Biology subjects

Tsetsos, K.

Publications and source records attributed to Tsetsos, K..

10 recordsLinked to original sources

bioRxiv2025

Inter-areal coupling for cognition through coincident oscillatory transients

How do large-scale functional brain networks dynamically emerge to enable cognition? Correlated oscillations, a mechanism for inter-areal interactions, can be expressed as phase coherence or correlated amplitude fluctuations. While the functional role of phase coherence has been addressed in the past, it remains unknown how large-scale amplitude coupling dynamically supports the rapid network reconfigurations necessary for adaptive behavior. Here, we demonstrate that cognitive processing relies on the temporal alignment of brief, high-amplitude oscillatory events across distant cortical regions, rather than sustained coupling of oscillations. Capitalizing on the spatio-temporal resolution of magnetoencephalography (MEG) during a task probing attention and decision-making, we show that oscillatory event coincidences form functionally relevant, transient networks: First, coincidences of high-amplitude oscillatory events in the alpha and beta frequency range increase prior to correct decisions within a parieto-frontal network. Second, transient theta/alpha oscillatory event coincidences within a medial parietal, temporo-parietal junction and lateral prefrontal network track the dynamics of spontaneous, covert spatial attention reallocation. In contrast, transient aperiodic and sustained network components exhibit only weak functional modulations, revealing a spatial and spectral dissociation between transient and sustained amplitude coupling. Overall, our findings demonstrate that efficient cognition relies on large-scale interactions rapidly formed through co-occurring transient oscillatory events. We propose that the brain-wide orchestration of high-amplitude oscillatory events can provide a new framework for understanding how the brain dynamically integrates information to guide flexible behavior.

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bioRxiv2025

Competing Neural Decision Variables in Human Frontal Cortex Shape Decision Confidence

Theories of the neural basis of decision-making postulate a competition between choice-selective populations of neurons that are distributed across many brain areas. We hypothesized that agents sense of confidence in a choice is shaped by the consensus among these distributed decision signals. To test this idea, we simultaneously tracked the dynamics of choice-selective signals within each of many cortical areas via magnetoencephalography, while human participants formed difficult decisions. Signals within premotor cortex that supported the choice option selected on a given trial, or the alternative, exhibited a signature of intra-cortical competition. Both competing neural signals made opposing and unique contributions to confidence. Critically, the number and strength of choice-selective signals in associative cortical areas supporting the selected option on each trial made an additional contribution to confidence. We conclude that confidence emerges from cortex-wide, competitive dynamics, through a mechanism that monitors the consensus across many local decision modules.

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bioRxiv2024

The timescale and functional form of context-dependence during human value-learning

AbstractThe way humans and other animals represent the values of alternatives can be distorted by inferior alternatives that are immediately available for choice (immediate context); or that were encountered in previous episodes (temporal context). Yet, the extent to which the immediate and temporal context (co-) shape such context-dependent value coding remains unclear. Here, we asked human participants (N = 30) to learn the values associated with three alternatives and to explicitly report their learned values before making binary and ternary choices among the alternatives. We show that context-dependent value coding is evident in the pre-choice value estimates and manifests equally in binary and ternary choices. Accordingly, we conclude that value representations are modulated by the temporal (and not the immediate) context. Interestingly, the functional form of context-dependence we report runs against extant value normalization theories and is best captured by a new memory-based valuation mechanism we propose.

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bioRxiv2023

Rhythmic sampling of multiple decision alternatives in the human brain

Humans and other animals navigate decisions by sequentially attending to (sampling) subsets of the available information. The internal dynamics of the selective sampling of decision-relevant information remain unknown. Here we use magnetoencephalography recordings and neural decoding to track the spontaneous dynamics of the locus and strength of covert attention as human participants performed a three-alternative perceptual choice task. The strength of covert attention fluctuated rhythmically around 11 Hz. A shift of attention from one alternative to another tends to occur at the trough of this oscillation, presumably enabling comparisons. These shifts further reset the attentional oscillation. By contrast, at the peak of the oscillation, attention tends to increase the focus on the currently sampled alternative, presumably deepening processing of that alternative. We propose intrinsic attentional oscillations as a core mechanism governing the flexible sampling of decision alternatives.

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bioRxiv2023

Decision bias and sampling asymmetry in reward-guided learning

Human economic decisions are highly sensitive to contexts. Deciding between two competing alternatives can be notoriously biased by their overall value ( magnitude effect) or by a third decoy option ( distractor effect). Some prominent explanations appeal to diminishing value sensitivity and divisive normalization in value representations, i.e., representational bias, that feed into the choice stage. However, these explanations have recently come under scrutiny due to empirical inconsistencies and mounting alternative theories. Here, we posit that context-sensitive choices may not stem from representational biases but rather emerge as by-products of asymmetric sampling during value learning. In a reward-guided choice task, participants aimed to maximize cumulative rewards through trial and error. The task introduced alternating blocks with either a colored distractor or a neutral notional distractor. We observed decreased choice accuracy when higher-value distractors were present, a pattern that persisted even in the notional distractor blocks. Using computational modeling, we show that this phenomenon falls out naturally from a simple learning rule without relying on any additional mechanism such as divisive normalization or nonlinear utility. Furthermore, we found that, contrary to divisive normalization, choice accuracy was not influenced by distractor value but strongly depended on the magnitude of the targets values per se. This magnitude sensitivity was also found in the notional distractor conditions and could lawfully be reproduced by the learning model. Importantly, when counterfactual feedback eliminated sampling asymmetry, the observed decision bias vanished. Our results suggest that the genesis of context-sensitive choices may lie in the learning dynamics themselves, specifically sampling asymmetry, rather than in pre-decisional representational biases. This finding reframes the discourse on irrational decision-making, attributing it to acquired biases during the learning process, not necessarily computational intricacies at the choice stage.

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bioRxiv2022

Clarifying the role of an unavailable distractor alternative in human multiattribute choice

Decisions between two economic goods can be swayed by a third unavailable decoy alternative, which does not compete for choice, notoriously violating the principles of rational choice theory. Although decoy effects typically depend on the decoys position in a multiattribute choice space, recent studies using risky prospects (i.e., varying in reward and probability) reported a novel positive decoy effect operating on a single value dimension: the higher the expected value of an unavailable (distractor) prospect was, the easier the discrimination between two available target prospects became, especially when their expected-value difference was small. Here we show that this unidimensional distractor effect affords alternative interpretations: it occurred because the distractors expected value covaried positively with the subjective utility difference between the two targets. Looking beyond this covariation, we report a modest negative distractor effect operating on subjective utility, as well as classic multiattribute decoy effects. A normatively meaningful model (selective integration), in which subjective utilities are shaped by intra-attribute information distortion, reproduces the multiattribute decoy effects, and as an epiphenomenon, the negative unidimensional distractor effect. These findings clarify the modulatory role of an unavailable distracting option, shedding fresh light on the mechanisms that govern multiattribute decisions.

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bioRxiv2020

Dissociation between catecholamines and acetylcholine in the human cerebral cortex

Influential accounts postulate distinct roles of the catecholamine and acetylcholine neuromodulatory systems in cognition and behavior. But previous work found similar effects of these modulators on the response properties of individual cortical neurons. Here, we report a double dissociation between catecholamine and acetylcholine effects at the level of cortex-wide network interactions in humans. A pharmacological boost of catecholamine levels increased cortex-wide interactions during a visual task, but not rest. Conversely, an acetylcholine-boost decreased correlations during rest, but not task. Cortical circuit modeling explained this dissociation by differential changes in two circuit properties: the local excitation-inhibition balance (more strongly altered by catecholamines) and intracortical transmission (more strongly reduced by acetylcholine). The inferred catecholaminergic mechanism also predicted increased behavioral exploration, which we confirmed in human behavior during both a perceptual and value-based choice task. In sum, we identified specific circuit mechanisms for shaping cortex-wide network interactions and behavior by key neuromodulatory systems.

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bioRxiv2020

Choices Change the Temporal Weighting of Decision Evidence

Decisions do not occur in isolation, but are embedded in sequences of other decisions, often pertaining to the same source of evidence. Here, we characterized the impact of intermittent choices on the accumulation of a protracted stream of decision-relevant evidence towards a final decision. Human participants performed two versions, based on perceptual or numerical evidence, of a decision task that required two successive judgments at different times during the evidence stream: an intermittent response consisting of a binary choice, and a continuous estimation at the end of the evidence stream. In a control condition, subjects executed a choice-independent motor response instead of binary choice as the intermittent response. In both, perceptual and numerical tasks, the intermittent choice reduced the sensitivity of subsequent evidence, and flipped the relative temporal weighting of early and late evidence in the final estimation judgment. The individual extent of the choice-induced overall (non-selective) sensitivity reduction predicted the extent of the selective down-weighting of subsequent evidence inconsistent with the initial choice, a form of confirmation bias. In sum, active decisions about a protracted evidence stream profoundly alter the dynamics of evidence accumulation, consistent with an active, modulatory mechanism triggered by the choice.

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bioRxiv2019

Selective integration during sequential sampling in posterior neural signals

Decisions are typically made after integrating information about multiple attributes of alternatives in a choice set. The computational mechanisms by which this integration occurs have been a focus of extensive research in humans and other animals. Where observers are obliged to consider attributes in turn, a framework known as \"selective integration\" can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used scalp electroencephalographic (EEG) recordings to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over posterior cortex. Over two sessions, human observers (of either sex) judged which of two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fit the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioural work.\n\nSignificance StatementWe often make choices about stimuli with multiple attributes, such as when deciding which car to buy on the basis of price, performance and fuel economy. A model of the choice process, known as selective integration, proposes that rather than taking all of the decision-relevant information equally into account when making choices, we discard or overlook a portion of it. Although information is discarded, this strategy can lead to better decisions when memory is limited. Here, we test and confirm predictions of the model about the brain signals that occur when different stimulus attributes of stimulus are being evaluated. Our work provides the first neural support for the selective integration model.

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bioRxiv2018

Phasic arousal optimizes decision computations in mice and humans

Decisions are often made by accumulating ambiguous evidence over time. The brains arousal systems are activated during such decisions. In previous work in humans, we showed that evoked responses of arousal centers during decisions are reported by rapid dilations of the pupil, and predict a suppression of biases in the accumulation of decision-relevant evidence (de Gee et al. 2017). Here, we show that this arousal-related suppression in decision bias acts on both conservative and liberal biases, and generalizes across species (humans / mice), sensory systems (visual / auditory), and domains of decision-making (perceptual / memory-based). In challenging sound-detection tasks, the impact of spontaneous or experimentally induced choice biases was reduced under high arousal. Similar bias suppression occurred when evidence was drawn from memory. All these behavioral effects were explained by reduced evidence accumulation biases. Our results pinpoint a general principle of the interplay between phasic arousal and decision-making.

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