scholarly journals Non-additive activity modulation during a decision making task involving tactic selection

2021 ◽  
Author(s):  
Wilhelm Braun ◽  
Yoshiya Matsuzaka ◽  
Hajime Mushiake ◽  
Georg Northoff ◽  
André Longtin
Keyword(s):  
Decision Making ◽  
Firing Rate ◽  
Regional Differences ◽  
Single Neuron ◽  
Principal Component ◽  
Motor Area ◽  
Perceptual Decision Making ◽  
Population Activity ◽  
Time Courses

AbstractHuman brain imaging has revealed that stimulus-induced activity does generally not simply add to the pre-stimulus activity, but rather builds in a non-additive way on this activity. Here we investigate this subject at the single neuron level and address the question whether and to what extent a strong form of non-additivity where activity drops post-cue is present in different areas of monkey cortex, including prefrontal and agranular frontal areas, during a perceptual decision making task involving action and tactic selection. Specifically we analyze spike train data recorded in vivo from the posterior dorsomedial prefrontal cortex (pmPFC), the supplementary motor area (SMA) and the presupplementary motor area (pre-SMA). For each neuron, we compute the ratio of the trial-averaged pre-stimulus spike count to the trial-averaged post-stimulus count. We also perform the ratio and averaging procedures in reverse order. We find that the statistics of these quantities behave differently across areas. pmPFC involved in tactic selection shows stronger non-additivity compared to the two other areas which more generically just increase their firing rate pos-stimulus. pmPFC behaved more similarly to pre-SMA, a likely consequence of the reciprocal connections between these areas. The trial-averaged ratio statistic was reproduced by a surrogate inhomogeneous Poisson process in which the measured trial-averaged firing rate for a given neuron is used as its time-dependent rate. Principal component analysis (PCA) of the trial-averaged firing rates of neuronal ensembles further reveals area-specific time courses of response to the stimulus, including latency to peak neural response, for the typical population activity. Our work demonstrates subtle forms of area-specific non-additivity based on the fine variability structure of pre- and post-stimulus spiking activity on the single neuron level. It also reveals significant differences between areas for PCA and surrogate analysis, complementing previous observations of regional differences based solely on post-stimulus responses. Moreover, we observe regional differences in non-additivity which are related to the monkey’s successful tactic selection and decision making.

scholarly journals Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

2020 ◽  
Author(s):  
Benjamin R. Cowley ◽  
Adam C. Snyder ◽  
Katerina Acar ◽  
Ryan C. Williamson ◽  
Byron M. Yu ◽  
...  
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Neural Activity ◽  
Internal State ◽  
Sensory Stimulus ◽  
Perceptual Decision Making ◽  
Population Activity ◽  
Perceptual Decision ◽  
Slow Drift ◽  
Sensory Evidence

AbstractAn animal’s decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This internal state is a product of cognitive factors, such as fatigue, motivation, and arousal, but it is unclear how these factors influence the neural processes that encode the sensory stimulus and form a decision. We discovered that, over the timescale of tens of minutes during a perceptual decision-making task, animals slowly shifted their likelihood of reporting stimulus changes. They did this unprompted by task conditions. We recorded neural population activity from visual area V4 as well as prefrontal cortex, and found that the activity of both areas slowly drifted together with the behavioral fluctuations. We reasoned that such slow fluctuations in behavior could either be due to slow changes in how the sensory stimulus is processed or due to a process that acts independently of sensory processing. By analyzing the recorded activity in conjunction with models of perceptual decision-making, we found evidence for the slow drift in neural activity acting as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in the population activity across multiple brain areas, hidden from typical trial-averaged analyses and revealed only when considering the passage of time within each experimental session. Knowledge of this cognitive factor was critical in elucidating how sensory signals and the internal state together contribute to the decision-making process.

scholarly journals Tracking dynamic adjustments to decision making and performance monitoring processes in conflict tasks

2019 ◽  
Author(s):  
Daniel Feuerriegel ◽  
Matthew Jiwa ◽  
William F Turner ◽  
Milan Andrejević ◽  
Robert Hester ◽  
...  
Keyword(s):  
Decision Making ◽  
Error Detection ◽  
Performance Monitoring ◽  
Flanker Task ◽  
Conflict Monitoring ◽  
Perceptual Decision Making ◽  
Evidence Accumulation ◽  
Time Courses ◽  
And Performance ◽  
Conflicting Stimulus

AbstractHow we exert control over our decision making has been investigated using conflict tasks, which involve stimuli containing elements that are either congruent or incongruent. In these tasks, participants adapt their decision making strategies following exposure to incongruent stimuli. According to conflict monitoring accounts, conflicting stimulus features are detected in medial frontal cortex, and the extent of experienced conflict scales with response time (RT) and frontal theta-band activity in the electroencephalogram (EEG). However, the consequent adjustments to decision processes following response conflict are not well-specified. To characterise these adjustments and their neural implementation we recorded EEG during a Flanker task. We traced the time-courses of performance monitoring processes (frontal theta) and multiple processes related to perceptual decision making. In each trial participants judged which of two overlaid gratings forming a plaid stimulus (termed the S1 target) was of higher contrast. The stimulus was divided into two sections, which each contained higher contrast gratings in either congruent or incongruent directions. Shortly after responding to the S1 target, an additional S2 target was presented, which was always congruent. Our EEG results suggest enhanced sensory evidence representations in visual cortex and reduced evidence accumulation rates for S2 targets following incongruent S1 stimuli. Frontal theta amplitudes positively correlated with RT following S1 targets (in line with conflict monitoring accounts). Following S2 targets there was no such correlation, and theta amplitude profiles instead resembled decision evidence accumulation trajectories. Based on these differing amplitude profiles across S1 and S2 we formulated a novel theory of frontal theta and performance monitoring, which accounts for differing theta amplitude profiles previously observed across tasks that do and do not involve conflict. We propose that frontal theta does not actually index conflict detection but instead reflects a more general performance monitoring process related to decision confidence and rapid error detection.

scholarly journals An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability

2018 ◽  
Author(s):  
Ziqiang Wei ◽  
Hidehiko Inagaki ◽  
Nuo Li ◽  
Karel Svoboda ◽  
Shaul Druckmann
Keyword(s):  
Decision Making ◽  
Brain Function ◽  
Premotor Cortex ◽  
Neural Correlates ◽  
Neural Population ◽  
Behavioral Variability ◽  
Perceptual Decision Making ◽  
Population Activity ◽  
Long Time ◽  
Dynamical Nature

AbstractAnimals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is understanding the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g. decision making), neuronal heterogeneity and signal to noise difficulties. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics was able to reveal previously unrecognized structure in the organization of population activity. This structure was similar on error and correct trials, suggesting what may be the underlying circuit mechanisms, was able to predict multiple aspects of behavioral variability and revealed long time-scale modulation of population activity.

scholarly journals Influence of data sampling methods on the representation of neural spiking activity in vivo

2022 ◽  
Author(s):  
Meike E van der Heijden ◽  
Amanda M Brown ◽  
Roy V Sillitoe
Keyword(s):  
Firing Rate ◽  
Single Neuron ◽  
Cerebellar Nuclei ◽  
Group Differences ◽  
Data Sampling ◽  
Disease States ◽  
Neural Spiking ◽  
Single Unit Recordings ◽  
Firing Properties

In vivo single-unit recordings distinguish the basal spiking properties of neurons in different experimental settings and disease states. Here, we examined over 300 spike trains recorded from Purkinje cells and cerebellar nuclei neurons to test whether data sampling approaches influence the extraction of rich descriptors of firing properties. Our analyses included neurons recorded in awake and anesthetized control mice, as well as disease models of ataxia, dystonia, and tremor. We find that recording duration circumscribes overall representations of firing rate and pattern. Notably, shorter recording durations skew estimates for global firing rate variability towards lower values. We also find that only some populations of neurons in the same mouse are more similar to each other than to neurons recorded in different mice. These data reveal that recording duration and approach are primary considerations when interpreting task-independent single-neuron firing properties. If not accounted for, group differences may be concealed or exaggerated.

scholarly journals Distinct neural dynamics underlie the encoding of visual speed in stationary and running mice

2021 ◽  
Author(s):  
Edward A B Horrocks ◽  
Aman B Saleem
Keyword(s):  
Single Neuron ◽  
Dynamic Range ◽  
Temporal Dynamics ◽  
Population Coding ◽  
Neural Responses ◽  
Population Activity ◽  
Visual Inputs ◽  
Structure Of Population ◽  
Time Courses ◽  
Visual Speed

Sensory experiences are often driven by an animal's self-motion and locomotion is known to modulate neural responses in the mouse visual system. This modulation is hypothesised to improve the processing of behaviourally relevant visual inputs, which may change rapidly during locomotion. However, little is known about how locomotion modulates the temporal dynamics (time courses) of visually-evoked neural responses. Here, we analysed the temporal dynamics of single neuron and population responses to dot field stimuli moving at a range of visual speeds using the Visual Coding dataset from the Allen Institute for Brain Science (Siegle et al, 2021). Single neuron responses had diverse temporal dynamics that varied between stationary and running sessions. Increased dynamic range and more reliable responses in running sessions enabled faster, stronger and more persistent encoding of visual speed. Population activity reflected the temporal dynamics of single neuron responses, including their modulation by locomotor state - neural trajectories of population activity made more direct transitions between baseline and stimulus steady states in running sessions. The structure of population coding also changed with locomotor state - population activity prioritised the encoding of visual speed in running, but not stationary sessions. Our results reveal a profound influence of locomotion on the temporal dynamics of neural responses. We demonstrate that during locomotion, mouse visual areas prioritise the encoding of potentially fast-changing, behaviourally relevant visual features.

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.

LPCD framework: Analytical tool or psychological model?

2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks
Keyword(s):  
Decision Making ◽  
Analytical Tool ◽  
Mathematical Framework ◽  
Bayesian Decision ◽  
Perceptual Decision Making ◽  
Psychological Model ◽  
Psychological Reality ◽  
Psychological Theories ◽  
Target Article ◽  
Bayesian Decision Making

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

The nature of metacognitive inefficiency in perceptual decision making

2020 ◽  
Author(s):  
Medha Shekhar ◽  
Dobromir Rahnev
Keyword(s):  
Decision Making ◽  
Mechanistic Model ◽  
Perceptual Decision Making ◽  
Large Dataset ◽  
Substantial Body ◽  
Confidence Levels ◽  
Continuous Scale ◽  
Competing Models ◽  
Metacognitive Ability ◽  
Confidence Ratings

Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across five different datasets and four different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally-distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically-validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency.

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