Biology subjects

Polania, R.

Publications and source records attributed to Polania, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Alpha oscillations in the temporoparietal junction causally shift feedback-based social learning computations in strategic negotiation

Human interactions span a range of contexts, from cooperation to competition. Negotiation, in particular, is a complex and extended social process in which individuals must reach mutually acceptable decisions despite conflicting incentives. The neural computations that support strategic behavior in such social dilemmas remain insufficiently understood. Here, we combine cognitive computational modeling, electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), and fMRI-guided Transcranial Magnetic Stimulation (TMS) to demonstrate that oscillatory activity anchored in the temporoparietal junction (TPJ) causally shifts social learning during strategic bargaining. We found that TPJ metabolic activity and alpha-band oscillations are associated with the use of a feedback-based learning strategy during bargaining. Causal perturbation with rhythmic alpha-frequency TMS selectively modulates this strategy, increasing endogenous alpha oscillations and shifting behavioral learning parameters. Together, these findings reveal a frequency-specific mechanism within the neural substrates of social cognition that implements adaptive social learning, offering insights into potential neuromodulatory targets for ameliorating social dysfunction in neuropsychiatric conditions. Significance StatementStrategic negotiation requires predicting how others will respond to our actions, yet the neural computations supporting this form of social learning have remained elusive. By integrating computational modeling with EEG, fMRI, and frequency-specific TMS, we identify a mechanistic link between alpha-band activity in the temporoparietal junction (TPJ) and feedback-based learning during social exchange. Trial-by-trial estimates of this learning strategy were tracked by TPJ metabolic and oscillatory signals, and rhythmic alpha TMS causally enhanced both the neural signature and the behavioral expression of this strategy. These findings provide causal evidence for a frequency-specific mechanism within the neural systems that supports adaptive social learning. They also highlight the TPJ-alpha system as a promising target for neuromodulatory interventions to improve social functioning in neuropsychiatric conditions. Key FindingsO_LIModel-based behavioral analyses revealed two distinct strategies during social negotiation: a feedback-based learning mechanism (U-strategy) and a reputation-based updating mechanism (A-strategy). C_LIO_LIBoth strategies robustly predicted participants adaptive behavior across samples and conditions, and their modulation accounted for differences in negotiation outcomes. C_LIO_LIEEG analyses revealed frequency-specific alpha and beta power modulation linked to U-strategy computations during partner anticipation, localized to right temporoparietal regions. C_LIO_LIfMRI analyses revealed that trial-by-trial U-strategy estimates selectively modulated BOLD activity within the temporoparietal network associated with mentalizing. C_LIO_LIRhythmic alpha-frequency TMS over individually localized Theory-of-Mind TPJ sites causally altered negotiation behavior, shifting U-learning parameters toward a more conservative strategy. C_LIO_LITMS-EEG analyses demonstrated that alpha-frequency TMS induced time-locked alpha activity in functionally connected frontal sites, consistent with enhanced anticipatory computations. C_LIO_LITogether, these multimodal findings establish a causal, frequency-specific mechanism in the TPJ that implements social value learning during strategic bargaining. C_LI

neuroscience↗

bioRxiv2026

A Neural Population Code for Value in Human Orbitofrontal Cortex.

Adaptive behavior depends on the ability to rapidly evaluate options and select those that promise the greatest benefit. Such decisions rely on neural representations of value distributed across multiple brain regions, including the orbitofrontal (OFC) and ventromedial prefrontal cortex (vmPFC), yet the neural code underlying these value representations remains unresolved. The dominant account proposes that OFC/vmPFC neurons encode value through a linear rate code, resulting in a single point estimate at the population level. However, this framework is difficult to reconcile with the heterogeneous tuning observed in individual OFC/vmPFC neurons, which can exhibit both positive and negative correlations with subjective value. Here, we test the alternative hypothesis--derived from theories of neural coding in perceptual systems-- that the OFC/vmPFC implements a probabilistic population code based on non-linear tuning functions. Such tuning allows population activity to represent not only subjective value but also the uncertainty surrounding it, in the form of a flexible posterior probability distribution. Using a population receptive field framework, we fitted non-linear value-tuning functions to functional magnetic resonance imaging data acquired during a value judgment task. Bayesian inversion of this encoding model enabled robust out-of-sample decoding of subjective value across several brain regions, including the OFC/vmPFC. Importantly, value uncertainty estimated from decoded medial OFC/vmPFC posteriors predicted within-subject preference instability, choice stochasticity, and confidence in option values, demonstrating its behavioral relevance and suggesting that participants had conscious access to this information. Complementary single-unit recordings from a subset of monkey OFC neurons similarly revealed nonlinear value-selective tuning. Together, these findings establish a probabilistic, non-linear population code for value in the OFC/vmPFC. This provides a neural foundation for the probabilistic code through which value, and uncertainty about value, can guide choice.

neuroscience↗

bioRxiv2025

Neurometabolic signaling and control of policy complexity

Cognition and adaptive behavior emerge from neural information processing. This must operate within finite metabolic constraints, since neural information processing is metabolically expensive. While neural implementations of action selection and learning are well-studied, systems allocating the informational capacity required to encode complex behavioral policies remain unknown. We hypothesized that hypothalamic hypocretin/orexin neurons (HONs) are uniquely positioned to signal and control policy complexity, given that they are activated by metabolic depletion and influence decision-making systems. To explore this, we employed a set of cell/neurotransmitter-specific imaging and causal manipulations during a multi-armed bandit task where freely behaving mice learned probabilistic state-action-reward relationships (together [~]100,000 decisions from >100 mice). Miniscope recordings of HON activity revealed that pre-choice, but not post-choice activity correlates with decision policy, dissociating decisions from feedback. Furthermore, manipulating HON signals with optogenetics and pharmacology confirmed that they causally regulate the development of complex policies. Finally, neurotransmitter-specific sensors revealed that hypocretin/orexin receptors modulate decision policy-related dopamine and noradrenaline dynamics in the nucleus accumbens and medial prefrontal cortex. These findings identify HONs as subcortical regulators of policy complexity, encoding critical signals for decision-making adjustments. This opens a new window for the development of comprehensive mechanistic models of strategic learning which account for interplay between decision-making policies and metabolic/informational constraints, and may guide the development of potential treatments for disorders with policy deficits such as autism and schizophrenia.

neuroscience↗

bioRxiv2024

Lateral Prefrontal Theta Oscillations Causally Drive a Computational Mechanism Underlying Conflict Expectation and Adaptation

Adapting our behavior to environmental demands relies on our capacity to perceive and manage potential conflicts within our surroundings. While evidence implicates the involvement of the lateral prefrontal cortex and theta oscillations in detecting conflict stimuli, their roles in conflict expectation remain elusive. Consequently, the exact computations and neural mechanisms underlying these cognitive processes still need to be determined. To address this gap, we employed an integrative approach involving cognitive computational modeling, fMRI, TMS, and EEG. Our results revealed a computational process underlying conflict expectation, which correlated with activity in the superior frontal gyrus (SFG). Furthermore, rhythmic TMS in the theta range applied over the SFG, but not over the inferior frontal junction, induced endogenous theta activity, enhancing computations associated with conflict expectation. These findings provide compelling evidence for the causal involvement of SFG theta activity in learning and allocating cognitive resources to address forthcoming conflict stimuli. Significant StatementAlterations in the processing of expectations of conflict events have been associated with several neuropsychiatric disorders that significantly affect the quality of life for many individuals. This article describes a cognitive computation underlying the conflict expectation and its causal neural mechanism involving theta brain activity in the superior frontal gyrus (SFG). Thus, unraveling this mechanism holds promise for developing interventions to address cognitive alterations related to anticipation of conflict events in neuropsychiatric disorders, improving overall cognitive function and quality of life.

neuroscience↗

bioRxiv2024

Functional-based parcellation of the mouse prefrontal cortex for network perturbation analysis

The prefrontal cortex (PFC) is a region of the brain involved in higher-order cognitive processes such as attention, emotional regulation, and social behavior, making it a hotspot for an ongoing clinical and fundamental research. Importantly, its functionality is intricately interconnected within a wide array of functional networks encompassing multiple other brain areas. However, the delineation of distinct subdivisions within the mouse PFC and their contributions to the broader brain network function remain topics of ongoing debate. In the current study, we used resting-state functional magnetic resonance imaging (rsfMRI) from a large cohort of wildtype animals to derive the functional-connectivity (FC) based parcellation of the mouse PFC with voxel resolution. Our findings indicate the presence of FC-based clusters that deviate from the established anatomical subdivisions within the cingulate and prelimbic areas, while they align in infralimbic and orbital cortices. To further underscore the association of these FC-based clusters with distinct functional networks, we performed network-specific perturbations using chemogenetics in the identified clusters in dorsal PFC and monitor the elicited effects with fMRI (chemo-fMRI). Our recordings revealed that FC perturbations were observed only within the functional networks linked to the targeted clusters and did not spread to neighbouring anatomical areas or functional clusters. We propose that FC-based parcellation is a valuable approach for tracking the impact of external activations and confirming the precise site of activation. O_FIG O_LINKSMALLFIG WIDTH=127 HEIGHT=200 SRC="FIGDIR/small/586664v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@11d4042org.highwire.dtl.DTLVardef@165cb5forg.highwire.dtl.DTLVardef@b0a4aorg.highwire.dtl.DTLVardef@d093c9_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Multipair phase-modulated temporal interference electrical stimulation combined with fMRI.

Temporal Interference stimulation (TIS) is a promising non-invasive brain stimulation technique exploiting frequency-shifted kHz fields to modulate oscillatory neural activity without invasive procedures. While human studies suggest TIS efficacy in targeting relatively deep brain structures, recent computational modeling and animal studies indicate potential off-target stimulations via the application of standard TIS protocols. Here, we computationally optimized TIS targeting for the prefrontal cortex (PFC) in mice. Combining in vivo electrophysiological recordings, intracellular calcium dynamics with fiber photometry, and functional MRI, we confirmed TIS-induced amplitude modulation, neuronal entrainment and hemodynamic responses in the PFC, while also identifying off-target modulations. To mitigate off-target effects, we propose a novel configuration with three electrode pairs, one of which is actively phase-shifted by 180 degrees. This cancelling field significantly enhanced TIS focality, reducing off-target effects without compromising the TIS efficacy in the target area. This work effectively addresses one of TIS most critical shortcomings and opens new avenues for research and clinical applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/571679v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@b04067org.highwire.dtl.DTLVardef@154549aorg.highwire.dtl.DTLVardef@17e63c3org.highwire.dtl.DTLVardef@c7a341_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

A neuropsychological basis for temptation-resistant voluntary exercise

Despite well-known health benefits of physical activity, many people under-exercise, and what drives prioritization of exercise over other alternatives is unclear. We implement a novel paradigm allowing to study how freely behaving mice rapidly display such prioritizing between time spent on wheel-running and other temptations such as palatable food. Causal manipulations and correlative analyses of underlying appetitive and consummatory psychobehavioral processes revealed this prioritizing to be instantiated by hypothalamic hypocretin/orexin neurons.

neuroscience↗

bioRxiv2023

Efficient numerosity estimation under limited time

The ability to rapidly estimate non-symbolic numerical quantities is a well-conserved sense across species with clear evolutionary advantages. Despite its importance, the rapid representation and estimation of numerosity is surprisingly imprecise and biased. However, a formal explanation for this seemingly irrational behavior remains unclear. We develop a unified normative theory of numerosity estimation that parsimoniously incorporates in a single framework information processing constraints alongside Brownian diffusion noise to capture the effects of time exposure of sensory estimations, logarithmic encoding of numerosity representations, and optimal inference via Bayesian decoding. We show that for a given allowable biological capacity constraint our model naturally endogenizes time perception during noisy efficient encoding to predict the complete posterior distribution of numerosity estimates. This model accurately predicts many features of human numerosity estimation as a function of temporal exposure, indicating that humans can rapidly and efficiently sample numerosity information over time. Additionally, we demonstrate how our model fundamentally differs from a thermodynamically-inspired formalization of bounded rationality, where information processing is modeled as acting to shift away from default states. The mechanism we propose is the likely origin of a variety of numerical cognition patterns observed in humans and other animals. Author summaryHumans have the ability to estimate the number of elements in a set without counting. We share this ability with other species, suggesting that it is evolutionarily relevant. However, despite its relevance, this sense is variable and biased. What is the origin of these imprecisions? We take the view that they are the result of an optimal use of limited neural resources. Because of these limitations, stimuli are encoded with noise. The observer then optimally decodes these noisy representations, taking into account its knowledge of the distribution of stimuli. We build on this view and incorporate stimulus presentation time (or contrast) directly into the encoding process using Brownian motion. This model can parsimoniously predict key characteristics of our perception and outperforms quantitatively and qualitatively a popular modeling approach that considers resource limitations at the stage of the response rather than the encoding.

animal behavior and cognition↗

bioRxiv2022

Individual risk attitudes arise from noise in neurocognitive magnitude representations

Humans are generally risk averse: they prefer options with smaller certain outcomes over those with larger uncertain ones. This risk aversion is classically explained with a concave utility function, meaning that successive increases in monetary payoffs should increase subjective valuations by progressively smaller amounts. Here, we provide neural and behavioural evidence that risk aversion may also arise from a purely perceptual bias: The noisy logarithmic coding of numerical magnitudes can lead individuals to underestimate the size of larger monetary payoffs, leading to apparent risk aversion even when subjective valuation increases linearly with the estimated amount. A formal model of this process predicts that risk aversion should systematically increase when individuals represent numerical magnitudes more noisily. We confirmed this prediction by measuring both the mental and neural acuity of magnitude representations during a purely perceptual task and relating these measures to individual risk attitudes during separate financial decisions. Computational model fitting suggested that subjects based both types of choices on similar mental magnitude representations, with correlated precision across the separate perceptual and risky choices. Increased stimulus noise due to the presentation format of risky outcomes led to increased risk aversion, just as predicted by the model. The precision of the underlying neural magnitude representations was estimated with a numerical population receptive field model fitted to the fMRI data of the perceptual task. Subjects with more precise magnitude representations in parietal cortex indeed showed less variable behaviour and less risk-aversion in the separate financial choices. Our results highlight that individual patterns of economic behaviour may, at least partially, be determined by capacity limitations in perceptual processing rather than by processes that assign subjective values to monetary rewards.

neuroscience↗

bioRxiv2021

Causal evidence for a domain-specific role of left superior frontal sulcus in human perceptual decision-making

Humans and animals can flexibly choose their actions based on different information, ranging from objective states of the environment (e.g., apples are bigger than cherries) to subjective preferences (e.g., cherries are tastier than apples). Whether the brain instantiates these different choices by recruiting either specialised or shared neural circuitry remains debated. Specifically, domain-general accounts of prefrontal cortex (PFC) function propose that prefrontal areas flexibly process either perceptual or value-based evidence depending on what is required for the present choice, whereas domain-specific theories posit that PFC sub-areas, such as the left superior frontal sulcus (SFS), selectively integrate evidence relevant for perceptual decisions. Here we comprehensively test the functional role of the left SFS for choices based on perceptual and value-based evidence, by combining fMRI with a behavioural paradigm, computational modelling, and transcranial magnetic stimulation. Confirming predictions by a sequential sampling model, we show that TMS-induced excitability reduction of the left SFS selectively changes the processing of decision-relevant perceptual information and associated neural processes. In contrast, value-based decision making and associated neural processes remain unaffected. This specificity of SFS function is evident at all levels of analysis (behavioural, computational, and neural, including functional connectivity), demonstrating that the left SFS causally contributes to evidence integration for perceptual but not value-based decisions.

neuroscience↗

bioRxiv2021

Rational inattention in mice

Behavior exhibited by humans and other organisms is generally inconsistent and biased, and thus is often labeled irrational. However, the origins of this seemingly suboptimal behavior remain elusive. We developed a behavioral task and normative framework to reveal how organisms should allocate their limited processing resources such that there is an advantage to being imprecise and biased for a given metabolic investment that guarantees maximal utility. We found that mice act as rational-inattentive agents by adaptively allocating their sensory resources in a way that maximizes reward consumption in novel stimulus-reward association environments. Surprisingly, perception to commonly occurring stimuli was relatively imprecise, however this apparent statistical fallacy implies "awareness" and efficient adaptation to their neurocognitive limitations. Interestingly, distributional reinforcement learning mechanisms efficiently regulate sensory precision via top-down normalization. These findings establish a neurobehavioral foundation for how organisms efficiently perceive and adapt to environmental states of the world within the constraints imposed by neurobiology.

neuroscience↗

bioRxiv2021

Neural codes in early sensory areas maximize fitness

It has generally been presumed that sensory information encoded by a nervous system should be as accurate as its biological limitations allow. However, perhaps counter intuitively, accurate representations of sensory signals do not necessarily maximize the organisms chances of survival. To test this hypothesis, we developed a unified normative framework for fitness-maximizing encoding by combining theoretical insights from neuroscience, computer science, and economics. Initially, we applied predictions of this model to neural responses from large monopolar cells (LMCs) in the blowfly retina. We found that neural codes that maximize reward expectation--and not accurate sensory representations--account for retinal LMC activity. We also conducted experiments in humans and find that early sensory areas flexibly adopt neural codes that promote fitness maximization in a retinotopically-specific manner, which impacted decision behavior. Thus, our results provide evidence that fitness-maximizing rules imposed by the environment are applied at the earliest stages of sensory processing.

neuroscience↗

bioRxiv2021

A causal role for the right frontal eye fields in value comparison

Recent studies have suggested close functional links between visual attention and decision making. This suggests that the corresponding mechanisms may interface in brain regions known to be crucial for guiding visual attention - such as the frontal eye field (FEF). Here, we combined brain stimulation, eye tracking and computational approaches to explore this possibility. We show that inhibitory transcranial magnetic stimulation (TMS) over the right FEF has a causal impact on decision-making, reducing the effect of gaze dwell time on choice while also increasing reaction times. We computationally characterize this putative mechanism by using the attentional drift diffusion model (aDDM), which reveals that FEF inhibition reduces the relative discounting of the non-fixated option in the comparison process. Our findings establish an important causal role of the right FEF in choice, elucidate the underlying mechanism, and provide support for one of the key causal hypotheses associated with the aDDM.

bioinformatics↗

bioRxiv2019

Integration of action value during action observation

Transcranial magnetic stimulation (TMS) studies demonstrated that observing the actions of other individuals leads to action-specific facilitation of primary motor cortex (M1) (i.e., "motor resonance"). Motor resonance is modulated by contextual information accompanying others actions, however, it is currently unknown whether action value influences behavioural and physiological outcomes during action observation in humans. Here we tested whether response times (RT) and muscle-specific changes of M1 excitability are modulated by the value an observer assigns to the action executed by another agent and whether this effect can be distinguished from attentional engagement. We show that observing highly-valued actions leads to a significant decrease in RT variability and a significant strengthening of action-specific neural representations in M1. This "sharpening" of behavioural and neural responses was observed over and beyond a control task requiring similar attentional engagement but did not include any rewards. Our finding that reward influences action specific representations in human M1 even if no motor response is required is new, suggesting that reward influences the transformation of action stimuli from the perceptual to the motor domain. We suggest that premotor areas are important for mediating the observed effect, most likely by optimizing grasp-specific PMv-M1 interactions which cause muscular facilitation patterns in M1 to be more distinct for rewarded actions.

neuroscience↗

bioRxiv2019

Neuro-Computational Foundations of Moral Preferences

Moral preferences pervade many aspects of our lives, dictating how we ought to behave, whom we can marry, and even what we eat. Despite their relevance, one fundamental question remains unanswered: Where do individual moral preferences come from? It is often thought that all types of preferences reflect properties of domain-general neural decision mechanisms that employ a common \"neural currency\" to value choice options in many different contexts. This assumption, however, appears at odds with the observation that many humans consider it intuitively wrong to employ the same scale to compare moral value (e.g., of a human life) with material value (e.g., of money). In this paper, we directly challenge the common-currency hypothesis by comparing the neural mechanisms that represent moral and financial subjective values. In a study combining fMRI with a novel behavioral paradigm, we identify neural representations of the subjective values of human lives or financial payoffs by means of structurally identical computational models. Correlating isomorphic model variables from both domains with brain activity reveals specific patterns of neural activity that selectively represent values in the moral (in the rTPJ) or financial (in the vmPFC) domain. Thus, our findings show that human lives and money are valued in distinct neural currencies, supporting theoretical proposals that human moral behavior is guided by processes that are distinct from those underlying behavior driven by personal material benefit.

neuroscience↗

bioRxiv2019

Efficient sampling and noisy decisions

The precision of human decisions is limited by both processing noise and basing decisions on finite information. But what determines the degree of such imprecision? Here we develop an efficient coding framework for higher-level cognitive processes, in which information is represented by a finite number of discrete samples. We characterize the sampling process that maximizes perceptual accuracy or fitness under the often-adopted assumption that full adaptation to an environmental distribution is possible, and show how the optimal process differs when detailed information about the current contextual distribution is costly. We tested this theory on a numerosity discrimination task, and found that humans efficiently adapt to contextual distributions, but in the way predicted by the model in which people must economize on environmental information. Thus, understanding decision behavior requires that we account for biological restrictions on information coding, challenging the often-adopted assumption of precise prior knowledge in higher-level decision systems.

neuroscience↗

bioRxiv2018

Dissociable mechanisms govern when and how strongly reward attributes affect decisions

Theories and computational models of decision making usually focus on how strongly different attributes are weighted in choice, e.g., as a function of their importance or salience to the decision-maker. However, when different attributes impact on the decision process is a question that has received far less attention. Here, we investigated whether attribute consideration timing has a unique influence on decision making using a time-varying drift diffusion model and data from four separate experiments. Experimental manipulations of attention and neural activity demonstrated that we can dissociate the processes that determine the relative weighting strength and timing of attribute consideration. Thus, the processes determining either the weighting strengths or the timing of attributes in decision making can adapt independently to changes in the environment or goals. Quantifying these separate influences of timing and weighting on choice improves our understanding and predictions of individual differences in decision behaviour.

neuroscience↗

bioRxiv2018

Efficient coding of subjective value

Preference-based decisions are essential for survival, for instance when deciding what we should (not) eat. Despite their importance, choices based on preferences are surprisingly variable and can appear irrational in ways that have defied mechanistic explanations. Here we propose that subjective valuation results from an inference process that accounts for the information structure of values in the environment and that maximizes information in value representations in line with demands imposed by limited coding resources. A model of this inference process explains the variability in both subjective value reports and preference-based choices, and predicts a new preference illusion that we validate with empirical data. Interestingly, the same model also explains the level of confidence associated with these reports. Our results imply that preference-based decisions reflect information-maximizing transmission and statistically optimal decoding of subjective values by a limited-capacity system. These findings provide a unified account of how humans perceive and valuate the environment to optimally guide behavior.

neuroscience↗