Basic functional trade-offs in cognition: An integrative framework

The role of uncertain reward in voluntary task-switching as revealed by pupillometry and gaze

Cognitive flexibility, the brain's ability to adjust to changes in the environment, is a critical component of executive functioning. Previous literature shows a robust relationship between reward dynamics and flexibility: flexibility is highest when reward changes, while flexibility decreases when reward remains stable. The purpose of this study was to examine the role of uncertain reward in a voluntary task switching paradigm on behavior, pupillometry, and eye gaze. We used pupil dilation as a neuropsychological correlate of arousal and accumulated fixations on a region (i.e. dwell time) to measure oculomotor attention capture. Results during the cue phase showed that pupil dilation under a deterministic, but not a stochastic reinforcement schedule tracked arousal from the magnitude of reward. In addition, dwell time was increased for the eventual choice and dwell-time was reduced under high reward. Taken together, results show that arousal and attentional capture by reward depends to some extent on reward certainty. Turning to reward outcome, pupil dilation was highest (and average dwell time was lowest) following Error feedback compared to correct rewarded feedback. Overall results show that uncertain reward cues may alter pupil-linked arousal and attention as compared to certain reward, highlighting the role of uncertainty as an important modulator affecting attention and reward processing in environments that demand cognitive flexibility.

Metacontrol Regulates Creative Thinking: An EEG Complexity Analysis Based on Multiscale Entropy

Previous studies have shown that creative thinking is associated with metacontrol, but its neural basis is unknown. The present study explored the neural basis of both by assessing EEG complexity through multiscale entropy. Subjects were engaged in a metacontrol task and an Alternative Uses Task, grouped according to task performance, and the EEG was analysed by multiscale entropy. The results showed that EEG complexity was significantly higher in the high-metacontrol and high-creativity groups than in the low-metacontrol and low-creativity groups, respectively, at high time scales. The metacontrol adaptability score and multipurpose task score were significantly and positively correlated with the EEG complexity at multiple electrode sites. It suggests that metacontrol and creativity are dependent on the activation of long-duration neural networks.

Human learning of hierarchical graphs

Humans are exposed to sequences of events in the environment, and the interevent transition probabilities in these sequences can be modeled as a graph or network. Many real-world networks are organized hierarchically and while much is known about how humans learn basic transition graph topology, whether and to what degree humans can learn hierarchical structures in such graphs remains unknown. We probe the mental estimates of transition probabilities via the surprisal effect phenomenon: humans react more slowly to less expected transitions. Using mean-field predictions and numerical simulations, we show that surprisal effects are stronger for finer-level than coarser-level hierarchical transitions, and that surprisal effects at coarser levels are difficult to detect for limited learning times or in small samples. Using a serial response experiment with human participants (n=100), we replicate our predictions by detecting a surprisal effect at the finer level of the hierarchy but not at the coarser level of the hierarchy. We then evaluate the presence of a trade-off in learning, whereby humans who learned the finer level of the hierarchy better also tended to learn the coarser level worse, and vice versa. This study elucidates the processes by which humans learn sequential events in hierarchical contexts. More broadly, our work charts a road map for future investigation of the neural underpinnings and behavioral manifestations of graph learning.

Bilingual disadvantages are systematically compensated by bilingual advantages across tasks and populations

Bilingualism is linked to both enhanced and hampered performance in various cognitive measures, yet the extent to which these bilingual advantages and disadvantages co-occur is unclear. To address this gap, we perform a systematic review and two quantitative analyses. First, we analyze results from 39 studies, obtained through the PRISMA method. Less than 50% of the studies that show up as results for the term "bilingual disadvantage" report exclusively a disadvantage, that shows bilinguals performing worse than monolinguals in a task. A Bayesian analysis reveals robust evidence for bilingual effects, but no evidence for differences in the proportion of advantages and disadvantages, suggesting that when results from different cognitive domains such as executive functions and verbal fluency are analyzed together, bilingual effects amount to a zero-sum game. This finding was replicated by repeating the analysis, using the datasets of two recent meta-analyses. We propose that the equilibrium we observe between positive and negative outcomes may not be accidental. Contrary to widespread belief, advantageous and disadvantageous effects are not stand-alone outcomes in free variation. We reframe them as the connatural components of a dynamic trade-off, whereby enhanced performance in one cognitive measure is offset by an incurred cost in another domain.

Octopus vulgaris Exhibits Interindividual Differences in Behavioural and Problem-Solving Performance

By presenting individual Octopus vulgaris with an extractive foraging problem with a puzzle box, we examined the possible correlation between behavioural performances (e.g., ease of adaptation to captive conditions, prevalence of neophobic and neophilic behaviours, and propensity to learn individually or by observing conspecifics), biotic (body and brain size, age, sex) and abiotic (seasonality and place of origin) factors. We found more neophilic animals showing shorter latencies to approach the puzzle box and higher probability of solving the task; also, shorter times to solve the task were correlated with better performance on the individual learning task. However, the most neophilic octopuses that approached the puzzle box more quickly did not reach the solution earlier than other individuals, suggesting that strong neophilic tendency may lead to suboptimal performance at some stages of the problem-solving process. In addition, seasonal and environmental characteristics of location of origin appear to influence the rate of expression of individual traits central to problem solving. Overall, our analysis provides new insights into the traits associated with problem solving in invertebrates and highlights the presence of adaptive mechanisms that promote population-level changes in octopuses' behavioural traits.

Redefining stroke rehabilitation: Mobilizing the embodied goal-oriented brain

Advancements in stroke rehabilitation remain limited and call for a reorientation. Based on recent results, this study proposes a network-centric perspective on stroke, positing that it not only causes localized deficits but also affects the brain's intricate network of networks, transiting it into a pathological state. Translating these system-level insights into interventions requires brain theory, and the Distributed Adaptive Control (DAC) theory offers such a framework. When applied in the rehabilitation gaming system, these principles demonstrate superior results over conventional methods. This impact stems from activating extensive brain networks, particularly the executive control network, focused motor learning, and maintaining excitatory-inhibitory balance, which is essential for neural repair and functional reorganization. The analysis stresses uniting preclinical and clinical research and placing the architecture of the embodied volitional brain at the centre of rehabilitation approaches.

A Classification Bias and an Exclusion Bias Jointly Overinflated the Estimation of Publication Biases in Bilingualism Research

A publication bias has been argued to affect the fate of results in bilingualism research. It was repeatedly suggested that studies presenting evidence for bilingual advantages are more likely to be published compared to studies that do not report results in favor of the bilingual advantage hypothesis. This work goes back to the original claim and re-examines both the dataset and the classification of the studies that were employed. We find that the exclusion of published works such as doctoral dissertations, book chapters, and conference proceedings from the original dataset significantly inflated the presumed publication bias. Moreover, the estimation of the publication bias was affected by a classification bias that uses a mega-category that consists of both null and negative outcomes. Yet finding evidence for a bilingual disadvantage is not synonymous with obtaining a result indistinguishable from zero. Consequently, grouping together null and negative findings in a mega-category has various ramifications, not only for the estimation of the presumed publication bias but also for the field's ability to appreciate the insofar hidden correlations between bilingual advantages and disadvantages. Tracking biases that inflate scientific results is important, but it is not enough. The next step is recognizing the nested Matryoshka doll effect of bias-within-bias, and this entails raising awareness for one's own bias blind spots in science.

Artificial selection for reversal learning reveals limited repeatability and no heritability of cognitive flexibility in great tits (Parus major)

Cognitive flexibility controls how animals respond to changing environmental conditions. Individuals within species vary considerably in cognitive flexibility but the micro-evolutionary potential in animal populations remains enigmatic. One prerequisite for cognitive flexibility to be able to evolve is consistent and heritable among-individual variation. Here we determine the repeatability and heritability of cognitive flexibility among great tits (Parus major) by performing an artificial selection experiment on reversal learning performance using a spatial learning paradigm over three generations. We found low, yet significant, repeatability (R = 0.15) of reversal learning performance. Our artificial selection experiment showed no evidence for narrow-sense heritability of associative or reversal learning, while we confirmed the heritability of exploratory behaviour. We observed a phenotypic, but no genetic, correlation between associative and reversal learning, showing the importance of prior information on reversal learning. We found no correlation between cognitive and personality traits. Our findings emphasize that cognitive flexibility is a multi-faceted trait that is affected by memory and prior experience, making it challenging to retrieve reliable values of temporal consistency and assess the contribution of additive genetic variation. Future studies need to identify what cognitive components underlie variation in reversal learning and study their between-individual and additive genetic components.

Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair

Due to its complex and multifaceted nature, developing effective treatments for epilepsy is still a major challenge. To deal with this complexity we introduce the concept of degeneracy to the field of epilepsy research: the ability of disparate elements to cause an analogous function or malfunction. Here, we review examples of epilepsy-related degeneracy at multiple levels of brain organisation, ranging from the cellular to the network and systems level. Based on these insights, we outline new multiscale and population modelling approaches to disentangle the complex web of interactions underlying epilepsy and to design personalised multitarget therapies.

Early adversity and the development of explore-exploit tradeoffs

Childhood adversity can have wide-ranging and long-lasting effects on later life. But what are the mechanisms that are responsible for these effects? This article brings together the cognitive science literature on explore-exploit tradeoffs, the empirical literature on early adversity, and the literature in evolutionary biology on 'life history' to explain how early experience influences later life. We propose one potential mechanism: early experiences influence 'hyperparameters' that determine the balance between exploration and exploitation. Adversity might accelerate a shift from exploration to exploitation, with broad and enduring effects on the adult brain and mind. These effects may be produced by life-history adaptations that use early experience to tailor development and learning to the likely future states of an organism and its environment.

Motherhood and DREADD manipulation of the nucleus accumbens weaken established pair bonds in female prairie voles

Monogamous pair bonding has evolved to enhance reproductive success and ensure offspring survival. Although the behavioral and neural mechanisms regulating the formation of pair bonds have been relatively well outlined, how these relationships are regulated and maintained across the lifetime of an individual remains relatively unexplored. One way to explore this is to study the maintenance of a social bond across a major life-history transition. The transition to motherhood is among the most poignant moments in the life history of a female, and is associated with significant neural and behavioral changes and shifting priorities. The nucleus accumbens (NAc) is known to modulate social valence and is central to mammalian pair bonding. In this study, we investigated two mechanisms driving variation in bond strength in the socially monogamous prairie vole (Microtus ochrogaster). We manipulated neural activity of the NAc at two distinct stages of life-history, before and after the birth of offspring, to assess how neural activity and social contexts modulate female pair bond strength. Our results showed DREADD (Designer Receptor Exclusively Activated by Designer Drugs) inhibition of the NAc decreases affiliative behavior towards the mating partner, whereas DREADD activation of the NAc increases affiliative behavior of strangers, thereby decreasing social selectivity. We also found a robust "birth effect" on pair bond strength, such that bonds with partners were weakened after the birth of offspring, an effect not attributable to the amount of cohabitation time with a partner. Overall, our data support the hypotheses that NAc activity modulates reward/saliency within the social brain in different ways, and that motherhood comes with a cost for the bond strength between mating partners.

Imbalanced weighting of proactive and reactive control as a marker of risk-taking propensity

According to the dual mechanisms of control (DMC), reactive and proactive control are involved in adjusting behaviors when maladapted to the environment. However, both contextual and inter-individual factors increase the weight of one control mechanism over the other, by influencing their cognitive costs. According to one of the DMC postulates, limited reactive control capacities should be counterbalanced by greater proactive control to ensure control efficiency. Moreover, as the flexible weighting between reactive and proactive control is key for adaptive behaviors, we expected that maladaptive behaviors, such as risk-taking, would be characterized by an absence of such counterbalance. However, to our knowledge, no studies have yet investigated this postulate. In the current study, we analyzed the performances of 176 participants on two reaction time tasks (Simon and Stop Signal tasks) and a risk-taking assessment (Balloon Analog Risk Taking, BART). The post-error slowing in the Simon task was used to reflect the spontaneous individuals' tendency to proactively adjust behaviors after an error. The Stop Signal Reaction Time was used to assess reactive inhibition capacities and the duration of the button press in the BART was used as an index of risk-taking propensity. Results showed that poorer reactive inhibition capacities predicted greater proactive adjustments after an error. Furthermore, the higher the risk-taking propensity, the less reactive inhibition capacities predicted proactive behavioral adjustments. The reported results suggest that higher risk-taking is associated with a smaller weighting of proactive control in response to limited reactive inhibition capacities. These findings highlight the importance of considering the imbalanced weighting of reactive and proactive control in the analysis of risk-taking, and in a broader sense, maladaptive behaviors.

Pareto optimality, economy-effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits.

A synergistic core for human brain evolution and cognition

How does the organization of neural information processing enable humans' sophisticated cognition? Here we decompose functional interactions between brain regions into synergistic and redundant components, revealing their distinct information-processing roles. Combining functional and structural neuroimaging with meta-analytic results, we demonstrate that redundant interactions are predominantly associated with structurally coupled, modular sensorimotor processing. Synergistic interactions instead support integrative processes and complex cognition across higher-order brain networks. The human brain leverages synergistic information to a greater extent than nonhuman primates, with high-synergy association cortices exhibiting the highest degree of evolutionary cortical expansion. Synaptic density mapping from positron emission tomography and convergent molecular and metabolic evidence demonstrate that synergistic interactions are supported by receptor diversity and human-accelerated genes underpinning synaptic function. This information-resolved approach provides analytic tools to disentangle information integration from coupling, enabling richer, more accurate interpretations of functional connectivity, and illuminating how the human neurocognitive architecture navigates the trade-off between robustness and integration.

Optimal Allocation of Finite Sampling Capacity in Accumulator Models of Multialternative Decision Making

When facing many options, we narrow down our focus to very few of them. Although behaviors like this can be a sign of heuristics, they can actually be optimal under limited cognitive resources. Here, we study the problem of how to optimally allocate limited sampling time to multiple options, modeled as accumulators of noisy evidence, to determine the most profitable one. We show that the effective sampling capacity of an agent increases with both available time and the discriminability of the options, and optimal policies undergo a sharp transition as a function of it. For small capacity, it is best to allocate time evenly to exactly five options and to ignore all the others, regardless of the prior distribution of rewards. For large capacities, the optimal number of sampled accumulators grows sublinearly, closely following a power law as a function of capacity for a wide variety of priors. We find that allocating equal times to the sampled accumulators is better than using uneven time allocations. Our work highlights that multialternative decisions are endowed with breadth–depth tradeoffs, demonstrates how their optimal solutions depend on the amount of limited resources and the variability of the environment, and shows that narrowing down to a handful of options is always optimal for small capacities.

Sexual selection and the ascent of women: Mate choice research since Darwin

Darwin's theory of sexual selection fundamentally changed how we think about sex and evolution. The struggle over mating and fertilization is a powerful driver of diversification within and among species. Contemporaries dismissed Darwin's conjecture of a "taste for the beautiful" as favoring particular mates over others, but there is now overwhelming evidence for a primary role of both male and female mate choice in sexual selection. Darwin's misogyny precluded much analysis of the "taste"; an increasing focus on mate choice mechanisms before, during, and after mating reveals that these often evolve in response to selection pressures that have little to do with sexual selection on chosen traits. Where traits and preferences do coevolve, they can do so whether fitness effects on choosers are positive, neutral, or negative. The spectrum of selection on traits and preferences, and how traits and preferences respond to social effects, determine how sexual selection and mate choice influence broader-scale processes like reproductive isolation and population responses to environmental change.

General intelligence disentangled via a generality metric for natural and artificial intelligence

Success in all sorts of situations is the most classical interpretation of general intelligence. Under limited resources, however, the capability of an agent must necessarily be limited too, and generality needs to be understood as comprehensive performance up to a level of difficulty. The degree of generality then refers to the way an agent's capability is distributed as a function of task difficulty. This dissects the notion of general intelligence into two non-populational measures, generality and capability, which we apply to individuals and groups of humans, other animals and AI systems, on several cognitive and perceptual tests. Our results indicate that generality and capability can decouple at the individual level: very specialised agents can show high capability and vice versa. The metrics also decouple at the population level, and we rarely see diminishing returns in generality for those groups of high capability. We relate the individual measure of generality to traditional notions of general intelligence and cognitive efficiency in humans, collectives, non-human animals and machines. The choice of the difficulty function now plays a prominent role in this new conception of generality, which brings a quantitative tool for shedding light on long-standing questions about the evolution of general intelligence and the evaluation of progress in Artificial General Intelligence.

Neural optimization: Understanding trade-offs with Pareto theory

Nervous systems, like any organismal structure, have been shaped by evolutionary processes to increase fitness. The resulting neural 'bauplan' has to account for multiple objectives simultaneously, including computational function, as well as additional factors such as robustness to environmental changes and energetic limitations. Oftentimes these objectives compete, and quantification of the relative impact of individual optimization targets is non-trivial. Pareto optimality offers a theoretical framework to decipher objectives and trade-offs between them. We, therefore, highlight Pareto theory as a useful tool for the analysis of neurobiological systems from biophysically detailed cells to large-scale network structures and behavior. The Pareto approach can help to assess optimality, identify relevant objectives and their respective impact, and formulate testable hypotheses.

Bilinguals are better than monolinguals in detecting manipulative discourse

One of the most contentious topics in cognitive science concerns the impact of bilingualism on cognitive functions and neural resources. Research on executive functions has shown that bilinguals often perform better than monolinguals in tasks that require monitoring and inhibiting automatic responses. The robustness of this effect is a matter of an ongoing debate, with both sides approaching bilingual cognition mainly through measuring abilities that fall outside the core domain of language processing. However, the mental juggling that bilinguals perform daily involves language. This study takes a novel path to bilingual cognition by comparing the performance of monolinguals and bilinguals in a timed task that features a special category of stimulus, which has the peculiar ability to manipulate the cognitive parser into treating it as well-formed while it is not: grammatical illusions. The results reveal that bilinguals outperform monolinguals in detecting illusions, but they are also slower across the board in judging the stimuli, illusory or not. We capture this trade-off by proposing the Plurilingual Adaptive Trade-off Hypothesis (PATH), according to which the adaptation of bilinguals' cognitive abilities may (i) decrease fallibility to illusions by means of recruiting sharpened top-down control processes, but (ii) this is part of a larger bundle of effects, not all of which are necessarily advantageous.

Rationalizing constraints on the capacity for cognitive control

Humans are remarkably limited in: (i) how many control-dependent tasks they can execute simultaneously, and (ii) how intensely they can focus on a single task. These limitations are universal assumptions of most theories of cognition. Yet, a rationale for why humans are subject to these constraints remains elusive. This feature review draws on recent insights from psychology, neuroscience, and machine learning, to suggest that constraints on cognitive control may result from a rational adaptation to fundamental, computational dilemmas in neural architectures. The reviewed literature implies that limitations in multitasking may result from a trade-off between learning efficacy and processing efficiency and that limitations in the intensity of commitment to a single task may reflect a trade-off between cognitive stability and flexibility.

Meta-control: From psychology to computational neuroscience

Research in the past decades shed light on the different mechanisms that underlie our capacity for cognitive control. However, the meta-level processes that regulate cognitive control itself remain poorly understood. Following the terminology from artificial intelligence, meta-control can be defined as a collection of mechanisms that (a) monitor the progress of controlled processing and (b) regulate the underlying control parameters in the service of current task goals and in response to internal or external constraints. From a psychological perspective, meta-control is an important concept because it may help explain and predict how and when human agents select different types of behavioral strategies. From a cognitive neuroscience viewpoint, meta-control is a useful concept for understanding the complex networks in the prefrontal cortex that guide higher-level behavior as well as their interactions with neuromodulatory systems (such as the dopamine or norepinephrine system). The purpose of the special issue is to integrate hitherto segregated strands of research across three different perspectives: 1) a psychological perspective that specifies meta-control processes on a functional level and aims to operationalize them in experimental tasks; 2) a computational perspective that builds on ideas from artificial intelligence to formalize normative solutions to meta-control problems; and 3) a cognitive neuroscience perspective that identifies neural correlates of and mechanisms underlying meta-control.

Understanding fish cognition: a review and appraisal of current practices

With over 30,000 recognized species, fishes exhibit an extraordinary variety of morphological, behavioural, and life-history traits. The field of fish cognition has grown markedly with numerous studies on fish spatial navigation, numeracy, learning, decision-making, and even theory of mind. However, most cognitive research on fishes takes place in a highly controlled laboratory environment and it can therefore be difficult to determine whether findings generalize to the ecology of wild fishes. Here, we summarize four prominent research areas in fish cognition, highlighting some of the recent advances and key findings. Next, we survey the literature, targeting these four areas, and quantify the nearly ubiquitous use of captive-bred individuals and a heavy reliance on lab-based research. We then discuss common practices that occur prior to experimentation and within experiments that could hinder our ability to make more general conclusions about fish cognition, and suggest possible solutions. By complementing ecologically relevant laboratory-based studies with in situ cognitive tests, we will gain further inroads toward unraveling how fishes learn and make decisions about food, mates, and territories.

Both Stationary and Dynamic Functional Interhemispheric Connectivity Are Strongly Associated With Performance on Cognitive Tests in Multiple Sclerosis

Although functional connectivity has been extensively studied in MS, robust estimates of both stationary (static connectivity at the time) and dynamic (connectivity variation across time) functional connectivity has not been commonly evaluated and neither has its association to cognition. In this study, we focused on interhemispheric connections as previous research has shown links between anatomical homologous connections and cognition. We examined functional interhemispheric connectivity (IC) in MS during resting-state functional MRI using both stationary and dynamic strategies and related connectivity measures to processing speed performance. Twenty-five patients with relapsing-remitting MS and 41 controls were recruited. Stationary functional IC was assessed between homologous Regions of Interest (ROIs) using correlation. For dynamic IC, a sliding window approach was used to quantify changes between homologous ROIs across time. We related IC measures to cognitive performance with correlation and regression. Compared to control subjects, MS demonstrated increased IC across homologous regions, which accurately predicted performance on the symbol digit modalities test (SDMT) (R ² = 0.96) and paced auditory serial addition test (PASAT) (R ² = 0.59). Dynamic measures were not different between the 2 groups, but dynamic IC was related to PASAT scores. The associations between stationary/dynamic connectivity and cognitive tests demonstrated that different aspects of functional IC were associated with cognitive processes. Processing speed measured in SDMT was associated with static interhemispheric connections and better PASAT performance, which requires working memory, sustain attention, and processing speed, was more related to rigid IC, underlining the neurophysiological mechanism of cognition in MS.