Rational regulation of learning dynamics by pupil-linked arousal systems

Attenuation of implicit motor learning with consecutive exposure to visual errors

Visual errors induced by movement drive implicit corrections of that movement. When similar errors are experienced consecutively, does sensitivity to the error remain consistent each time? This study aimed to investigate the modulation of implicit error sensitivity through continuous exposure to the same errors. In the reaching task using visual error-clamp feedback, participants were presented with the same error in direction and magnitude for four consecutive trials. We found that implicit error sensitivity decreased after exposure to the second error. These results indicate that when visual errors occur consecutively, the sensorimotor system exhibits different responses, even for identical errors. The continuity of errors may be a factor that modulates error sensitivity.

Toward a computational role for locus coeruleus/norepinephrine arousal systems

Brain and behavior undergo measurable changes in their underlying state and neuromodulators are thought to contribute to these fluctuations. Why do we undergo such changes, and what function could the underlying neuromodulatory systems perform? Here we examine theoretical answers to these questions with respect to the locus coeruleus/norepinephrine system focusing on peripheral markers for arousal, such as pupil diameter, that are thought to provide a window into brain wide noradrenergic signaling. We explore a computational role for arousal systems in facilitating internal state transitions that facilitate credit assignment and promote accurate perceptions in non-stationary environments. We summarize recent work that supports this idea and highlight open questions as well as alternative views of how arousal affects cognition.

Consistency within change: Evaluating the psychometric properties of a widely used predictive-inference task

Rapid adaptation to sudden changes in the environment is a hallmark of flexible human behaviour. Many computational, neuroimaging, and even clinical investigations studying this cognitive process have relied on a behavioural paradigm known as the predictive-inference task. However, the psychometric quality of this task has never been examined, leaving unanswered whether it is indeed suited to capture behavioural variation on a within- and between-subject level. Using a large-scale test-retest design (T1: N = 330; T2: N = 219), we assessed the internal (internal consistency) and temporal (test-retest reliability) stability of the task's most used measures. We show that the main measures capturing flexible belief and behavioural adaptation yield good internal consistency and overall satisfying test-retest reliability. However, some more complex markers of flexible behaviour show lower psychometric quality. Our findings have implications for the large corpus of previous studies using this task and provide clear guidance as to which measures should and should not be used in future studies.

Stepwise updating of probabilities is neither universal nor fully explained by motor costs

Often our expectations are set by sampling many times from the same distribution. When that distribution changes, so should our expectations. If we want to decide whether to take our umbrella today we need to have tracked, and updated, our estimate of the probability of rain by reference to recent temperatures and precipitation. Under debate is whether we update our mental estimates of probabilities given each new incoming piece of evidence or whether we stick with a current estimate until it becomes clearly in need of change. Previous research has suggested that participant estimates of running probabilities are not updated on every trial, but only intermittently. This has been used to support change-point models of probability updating. However, such a pattern could also be explained by the way common laboratory procedures impose a motor cost to update the probability report. This study was designed to remove the motor confound. Our procedure required similar motor actions for both changing and maintaining one's probability estimate. At a group level, motor cost did affect updating frequency and removing the default response option encouraged more frequent updating. However, intermittent updating response patterns did not disappear completely. Despite this equivalence in response effort, some participants, even when forced to make a new estimate on every trial, continued to update rarely while other participants meticulously updated every trial. We conclude deliberate updating frequency is heterogenous but intermittent updating is not simply an artifact of motor cost.

Individual differences in belief updating and phasic arousal are related to psychosis proneness

Many decisions entail the updating of beliefs about the state of the environment by accumulating noisy sensory evidence. This form of probabilistic reasoning may go awry in psychosis. Computational theory shows that optimal belief updating in environments subject to hidden changes in their state requires a dynamic modulation of the evidence accumulation process. Recent empirical findings implicate transient responses of pupil-linked central arousal systems to individual evidence samples in this modulation. Here, we analyzed behavior and pupil responses during evidence accumulation in a changing environment in a community sample of human participants. We also assessed their subclinical psychotic experiences (psychosis proneness). Participants most prone to psychosis showed overall less flexible belief updating profiles, with diminished behavioral impact of evidence samples occurring late during decision formation. These same individuals also exhibited overall smaller pupil responses and less reliable pupil encoding of computational variables governing the dynamic belief updating. Our findings provide insights into the cognitive and physiological bases of psychosis proneness and open paths to unraveling the pathophysiology of psychotic disorders.

The effects of emotional arousal on pupil size depend on luminance

Pupillometry is widely used to measure arousal states. The primary functional role of the pupil, however, is to respond to the luminance of visual inputs. We previously demonstrated that cognitive effort-related arousal interacted multiplicatively with luminance, with the strongest pupillary effects of arousal occurring at low-to-mid luminances (< 37 cd/m2), implying a narrow range of conditions ideal for assessing cognitive arousal-driven pupillary differences. Does this generalize to other forms of arousal? To answer this, we assessed luminance-driven pupillary response functions while manipulating emotional arousal, using well-established visual and auditory stimulus sets. At the group level, emotional arousal interacted with the pupillary light response differently from cognitive arousal: the effects occurred primarily at much lower luminances (< 20 cd/m2). Analyses at the individual-participant level revealed qualitatively distinct patterns of modulation, with a sizable number of individuals displaying no arousal response to the visual or auditory stimuli, regardless of luminance. Together, our results suggest that effects of arousal on pupil size are not monolithic: different forms of arousal exert different patterns of effects. More practically, our findings suggest that lower luminances create better conditions for measuring pupil-linked arousal, and when selecting ambient luminance levels, consideration of the arousal manipulation and individual differences is critical.

Neurons of Macaque Frontal Eye Field Signal Reward-Related Surprise

The frontal eye field (FEF) plays a well-established role in the control of visual attention. The strength of an FEF neuron's response to a visual stimulus presented in its receptive field is enhanced if the stimulus captures spatial attention by virtue of its salience. A stimulus can be rendered salient by cognitive factors as well as by physical attributes. These include surprise. The aim of the present experiment was to determine whether surprise-induced salience would result in enhanced visual-response strength in the FEF. Toward this end, we monitored neuronal activity in two male monkeys while presenting first a visual cue predicting with high probability that the reward delivered at the end of the trial would be good or bad (large or small) and then a visual cue announcing the size of the impending reward with certainty. The second cue usually confirmed but occasionally violated the expectation set up by the first cue. Neurons responded more strongly to the second cue when it violated than when it confirmed expectation. The increase in the firing rate was accompanied by a decrease in spike-count correlation as expected from capture of attention. Although both good surprise and bad surprise induced enhanced firing, the effects appeared to arise from distinct mechanisms as indicated by the fact that the bad-surprise signal appeared at a longer latency than the good-surprise signal and by the fact that the strength of the two signals varied independently across neurons.

Dynamic estimation of the attentional field from visual cortical activity

Navigating around the world, we must adaptively allocate attention to our surroundings based on anticipated future stimuli and events. This allocation of spatial attention boosts visuocortical representations at attended locations and locally enhances perception. Indeed, spatial attention has often been analogized to a "spotlight" shining on the item of relevance. Although the neural underpinnings of the locus of this attentional spotlight have been relatively well studied, less is known about the size of the spotlight: to what extent can the attentional field be broadened and narrowed in accordance with behavioral demands? In this study, we developed a paradigm for dynamically estimating the locus and spread of covert spatial attention, inferred from visuocortical activity using fMRI in humans. We measured BOLD activity in response to an annulus while participants (4 female, 4 male) used covert visual attention to determine whether more numbers or letters were present in a cued region of the annulus. Importantly, the width of the cued area was systematically varied, calling for different sizes of the attentional spotlight. The deployment of attention was associated with an increase in BOLD activity in corresponding retinotopic regions of visual areas V1-V3. By modeling the visuocortical attentional modulation, we could reliably recover the cued location, as well as a broadening of the attentional enhancement with wider attentional cues. This modeling approach offers a useful window into the dynamics of attention and spatial uncertainty.

Dopamine transients encode reward prediction errors independent of learning rates

Biological accounts of reinforcement learning posit that dopamine encodes reward prediction errors (RPEs), which are multiplied by a learning rate to update state or action values. These values are thought to be represented in synaptic weights in the striatum, and updated by dopamine-dependent plasticity, suggesting that dopamine release might reflect the product of the learning rate and RPE. Here, we leveraged the fact that animals learn faster in volatile environments to characterize dopamine encoding of learning rates in the nucleus accumbens core (NAcc). We trained rats on a task with semi-observable states offering different rewards, and rats adjusted how quickly they initiated trials across states using RPEs. Computational modeling and behavioral analyses showed that learning rates were higher following state transitions, and scaled with trial-by-trial changes in beliefs about hidden states, approximating normative Bayesian strategies. Notably, dopamine release in the NAcc encoded RPEs independent of learning rates, suggesting that dopamine-independent mechanisms instantiate dynamic learning rates.

Plinko: Eliciting beliefs to build better models of statistical learning and mental model updating

Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when updating a particular model. Despite this, prior and posterior beliefs are either inferred from sequential participant actions or elicited through impoverished means. We had participants to play a version of the game 'Plinko', to first elicit individual participant priors in a theoretically agnostic manner. Subsequent learning and updating of participant beliefs was then directly measured. We show that participants hold various priors that cluster around prototypical probability distributions that in turn influence learning. In follow-up studies, we show that participant priors are stable over time and that the ability to update beliefs is influenced by a simple environmental manipulation (i.e., a short break). These data reveal the importance of directly measuring participant beliefs rather than assuming or inferring them as has been widely done in the literature to date. The Plinko game provides a flexible and fecund means for examining statistical learning and mental model updating.

Impact of light on task-evoked pupil responses during cognitive tasks

Light has many non-image-forming functions including modulation of pupil size and stimulation of alertness and cognition. Part of these non-image-forming effects may be mediated by the brainstem locus coeruleus. The processing of sensory inputs can be associated with a transient pupil dilation that is likely driven in part by the phasic activity of the locus coeruleus. In the present study, we aimed to characterise the task-evoked pupil response associated with auditory inputs under different light levels and across two cognitive tasks. We continuously monitored the pupil of 20 young healthy participants (mean [SD] 24.05 [4.0] years; 14 women) whilst they completed an attentional and an emotional auditory task whilst exposed to repeated 30-40-s blocks of light interleaved with darkness periods. Blocks could either consist of monochromatic orange light (0.16 melanopic equivalent daylight illuminance (EDI) lux) or blue-enriched white light of three different levels [37, 92, 190 melanopic EDI lux; 6500 K]. For the analysis, 15 and then 14 participants were included in the attentional and emotional tasks, respectively. Generalised linear mixed models showed a significant main effect of light level on the task-evoked pupil responses triggered by the attentional and emotional tasks (p ≤ 0.0001). The impact of light was different for the target versus non-target stimulus of the attentional task but was not different for the emotional and neutral stimulus of the emotional task. There is a smaller sustained pupil size during brighter light blocks but, a higher light level triggers a stronger task-evoked pupil response to auditory stimulation, presumably through the recruitment of the locus coeruleus.

Influence of surprise on reinforcement learning in younger and older adults

Surprise is a key component of many learning experiences, and yet its precise computational role, and how it changes with age, remain debated. One major challenge is that surprise often occurs jointly with other variables, such as uncertainty and outcome probability. To assess how humans learn from surprising events, and whether aging affects this process, we studied choices while participants learned from bandits with either Gaussian or bi-modal outcome distributions, which decoupled outcome probability, uncertainty, and surprise. A total of 102 participants (51 older, aged 50-73; 51 younger, 19-30 years) chose between three bandits, one of which had a bimodal outcome distribution. Behavioral analyses showed that both age-groups learned the average of the bimodal bandit less well. A trial-by-trial analysis indicated that participants performed choice reversals immediately following large absolute prediction errors, consistent with heightened sensitivity to surprise. This effect was stronger in older adults. Computational models indicated that learning rates in younger as well as older adults were influenced by surprise, rather than uncertainty, but also suggested large interindividual variability in the process underlying learning in our task. Our work bridges between behavioral economics research that has focused on how outcomes with low probability affect choice in older adults, and reinforcement learning work that has investigated age differences in the effects of uncertainty and suggests that older adults overly adapt to surprising events, even when accounting for probability and uncertainty effects.

Thalamocortical architectures for flexible cognition and efficient learning

The brain exhibits a remarkable ability to learn and execute context-appropriate behaviors. How it achieves such flexibility, without sacrificing learning efficiency, is an important open question. Neuroscience, psychology, and engineering suggest that reusing and repurposing computations are part of the answer. Here, we review evidence that thalamocortical architectures may have evolved to facilitate these objectives of flexibility and efficiency by coordinating distributed computations. Recent work suggests that distributed prefrontal cortical networks compute with flexible codes, and that the mediodorsal thalamus provides regularization to promote efficient reuse. Thalamocortical interactions resemble hierarchical Bayesian computations, and their network implementation can be related to existing gating, synchronization, and hub theories of thalamic function. By reviewing recent findings and providing a novel synthesis, we highlight key research horizons integrating computation, cognition, and systems neuroscience.

Keeping an Eye Out for Change: Anxiety Disrupts Adaptive Resolution of Policy Uncertainty

BACKGROUND

Human learning unfolds under uncertainty. Uncertainty is heterogeneous with different forms exerting distinct influences on learning. While one can be uncertain about what to do to maximize rewarding outcomes, known as policy uncertainty, one can also be uncertain about general world knowledge, known as epistemic uncertainty (EU). In complex and naturalistic environments such as the social world, adaptive learning may hinge on striking a balance between attending to and resolving each type of uncertainty. Prior work illustrates that people with anxiety-those with increased threat and uncertainty sensitivity-learn less from aversive outcomes, particularly as outcomes become more uncertain. How does a learner adaptively trade-off between attending to these distinct sources of uncertainty to successfully learn about their social environment?

METHODS

We developed a novel eye-tracking method to capture highly granular estimates of policy uncertainty and EU based on gaze patterns and pupil diameter (a physiological estimate of arousal).

RESULTS

These empirically derived uncertainty measures revealed that humans (N = 94) flexibly switched between resolving policy uncertainty and EU to adaptively learn about which individuals can be trusted and which should be avoided. However, those with increased anxiety (n = 49) did not flexibly switch between resolving policy uncertainty and EU and instead expressed less uncertainty overall.

CONCLUSIONS

Combining modeling and eye-tracking techniques, we show that altered learning in people with anxiety emerged from an insensitivity to policy uncertainty and rigid choice policies, leading to maladaptive behaviors with untrustworthy people.

Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance

The ability to calibrate learning according to new information is a fundamental component of an organism's ability to adapt to changing conditions. Yet, the exact neural mechanisms guiding dynamic learning rate adjustments remain unclear. Catecholamines appear to play a critical role in adjusting the degree to which we use new information over time, but individuals vary widely in the manner in which they adjust to changes. Here, we studied the effects of a low dose of methamphetamine (MA), and individual differences in these effects, on probabilistic reversal learning dynamics in a within-subject, double-blind, randomized design. Participants first completed a reversal learning task during a drug-free baseline session to provide a measure of baseline performance. Then they completed the task during two sessions, one with MA (20 mg oral) and one with placebo (PL). First, we showed that, relative to PL, MA modulates the ability to dynamically adjust learning from prediction errors. Second, this effect was more pronounced in participants who performed poorly at baseline. These results present novel evidence for the involvement of catecholaminergic transmission on learning flexibility and highlights that baseline performance modulates the effect of the drug.

Beyond peaks and troughs: Multiplexed performance monitoring signals in the EEG

With the discovery of event-related potentials elicited by errors more than 30 years ago, a new avenue of research on performance monitoring, cognitive control, and decision making emerged. Since then, the field has developed and expanded fulminantly. After a brief overview on the EEG correlates of performance monitoring, this article reviews recent advancements based on single-trial analyses using independent component analysis, multiple regression, and multivariate pattern classification. Given the close interconnection between performance monitoring and reinforcement learning, computational modeling and model-based EEG analyses have made a particularly strong impact. The reviewed findings demonstrate that error- and feedback-related EEG dynamics represent variables reflecting how performance-monitoring signals are weighted and transformed into an adaptation signal that guides future decisions and actions. The model-based single-trial analysis approach goes far beyond conventional peak-and-trough analyses of event-related potentials and enables testing mechanistic theories of performance monitoring, cognitive control, and decision making.

Neurobehavioral meaning of pupil size

Pupil size is a widely used metric of brain state. It is one of the few signals originating from the brain that can be readily monitored with low-cost devices in basic science, clinical, and home settings. It is, therefore, important to investigate and generate well-defined theories related to specific interpretations of this metric. What exactly does it tell us about the brain? Pupils constrict in response to light and dilate during darkness, but the brain also controls pupil size irrespective of luminosity. Pupil size fluctuations resulting from ongoing "brain states" are used as a metric of arousal, but what is pupil-linked arousal and how should it be interpreted in neural, cognitive, and computational terms? Here, we discuss some recent findings related to these issues. We identify open questions and propose how to answer them through a combination of well-defined tasks, neurocomputational models, and neurophysiological probing of the interconnected loops of causes and consequences of pupil size.

The expression of decision and learning variables in movement patterns related to decision actions

Decisions are not necessarily easy to separate into a planning and an execution phase and the decision-making process can often be reflected in the movement associated with the decision. Here, we used formalized definitions of concepts relevant in decision-making and learning to explore if and how these concepts correlate with decision-related movement paths, both during and after a choice is made. To this end, we let 120 participants (46 males, mean age = 24.5 years) undergo a repeated probabilistic two-choice task with changing probabilities where we used mouse-tracking, a simple non-invasive technique, to study the movements related to decisions. The decisions of the participants were modelled using Bayesian inference which enabled the computation of variables related to decision-making and learning. Analyses of the movement during the decision showed effects of relevant decision variables, such as confidence, on aspects related to, for instance, timing and pausing, range of movement and deviation from the shortest distance. For the movements after a decision there were some effects of relevant learning variables, mainly related to timing and speed. We believe our findings can be of interest for researchers within several fields, spanning from social learning to experimental methods and human-machine/robot interaction.

Correlated P300b and phasic pupil-dilation responses to motivationally significant stimuli

Motivationally significant events like oddball stimuli elicit both a characteristic event-related potential (ERPs) known as P300 and a set of autonomic responses including a phasic pupil dilation. Although co-occurring, P300 and pupil-dilation responses to oddball events have been repeatedly found to be uncorrelated, suggesting separate origins. We re-examined their relationship in the context of a three-stimulus version of the auditory oddball task, independently manipulating the frequency (rare vs. repeated) and motivational significance (relevance for the participant's task) of the stimuli. We used independent component analysis to derive a P300b component from EEG traces and linear modeling to separate a stimulus-related pupil-dilation response from a potentially confounding action-related response. These steps revealed that, once the complexity of ERP and pupil-dilation responses to oddball targets is accounted for, the amplitude of phasic pupil dilations and P300b are tightly and positively correlated (across participants: r = .69 p = .002), supporting their coordinated generation.

Spontaneous movements and their impact on neural activity fluctuate with latent engagement states

Existing work demonstrates that animals alternate between engaged and disengaged states during perceptual decision-making. To understand the neural signature of these states, we performed cortex-wide measurements of neural activity in mice making auditory decisions. The trial-averaged magnitude of neural activity was similar in the two states. However, the trial-to-trial variance in neural activity was higher during disengagement. To understand this increased variance, we trained separate linear encoding models on neural data from each state. The models demonstrated that although task variables and task-aligned movements impacted neural activity similarly during the two states, movements that are independent of task events explained more variance during disengagement. Behavioral analyses uncovered that during disengagement, movements become uncoupled to task events. Taken together, these results argue that the neural signature of disengagement, though obscured in trial-averaged neural activity, is evident in trial-to-trial variability driven by changing patterns of spontaneous movements.

A common computational and neural anomaly across mouse models of autism

Computational psychiatry has suggested that humans within the autism spectrum disorder (ASD) inflexibly update their expectations (i.e., Bayesian priors). Here, we leveraged high-yield rodent psychophysics (n = 75 mice), extensive behavioral modeling (including principled and heuristics), and (near) brain-wide single cell extracellular recordings (over 53k units in 150 brain areas) to ask (1) whether mice with different genetic perturbations associated with ASD show this same computational anomaly, and if so, (2) what neurophysiological features are shared across genotypes in subserving this deficit. We demonstrate that mice harboring mutations in Fmr1 , Cntnap2 , and Shank3B show a blunted update of priors during decision-making. Neurally, the differentiating factor between animals flexibly and inflexibly updating their priors was a shift in the weighting of prior encoding from sensory to frontal cortices. Further, in mouse models of ASD frontal areas showed a preponderance of units coding for deviations from the animals' long-run prior, and sensory responses did not differentiate between expected and unexpected observations. These findings demonstrate that distinct genetic instantiations of ASD may yield common neurophysiological and behavioral phenotypes.

The pupil dilation response as an indicator of visual cue uncertainty and auditory outcome surprise

In everyday perception, we combine incoming sensory information with prior expectations. Expectations can be induced by cues that indicate the probability of following sensory events. The information provided by cues may differ and hence lead to different levels of uncertainty about which event will follow. In this experiment, we employed pupillometry to investigate whether the pupil dilation response to visual cues varies depending on the level of cue-associated uncertainty about a following auditory outcome. Also, we tested whether the pupil dilation response reflects the amount of surprise about the subsequently presented auditory stimulus. In each trial, participants were presented with a visual cue (face image) which was followed by an auditory outcome (spoken vowel). After the face cue, participants had to indicate by keypress which of three auditory vowels they expected to hear next. We manipulated the cue-associated uncertainty by varying the probabilistic cue-outcome contingencies: One face was most likely followed by one specific vowel (low cue uncertainty), another face was equally likely followed by either of two vowels (intermediate cue uncertainty) and the third face was followed by all three vowels (high cue uncertainty). Our results suggest that pupil dilation in response to task-relevant cues depends on the associated uncertainty, but only for large differences in the cue-associated uncertainty. Additionally, in response to the auditory outcomes, the pupil dilation scaled negatively with the cue-dependent probabilities, likely signalling the amount of surprise.

Cholinergic-related pupil activity reflects level of emotionality during motor performance

Pupil size covaries with the diffusion rate of the cholinergic and noradrenergic neurons throughout the brain, which are essential to arousal. Recent findings suggest that slow pupil fluctuations during locomotion are an index of sustained activity in cholinergic axons, whereas phasic dilations are related to the activity of noradrenergic axons. Here, we investigated movement induced arousal (i.e., by singing and swaying to music), hypothesising that actively engaging in musical behaviour will provoke stronger emotional engagement in participants and lead to different qualitative patterns of tonic and phasic pupil activity. A challenge in the analysis of pupil data is the turbulent behaviour of pupil diameter due to exogenous ocular activity commonly encountered during motor tasks and the high variability typically found between individuals. To address this, we developed an algorithm that adaptively estimates and removes pupil responses to ocular events, as well as a functional data methodology, derived from Pfaffs' generalised arousal, that provides a new statistical dimension on how pupil data can be interpreted according to putative neuromodulatory signalling. We found that actively engaging in singing enhanced slow cholinergic-related pupil dilations and having the opportunity to move your body while performing amplified the effect of singing on pupil activity. Phasic pupil oscillations during motor execution attenuated in time, which is often interpreted as a measure of sense of agency over movement.

Pupil diameter as an indicator of sound pair familiarity after statistically structured auditory sequence

Inspired by recent findings in the visual domain, we investigated whether the stimulus-evoked pupil dilation reflects temporal statistical regularities in sequences of auditory stimuli. We conducted two preregistered pupillometry experiments (experiment 1, n = 30, 21 females; experiment 2, n = 31, 22 females). In both experiments, human participants listened to sequences of spoken vowels in two conditions. In the first condition, the stimuli were presented in a random order and, in the second condition, the same stimuli were presented in a sequence structured in pairs. The second experiment replicated the first experiment with a modified timing and number of stimuli presented and without participants being informed about any sequence structure. The sound-evoked pupil dilation during a subsequent familiarity task indicated that participants learned the auditory vowel pairs of the structured condition. However, pupil diameter during the structured sequence did not differ according to the statistical regularity of the pair structure. This contrasts with similar visual studies, emphasizing the susceptibility of pupil effects during statistically structured sequences to experimental design settings in the auditory domain. In sum, our findings suggest that pupil diameter may serve as an indicator of sound pair familiarity but does not invariably respond to task-irrelevant transition probabilities of auditory sequences.

Linking tonic and phasic pupil responses to P300 amplitude in an emotional face-word Stroop task

The locus coeruleus-norepinephrine (LC-NE) system, which regulates arousal levels, is important for cognitive control, including emotional conflict resolution. Additionally, the LC-NE system is implicated in P300 generation. If the P300 is mediated by the LC-NE system, and considering the established correlations between LC activity and pupil dilation, P300 amplitude should correlate with task-evoked (phasic) pupil dilation on a trial-by-trial basis. However, prior studies, predominantly utilizing oddball-type paradigms, have not demonstrated correlations between concurrently recorded task-evoked pupil dilation and P300 responses. Using a recently developed emotional face-word Stroop task that links pupil dilation to the LC-NE system, here, we examined both intra- and inter-individual correlations between task-evoked pupil dilation and P300 amplitude. We found that lower accuracy, slower reaction times, and larger task-evoked pupil dilation were obtained in the incongruent compared to the congruent condition. Furthermore, we observed intra-individual correlations between task-evoked pupil dilation and P300 amplitude, with larger pupil dilation correlating with a greater P300 amplitude. In contrast, pupil dilation did not exhibit consistent correlations with N450 and N170 amplitudes. Baseline (tonic) pupil size also showed correlations with P300 and N170 amplitudes, with smaller pupil size corresponding to larger amplitude. Moreover, inter-individual differences in task-evoked pupil dilation between the congruent and incongruent conditions correlated with differences in reaction time and P300 amplitude, though these effects only approached significance. To summarize, our study provides evidence for a connection between task-evoked pupil dilation and P300 amplitude at the single-trial level, suggesting the involvement of the LC-NE system in P300 generation.

Do dogs preferentially encode the identity of the target object or the location of others' actions?

The ability to make sense of and predict others' actions is foundational for many socio-cognitive abilities. Dogs (Canis familiaris) constitute interesting comparative models for the study of action perception due to their marked sensitivity to human actions. We tested companion dogs (N = 21) in two screen-based eye-tracking experiments, adopting a task previously used with human infants and apes, to assess which aspects of an agent's action dogs consider relevant to the agent's underlying intentions. An agent was shown repeatedly acting upon the same one of two objects, positioned in the same location. We then presented the objects in swapped locations and the agent approached the objects centrally (Experiment 1) or the old object in the new location or the new object in the old location (Experiment 2). Dogs' anticipatory fixations and looking times did not reflect an expectation that agents should have continued approaching the same object nor the same location as witnessed during the brief familiarization phase; this contrasts with some findings with infants and apes, but aligns with findings in younger infants before they have sufficient motor experience with the observed action. However, dogs' pupil dilation and latency to make an anticipatory fixation suggested that, if anything, dogs expected the agents to keep approaching the same location rather than the same object, and their looking times showed sensitivity to the animacy of the agents. We conclude that dogs, lacking motor experience with the observed actions of grasping or kicking performed by a human or inanimate agent, might interpret such actions as directed toward a specific location rather than a specific object. Future research will need to further probe the suitability of anticipatory looking as measure of dogs' socio-cognitive abilities given differences between the visual systems of dogs and primates.

Arousal coherence, uncertainty, and well-being: an active inference account

Here we build on recent findings which show that greater alignment between our subjective experiences (how we feel) and physiological states (measurable changes in our body) plays a pivotal role in the overall psychological well-being. Specifically, we propose that the alignment or 'coherence' between affective arousal (e.g. how excited we 'feel') and autonomic arousal (e.g. heart rate or pupil dilation) may be key for maintaining up-to-date uncertainty representations in dynamic environments. Drawing on recent advances in interoceptive and affective inference, we also propose that arousal coherence reflects interoceptive integration, facilitates adaptive belief updating, and impacts our capacity to adapt to changes in uncertainty, with downstream consequences to well-being. We also highlight the role of meta-awareness of arousal, a third level of inference, which may permit conscious awareness, learning about, and intentional regulation of lower-order sources of arousal. Practices emphasizing meta-awareness of arousal (like meditation) may therefore elicit some of their known benefits via improved arousal coherence. We suggest that arousal coherence is also likely to be associated with markers of adaptive functioning (like emotional awareness and self-regulatory capacities) and discuss mind-body practices that may increase coherence.

Cross-Modal Interactions Between Auditory Attention and Oculomotor Control

Microsaccades are small, involuntary eye movements that occur during fixation. Their role is debated with recent hypotheses proposing a contribution to automatic scene sampling. Microsaccadic inhibition (MSI) refers to the abrupt suppression of microsaccades, typically evoked within 0.1 s after new stimulus onset. The functional significance and neural underpinnings of MSI are subjects of ongoing research. It has been suggested that MSI is a component of the brain's attentional re-orienting network which facilitates the allocation of attention to new environmental occurrences by reducing disruptions or shifts in gaze that could interfere with processing. The extent to which MSI is reflexive or influenced by top-down mechanisms remains debated. We developed a task that examines the impact of auditory top-down attention on MSI, allowing us to disentangle ocular dynamics from visual sensory processing. Participants (N = 24 and 27; both sexes) listened to two simultaneous streams of tones and were instructed to attend to one stream while detecting specific task "targets." We quantified MSI in response to occasional task-irrelevant events presented in both the attended and unattended streams (frequency steps in Experiment 1, omissions in Experiment 2). The results show that initial stages of MSI are not affected by auditory attention. However, later stages (∼0.25 s postevent onset), affecting the extent and duration of the inhibition, are enhanced for sounds in the attended stream compared to the unattended stream. These findings provide converging evidence for the reflexive nature of early MSI stages and robustly demonstrate the involvement of auditory attention in modulating the later stages.

From pre-processing to advanced dynamic modeling of pupil data

The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants' pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.

Attention deficits linked with proclivity to explore while foraging

All mobile organisms forage for resources, choosing how and when to search for new opportunities by comparing current returns with the average for the environment. In humans, nomadic lifestyles favouring exploration have been associated with genetic mutations implicated in attention deficit hyperactivity disorder (ADHD), inviting the hypothesis that this condition may impact foraging decisions in the general population. Here we tested this pre-registered hypothesis by examining how human participants collected resources in an online foraging task. On every trial, participants chose either to continue to collect rewards from a depleting patch of resources or to replenish the patch. Participants also completed a well-validated ADHD self-report screening assessment at the end of sessions. Participants departed resource patches sooner when travel times between patches were shorter than when they were longer, as predicted by optimal foraging theory. Participants whose scores on the ADHD scale crossed the threshold for a positive screen departed patches significantly sooner than participants who did not meet this criterion. Participants meeting this threshold for ADHD also achieved higher reward rates than individuals who did not. Our findings suggest that ADHD attributes may confer foraging advantages in some environments and invite the possibility that this condition may reflect an adaptation favouring exploration over exploitation.

Specifying the timescale of early life unpredictability helps explain the development of internalising and externalising behaviours

Early life unpredictability is associated with both physical and mental health outcomes throughout the life course. Here, we classified adverse experiences based on the timescale on which they are likely to introduce variability in children's environments: variations unfolding over short time scales (e.g., hours, days, weeks) and labelled Stochasticity vs variations unfolding over longer time scales (e.g., months, years) and labelled Volatility and explored how they contribute to the development of problem behaviours. Results indicate that externalising behaviours at age 9 and 15 and internalising behaviours at age 15 were better accounted for by models that separated Stochasticity and Volatility measured at ages 3 to 5. Both externalising and internalising behaviours were specifically associated with Volatility, with larger effects for externalising behaviours. These findings are interpreted in light of evolutionary-developmental models of psychopathology and reinforcement learning models of learning under uncertainty.

Preverbal infants' understanding of social norms

Social norms are foundational to human cooperation and co-existence in social groups. A crucial marker of social norms is that a behavior is not only shared, but that the conformity to the behavior of others is a basis for social evaluation (i.e., reinforcement and sanctioning), taking the is, how individuals usually behave, to an ought, how individuals should behave to be socially approved by others. In this preregistered study, we show that 11-month-old infants grasp this fundamental aspect about social norms already in their first year. They showed a pupillary surprise response for unexpected social responses, namely the disapproval and exclusion of an individual who showed the same behavior like others or the approval and inclusion of an individual who behaved differently. That preverbal infants link the conformity with others' behavior to social evaluations, before they respond to norm violations themselves, indicates that the foundations of social norm understanding lie in early infancy.

Fast adaptation to rule switching using neuronal surprise

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signal is extracted from an increase in neural activity after an imbalance of excitation and inhibition. The surprise signal modulates synaptic plasticity via a three-factor learning rule which increases plasticity at moments of surprise. The surprise signal remains small when transitions between sensory events follow a previously learned rule but increases immediately after rule switching. In a spiking network with several modules, previously learned rules are protected against overwriting, as long as the number of modules is larger than the total number of rules-making a step towards solving the stability-plasticity dilemma in neuroscience. Our model relates the subjective notion of surprise to specific predictions on the circuit level.

Synthetic surprise as the foundation of the psychedelic experience

Psychedelic agents, such as LSD and psilocybin, induce marked alterations in consciousness via activation of the 5-HT2A receptor (5-HT2ARs). We hypothesize that psychedelics enforce a state of synthetic surprise through the biased activation of the 5-HTRs system. This idea is informed by recent insights into the role of 5-HT in signaling surprise. The effects on consciousness, explained by the cognitive penetrability of perception, can be described within the predictive coding framework where surprise corresponds to prediction error, the mismatch between predictions and actual sensory input. Crucially, the precision afforded to the prediction error determines its effect on priors, enabling a dynamic interaction between top-down expectations and incoming sensory data. By integrating recent findings on predictive coding circuitry and 5-HT2ARs transcriptomic data, we propose a biological implementation with emphasis on the role of inhibitory interneurons. Implications arise for the clinical use of psychedelics, which may rely primarily on their inherent capacity to induce surprise in order to disrupt maladaptive patterns.

The locus coeruleus as a global model failure system

Predictive processing models posit that brains constantly attempt to predict their sensory inputs. Prediction errors signal when these predictions are incorrect and are thought to be instructive signals that drive corrective plasticity. Recent findings support the idea that the locus coeruleus (LC) - a brain-wide neuromodulatory system - signals several types of prediction error. I discuss how these findings support models proposing that the LC signals global model failures: instances where predictions about the world are strongly violated. Focusing on the cortex, I explore the utility of this signal in learning rate control, how the LC circuit may compute the signal, and how this view may aid our understanding of neurodivergence.

Multiple timescales of temporal context in risky choice: Behavioral identification and relationships to physiological arousal

Context-dependence is fundamental to risky monetary decision-making. A growing body of evidence suggests that temporal context, or recent events, alters risk-taking at a minimum of three timescales: immediate (e.g. trial-by-trial), neighborhood (e.g. a group of consecutive trials), and global (e.g. task-level). To examine context effects, we created a novel monetary choice set with intentional temporal structure in which option values shifted between multiple levels of value magnitude ("contexts") several times over the course of the task. This structure allowed us to examine whether effects of each timescale were simultaneously present in risky choice behavior and the potential mechanistic role of arousal, an established correlate of risk-taking, in context-dependency. We found that risk-taking was sensitive to immediate, neighborhood, and global timescales: risk-taking decreased following large (vs. small) outcome amounts, increased following large positive (but not negative) shifts in context, and increased when cumulative earnings exceeded expectations. We quantified arousal with skin conductance responses, which were related to the global timescale, increasing with cumulative earnings, suggesting that physiological arousal captures a task-level assessment of performance. Our results both replicate and extend prior research by demonstrating that risky decision-making is consistently dynamic at multiple timescales and that the role of arousal in risk-taking extends to some, but not all timescales of context-dependence.

Out of control: computational dynamic control dysfunction in stress- and anxiety-related disorders

Control theory, which has played a central role in technological progress over the last 150 years, has also yielded critical insights into biology and neuroscience. Recently, there has been a surging interest in integrating control theory with computational psychiatry. Here, we review the state of the field of using control theory approaches in computational psychiatry and show that recent research has mapped a neural control circuit consisting of frontal cortex, parietal cortex, and the cerebellum. This basic feedback control circuit is modulated by estimates of reward and cost via the basal ganglia as well as by arousal states coordinated by the insula, dorsal anterior cingulate cortex, amygdala, and locus coeruleus. One major approach within the broader field of control theory, known as proportion-integral-derivative (PID) control, has shown promise as a model of human behavior which enables precise and reliable estimates of underlying control parameters at the individual level. These control parameters correlate with self-reported fear and with both structural and functional variation in affect-related brain regions. This suggests that dysfunctional engagement of stress and arousal systems may suboptimally modulate parameters of domain-general goal-directed control algorithms, impairing performance in complex tasks involving movement, cognition, and affect. Future directions include clarifying the causal role of control deficits in stress- and anxiety-related disorders and developing clinically useful tools based on insights from control theory.

Neural Representations of Post-Decision Accuracy and Reward Expectation in the Caudate Nucleus and Frontal Eye Field

Performance monitoring that supports ongoing behavioral adjustments is often examined in the context of either choice confidence for perceptual decisions (i.e., "did I get it right?") or reward expectation for reward-based decisions (i.e., "what reward will I receive?"). However, our understanding of how the brain encodes these distinct evaluative signals remains limited because they are easily conflated, particularly in commonly used two-alternative tasks with symmetric rewards for correct choices. Previously we used a motion-discrimination task with asymmetric rewards to identify neural substrates of forming reward-biased perceptual decisions in the caudate nucleus (part of the striatum in the basal ganglia) and the frontal eye field (FEF, in prefrontal cortex). Here we leveraged this task design to partially decouple estimates of accuracy and reward expectation and examine their impacts on subsequent decisions and their representations in those two brain areas. We identified distinguishable representations of these two evaluative signals in individual caudate and FEF neurons, with regional differences in their distribution patterns and time courses. We observed that well-trained monkeys (both sexes) used both evaluative signals, infrequently but consistently, to adjust their subsequent decisions. We found further that these behavioral adjustments had reliable relationships with the neural representations of both evaluative signals in caudate, but not FEF. These results suggest that the cortico-striatal decision network may use diverse evaluative signals to monitor and adjust decision-making behaviors, adding to our understanding of the different roles that the FEF and caudate nucleus play in a diversity of decision-related computations.

Mapping the bioimaging marker of Alzheimer's disease based on pupillary light response-driven brain-wide fMRI in awake mice

Pupil dynamics has emerged as a critical non-invasive indicator of brain state changes. In particular, pupillary-light-responses (PLR) in Alzheimer's disease (AD) patients may be used as biomarkers of brain degeneration. To characterize AD-specific PLR and its underlying neuromodulatory sources, we combined high-resolution awake mouse fMRI with real-time pupillometry to map brain-wide event-related correlation patterns based on illumination-driven pupil constriction ( P c ) and post-illumination pupil dilation recovery (amplitude, P d , and time, T ). The P c -driven differential analysis revealed altered visual signal processing coupled with reduced thalamocortical activation in AD mice compared with the wild-type normal mice. In contrast, the post-illumination pupil dilation recovery-based fMRI highlighted multiple brain areas related to AD brain degeneration, including the cingulate cortex, hippocampus, septal area of the basal forebrain, medial raphe nucleus, and pontine reticular nuclei (PRN). Also, brain-wide functional connectivity analysis highlighted the most significant changes in PRN of AD mice, which serves as the major subcortical relay nuclei underlying oculomotor function. This work combined non-invasive pupil-fMRI measurements in preclinical models to identify pupillary biomarkers based on neuromodulatory dysfunction coupled with AD brain degeneration.

Interpersonal alignment of neural evidence accumulation to social exchange of confidence

Private, subjective beliefs about uncertainty have been found to have idiosyncratic computational and neural substrates yet, humans share such beliefs seamlessly and cooperate successfully. Bringing together decision making under uncertainty and interpersonal alignment in communication, in a discovery plus pre-registered replication design, we examined the neuro-computational basis of the relationship between privately held and socially shared uncertainty. Examining confidence-speed-accuracy trade-off in uncertainty-ridden perceptual decisions under social vs isolated context, we found that shared (i.e. reported confidence) and subjective (inferred from pupillometry) uncertainty dynamically followed social information. An attractor neural network model incorporating social information as top-down additive input captured the observed behavior and demonstrated the emergence of social alignment in virtual dyadic simulations. Electroencephalography showed that social exchange of confidence modulated the neural signature of perceptual evidence accumulation in the central parietal cortex. Our findings offer a neural population model for interpersonal alignment of shared beliefs.

Pupil-linked arousal correlates with neural activity prior to sensorimotor decisions

Objective.Sensorimotor decisions require the brain to process external information and combine it with relevant knowledge prior to actions. In this study, we explore the neural predictors of motor actions in a novel, realistic driving task designed to study decisions while driving.Approach.Through a spatiospectral assessment of functional connectivity during the premotor period, we identified the organization of visual cortex regions of interest into a distinct scene processing network. Additionally, we identified a motor action selection network characterized by coherence between the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC).Main results.We show that steering behavior can be predicted from oscillatory power in the visual cortex, DLPFC, and ACC. Power during the premotor periods (specific to the theta and beta bands) correlates with pupil-linked arousal and saccade duration.Significance.We interpret our findings in the context of network-level correlations with saccade-related behavior and show that the DLPFC is a key node in arousal circuitry and in sensorimotor decisions.

Time-on-task effects on human pupillary and saccadic metrics after theta burst transcranial magnetic stimulation over the frontal eye field

Pupil size undergoes constant changes primarily influenced by ambient luminance. These changes are referred to as the pupillary light reflex (PLR), where the pupil transiently constricts in response to light. PLR kinematics provides valuable insights into autonomic nervous system function and have significant clinical applications. Recent research indicates that attention plays a role in modulating the PLR, and the circuit involving the frontal eye field (FEF) and superior colliculus is causally involved in controlling this pupillary modulation. However, there is limited research exploring the role of the human FEF in these pupillary responses, and its impact on PLR metrics remains unexplored. Additionally, although the protocol of continuous theta-burst stimulation (cTBS) is well-established, the period of disruption after cTBS is yet to be examined in pupillary responses. Our study aimed to investigate the effects of FEF cTBS on pupillary and saccadic metrics in relation to time spent performing a task (referred to as time-on-task). We presented a bright stimulus to induce the PLR in visual- and memory-delay saccade tasks following cTBS over the right FEF or vertex. FEF cTBS, compared to vertex cTBS, resulted in decreased baseline pupil size, peak constriction velocities, and amplitude. Furthermore, the time-on-task effects on baseline pupil size, peak amplitude, and peak time differed between the two stimulation conditions. In contrast, the time-on-task effects on saccadic metrics were less pronounced between the two conditions. In summary, our study provides the first evidence that FEF cTBS affects human PLR metrics and that these effects are modulated by time-on-task.

Adaptive decision-making depends on pupil-linked arousal in rats performing tactile discrimination tasks

Perceptual decision-making is a dynamic cognitive process and is shaped by many factors, including behavioral state, reward contingency, and sensory environment. To understand the extent to which adaptive behavior in decision-making is dependent on pupil-linked arousal, we trained head-fixed rats to perform perceptual decision-making tasks and systematically manipulated the probability of Go and No-go stimuli while simultaneously measuring their pupil size in the tasks. Our data demonstrated that the animals adaptively modified their behavior in response to the changes in the sensory environment. The response probability to both Go and No-go stimuli decreased as the probability of the Go stimulus being presented decreased. Analyses within the signal detection theory framework showed that while the animals' perceptual sensitivity was invariant, their decision criterion increased as the probability of the Go stimulus decreased. Simulation results indicated that the adaptive increase in the decision criterion will increase possible water rewards during the task. Moreover, the adaptive decision-making is dependent on pupil-linked arousal as the increase in the decision criterion was the largest during low pupil-linked arousal periods. Taken together, our results demonstrated that the rats were able to adjust their decision-making to maximize rewards in the tasks, and that adaptive behavior in perceptual decision-making is dependent on pupil-linked arousal.NEW & NOTEWORTHY Perceptual decision-making is a dynamic cognitive process and is shaped by many factors. However, the extent to which changes in sensory environment result in adaptive decision-making remains poorly understood. Our data provided new experimental evidence demonstrating that the rats were able to adaptively modify their decision criterion to maximize water reward in response to changes in the statistics of the sensory environment. Furthermore, the adaptive decision-making is dependent on pupil-linked arousal.

Distinct value computations support rapid sequential decisions

The value of the environment determines animals' motivational states and sets expectations for error-based learning1-3. How are values computed? Reinforcement learning systems can store or cache values of states or actions that are learned from experience, or they can compute values using a model of the environment to simulate possible futures3. These value computations have distinct trade-offs, and a central question is how neural systems decide which computations to use or whether/how to combine them4-8. Here we show that rats use distinct value computations for sequential decisions within single trials. We used high-throughput training to collect statistically powerful datasets from 291 rats performing a temporal wagering task with hidden reward states. Rats adjusted how quickly they initiated trials and how long they waited for rewards across states, balancing effort and time costs against expected rewards. Statistical modeling revealed that animals computed the value of the environment differently when initiating trials versus when deciding how long to wait for rewards, even though these decisions were only seconds apart. Moreover, value estimates interacted via a dynamic learning rate. Our results reveal how distinct value computations interact on rapid timescales, and demonstrate the power of using high-throughput training to understand rich, cognitive behaviors.

Adaptive learning from outcome contingencies in eating-disorder risk groups

Eating disorders are characterised by altered eating patterns alongside overvaluation of body weight or shape, and have relatively low rates of successful treatment and recovery. Notably, cognitive inflexibility has been implicated in both the development and maintenance of eating disorders, and understanding the reasons for this inflexibility might indicate avenues for treatment development. We therefore investigate one potential cause of this inflexibility: an inability to adjust learning when outcome contingencies change. We recruited (n = 82) three groups of participants: those who had recovered from anorexia nervosa (RA), those who had high levels of eating disorder symptoms but no formal diagnosis (EA), and control participants (HC). They performed a reinforcement learning task (alongside eye-tracking) in which the volatility of wins and losses was independently manipulated. We predicted that both the RA and EA groups would adjust their learning rates less than the control participants. Unexpectedly, the RA group showed elevated adjustment of learning rates for both win and loss outcomes compared to control participants. The RA group also showed increased pupil dilation to stable wins and reduced pupil dilation to stable losses. Their learning rate adjustment was associated with the difference between their pupil dilation to volatile vs. stable wins. In conclusion, we find evidence that learning rate adjustment is unexpectedly higher in those who have recovered from anorexia nervosa, indicating that the relationship between eating disorders and cognitive inflexibility may be complex. Given our findings, investigation of noradrenergic agents may be valuable in the field of eating disorders.

Competing neural representations of choice shape evidence accumulation in humans

Making adaptive choices in dynamic environments requires flexible decision policies. Previously, we showed how shifts in outcome contingency change the evidence accumulation process that determines decision policies. Using in silico experiments to generate predictions, here we show how the cortico-basal ganglia-thalamic (CBGT) circuits can feasibly implement shifts in decision policies. When action contingencies change, dopaminergic plasticity redirects the balance of power, both within and between action representations, to divert the flow of evidence from one option to another. When competition between action representations is highest, the rate of evidence accumulation is the lowest. This prediction was validated in in vivo experiments on human participants, using fMRI, which showed that (1) evoked hemodynamic responses can reliably predict trial-wise choices and (2) competition between action representations, measured using a classifier model, tracked with changes in the rate of evidence accumulation. These results paint a holistic picture of how CBGT circuits manage and adapt the evidence accumulation process in mammals.

Surprise and novelty in the brain

Notions of surprise and novelty have been used in various experimental and theoretical studies across multiple brain areas and species. However, 'surprise' and 'novelty' refer to different quantities in different studies, which raises concerns about whether these studies indeed relate to the same functionalities and mechanisms in the brain. Here, we address these concerns through a systematic investigation of how different aspects of surprise and novelty relate to different brain functions and physiological signals. We review recent classifications of definitions proposed for surprise and novelty along with links to experimental observations. We show that computational modeling and quantifiable definitions enable novel interpretations of previous findings and form a foundation for future theoretical and experimental studies.

Investigating social comparison behaviour in an immersive virtual reality classroom based on eye-movement data

Higher-achieving peers have repeatedly been found to negatively impact students' evaluations of their own academic abilities (i.e., Big-Fish-Little-Pond Effect). Building on social comparison theory, this pattern is assumed to result from students comparing themselves to their classmates; however, based on existing research designs, it remains unclear how exactly students make use of social comparison information in the classroom. To determine the extent to which students (N = 353 sixth graders) actively attend and respond to social comparison information in the form of peers' achievement-related behaviour, we used eye-tracking data from an immersive virtual reality (IVR) classroom. IVR classrooms offer unprecedented opportunities for psychological classroom research as they allow to integrate authentic classroom scenarios with maximum experimental control. In the present study, we experimentally varied virtual classmates' achievement-related behaviour (i.e., their hand-raising in response to the teacher's questions) during instruction, and students' eye and gaze data showed that they actively processed this social comparison information. Students who attended more to social comparison information (as indicated by more frequent and longer gaze durations at peer learners) had less favourable self-evaluations. We discuss implications for the future use of IVR environments to study behaviours in the classroom and beyond.

Multiple routes to enhanced memory for emotionally relevant events

Events associated with aversive or rewarding outcomes are prioritized in memory. This memory boost is commonly attributed to the elicited affective response, closely linked to noradrenergic and dopaminergic modulation of hippocampal plasticity. Herein we review and compare this 'affect' mechanism to an additional, recently discovered, 'prediction' mechanism whereby memories are strengthened by the extent to which outcomes deviate from expectations, that is, by prediction errors (PEs). The mnemonic impact of PEs is separate from the affective outcome itself and has a distinct neural signature. While both routes enhance memory, these mechanisms are linked to different - and sometimes opposing - predictions for memory integration. We discuss new findings that highlight mechanisms by which emotional events strengthen, integrate, and segment memory.

Distinct value computations support rapid sequential decisions

The value of the environment determines animals' motivational states and sets expectations for error-based learning1-3. How are values computed? Reinforcement learning systems can store or "cache" values of states or actions that are learned from experience, or they can compute values using a model of the environment to simulate possible futures3. These value computations have distinct trade-offs, and a central question is how neural systems decide which computations to use or whether/how to combine them4-8. Here we show that rats use distinct value computations for sequential decisions within single trials. We used high-throughput training to collect statistically powerful datasets from 291 rats performing a temporal wagering task with hidden reward states. Rats adjusted how quickly they initiated trials and how long they waited for rewards across states, balancing effort and time costs against expected rewards. Statistical modeling revealed that animals computed the value of the environment differently when initiating trials versus when deciding how long to wait for rewards, even though these decisions were only seconds apart. Moreover, value estimates interacted via a dynamic learning rate. Our results reveal how distinct value computations interact on rapid timescales, and demonstrate the power of using high-throughput training to understand rich, cognitive behaviors.