Stimulus expectation alters decision criterion but not sensory signal in perceptual decision making

Recent research has shown that adults and children with autism spectrum disorders have a more conservative decision criterion in perceptual decision making compared to neurotypical individuals, meaning that autistic participants prioritise accuracy over speed of a decision. Here, we test whether autistic traits in the neurotypical population correlate with increased response conservativeness. We employed three different tasks; for two tasks we recruited participants from China ( N = 39) and for one task from the United Kingdom ( N = 37). Our results show that autistic traits in the neurotypical population do not predict variation in response criterion. We also failed to replicate previous work showing a relationship between autistic traits and sensitivity to coherent motion and static orientation. Following the argument proposed by Gregory and Plaisted-Grant, we discuss why perceptual differences between autistic and neurotypical participants do not necessarily predict perceptual differences between neurotypical participants with high and low autistic traits.

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Re-thinking the fixed-criterion model of perceptual decision-making

10.31234/osf.io/ys8mb ◽

2021 ◽

Author(s):

Jennifer Laura Lee ◽

Rachel N. Denison ◽

Wei Ji Ma

Keyword(s):

Decision Making ◽

Probabilistic Reasoning ◽

Sensory Information ◽

Feature Space ◽

Decision Criterion ◽

Decision Variable ◽

Perceptual Decision Making ◽

Perceptual Decision ◽

Response Variance ◽

Sensory Uncertainty

Perceptual decision-making is often conceptualized as the process of comparing an internal decision variable to a categorical boundary, or criterion. How the mind sets such a criterion has been studied from at least two perspectives. First, researchers interested in consciousness have proposed that criterion-crossing determines whether a stimulus is consciously perceived. Second, researchers interested in decision-making have studied how the criterion depends on a range of stimulus and task variables. Both communities have considered the question of how the criterion behaves when sensory information is weak or uncertain. Interestingly, however, they have arrived at different conclusions. Consciousness researchers investigating a phenomenon called "subjective inflation" – a form of metacognitive mismatch in which observers overestimate the quality of their sensory representations in the periphery or at an unattended location – have proposed that the criterion governing subjective visibility is fixed. That is, it does not adjust to changes in sensory uncertainty. Decision-making researchers, on the other hand, have concluded that the criterion does adjust to account for sensory uncertainty, including under inattention. Here, we mathematically demonstrate that previous empirical findings supporting subjective inflation are consistent with either a fixed or a flexible decision criterion. We further show that specific experimental task requirements are necessary to make inferences about the flexibility of the criterion: 1) a clear mapping from decision variable space to stimulus feature space, and 2) a task incentive for observers to adjust their decision criterion as response variance increases. We conclude that the fixed-criterion model of subjective inflation requires re-thinking in light of new evidence from the probabilistic reasoning literature that decision criteria flexibly adjust according to response variance.

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Contribution of sensory encoding to measured bias

10.1101/444430 ◽

2018 ◽

Cited By ~ 2

Author(s):

Miaomiao Jin ◽

Lindsey L. Glickfeld

Keyword(s):

Decision Making ◽

Neuronal Response ◽

Decision Criterion ◽

Neuronal Firing ◽

Mathematical Framework ◽

Perceptual Decision Making ◽

Stimulus Interval ◽

Perceptual Decision ◽

Behavioral Bias ◽

Sensory Encoding

AbstractPerceptual decision-making is a complex process that involves sensory integration followed by application of a cognitive threshold. Signal detection theory (SDT) provides a mathematical framework for attributing the underlying neurobiological processes to these distinct phases of perceptual decision-making. In particular, SDT reveals the sensitivity (d’) of the neuronal response distributions and the bias (c) of the decision criterion, which are commonly thought to reflect sensory and cognitive processes, respectively. However, neuronal representations of bias have been observed in sensory areas, suggesting that some changes in bias are due to effects on sensory encoding. To directly test whether sensory encoding can influence bias, we optogenetically manipulated neuronal excitability in primary visual cortex (V1) during a detection task. Increasing excitability in V1 significantly decreased behavioral bias, while decreasing excitability had the opposite effect. To determine whether this change in bias is consistent with the effects on sensory encoding, we made extracellular recordings from V1 neurons in passively viewing mice. Indeed, we found that optogenetic manipulation of excitability shifted the neuronal bias in the same direction as the behavioral bias, despite using a fixed artificial decision criterion to predict hit and false alarm rates from the neuronal firing rates. To test the generality these effects, we also manipulated the quality of V1 encoding by changing stimulus contrast or inter-stimulus interval. These stimulus manipulations also resulted in consistent changes in bias measured both behaviorally and neuronally. Thus, changes in sensory encoding are sufficient to drive changes in bias measured using SDT.

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Discarding optimality: Throwing out the baby with the bathwater?

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001401 ◽

2018 ◽

Vol 41 ◽

Cited By ~ 1

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.

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Mesoscale cortical dynamics reflect the interaction of sensory evidence and temporal expectation during perceptual decision-making

Neuron ◽

10.1016/j.neuron.2021.03.031 ◽

2021 ◽

Author(s):

Ivana Orsolic ◽

Maxime Rio ◽

Thomas D. Mrsic-Flogel ◽

Petr Znamenskiy

Keyword(s):

Decision Making ◽

Perceptual Decision Making ◽

Cortical Dynamics ◽

Perceptual Decision ◽

Temporal Expectation ◽

Sensory Evidence

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Flexible categorization in perceptual decision making

Nature Communications ◽

10.1038/s41467-021-21501-z ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 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.

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

Mindfulness ◽

10.1007/s12671-020-01562-9 ◽

2021 ◽

Author(s):

Sungjin Im ◽

Maya A. Marder ◽

Gabriella Imbriano ◽

Tamara J. Sussman ◽

Aprajita Mohanty

Keyword(s):

Decision Making ◽

Perceptual Decision Making ◽

Perceptual Decision

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

Sensors ◽

10.3390/s21072461 ◽

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).

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