Biology subjects

Peters, M. A. K.

Publications and source records attributed to Peters, M. A. K..

11 recordsLinked to original sources

bioRxiv2026

Memory retrieval explains dynamic effects of expectations on perceptual decisions

Expectations--prior knowledge of environmental statistics--support adaptive behavior by reducing uncertainty during inference. However, the questions of where expectations come from and how that ought to impact their integration has remained largely unexplored. Here, we present a novel model formalizing the role of memory retrieval in the process of integrating expectations with uncertain sensory information. To do this, we first articulate how uncertainty about expectations--omitted from existing normative models--ought to induce memory retrieval processes. We then model expectations as a dynamic source of internal evidence that observers sample from in parallel to vision, and that are weighed according to a time-evolving estimate of their reliability relative to sensory evidence. Finally, we show that this single, principled model can reproduce several previously disparate observations of time-varying effects of expectations in neural and behavioral data, each of which was previously explained using different neural and computational mechanisms. These findings suggest that our model identifies a basic mechanism governing the integration of expectations with sensory evidence on assumptions that more closely approximate the dynamic, heterogeneous, and uncertain nature of real-world decision settings.

neuroscience↗

bioRxiv2024

Whole-brain causal connectivity during decoded neurofeedback: a meta study

Decoded Neurofeedback (DecNef) enables modulation of targeted brain activity patterns without subjective awareness through multivariate pattern analysis, reinforcement learning, and real-time fMRI feedback. Despite its proven effectiveness, the causal mechanisms underlying DecNef and the neural dynamics that distinguish successful learners from those who struggle remain poorly understood. We conducted a meta-study using fMRI data from five DecNef experiments and extracted causal network dynamics as well as their associations with performance differences. Across studies, we found that connectivity within a posterior control hub-consisting of posterior cingulate, precuneus, and lateral posterior parietal cortices-is stronger during DecNef and positively correlates with neurofeedback success. Comparisons across cognition- and perception-targeted DecNef revealed separation in connections to somatomotor network, where connections between somatomotor and control-default-attention networks are larger during cognitive neurofeedback while connections between somatomotor and subcortical-visual-limbic networks are larger during perceptive DecNef. Whole-brain causal connectivity during DecNef further exhibited distinct network reorganizations, with greater subject-to-subject variability, increased engagement of control, limbic and visual, and decreased engagement of ventral and dorsal attention networks. Our results distill the complex and distributed network mechanisms underlying DecNef into dissociable roles for well-known functional subnetworks, thus advancing the research and clinical applications of decoded neurofeedback.

neuroscience↗

bioRxiv2023

Whole-Brain Causal Discovery Using fMRI

Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger Causality and Dynamic Causal Modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested alternatives. From resting-state human fMRI, CaLLTiF learns causal connectomes that are highly consistent across individuals, show clear top-down flow of causal effect from attention and default mode to sensorimotor networks, exhibit Euclidean distance-dependence in causal interactions, and are highly dominated by contemporaneous effects. Overall, this work takes a major step in enhancing causal discovery from whole-brain fMRI and defines a new standard for future investigations. AUTHOR SUMMARYDiscovering causal relationships from fMRI data is challenging due to contemporaneous effects and latent causes. Popular methods like Granger Causality and Dynamic Causal Modeling struggle with these issues, especially in large networks. To address this, we introduce CaLLTiF, a scalable method that uses both lagged and contemporaneous variables to identify causal relationships. CaLLTiF outperforms various existing techniques in accuracy and scalability on simulated fMRI data. When applied to human resting-state fMRI, it reveals consistent and biologically-plausible patterns across individuals, with a clear top-down causal flow from attention and default mode networks to sensorimotor areas. Overall, this work advances the field of causal discovery in large-scale fMRI studies.

neuroscience↗

bioRxiv2022

Locus coeruleus neuromelanin predicts ease of attaining and maintaining neural states of arousal

The locus coeruleus (LC), a small subcortical structure in the brainstem, is the brains principal source of norepinephrine. It plays a primary role in regulating stress, the sleep-wake cycle, and attention, and its degradation is associated with aging and neurodegenerative diseases associated with cognitive deficits (e.g., Parkinsons, Alzheimers). Yet precisely how norepinephrine drives brain networks to support healthy cognitive function remains poorly understood - partly because LCs small size makes it difficult to study noninvasively in humans. Here, we characterized LCs influence on brain dynamics using a hidden Markov model fitted to functional neuroimaging data from healthy young adults across four attention-related brain networks and LC. We modulated LC activity using a behavioral paradigm and measured individual differences in LC magnetization transfer contrast. The model revealed five hidden states, including a stable state dominated by salience-network activity that occurred when subjects actively engaged with the task. LC magnetization transfer contrast correlated with this states stability across experimental manipulations and with subjects propensity to enter into and remain in this state. These results provide new insight into LCs role in driving spatiotemporal neural patterns associated with attention, and demonstrate that variation in LC integrity can explain individual differences in these patterns even in healthy young adults.

neuroscience↗

bioRxiv2022

A novel hidden Markov approach to studying dynamic functional connectivity states in human neuroimaging

IntroductionHidden Markov models are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse hidden Markov models have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: intensity-based states are unlikely to provide comprehensive information about dynamic connectivity patterns. MethodsWe addressed this problem by introducing a new hidden Markov model that defines states based on full functional connectivity profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on intensity-based or summed functional connectivity states using the HCP unrelated 100 functional magnetic resonance imaging "resting state" dataset. ResultsOur full functional connectivity model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. DiscussionThus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods which miss crucial information about the evolution of functional connectivity in the brain. Impact statementHidden Markov models can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity summed across nodes. Here we introduce a novel connectivity-based hidden Markov model and show how it can reveal true connectivity hidden states under minimal assumptions.

neuroscience↗

bioRxiv2021

Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery

Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed subjective inflation. Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise, i.e. variance misperception. However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors -- but used them incorrectly -- showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system. SignificancePerceptual confidence can dissociate from accuracy in peripheral visual perception, a phenomenon known as peripheral inflation. No previous model has considered how this phenomenon may arise from metacognitive noise estimation which depends on empirical noise statistics. Here we simulate Bayesian-inspired noise estimation algorithms to show that the systems erroneous beliefs about distributions of noise in the visual periphery can explain the occurrence of peripheral inflation, including how inflation varies with attentional manipulations in surprising ways. Our results explain why peripheral inflation occurs by positing a new Bayesian metacognitive noise estimation mechanism, paving the way for future psychophysical studies.

neuroscience↗

bioRxiv2019

Tuned normalization in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior

Current dominant views hold that perceptual confidence reflects the probability that a decision is correct. Although these views have enjoyed some empirical support, recent behavioral results indicate that confidence and the probability of being correct can be dissociated. An alternative hypothesis suggests that confidence instead reflects the magnitude of evidence in favor of a decision while being relatively insensitive to the evidence opposing the decision. We considered how this alternative hypothesis might be biologically instantiated by developing a simple leaky competing accumulator neural network model incorporating a known property of sensory neurons: tuned normalization. The key idea of the model is that each accumulator neurons normalization tuning dictates its contribution to perceptual decisions versus confidence judgments. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature, where confidence and decision accuracy were shown to dissociate -- and that the differential contribution a neuron makes to decisions versus confidence judgments based on its normalization tuning is vital to capturing some of these effects. One critical prediction of the model is that systematic variability in normalization tuning exists not only in sensory cortices but also in the decision-making circuitry. We tested and validated this prediction in macaque superior colliculus (SC; a region implicated in decision-making). The confirmation of this novel prediction provides direct support for our model. These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by capitalizing on the diversity of neural resources available. SignificanceThe dominant view of perceptual confidence proposes that confidence optimally reflects the probability that a decision is correct. But recent empirical evidence suggests that perceptual confidence exhibits a suboptimal confirmation bias, just as in human decision-making in general. We tested how this bias might be neurally implemented by building a biologically plausible neural network model, and showed that the bias emerges when each neurons degree of divisive normalization dictates how it drives decisions versus confidence judgments. We confirmed the models biological substrate using electrophysiological recordings in monkeys. These results challenge the dominant model, suggesting that the brain instead capitalizes on the diversity of available machinery (i.e., neuronal resources) to implement heuristic -- not optimal -- strategies to compute subjective confidence.

neuroscience↗

bioRxiv2018

Normal observers show no evidence for blindsight in facial emotion perception

It is commonly assumed that normal human observers can exhibit blindsight-like behavior: the ability to discriminate or identify a stimulus without being aware of it. However, we recently used a bias-free task to show that what looks like blindsight may in fact be an artifact of typical experimental paradigms susceptibility to response bias. While those findings challenge many previous reports of blindsight in normal observers, they do not rule out the possibility that different stimuli or techniques could reveal such perception without awareness. One particularly intriguing candidate for this unconscious perception is emotion processing, as processing of emotional stimuli (e.g. fearful/happy faces) has been reported to potentially bypass conscious visual circuits. Here we used the bias-free blindsight paradigm to investigate whether emotion processing might reveal featural blindsight, i.e. the ability to identify a faces emotion without having introspective access to the task-relevant features of the face that led to the discrimination decision. However, despite the purported ability of emotional stimuli to bypass conscious visual processing, we saw no evidence for such emotion processing featural blindsight: like in our previous study, as soon as participants could identify a faces emotion they reported introspective access to the task-relevant features, matching predictions of a Bayesian ideal observer. The present results challenge dominant theory, adding to the growing body of evidence that perceptual discrimination ability in the complete absence of introspective access may not be possible for neurologically intact observers.

neuroscience↗

bioRxiv2018

Continuous flash suppression and monocular pattern masking impact subjective awareness similarly

Peters & Lau (2015) found that when criterion bias is controlled for, there is no evidence for unconscious visual perception in normal observers, in the sense that they cannot directly discriminate a target above chance without knowing it. One criticism of that study is that the visual suppression method used, forward and backward masking (FBM), may be too blunt in the way it interferes with visual processing to allow for unconscious forced-choice discrimination. To investigate this question we compared FBM directly to continuous flash suppression (CFS) in a two-interval forced choice task. Although CFS is popular, and may be thought of as a more powerful visual suppression technique, we found no difference in the degree of perceptual impairment between the two suppression types. To the extent that CFS impairs perception, both objective discrimination and subjective awareness are impaired to similar degrees under FBM. This pattern was consistently observed across 3 experiments in which various experimental parameters were varied. These findings provide evidence for an ongoing debate about unconscious perception: normal observers cannot perform forced-choice discrimination tasks unconsciously.

animal behavior and cognition↗

bioRxiv2017

On a ‘failed’ attempt to manipulate conscious perception with transcranial magnetic stimulation to prefrontal cortex

It has been reported that continuous theta burst transcranial magnetic stimulation (TMS) to the dorsolateral prefrontal cortex (DLPFC) impairs metacognitive awareness in visual perception (Rounis et al., 2010). Bor et al. (2017) recently attempted to replicate this result. However, the authors modified the experimental design of the original study considerably, meaning that this was not strictly a replication. In some cases, the changes are a priori expected to lower the chance of obtaining positive findings. Despite these changes, the researchers in fact still found an effect similar to Rounis et al.s, but they claimed that it was necessary to adopt certain criteria to discard {bsim}30% of their subjects, after which a null result was reported. Using computer simulations, we evaluated whether the subject exclusion criteria Bor et al. adopted was appropriate or beneficial. We found that, contrary to their intended purpose, excluding subjects by their criteria does not actually reduce false positive rates. Taking into account both their positive result (without subject exclusion) and negative result (after exclusion) in a Bayesian framework, we further found that their results suggest a 75% or greater likelihood that TMS to DLPFC does in fact impair metacognition, directly contradicting their claim of replication failure. That lesion and chemical inactivation studies are known to demonstrate positive effects in similar paradigms further suggests that Bor et al.s alleged negative finding cannot be taken as evidence against the role of the prefrontal cortex in conscious perception in general.

neuroscience↗

bioRxiv2017

Superior Colliculus Neuronal Ensemble Activity Signals Optimal Rather Than Subjective Confidence

Recent studies suggest that neurons in sensorimotor circuits involved in perceptual decision-making also play a role in decision confidence. In these studies, confidence is often considered to be an optimal readout of the probability that a decision is correct. However, the information leading to decision accuracy and the report of confidence often co-varied, leaving open the possibility that there are actually two dissociable signal types in the brain: signals that correlate with decision accuracy (optimal confidence) and signals that correlate with subjects behavioral reports of confidence (subjective confidence). We recorded neuronal activity from a sensorimotor decision area, the superior colliculus (SC) of monkeys, while they performed two different tasks. In our first task, decision accuracy and confidence co-varied, as in previous studies. In our second task, we implemented a novel motion discrimination task with stimuli that were matched for decision accuracy but produced different levels of confidence as reflected by behavioral reports. We used a multivariate decoder to predict monkeys choices from neuronal population activity. As in previous studies on perceptual decision-making mechanisms, we found that neuronal decoding performance increased as decision accuracy increased. However, when decision accuracy was matched, performance of the decoder was similar between high and low subjective confidence conditions. These results show that the SC likely signals optimal decision confidence similar to previously reported cortical mechanisms, but is unlikely to play a critical role in subjective confidence. The results also motivate future investigations to determine where in the brain signals related to subjective confidence reside.\n\nSignificance StatementConfidence is thought to reflect the rational or optimal belief concerning ones choice accuracy. Here, we introduce a novel version of the dot-motion discrimination task with stimulus conditions that produce similar accuracy but different subjective behavioral reports of confidence. We decoded decision performance of this task from neuronal signals in the superior colliculus (SC), a subcortical region involved in decision-making. We found that SC activity signaled a perceptual decision for visual stimuli, with the strength of this activity reflecting decision accuracy, but not the subjective level of confidence as reflected by behavioral reports. These results demonstrate an important role for the SC in perceptual decision-making and challenge current ideas about how to measure subjective confidence in monkeys and humans.

neuroscience↗