Biology subjects

Moreno-Bote, R.

Publications and source records attributed to Moreno-Bote, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Metacognitive Efficiency Reduces Confirmation Bias in Perceptual Decision Making

Humans exhibit a pervasive drive toward self-consistency, often failing to revise previous decisions even when confronted with contradictory evidence. Here, we investigate the computational mechanisms underlying decision revision in perceptual tasks, examining the regulatory role of metacognition. To do so, we capitalize on a novel paradigm in which participants are repeatedly presented with identical sensory information and allowed to revise their choices after each exposure. Our results reveal that repeated exposure to the same stimulus systematically biases subsequent judgments toward prior responses. Using drift-diffusion modeling, we tested competing explanations incorporating different assumptions about how prior choices affect evidence accumulation. Our findings indicate that consistency biases emerge from asymmetric sensory weighting, selectively amplifying information consistent with previous choices--a phenomenon akin to confirmation bias. Crucially, individuals with higher metacognitive skills exhibited weaker confirmatory biases and more flexible integration of repeated sensory information, enabling greater adaptability in decision-making. These findings highlight the continuous nature of perceptual inference and underscore metacognitions pivotal role in mitigating bias and optimizing decision flexibility.

neuroscience↗

bioRxiv2025

Accumulation of virtual tokens towards a jackpot reward enhances performance and value encoding in dorsal anterior cingulate cortex

Normatively, our decisions ought to be made relative to our total wealth, but in practice, we make our decisions relative to variable, decision-time-specific set points. This predilection introduces a major behavior bias that is known as reference-point dependence in Prospect Theory, and that has close links to mental accounting. Here we examined neural activity in the dorsal anterior cingulate cortex (dACC) of macaques performing a token-based risky choice task, in which the acquisition of six tokens (accumulated over several trials) resulted in a jackpot reward. We find that subjects make faster and more accurate choices, and that they are less prone to risk-taking as offer contingencies are easier and the jackpot reward becomes more likely to be achieved. By comparing alternative models that accounted for progressive token accumulation, we found that subjective evaluations are best explained by a reference-dependent value RDV model where offer values are considered as potential gains or losses with respect to a token-dependent reference. The reference-dependent model allows to implement a dynamical comparison of the two offered values to each other and to the number of missing tokens to reach the six tokens threshold as jackpot approached. In dACC, we find that gains in subjective values entail higher fractions of encoding cells than losses, and that the encoding tuning of expected utility variables is best aligned to choices in gains than in losses. These results suggest a neural basis of reference dependence biases in shaping decision-making behavior and highlight the critical role of value representations in dACC in driving evaluations.

neuroscience↗

bioRxiv2024

Rational inattention in neural coding for economic choice

Mental operations like computing the value of an option are computationally expensive. Even before we evaluate options, we must decide how much attentional effort to invest in the evaluation process. More precise evaluation will improve choice accuracy, and thus reward yield, but the gain may not justify the cost. Rational Inattention theories provide an accounting of the internal economics of attentionally effortful economic decisions. To understand this process, we examined choices and neural activity in several brain regions in six macaques making risky choices. We extended the rational inattention framework to incorporate the foraging theoretic understanding of local environmental richness or reward rate, which we predict will promote attentional effort. Consistent with this idea, we found local reward rate positively predicted choice accuracy. Supporting the hypothesis that this effect reflects variations in attentional effort, richer contexts were associated with increased baseline and evoked pupil size. Neural populations likewise showed systematic baseline coding of reward rate context. During increased reward rate contexts, ventral striatum and orbitofrontal cortex showed both an increase in value decodability and a rotation in the population geometries for value. This confluence of these results suggests a mechanism of attentional effort that operates by controlling gain through using partially distinct population codes for value. Additionally, increased reward rate accelerated value code dynamics, which have been linked to improved signal-to-noise. These results extend the theory of rational inattention to static and stationary contexts and align theories of rational inattention with specific costly, neural processes.

neuroscience↗

bioRxiv2024

The relationship between confidence and gaze-at-nothing oculomotor dynamics during decision-making

How does confidence relate to oculomotor dynamics during decision-making? Do oculomotor dynamics reflect deliberation and the buildup of confidence in the absence of visual stimuli? Here we examine the hypothesis that working memory, deliberation, and confidence warp oculomotor dynamics, both in the presence and absence of visual stimuli. We analyzed oculomotor dynamics in a decision-making task in which participants were provided with an abstract context in which to make the decision, and two similar option images which became eventually invisible. We show that fixations between the empty locations in which the images were formerly shown continued after the options disappeared, consistently with a sustained deliberative process facilitated by oculomotor dynamics. Both, oculomotor dynamics and decision patterns remained unchanged regardless of whether the stimuli were visible. Furthermore, our analyses show that the number of alternative fixations between stimuli correlated negatively with the confidence reported after each decision, while the observation time of the selected target correlated positively. Given that decisions in our experimental paradigm are reported in the absence of the stimuli, this suggests a relationship between evidence retrieval from working memory, confidence gathering and oculomotor dynamics. Finally, we performed a model comparison based on predictions from drift-diffusion models to assess the relationship between sequential fixations between images, deliberation and confidence gathering, and the ensuing choice. These analyses supported confidence as a contributing cognitive process encompassing serial evidence-gathering and parallel option consideration during decision-making. One-Sentence SummaryThe dynamics of oculomotor dynamics between absent stimuli are related with the participants confidence during value-based decision-making.

neuroscience↗

bioRxiv2024

A Neural Mechanism for Optic Flow Parsing in Macaque Visual Cortex

For the brain to compute object motion in the world during self-motion, it must discount the global patterns of image motion (optic flow) caused by self-motion. Optic flow parsing is a proposed visual mechanism for computing object motion in the world, and studies in both humans and monkeys have demonstrated perceptual biases consistent with the operation of a flow parsing mechanism. However, the neural basis of flow parsing remains unknown. We demonstrate, at both the individual unit and population levels, that neural activity in macaque area MT is biased by peripheral optic flow in a manner that can at least partially account for perceptual biases induced by flow parsing. These effects cannot be explained by conventional surround suppression mechanisms or choice-related activity, and have a substantial neural latency. Together, our findings establish the first neural basis for the computation of scene-relative object motion based on flow parsing.

neuroscience↗

bioRxiv2023

Gaze-centered gating and re-activation of value encoding in orbitofrontal cortex

During economic choice, we often consider options in alternation, until we commit to one. Nonetheless, neuroeconomics typically ignores the dynamic aspects of deliberation. We trained macaques to perform a value-based decision-making task where two risky offers were presented in sequence at different locations of the visual field, each followed by a delay epoch where offers were invisible. Subjects looked at the offers in sequence, as expected. Surprisingly, during the delay epochs, we found that subjects still tend to look at empty locations where the visual offers were formerly presented; and, moreover, longer fixation to given empty location increases the probability of choosing the associated offer, even after controlling for the offer values. We show that activity in orbitofrontal cortex (OFC) reflects the value of the gazed offer, but also the value of the offer associated to the gazed spatial location, even if it is not the most recently viewed. This reactivation reflects a reevaluation process, as fluctuations in neural spiking during offer stimuli presentation and delays correlate with upcoming choice. Our results suggest that look-at-nothing gazing triggers the reactivation of a previously seen offer for further reevaluation, revealing novel aspects of deliberation.

neuroscience↗

bioRxiv2023

A biologically plausible decision-making model based on interacting cortical columns

We present a novel decision-making model with two populations. Each population is composed of Regularly Spiking (excitatory) and Fast Spiking (inhibitory) cells in cortical layer 2/3. Each population votes for one of the two visual alternatives shown on a monitor in human and macaque experiments. The model is biophysically plausible since it is based on long-range cortico-cortical connections between the layer 2/3 populations. These connections are excitatory. They contact both Regularly Spiking and Fast Spiking cells. This long-range excitation is conflicted by an inhibition based on local connections within the populations. This configuration introduces a competition between the layer 2/3 populations, sufficient for making a decision to choose between two alternatives shown on the monitor. We integrate the model with a reward-driven learning mechanism. This allows the model to learn the optimal strategy maximizing the cumulative reward in the long term. We test the model on two decision-making tasks applied on human and macaque. This model elaborates certain biophysical details which were not considered by simpler phenomenological models proposed for similar decision-making tasks. Finally, the model can be embedded in a brain simulator such as The Virtual Brain to study decision-making in terms of large-scale brain dynamics.

neuroscience↗

bioRxiv2023

A Theoretical Formalization of Consequence-Based Decision-Making

Learning to make adaptive decisions depends on exploring options, experiencing their consequence, and reassessing ones strategy for the future. Although several studies have analyzed various aspects of value-based decision-making, most of them have focused on decisions in which gratification is cued and immediate. By contrast, how the brain gauges delayed consequence for decision-making remains poorly understood. To investigate this, we designed a decision-making task in which each decision altered future options. The task was organized in groups of consecutively dependent trials, and the participants were instructed to maximize the cumulative reward value within each group. In the absence of any explicit performance feedback, the participants had to test and internally assess specific criteria to make decisions. This task was designed to specifically study how the assessment of consequence forms and influences decisions as learning progresses. We analyzed behavior results to characterize individual differences in reaction times, decision strategies, and learning rates. We formalized this operation mathematically by means of a multi-layered decision-making model. By using a mean-field approximation, the first layer of the model described the dynamics of two populations of neurons which characterized the binary decision-making process. The other two layers modulated the decision-making policy by dynamically adapting an oversight learning mechanism. The model was validated by fitting each individual participants behavior and it faithfully predicted non-trivial patterns of decision-making, regardless of performance level. These findings provided an explanation to how delayed consequence may be computed and incorporated into the neural dynamics of decision-making, and to how learning occurs in the absence of explicit feedback.

neuroscience↗

bioRxiv2021

Covariance-based information processing in reservoir computing systems

In biological neuronal networks, information representation and processing are achieved through plasticity learning rules that have been empirically characterized as sensitive to second and higher-order statistics in spike trains. However, most models in both computational neuroscience and machine learning aim to convert diverse statistical properties in inputs into first-order statistics in outputs, like in modern deep learning networks. In the context of classification, such schemes have merit for inputs like static images, but they are not well suited to capture the temporal structure in time series. In contrast, the recently developed covariance perceptron uses second-order statistics by mapping input covariances to output covariances in a consistent fashion. Here, we explore the applicability of covariance-based perceptron readouts in reservoir computing networks to classify synthetic multivariate time series structured at different statistical orders (first and second). We show that the second-order framework outperforms or matches the classical mean paradigm in terms of accuracy. Our results highlight a nontrivial relationship between input and reservoir properties in generating the output reservoir activity, which suggests an important role for recurrent connectivity in transforming information representations in biologically inspired architectures. Finally, we solve a speech recognition task for the classification of spoken digits to further demonstrate the potential of covariance-based decoding for real data.

neuroscience↗

bioRxiv2021

The dynamics of decision making and action during active sampling

Embodied Cognition Theories (ECTs) propose that the decision process continues to unfold during the execution of choice actions, and its outcome manifests itself in these actions. Scenarios where actions not only express choice but also help sample information can provide a valuable test of this framework. Remarkably almost no studies so far have addressed this scenario. Here, we present a study testing just this paradigmatic situation with humans. On each trial, subjects categorized a central object image, blurred to different extents (2AFC task) by moving a cursor toward the left or right of the display. Upward cursor movements, orthogonal with respect to choice options, reduced the image blur and could be freely used to actively sample information. Thus, actions for decision and actions for sampling were made orthogonal to each other. We analyzed response trajectories to test a central prediction of ECTs; whether information-sampling movements co-occurred with the ongoing decision process. Trajectory data revealed were bimodally distributed, with one kind being direct towards one response option (non-sampling trials), and the other kind containing an initial upward component before veering off towards an option (sampling trials). This implies that there was an initial decision at the early stage of a trial whether to sample information or not. Importantly, the trajectories in sampling trials were not purely upward, but rather had a significant horizontal deviation that was visible early on in the movement. This result suggests that movements to sample information exhibit an online interaction with the decision process. The finding that decision processes interact with actions to sample information supports the ECT under novel, ecologically relevant constrains.

neuroscience↗

bioRxiv2021

Sense of Agency for Mental Actions: Insights from a Belief-Based Action-Effect Paradigm

A substantial body of research in the past few decades has converged on the idea that the so-called "sense of agency", the feeling of being in control of our own actions, arises from the integration of multiple sources of information at different levels. In this study, we investigated whether a measurable sense of agency can be detected for mental actions, without the contribution of motor components. We used a fake action-effect paradigm, where participants were led to think that a motor action or a particular thought could trigger a sound. Results showed that the high-level sense of agency, measured through explicit reports, was of comparable strength for motor and mental actions. The intentional binding effect, a phenomenon typically associated with the experience of agency, was also observed for both motor and mental actions, with the only exception of short action-effect delays. Furthermore, a consistent relationship between explicit reports of agency and intentional binding was found. Taken together, our results provide novel insights into the specific role of intentional cues in instantiating a sense of agency, even in the absence of motor signals. These results may have important implications for future brain-computer interfaces as well as for the study of pathological disruptions of agency.

neuroscience↗

bioRxiv2020

Naturalistic coding of working memory in primate prefrontal cortex

The primate lateral prefrontal cortex (LPFC) is considered fundamental for temporarily maintaining and manipulating mental representations that serve behavior, a cognitive function known as working memory1. Studies in non-human primates have shown that LPFC lesions impair working memory2 and that LPFC neuronal activity encodes working memory representations3. However, such studies have used simple displays and constrained gaze while subjects held information in working memory3, which put into question their ethological validity4,5. Currently, it remains unclear whether LPFC microcircuits can support working memory function during natural behavior. We tested macaque monkeys in a working memory navigation task in a life-like virtual environment while their gaze was unconstrained. We show that LPFC neuronal populations robustly encode working memory representations in these conditions. Furthermore, low doses of the NMDA receptor antagonist, ketamine, impaired working memory performance while sparing perceptual and motor skills. Ketamine decreased the firing of narrow spiking inhibitory interneurons and increased the firing of broad spiking cells reducing population decoding accuracy for remembered locations. Our results show that primate LPFC generates robust neural codes for working memory in naturalistic settings and that such codes rely upon a fine balance between the activation of excitatory and inhibitory neurons.

neuroscience↗

bioRxiv2020

Prefrontal cortex represents heuristics that shape choice bias and its integration into future behavior.

Goal-directed behavior requires integrating sensory information with prior knowledge about the environment. Behavioral biases that arise from these priors could increase positive outcomes when the priors match the true structure of the environment, but mismatches also happen frequently and could cause unfavorable outcomes. Biases that reduce gains and fail to vanish with training indicate fundamental suboptimalities arising from ingrained heuristics of the brain. Here, we report systematic, gain-reducing choice biases in highly-trained monkeys performing a motion direction discrimination task where only the current stimulus is behaviorally relevant. The monkeys bias fluctuated at two distinct time scales: slow, spanning tens to hundreds of trials, and fast, arising from choices and outcomes of the most recent trials. Our finding enabled single trial prediction of biases, which influenced the choice especially on trials with weak stimuli. The pre-stimulus activity of neuronal ensembles in the monkey prearcuate gyrus represented these biases as an offset along the decision axis in the state space. This offset persisted throughout the stimulus viewing period, when sensory information was integrated, leading to a biased choice. The pre-stimulus representation of history-dependent bias was functionally indistinguishable from the neural representation of upcoming choice before stimulus onset, validating our model of single-trial biases and suggesting that pre-stimulus representation of choice could be fully defined by biases inferred from behavioral history. Our results indicate that the prearcuate gyrus reflects intrinsic heuristics that compute bias signals, as well as the mechanisms that integrate them into the oculomotor decision-making process.

animal behavior and cognition↗

bioRxiv2020

The breadth-depth dilemma in a finite capacity model of decision-making

Decision-makers are often faced with limited information about the outcomes of their choices. Current formalizations of uncertain choice, such as the explore-exploit dilemma, do not apply well to decisions in which search capacity can be allocated to each option in variable amounts. Such choices confront decision-makers with the need to tradeoff between breadth - allocating a small amount of capacity to each of many options - and depth - focusing capacity on a few options. We formalize the breadth-depth dilemma through a finite sample capacity model. We find that, if capacity is smaller than 4-7 samples, it is optimal to draw one sample per alternative, favoring breadth. However, for larger capacities, a sharp transition is observed, and it becomes best to deeply sample a very small fraction of alternatives, that decreases with the square root of capacity. Thus, ignoring most options, even when capacity is large enough to shallowly sample all of them, reflects a signature of optimal behavior. Our results also provide a rich casuistic for metareasoning in multi-alternative decisions with bounded capacity.

neuroscience↗

bioRxiv2020

Scaling of information in large neural populations reveals signatures of information-limiting correlations

How is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into information-limiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations.

neuroscience↗

bioRxiv2019

Identifying the most influential features of neural population responses for information encoding and behavior

Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding the neural code. Statistical features such as tuning properties, individual and shared response variability, and global activity modulations could all affect the amount of information encoded and modulate behavioral performance. We show that two features in particular affect information: the modulation of population responses across conditions and the projection of the inverse population variability along the modulation axis. We demonstrate that fluctuations of these two quantities are correlated with fluctuations of behavioral performance in various tasks and brain regions. In contrast, fluctuations in mean correlations among neurons and global activity have negligible or inconsistent effects on the amount of information encoded and behavioral performance. Our results are consistent with predictions of a model that optimally decodes population responses, which suggests that in our behavioral tasks the readout of information is near-optimal.

neuroscience↗

bioRxiv2019

The covariance perceptron: A new framework for classification and processing of time series in recurrent neural networks

Learning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing. Implementations have been inspired by stereotypical neuronal responses like tuning curves in the visual system, where, for example, ON/OFF cells fire or not depending on the contrast in their receptive fields. Classical models of neuronal networks therefore map a set of input signals to a set of activity levels in the output of the network. Each category of inputs is thereby predominantly characterized by its mean. In the case of time series, fluctuations around this mean constitute noise in this view. For this paradigm, the high variability exhibited by the cortical activity may thus imply limitations or constraints, which have been discussed for many years. For example, the need for averaging neuronal activity over long periods or large groups of cells to assess a robust mean and to diminish the effect of noise correlations. To reconcile robust computations with variable neuronal activity, we here propose a conceptual change of perspective by employing variability of activity as the basis for stimulus-related information to be learned by neurons, rather than merely being the noise that corrupts the mean signal. In this new paradigm both afferent and recurrent weights in a network are tuned to shape the input-output mapping for covariances, the second-order statistics of the fluctuating activity. When including time lags, covariance patterns define a natural metric for time series that capture their propagating nature. We develop the theory for classification of time series based on their spatio-temporal covariances, which reflect dynamical properties. We demonstrate that recurrent connectivity is able to transform information contained in the temporal structure of the signal into spatial covariances. Finally, we use the MNIST database to show how the covariance perceptron can capture specific second-order statistical patterns generated by moving digits. Author summaryThe dynamics in cortex is characterized by highly fluctuating activity: Even under the very same experimental conditions the activity typically does not reproduce on the level of individual spikes. Given this variability, how then does the brain realize its quasi-deterministic function? One obvious solution is to compute averages over many cells, assuming that the mean activity, or rate, is actually the decisive signal. Variability across trials of an experiment is thus considered noise. We here explore the opposite view: Can fluctuations be used to actually represent information? And if yes, is there a benefit over a representation using the mean rate? We find that a fluctuation-based scheme is not only powerful in distinguishing signals into several classes, but also that networks can efficiently be trained in the new paradigm. Moreover, we argue why such a scheme of representation is more consistent with known forms of synaptic plasticity than rate-based network dynamics.

neuroscience↗

bioRxiv2018

Human confidence judgments reflect reliability-based hierarchical integration of contextual information

Because of uncertainty inherent in perception, our immediate observations must be supplemented with contextual information to resolve ambiguities. However, often context too is ambiguous, and thus it should be inferred itself to guide behavior. We developed a novel hierarchical task where participants should infer a higher-level, contextual variable to inform probabilistic inference about a hidden dependent variable at a lower level. By controlling the reliability of the past sensory evidence through sample size, we found that humans estimate the reliability of the context and combine it with current sensory uncertainty to inform their confidence reports. Indeed, behavior closely follows inference by probabilistic message passing between latent variables across hierarchical state representations. Despite the sophistication of our task, commonly reported inferential fallacies, such as sample size insensitivity, are not present, and neither do participants appear to rely on simple heuristics. Our results reveal ubiquitous probabilistic representations of uncertainty at different hierarchical levels and temporal scales of the environment.

neuroscience↗