set of the space formed by the stimuli features. Because neural preference can be expressed by feature combinations, we can track neural dynamics in geometrical terms in this space, allowing a compact description of neural trajectories by observing changes in either vector-magnitude and/or angle-to- rule. We find that a large fraction of cells in both regions follow the behavior during learning. Neurons in the dACC mainly rotate towards the policy, suggesting an increase in selectivity that approximates the rule; whereas in the Putamen we also find a prominent magnitude increase, suggesting strengthening of confidence. Additionally, magnitude increases in the striatum followed rotation in the dACC. Finally, the neural representation at the end of the session predicted next-day behavior. The use of this novel framework enables tracking of neural dynamics during learning and suggests differential yet complementing roles for these brain regions.

scholarly journals Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Xiaoxuan Jia ◽  
Ha Hong ◽  
Jim DiCarlo
Keyword(s):  
Object Recognition ◽  
Neural Plasticity ◽  
Time Course ◽  
Human Performance ◽  
Temporal Cortex ◽  
Neural Representation ◽  
Temporal Contiguity ◽  
Inferior Temporal Cortex ◽  
Object Identity ◽  
Visual Stream

Temporal continuity of object identity is a feature of natural visual input, and is potentially exploited -- in an unsupervised manner -- by the ventral visual stream to build the neural representation in inferior temporal (IT) cortex. Here we investigated whether plasticity of individual IT neurons underlies human core-object-recognition behavioral changes induced with unsupervised visual experience. We built a single-neuron plasticity model combined with a previously established IT population-to-recognition-behavior linking model to predict human learning effects. We found that our model, after constrained by neurophysiological data, largely predicted the mean direction, magnitude and time course of human performance changes. We also found a previously unreported dependency of the observed human performance change on the initial task difficulty. This result adds support to the hypothesis that tolerant core object recognition in human and non-human primates is instructed -- at least in part -- by naturally occurring unsupervised temporal contiguity experience.

scholarly journals A distributed fMRI-based signature for the subjective experience of fear

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Feng Zhou ◽  
Weihua Zhao ◽  
Ziyu Qi ◽  
Yayuan Geng ◽  
Shuxia Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Distributed Systems ◽  
Negative Affect ◽  
Basal Forebrain ◽  
Subjective Experience ◽  
Neural Representation ◽  
Neural Systems ◽  
Subjective Feeling ◽  
Affective Neuroscience ◽  
Neural Signature

AbstractThe specific neural systems underlying the subjective feeling of fear are debated in affective neuroscience. Here, we combine functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n = 67), validation (n = 20) and generalization (n = 31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated ‘fear centers’.

scholarly journals Probabilistic representation in human visual cortex reflects uncertainty in serial decisions

2019 ◽  
Author(s):  
R.S. van Bergen ◽  
J.F.M. Jehee
Keyword(s):  
Visual Cortex ◽  
Neural Coding ◽  
Probability Distributions ◽  
Sensory Information ◽  
Population Level ◽  
Neural Representation ◽  
Serial Dependence ◽  
Population Activity ◽  
Human Visual Cortex ◽  
Sensory Uncertainty

AbstractHow does the brain represent the reliability of its sensory evidence? Here, we test whether sensory uncertainty is encoded in cortical population activity as the width of a probability distribution – a hypothesis that lies at the heart of Bayesian models of neural coding. We probe the neural representation of uncertainty by capitalizing on a well-known behavioral bias called serial dependence. Human observers of either sex reported the orientation of stimuli presented in sequence, while activity in visual cortex was measured with fMRI. We decoded probability distributions from population-level activity and found that serial dependence effects in behavior are consistent with a statistically advantageous sensory integration strategy, in which uncertain sensory information is given less weight. More fundamentally, our results suggest that probability distributions decoded from human visual cortex reflect the sensory uncertainty that observers rely on in their decisions, providing critical evidence for Bayesian theories of perception.

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