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Methods for data analysis in the biomedical, life, and social (BLS) sciences are developing at a rapid pace. At the same time, there is increasing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of analytic methods. Our survey of peer-reviewed literature analyzed approximately 1.3 million openly available research articles to monitor the cross-disciplinary mentions of analytic methods in the past decade. We applied data-driven text mining analyses to the “Methods” and “Results” sections of a large subset of this corpus to identify trends in analytic method mentions shared across disciplines, as well as those unique to each discipline. We found that the t test, analysis of variance (ANOVA), linear regression, chi-squared test, and other classical statistical methods have been and remain the most mentioned analytic methods in biomedical, life science, and social science research articles. However, mentions of these methods have declined as a percentage of the published literature between 2009 and 2020. On the other hand, multivariate statistical and machine learning approaches, such as artificial neural networks (ANNs), have seen a significant increase in the total share of scientific publications. We also found unique groupings of analytic methods associated with each BLS science discipline, such as the use of structural equation modeling (SEM) in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration., A quantitative survey of >1 million published research articles reveals that while classical statistical methods remain in widespread use, multivariate statistical and machine-learning approaches have seen a significant increase; statistics curricula should be revised to take full advantage of these new analytical tools.

International audience; Does rhythmic neural activity merely echo the rhythmic features of the environment, or does it reflect a fundamental computational mechanism of the brain? This debate has generated a series of clever experimental studies attempting to find an answer. Here, we argue that the field has been obstructed by predictions of oscillators that are based more on intuition rather than biophysical models compatible with the observed phenomena. What follows is a series of cautionary examples that serve as reminders to ground our hypotheses in well-developed theories of oscillatory behavior put forth by theoretical study of dynamical systems. Ultimately, our hope is that this exercise will push the field to concern itself less with the vague question of "oscillation or not" and more with specific biophysical models that can be readily tested.

The motor system demonstrates an exquisite ability to adapt to changes in the environment and to quickly reset when these changes prove transient. If similar environmental changes are encountered in the future, learning may be faster, a phenomenon known as savings. In studies of sensorimotor learning, a central component of savings is attributed to the explicit recall of the task structure and appropriate compensatory strategies. Whether implicit adaptation also contributes to savings remains subject to debate. We tackled this question by measuring, in parallel, explicit and implicit adaptive responses in a visuomotor rotation task, employing a protocol that typically elicits savings. While the initial rate of learning was faster in the second exposure to the perturbation, an analysis decomposing the 2 processes showed the benefit to be solely associated with explicit re-aiming. Surprisingly, we found a significant decrease after relearning in aftereffect magnitudes during no-feedback trials, a direct measure of implicit adaptation. In a second experiment, we isolated implicit adaptation using clamped visual feedback, a method known to eliminate the contribution of explicit learning processes. Consistent with the results of the first experiment, participants exhibited a marked reduction in the adaptation function, as well as an attenuated aftereffect when relearning from the clamped feedback. Motivated by these results, we reanalyzed data from prior studies and observed a consistent, yet unappreciated pattern of attenuation of implicit adaptation during relearning. These results indicate that explicit and implicit sensorimotor processes exhibit opposite effects upon relearning: Explicit learning shows savings, while implicit adaptation becomes attenuated, Humans learning a new motor task typically improve with repeated practice due to the faster expression of more effective explicit strategies; this study reveals that when motor learning occurs without awareness, performance deteriorates upon relearning.

While there is no doubt that social signals affect human reinforcement learning, there is still no consensus about how this process is computationally implemented. To address this issue, we compared three psychologically plausible hypotheses about the algorithmic implementation of imitation in reinforcement learning. The first hypothesis, decision biasing (DB), postulates that imitation consists in transiently biasing the learner’s action selection without affecting their value function. According to the second hypothesis, model-based imitation (MB), the learner infers the demonstrator’s value function through inverse reinforcement learning and uses it to bias action selection. Finally, according to the third hypothesis, value shaping (VS), the demonstrator’s actions directly affect the learner’s value function. We tested these three hypotheses in 2 experiments (N = 24 and N = 44) featuring a new variant of a social reinforcement learning task. We show through model comparison and model simulation that VS provides the best explanation of learner’s behavior. Results replicated in a third independent experiment featuring a larger cohort and a different design (N = 302). In our experiments, we also manipulated the quality of the demonstrators’ choices and found that learners were able to adapt their imitation rate, so that only skilled demonstrators were imitated. We proposed and tested an efficient meta-learning process to account for this effect, where imitation is regulated by the agreement between the learner and the demonstrator. In sum, our findings provide new insights and perspectives on the computational mechanisms underlying adaptive imitation in human reinforcement learning., This study investigates imitation from a computational perspective; three experiments show that, in the context of reinforcement learning, imitation operates via a durable modification of the learner's values, shedding new light on how imitation is computationally implemented and shapes learning and decision-making.

Predicting individual mental traits and behavioral dispositions from brain imaging data through machine-learning approaches is becoming a rapidly evolving field in neuroscience. Beyond scientific and clinical applications, such approaches also hold the potential to gain substantial influence in fields such as human resource management, education, or criminal law. Although several challenges render real-life applications of such tools difficult, future conflicts of individual, economic, and public interests are preprogrammed, given the prospect of improved personalized predictions across many domains. In this Perspective paper, we thus argue for the need to engage in a discussion on the ethical, legal, and societal implications of the emergent possibilities for brain-based predictions and outline some of the aspects for this discourse., Advances in machine learning on neuroimaging data have opened up the possibility of objectively predicting individual traits like intelligence, personality, or clinical risks from brain scans. This Perspective article discusses the methodological and ethical challenges arising from these advances.

Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree of impairment in neurological disorders such as stroke, Parkinson disease, or Huntington disease. The analysis of such movement abnormalities is notoriously difficult and requires a trained evaluator. Here, we show that a deep neural network is able to score behavioral impairments with expert accuracy in rodent models of stroke. The same network was also trained to successfully score movements in a variety of other behavioral tasks. The neural network also uncovered novel movement alterations related to stroke, which had higher predictive power of stroke volume than the movement components defined by human experts. Moreover, when the regression network was trained only on categorical information (control = 0; stroke = 1), it generated predictions with intermediate values between 0 and 1 that matched the human expert scores of stroke severity. The network thus offers a new data-driven approach to automatically derive ratings of motor impairments. Altogether, this network can provide a reliable neurological assessment and can assist the design of behavioral indices to diagnose and monitor neurological disorders., This study demonstrates that a state-of-the-art neural network can provide automated scoring of motor deficits with an accuracy equivalent to that of human experts and has the potential teach us to develop more-sensitive behavioral tests.

Genetic data indicate differences in speciation rate across latitudes, but underlying causes have been difficult to assess because a critical phase of the speciation process is initiated in allopatry, in which, by definition, individuals from different taxa do not interact. We conducted song playback experiments between 109 related pairs of mostly allopatric bird species or subspecies in Amazonia and North America to compare the rate of evolution of male discrimination of songs. Relative to local controls, the number of flyovers and approach to the speaker were higher in Amazonia. We estimate that responses to songs of relatives are being lost about 6 times more slowly in Amazonia than in North America. The slow loss of response holds even after accounting for differences in song frequency and song length. Amazonian species with year-round territories are losing aggressive responses especially slowly. We suggest the presence of many species and extensive interspecific territoriality favors recognition of songs sung by sympatric heterospecifics, which results in a broader window of recognition and hence an ongoing response to novel similar songs. These aggressive responses should slow the establishment of sympatry between recently diverged forms. If male responses to novel allopatric taxa reflect female responses, then premating reproductive isolation is also evolving more slowly in Amazonia. The findings are consistent with previously demonstrated slower recent rates of expansion of sister taxa into sympatry, slower rates of evolution of traits important for premating isolation, and slower rates of speciation in general in Amazonia than in temperate North America., The use of more than 100 song playback experiments reveals that birds give stronger responses to songs of closely related species in the Amazon than in temperate North America, suggesting that speciation is currently happening more slowly in the species-rich tropics than in species-poor temperate regions.

The encoding of sensory information in the human brain is thought to be optimised by two principal processes: ‘prediction’ uses stored information to guide the interpretation of forthcoming sensory events, and ‘attention’ prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Because prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models propose that attention increases the activity of prediction (or ‘representation’) neurons and would therefore suggest that attention and prediction synergistically modulate selectivity for ‘feature information’ in the brain. Here, we applied forward encoding models to neural activity recorded via electroencephalography (EEG) as human observers performed a simple visual task to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, as well as support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors., Author summary The human brain is theorised to operate like a sophisticated hypothesis tester, using past experience to generate a model of the external world, testing predictions of this model against incoming sensory evidence, and generating a ‘prediction error’ signal that updates the model when predictions and sensory evidence do not match. In addition to predicting the content of sensory signals, an optimal system should also predict the reliability (or ‘precision’) of those signals to minimise the influence of unreliable sensory information. It has been proposed that attention optimises this process by boosting prediction error signals, which are coded as the difference (or ‘mismatch’) between predicted and observed stimulus features. Accordingly, this theory predicts that attention should increase the selectivity for mismatch information in the neural response to surprising stimuli. We tested this hypothesis in human participants by training a decoding algorithm to identify ‘mismatch information’ in the brain, recorded by electroencephalography (EEG), following the presentation of surprising stimuli that were either attended or ignored. We found that attention did indeed increase the selectivity for mismatch information in the neural response, supporting the notion that attention and prediction are intricately related processes.

Many decisions rely on how we evaluate potential outcomes and estimate their corresponding probabilities of occurrence. Outcome evaluation is subjective because it requires consulting internal preferences and is sensitive to context. In contrast, probability estimation requires extracting statistics from the environment and therefore imposes unique challenges to the decision maker. Here, we show that probability estimation, like outcome evaluation, is subject to context effects that bias probability estimates away from other events present in the same context. However, unlike valuation, these context effects appeared to be scaled by estimated uncertainty, which is largest at intermediate probabilities. Blood-oxygen-level-dependent (BOLD) imaging showed that patterns of multivoxel activity in the dorsal anterior cingulate cortex (dACC), ventromedial prefrontal cortex (VMPFC), and intraparietal sulcus (IPS) predicted individual differences in context effects on probability estimates. These results establish VMPFC as the neurocomputational substrate shared between valuation and probability estimation and highlight the additional involvement of dACC and IPS that can be uniquely attributed to probability estimation. Because probability estimation is a required component of computational accounts from sensory inference to higher cognition, the context effects found here may affect a wide array of cognitive computations., This study shows how probability estimation can be affected by the context of our recent experience, namely, how the presence of multiple events experienced closed in time can influence their respective probability estimates.

What is the best way to teach evolution? As microevolution may be configured as a branch of genetics, it being a short conceptual leap from understanding the concepts of mutation and alleles (i.e., genetics) to allele frequency change (i.e., evolution), we hypothesised that learning genetics prior to evolution might improve student understanding of evolution. In the UK, genetics and evolution are typically taught to 14- to 16-y-old secondary school students as separate topics with few links, in no particular order and sometimes with a large time span between. Here, then, we report the results of a large trial into teaching order of evolution and genetics. We modified extant questionnaires to ascertain students’ understanding of evolution and genetics along with acceptance of evolution. Students were assessed prior to teaching, immediately post teaching and again after several months. Teachers were not instructed what to teach, just to teach in a given order. Regardless of order, teaching increased understanding and acceptance, with robust signs of longer-term retention. Importantly, teaching genetics before teaching evolution has a significant (p < 0.001) impact on improving evolution understanding by 7% in questionnaire scores beyond the increase seen for those taught in the inverse order. For lower ability students, an improvement in evolution understanding was seen only if genetics was taught first. Teaching genetics first additionally had positive effects on genetics understanding, by increasing knowledge. These results suggest a simple, minimally disruptive, zero-cost intervention to improve evolution understanding: teach genetics first. This same alteration does not, however, result in a significantly increased acceptance of evolution, which reflects a weak correlation between knowledge and acceptance of evolution. Qualitative focus group data highlights the role of authority figures in determination of acceptance., Author summary What is the best way to teach evolution? We hypothesised that if students know the fundamental concepts of genetics, then this might help them understand evolution better. To evaluate this, we performed a large trial in which pupils in United Kingdom secondary schools were either taught genetics and then evolution or evolution and then genetics. We found that the students being taught genetics first had a 5%–10% improvement in their understanding of evolution, above that shown in the group taught evolution first. The change was seen in both higher- and foundation-ability classes. Indeed, in the foundation classes the genetics-first approach was the only approach that enabled an increase in evolution understanding. Teaching genetics first comes at no cost to genetics understanding (and may even improve it). However, the genetics-first approach was no different from the evolution-first approach in helping the acceptance of evolution. Qualitative follow-up studies indicated a major role for authority figures in determining acceptance, potentially explaining the weak correlation between understanding and acceptance. These results suggest a simple, minimally disruptive, zero-cost intervention to improve evolution understanding: teach genetics first.

Psychological and personality factors, socioeconomic status, and brain properties all contribute to chronic pain but have essentially been studied independently. Here, we administered a broad battery of questionnaires to patients with chronic back pain (CBP) and collected repeated sessions of resting-state functional magnetic resonance imaging (fMRI) brain scans. Clustering and network analyses applied on the questionnaire data revealed four orthogonal dimensions accounting for 56% of the variance and defining chronic pain traits. Two of these traits—Pain-trait and Emote-trait—were associated with back pain characteristics and could be related to distinct distributed functional networks in a cross-validation procedure, identifying neurotraits. These neurotraits showed good reliability across four fMRI sessions acquired over five weeks. Further, traits and neurotraits all related to the income, emphasizing the importance of socioeconomic status within the personality space of chronic pain. Our approach is a first step in providing metrics aimed at unifying the psychology and the neurophysiology of chronic pain applicable across diverse clinical conditions., A neuroimaging study shows that psychological contributors of chronic pain are represented in patterns of resting state functional connectivity; clustering analysis of these patterns suggests the existence of subtypes of chronic pain patients with specific clinical characteristics.

Persistent neural activity that encodes online mental representations plays a central role in working memory (WM). However, there has been debate regarding the number of items that can be concurrently represented in this active neural state, which is often called the “focus of attention.” Some models propose a strict single-item limit, such that just 1 item can be neurally active at once while other items are relegated to an activity-silent state. Although past studies have decoded multiple items stored in WM, these studies cannot rule out a switching account in which only a single item is actively represented at a time. Here, we directly tested whether multiple representations can be held concurrently in an active state. We tracked spatial representations in WM using alpha-band (8–12 Hz) activity, which encodes spatial positions held in WM. Human observers remembered 1 or 2 positions over a short delay while we recorded electroencephalography (EEG) data. Using a spatial encoding model, we reconstructed active stimulus-specific representations (channel-tuning functions [CTFs]) from the scalp distribution of alpha-band power. Consistent with past work, we found that the selectivity of spatial CTFs was lower when 2 items were stored than when 1 item was stored. Critically, data-driven simulations revealed that the selectivity of spatial representations in the two-item condition could not be explained by models that propose that only a single item can exist in an active state at once. Thus, our findings demonstrate that multiple items can be concurrently represented in an active neural state., An electroencephalography-based study shows that working memory delay activity concurrently represents two items, posing a challenge for models which propose that only one item can be actively represented at a time.

Although numerous studies have convincingly demonstrated that sleep plays a critical role in motor sequence learning (MSL) consolidation, the specific contribution of the different sleep stages in this type of memory consolidation is still contentious. To probe the role of stage 2 non-REM sleep (NREM2) in this process, we used a conditioning protocol in three different groups of participants who either received an odor during initial training on a motor sequence learning task and were re-exposed to this odor during different sleep stages of the post-training night (i.e., NREM2 sleep [Cond-NREM2], REM sleep [Cond-REM], or were not conditioned during learning but exposed to the odor during NREM2 [NoCond]). Results show that the Cond-NREM2 group had significantly higher gains in performance at retest than both the Cond-REM and NoCond groups. Also, only the Cond-NREM2 group yielded significant changes in sleep spindle characteristics during cueing. Finally, we found that a change in frequency of sleep spindles during cued-memory reactivation mediated the relationship between the experimental groups and gains in performance the next day. These findings strongly suggest that cued-memory reactivation during NREM2 sleep triggers an increase in sleep spindle activity that is then related to the consolidation of motor sequence memories., Targeted reactivation of a motor sequence memory through re-exposure with a conditioned olfactory stimulus during the night reveals the specific role of stage 2 non-REM sleep and spindles in the consolidation process., Author Summary There is ample evidence that sleep contributes to the consolidation (from a labile to a robust state) of a memory trace formed through learning of a new motor sequence, such as piano-playing. Sleep, however, is a complex neurophysiological state comprising several phases, and the role that each phase plays in this memory process remains controversial. To address this issue, we used a conditioning paradigm with human volunteers in which an odor was first associated (or not) with the practice of a motor sequence learning task. The odor was then presented again in one of two different sleep phases—rapid-eye movement (REM) or stage 2 non-REM (NREM2)—during the second half of the night, as a means of facilitating reactivation of the memory trace. As predicted, we found that participants cued with the odor during NREM2 showed greater gains in performance (reflecting better consolidation) the following morning compared with those who were cued during REM sleep or who had not previously been exposed to the odor during training. We also observed that reactivation during NREM2 sleep prompted significant changes in a type of brain activity known as sleep spindles. We conclude that NREM2 sleep—and spindles in particular—contribute critically to the consolidation of motor sequence memories.

In human studies, how averaged activation in a brain region relates to human behavior has been extensively investigated. This approach has led to the finding that positive and negative facial preferences are represented by different brain regions. However, using a functional magnetic resonance imaging (fMRI) decoded neurofeedback (DecNef) method, we found that different patterns of neural activations within the cingulate cortex (CC) play roles in representing opposite directions of facial preference. In the present study, while neutrally preferred faces were presented, multi-voxel activation patterns in the CC that corresponded to higher (or lower) preference were repeatedly induced by fMRI DecNef. As a result, previously neutrally preferred faces became more (or less) preferred. We conclude that a different activation pattern in the CC, rather than averaged activation in a different area, represents and suffices to determine positive or negative facial preference. This new approach may reveal the importance of an activation pattern within a brain region in many cognitive functions., A newly developed fMRI method, decoded neurofeedback (DecNef), reveals that specific activation patterns in the cingulate cortex are largely responsible for determining human facial preferences., Author Summary Although it is well studied how averaged activation of a brain region relates to behavior, it is still unclear if specific patterns of activation within regions also relate to cognitive function. In recent years, several methods have been developed for manipulating brain activity in humans. Real-time functional magnetic resonance imaging decoded neurofeedback (fMRI DecNef) is a method that allows the induction of specific patterns of brain activity by measuring the current pattern, comparing this to the pattern to be induced, and giving the subjects feedback on how close the two patterns of neuronal activity are. Using fMRI DecNef, we manipulated the pattern of activation in the cingulate cortex—a part of the cerebral cortex that plays a role in preference to different categories including faces and daily items—and tested whether we could change these preferences. In the experiment, a specific activation pattern in the cingulate cortex corresponding to higher (or lower) preference was induced by fMRI DecNef while subjects were seeing a neutrally preferred face. As a result, these neutrally preferred faces became more (or less) preferred. Our finding suggests that different patterns of activation in the cingulate cortex represent, and are sufficient to determine, different emotional states. Our new approach using fMRI DecNef may reveal the importance of activation patterns within a brain region, rather than activation in a whole region, in many cognitive functions.

Vocal learning, the substrate of human language acquisition, has rarely been described in other mammals. Often, group-specific vocal dialects in wild populations provide the main evidence for vocal learning. While social learning is often the most plausible explanation for these intergroup differences, it is usually impossible to exclude other driving factors, such as genetic or ecological backgrounds. Here, we show the formation of dialects through social vocal learning in fruit bats under controlled conditions. We raised 3 groups of pups in conditions mimicking their natural roosts. Namely, pups could hear their mothers' vocalizations but were also exposed to a manipulation playback. The vocalizations in the 3 playbacks mainly differed in their fundamental frequency. From the age of approximately 6 months and onwards, the pups demonstrated distinct dialects, where each group was biased towards its playback. We demonstrate the emergence of dialects through social learning in a mammalian model in a tightly controlled environment. Unlike in the extensively studied case of songbirds where specific tutors are imitated, we demonstrate that bats do not only learn their vocalizations directly from their mothers, but that they are actually influenced by the sounds of the entire crowd. This process, which we term “crowd vocal learning,” might be relevant to many other social animals such as cetaceans and pinnipeds., Author summary The spontaneous acquisition of speech by human infants is considered a keystone of human language, but the ability to reproduce vocalizations acquired by hearing is not commonly described in other mammals. This skill, termed vocal learning, is challenging to study in nonhuman animals since such investigation requires the detection and exclusion of innate developmental effects. The recognition of vocal dialects among different populations can open a window on the vocal learning abilities of animals, but such findings in the wild may reflect genetic or ecological differences between groups rather than the learning of group-specific vocal behavior. In this study, we used a playback-based lab experiment to induce vocal dialects in fruit bat pups. By exposing groups of pups to different playbacks of conspecific calls, we could establish separate dialects, demonstrating the vocal learning skill of these bats. Furthermore, while songbirds, for instance, learn their songs directly from a specific tutor, our bats showed the ability to pick up vocal variations from the surrounding crowd, without direct interaction with any given tutor.

In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers’ decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research., Author summary In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. We conducted a systematic review to explore the nature and prevalence of spin in the biomedical literature. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers’ decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research.

In a crowded visual scene, attention must be distributed efficiently and flexibly over time and space to accommodate different contexts. It is well established that selective attention enhances the corresponding neural responses, presumably implying that attention would persistently dwell on the task-relevant item. Meanwhile, recent studies, mostly in divided attentional contexts, suggest that attention does not remain stationary but samples objects alternately over time, suggesting a rhythmic view of attention. However, it remains unknown whether the dynamic mechanism essentially mediates attentional processes at a general level. Importantly, there is also a complete lack of direct neural evidence reflecting whether and how the brain rhythmically samples multiple visual objects during stimulus processing. To address these issues, in this study, we employed electroencephalography (EEG) and a temporal response function (TRF) approach, which can dissociate responses that exclusively represent a single object from the overall neuronal activity, to examine the spatiotemporal characteristics of attention in various attentional contexts. First, attention, which is characterized by inhibitory alpha-band (approximately 10 Hz) activity in TRFs, switches between attended and unattended objects every approximately 200 ms, suggesting a sequential sampling even when attention is required to mostly stay on the attended object. Second, the attentional spatiotemporal pattern is modulated by the task context, such that alpha-mediated switching becomes increasingly prominent as the task requires a more uniform distribution of attention. Finally, the switching pattern correlates with attentional behavioral performance. Our work provides direct neural evidence supporting a generally central role of temporal organization mechanism in attention, such that multiple objects are sequentially sorted according to their priority in attentional contexts. The results suggest that selective attention, in addition to the classically posited attentional “focus,” involves a dynamic mechanism for monitoring all objects outside of the focus. Our findings also suggest that attention implements a space (object)-to-time transformation by acting as a series of concatenating attentional chunks that operate on 1 object at a time., Author summary In a crowded visual scene, attention must be efficiently and flexibly distributed over time and space to accommodate different contexts in a task. Recent studies have proposed that attention is a dynamic process that organizes copious information temporally. However, how the brain coordinates attention among multiple visual objects and whether this dynamic mechanism mediates attention at a general level remain largely unknown. In this study, we analyze electroencephalography (EEG) recordings during a multi-object selective attention task to extract object-specific neuronal responses. We demonstrate that attention rhythmically switches between visual objects every approximately 200 ms. Furthermore, the spatiotemporal sampling profile of attention adaptively changes in various task contexts and correlates with behavioral performance during attention. Our work provides direct neural evidence supporting the idea that multiple objects are sequentially sorted according to their priority in attentional contexts. The results suggest that attention is intrinsically dynamic, acting as a series of concatenating chunks of attention that operate on 1 object at a time to maintain awareness of the whole scene.

An animal’s ability to survive depends on its sensory systems being able to adapt to a wide range of environmental conditions, by maximizing the information extracted and reducing the noise transmitted. The visual system does this by adapting to luminance and contrast. While luminance adaptation can begin at the retinal photoreceptors, contrast adaptation has been shown to start at later stages in the retina. Photoreceptors adapt to changes in luminance over multiple time scales ranging from tens of milliseconds to minutes, with the adaptive changes arising from processes within the phototransduction cascade. Here we show a new form of adaptation in cones that is independent of the phototransduction process. Rather, it is mediated by voltage-gated ion channels in the cone membrane and acts by changing the frequency response of cones such that their responses speed up as the membrane potential modulation depth increases and slow down as the membrane potential modulation depth decreases. This mechanism is effectively activated by high-contrast stimuli dominated by low frequencies such as natural stimuli. However, the more generally used Gaussian white noise stimuli were not effective since they did not modulate the cone membrane potential to the same extent. This new adaptive process had a time constant of less than a second. A critical component of the underlying mechanism is the hyperpolarization-activated current, Ih, as pharmacologically blocking it prevented the long- and mid- wavelength sensitive cone photoreceptors (L- and M-cones) from adapting. Consistent with this, short- wavelength sensitive cone photoreceptors (S-cones) did not show the adaptive response, and we found they also lacked a prominent Ih. The adaptive filtering mechanism identified here improves the information flow by removing higher-frequency noise during lower signal-to-noise ratio conditions, as occurs when contrast levels are low. Although this new adaptive mechanism can be driven by contrast, it is not a contrast adaptation mechanism in its strictest sense, as will be argued in the Discussion., Author summary An animal’s ability to survive depends on its ability to adapt to a wide range of light conditions, by maximizing the information flow through the retina. Here, we show a new form of adaptation in cone photoreceptors that helps them optimize the information they transmit by adjusting their response kinetics to better match the visual conditions. The adaptive mechanism we describe is independent of the cone phototransduction process and is instead mediated by membrane processes in which the hyperpolarization-activated current, Ih, plays a critical role. Consistent with the critical role of this current, we also found that cones sensitive to short wavelengths lacked a prominent Ih current and did not show this new form of adaptation. As voltage-dependent processes underlie the adaptational mechanism, it is only apparent when the stimuli are able to sufficiently modulate the membrane potential of cones. This happens with natural stimuli, which are able to deliver high levels of “effective” contrast. However, even though this new adaptive mechanism can be driven by contrast, we argue in the Discussion that in its strictest sense it is not a contrast adaptation mechanism per se.