With an ageing population, an increasing number of people are seeking medical advice for concerns about cognitive decline (Deary et al., 2009; Harada et al., 2013). While in some individuals these complaints might be related to a progressive pathological process such as Alzheimer’s disease (AD), they can also be expressed by people without an underlying neurodegenerative disorder (McWhirter et al., 2020). When objective clinical evidence of significant cognitive impairment is not evident alongside self-reported cognitive complaints, individuals are categorised as having subjective cognitive impairment/decline (SCI/D) (Jessen et al., 2014; Jessen et al., 2020; Reid and Maclullich, 2006). Although most follow a relatively benign course, a small proportion develops objective cognitive impairment and subsequently progress to dementia (Arvanitakis et al., 2018; Kryscio et al., 2014; Mendonça et al., 2016). Nevertheless, it remains unclear what drives subjective cognitive complaints in those people who do not have evidence of underlying neurodegeneration. Understanding the mechanisms of cognitive, behavioural, and psychiatric manifestations in SCI is thus crucial to improve clinical outcomes and enhance understanding of presentation of people who present with cognitive concerns.

A wealth of evidence suggests a particularly high prevalence of a range of mental health problems associated with SCI, in particular affective disorders such as anxiety and depression (Hill et al., 2016; Hohman et al., 2011; Pavisic et al., 2021; Reid and Maclullich, 2006). Similarly, people who primarily suffer from these psychiatric disorders often report sub-optimal cognitive performance, further emphasising the intertwined relationship between affective burden and subjective cognitive experience (Millan et al., 2012). Moreover, treating anxiety and depression may improve subjective cognitive complaints in individuals with SCI (Allott et al., 2020). Despite the association between SCI and affective burden being increasingly recognised, little is understood about the underlying cognitive mechanisms and brain networks involved.

A rich body of theoretical and empirical work suggests that affective dysregulation might be related to uncertainty processing and related behaviours (Bishop and Gagne, 2018; Carleton, 2016; Grupe and Nitschke, 2013; Gu et al., 2020). People who express higher levels of anxiety and depression often report higher levels of intolerance to uncertainty (Boelen et al., 2016; Boswell et al., 2013; Carleton et al., 2012; McEvoy and Mahoney, 2011; Saulnier et al., 2019). Mechanistically, intolerance to uncertainty might be reflected in several cognitive and behavioural processes underpinning goal-directed behaviour when people decide and act under uncertainty (Grupe and Nitschke, 2013). For example, when someone is crossing the road, they make their decision based on how confident they are that the environment is safe (i.e. they have an assessment of how uncertain the environment is for their intended action) (Bach and Dolan, 2012; Gottlieb and Oudeyer, 2018). If uncertainty is high, agents often try to reduce it by gathering information to inform their decision (e.g. checking passing cars and traffic lights and looking for a safer place to cross). People who are more sensitive to uncertainty might have an exaggerated estimation of uncertainty or preparedness when required to face it, eventually affecting their decisions and outcomes (Grupe and Nitschke, 2013). Similarly, uncertainty sensitivity might affect self evaluation of cognitive abilities (e.g. having lower confidence in recollection) amplifying memory complaints and subsequent emotional reaction (Fitzgerald et al., 2017; Nelson, 1990). Such a framework, which involves estimation, valuation, preparation, and learning under uncertainty allows a detailed investigation of the psychopathology of affective dysfunction (Gottlieb and Oudeyer, 2018; Grupe and Nitschke, 2013; Sharot and Sunstein, 2020).

Investigation of the dynamics of how people decide and act under uncertainty might hold an important key to understanding the relationship between SCI and affective dysfunction. This might be challenging to achieve using classical behavioural paradigms, for example beads task or variants of it (Phillips et al., 1966). These paradigms often involve randomly drawing samples from a distribution to make inferences about the distribution (e.g, deciding the predominant colour of beads in a jar). However, when people gather information to reduce uncertainty, they dynamically assess their environment and update their expectations in order to decide whether a new piece of evidence is needed and whether they can tolerate its cost (Juni et al., 2016; Petitet et al., 2021). Although the economic aspect of this behaviour has been extensively examined in previous studies (Clark et al., 2006; Jones et al., 2019; Juni et al., 2016), investigation into how information is gathered is limited. Capturing behavioural markers that might not be directly apparent using such tasks (e.g. sampling speed and efficiency) might provide important insights into underpinning mechanisms of affective disorders. This distinction has been formalised as ‘active’ information gathering, characterised by situations in which participants have agency over not only how much information they gather but also how information is collected in face of uncertainty (e.g. what resources to consult and when) (Gottlieb and Oudeyer, 2018; Petitet et al., 2021).

In this study, we adopted this approach using a recently developed behavioural paradigm to investigate how people with SCI decide and act (gather information) under uncertainty (Petitet et al., 2021). A crucial question of this study was whether uncertainty processing is associated with affective burden. Further, to investigate the underlying brain structures and networks that might be implicated in the process, brain resting functional neuroimaging (rfMRI) data were also collected. In an active form of the task, participants collected informative clues, which came at a known cost, to reduce their uncertainty before committing to decisions. Crucially, they were allowed to freely gather information whenever and in whichever way they wanted. In a passive form of the task, this agency over uncertainty was removed. Participants were allowed only to accept or reject offers that had fixed levels of uncertainty weighed against potential reward. Decisions were made based on whether tolerating uncertainty was worth the reward on offer. This enabled us to calculate how people weigh uncertainty against reward in a passive environment where agency over uncertainty is absent. Before decisions, participants also reported their subjective uncertainty, enabling us to measure the accuracy of uncertainty estimation that might influence both active and passive behaviour.

The results from the behavioural tasks showed that individuals with SCI gather significantly more information than healthy matched controls before they commit to final decisions. They did this regardless of the cost of information and at a faster rate than controls. Despite this faster sampling, SCI participants impressively were capable of maintaining their sampling efficiency (i.e. gathering samples that were as informative as controls). This meant that they exceeded the speed-efficiency trade-off that characterises sampling behaviour of healthy controls. Crucially, in individuals with SCI, sampling speed and over-sampling (indices of heightened reactivity to uncertainty) were associated with affective burden (derived from self-report measures of anxiety and depression).

By contrast, when they had no agency over the reward and uncertainty on offer (passive choice task), SCI participants had intact metacognitive assessment and valuation of uncertainty. This suggests that controllability when dealing with uncertainty might be a crucial aspect in affective dysfunction, as differences between SCI participants and controls were apparent only when they had agency over uncertainty (i.e. during the active information gathering phase preceding decisions).

Functional neuroimaging analysis investigating whole-brain resting connectivity between regions of interest across all known brain networks revealed that individuals with SCI, in comparison to healthy controls, had increased insular hippocampal connectivity. Further, the strength of this connectivity in SCI correlated with reactivity to uncertainty indexed by sampling speed.

Taken together, the results indicate that hyperreactivity to uncertainty might be a key mechanism in SCI, and link this process to the insular cortex and hippocampus.

A rich body of literature indicates a strong association between SCI and affective dysregulation such as anxiety and depression (Hill et al., 2016; Hohman et al., 2011; Pavisic et al., 2021; Reid and Maclullich, 2006). However, the mechanisms underlying such a burden are not fully established. In this study, we hypothesised that affective dysfunction in SCI might be related to deficits in processing uncertainty. Using a purpose-designed behavioural paradigm, we investigated how people decide and act under uncertainty in active and passive contexts. In the active form, participants could gather information at a cost to reduce uncertainty before committing to decisions. In the passive form, they made decisions responding to offers that had fixed levels of uncertainty and potential reward. The results showed that when participants had agency (i.e. in the active form), individuals with SCI exhibited behaviour indicative of increased reactivity to uncertainty, manifested as more rapid and extensive sampling compared to age- and gender-matched healthy controls. These behavioural markers of heightened reactivity to uncertainty correlated with the severity of affective burden in SCI participants. Furthermore, resting functional neuroimaging analysis showed that individuals with SCI had increased insular-hippocampal connectivity, which in turn correlated with reactivity to uncertainty indexed by sampling speed. By contrast, estimation and weighing of uncertainty in passive decisions were both intact in SCI participants, suggesting that their hyperreactivity to uncertainty was specifically expressed in an active situation in which uncertainty is controllable. Overall, the results point to a specific deficit in processing uncertainty in individuals with SCI that might be underlying their affective dysregulation and is related to increased insular-hippocampal connectivity in the condition.

These results resonate with previous research indicating that people with depression and anxiety might have altered uncertainty processing (Bishop and Gagne, 2018; Boswell et al., 2013; Carleton et al., 2012; Grupe and Nitschke, 2013; Gu et al., 2020; Hartley and Phelps, 2012; Pulcu and Browning, 2019; Saulnier et al., 2019). A recent investigation, for example, showed that a common factor accounting for shared variance between both syndromes was associated with disrupted learning in probabilistic environments reflecting impaired uncertainty processing (Gagne et al., 2020). Other reports pointed to a possible alteration of uncertainty estimation and reward-related valuation in these syndromes affecting decision making under uncertainty (Pizzagalli et al., 2005; Pizzagalli et al., 2008; Pulcu and Browning, 2019). However, evidence on how affective dysfunction relates to more active forms of behaviour such as information gathering prior to committing decisions is limited. One early investigation in social psychology showed that individuals suffering from depression tend to acquire more high-utility information than non-depressed individuals in a simulated interview environment where participants played the role of the interviewer and had to select interview questions from a standardised list of questions that differed in their diagnostic utility (Hildebrand-Saints and Weary, 1989). While this investigation along with other similar reports from social psychology literature advance the notion that disrupted information gathering might be a key feature in affective disorders, a mechanistic account supporting this claim is still not established (Aderka et al., 2013; Camp, 1986; Joiner et al., 2009; Locander and Hermann, 1979). For example, previous studies using classical behavioural paradigms of information seeking such as the beads task failed to report consistent effects of anxiety and depression on performance (Jacoby et al., 2014). Such inconsistencies might be due to the fact that the behavioural paradigms used in these prior studies often neglect an important aspect of information gathering – controllability – which has been hypothesised to be a crucial feature of both anxiety and depression (Abramson et al., 1978; Barlow, 1991). In the present study, this important component was accounted for by using a novel behavioural paradigm allowing participants to gather information with minimal limitations to the speed and efficiency of sampling. This in turn revealed an insightful aspect of information gathering behaviour in individuals with SCI who not only sampled more than controls, but also showed that they do this more rapidly without losing efficiency.

One might argue that rather than suggesting that SCI individuals are hyperreactive to uncertainty in the active task, our findings might alternatively be explained by a lower reactivity to reward – as participants lost more credits to obtain the extra information. However, some observations in the study suggest that this might not be the case. First, the influence of economic constraints (R₀, ${\eta }_s$) on sampling behaviour and speed did not significantly differ between individuals with SCI and controls. If SCI participants were less reactive to reward, then one would expect economic constraints to affect sampling behaviour in SCI participants to a lesser degree than age- and gender-matched controls. Instead, the group effect (i.e. acquiring more samples in individuals with SCI) was equally manifested in all experimental conditions, regardless of the current cost-benefit structure. Second, acquiring more samples indeed allowed SCI participants to achieve lower uncertainty levels prior to decisions, suggesting that these additional samples carried instrumental utility and were not merely reflective of wasteful sampling behaviour driven by insensitivity to reward.

Another account of SCI individuals’ extensive sampling behaviour might involve subjective costs that are not directly specified in economic terms in the task. Namely, SCI individuals and controls might differ in the subjective cost they assign to sampling speed and efficiency. As demonstrated previously, these additional agent-related factors contribute to the utility of the samples being acquired and thus influence the extensiveness of sampling (Petitet et al., 2021). Given that SCI participants sampled faster but as efficiently as controls, these subjective costs appear to be lower in this population (see Appendix 1 for a formal demonstration using a computational model). As a result, samples carry higher utility overall, which promotes more extended data gathering. Potentially, there are several other subjective costs that people might be considering when gathering information. For example, SCI participants might have exaggerated cost linked to bad performance or inaccurate placements of the circle. We aimed to limit this specific cost by not displaying the hidden circle as feedback and showing the credits won or lost instead (Figure 1). In addition, since the task depends on touching the screen to obtain clues, the cost of these motor actions might have influenced sampling behaviour (Carland et al., 2019; Cisek and Kalaska, 2010; Morel et al., 2017; Pierrieau et al., 2021; Rangel and Hare, 2010; Scott, 2012). It is possible, for example, that SCI participants might be regarding reaching and touching the screen as less effortful than controls or assigning lower costs for motor precision. While we tried to limit the influence of the motor aspects on performance by strictly controlling the settings (e.g. fixed distance from the screen, using only index finger of dominant hand), the contribution of motor elements to the observed behaviour cannot be entirely determined or excluded and might pose a limitation. Such inter-individual differences could be in part captured by the intercept term in the computational model used to fit active information sampling data (w₀), which was not significantly different between the two groups (see Appendix 1).

In a similar vein, while the results from the passive task suggest that SCI individuals have intact estimation of uncertainty, this might not necessarily apply to a dynamic situation such as the active task. The distinct contexts of the two tasks (passive viewing vs. active gathering) may impose different computations involved in uncertainty estimation. For example, SCI individuals and controls might specifically differ in how they judge self-generated configurations of uncertainty (active task) vs. externally predefined ones (passive task) (Acosta, 1982; Gaschler et al., 2014; Kemper et al., 2012; Kemper and Gaschler, 2017). People might also differ in the reference points (temporal or spatial) they compare updated environments with as more information is added (Koop and Johnson, 2012; O’Donoghue and Sprenger, 2018; Sher and McKenzie, 2006; Tversky and Kahneman, 1986). It is also possible that these computations might be cognitively effortful, influencing how frequently or/and efficiently people re-estimate and update uncertainty during their search (Bhui et al., 2021; Lieder and Griffiths, 2019). All these factors might have contributed with different degrees to the pattern of the results observed in the study and might be a limitation to active information sampling investigations in general.

It should also be noted that the measure of subjective uncertainty based on self-reports in the passive may map imperfectly onto actual internal uncertainty (Navajas et al., 2017; Peters, 2022). Self-reports of confidence might be contaminated by other factors such as perceptual uncertainty (e.g. meaning of perceived stimuli on the task), memory (e.g. remembering task instructions or previous estimations) and decisions (e.g. confidence in estimation). The contribution of these metacognitive dimensions could be explored in future studies, especially given that SCI – by definition – is characterised by inconsistency between self-report estimates and objective measures (Jessen et al., 2020). Similarly, SCI might involve different cognitive domains (e.g. memory, attention, language, etc.) with different levels of severity (Diaz-Galvan et al., 2021; Jessen et al., 2020; Miebach et al., 2019; Si et al., 2020; Smart et al., 2014). How such differences relate to uncertainty processing and affective burden will require a fine-grained approach aimed specifically at characterising SCI cognitive complaints in-depth to disentangle the dimensions involved.

Various brain regions have previously been implicated in anxiety and depression and their mechanistic characterisation as deficits in uncertainty processing including amygdala (Grupe and Nitschke, 2013; Morriss et al., 2019), hippocampus (Gray and McNaughton, 2003; Harrison et al., 2006; Rigoli et al., 2019; Strange et al., 2005; Tobia et al., 2012), and insular cortex (Grupe and Nitschke, 2013; Morriss et al., 2019; Tanovic et al., 2018). Consistent with these reports, we found that individuals with SCI displayed heightened connectivity between these regions (insular-limbic). Conceptually, the insula stands out in the context of SCI not only because of its consistent implications in various forms of uncertainty processing and affective syndromes in health and disease (Morriss et al., 2019; Namkung et al., 2017; Paulus and Stein, 2006; Singer et al., 2009; Tanovic et al., 2018), but also because of its potential role in subjective awareness and interoception (Craig, 2009). The insular cortex receives input from different brain regions carrying interoceptive information about various bodily sensations, such as temperature, heartbeat, bowel distension and more (Namkung et al., 2017; Uddin et al., 2017). Subjective experience of these stimuli has been shown to correlate with insular activity on functional neuroimaging using MRI or positron emission tomography (Craig et al., 2000; Critchley et al., 2004). More recent accounts of insular function extend this contribution of subjective awareness to involve emotional states and higher subjective awareness (Chang et al., 2013; Craig, 2009; Namkung et al., 2017). It is thus not surprising to find insular involvement in a condition that is primarily defined by altered subjective experience. A few studies have demonstrated altered insular task-related activity in SCI linked to impaired memory performance and future-guided decision making, however, without pointing to how these findings are related to affective burden or subjectivity (Cai et al., 2020; Hu et al., 2017). By contrast, damage to the insula might impair subjectivity and self-awareness as seen in patients with anosognosia for hemiplegia and other forms of insular injury (Fotopoulou et al., 2010; Karnath et al., 2005; Spinazzola et al., 2008), as well as resulting in dysfunctional emotional awareness (e.g. as seen in fronto-temporal dementia), under-reactivity, lack of self-monitoring and passivity (Kleiner et al., 2007; Manes et al., 1999; Sturm et al., 2006). Such findings are opposite to what is observed in individuals with SCI who often report heightened levels of subjective affective dysfunction and were found in this study to be behaviourally more reactive.

This insula-centred formalisation of affective dysfunction, uncertainty processing, and subjectivity might be further supported by taking into consideration hippocampal contribution. One prominent view of hippocampal involvement in goal-directed behaviour suggests that it constitutes a crucial part of a behavioural inhibition system (BIS) that is concerned with processing aversive cues such as uncertainty (Gray and McNaughton, 2003). According to this view, the hippocampus acts as a comparator between one’s expectations and the environment, resulting in behavioural aversion to threatening and negative stimuli. It has therefore been suggested that hyperactivity of the BIS might be an important neurobiological basis of anxiety and affective dysregulation (Gray and McNaughton, 2003). When anticipating decisions and actions under uncertainty, the hippocampus might be encoding possible future states and their associated risks (Addis et al., 2011; Martin et al., 2011; Schacter et al., 2008; Schacter et al., 2017; Weiler et al., 2010). However, when such situations are avoidable (as in the active task), aversion might be expressed as a propensity to quickly collect information to avoid facing uncertainty in the future. Hippocampal signals encoding future trajectories and prior experiences from these contexts might be shared with the insula, which in turn process emotional responses resulting in a feeling of anxiety and depression (Chang et al., 2013; Craig, 2009; Paulus and Stein, 2006). Additionally, because of the hippocampus’ well-established mnemonic function, insular-hippocampal processing of uncertainty might also affect one’s awareness of memory performance, giving rise to subjective cognitive complaints characterising SCI. Testing such a hypothesis will require future research with a detailed examination of the nature of subjective complaints, their severity, and association with uncertainty and expression of concerns.

These neuroimaging findings are consistent with previous fMRI studies (task-related and resting) in SCI showing altered connectivity between temporal brain regions (e.g. hippocampus and para-hippocampus) and several other areas and networks including default mode and salience networks (Cai et al., 2020; Dillen et al., 2017; Hafkemeijer et al., 2013; Hu et al., 2017; Rodda et al., 2009; Verfaillie et al., 2018; Viviano and Damoiseaux, 2020). These investigations have reported both increased and decreased activation in these networks without converging spatial specificity or clear relationship with behaviour. While the hippocampus seems to be a key region commonly involved in SCI by many fMRI studies, a consistent pattern of its activity in the condition is still lacking (Dillen et al., 2017; Hafkemeijer et al., 2013; Rodda et al., 2009; Verfaillie et al., 2018). This might be due to issues related to sample size, methods used and importantly, phenotyping of SCI group included in such studies.

A recent account of these fMRI studies has tried to reconcile these apparent inconsistencies by promoting the idea that SCI might be related to general inefficiency in signal processing across whole-brain networks (Viviano and Damoiseaux, 2020). Our findings, however, point to a specific hippocampus-related network that might be involved in uncertainty processing and affective regulation. This could have future theoretical implications in SCI as converging reports support the role of regions of this network (limbic region and insula) in uncertainty processing and affective functioning (Gray and McNaughton, 2003; Grupe and Nitschke, 2013; Harrison et al., 2006; Morriss et al., 2019; Rigoli et al., 2019; Strange et al., 2005; Tanovic et al., 2018; Tobia et al., 2012). Further validation of our neuroimaging results might be needed using other techniques (e.g. task-related MRI, lesion studies) as well as replication efforts, ideally with larger samples.

One major future direction is to investigate how the mechanisms and brain networks uncovered in this study relate to AD spectrum and prospective risk of developing dementia. For example, while not needed to make a clinical diagnosis of SCI (Jessen et al., 2014), AD-related biological indicators (e.g. CSF biomarkers and amyloid and tau PET imaging) might provide valuable information on how processing uncertainty and information gathering relates to AD pathology in preclinical population with SCI. This could be further supported by evidence from longitudinal follow-up of individuals with SCI to establish risk factors and outcomes. Another line of research might benefit from adopting a transdiagnostic approach to examine whether and how cognitive mechanisms of information seeking and related affective dysfunction are shared (or distinguished) in different stages of AD and other forms of neurodegeneration. Similarly, examining patients who suffer from anxiety and depression without expression of cognitive complaints would help further delineate the association between affective burden and uncertainty-related behaviours.

In conclusion, our results provide evidence that hyperreactivity to uncertainty might be a key manifestation of SCI and is related to heightened functional connectivity between the insula and the hippocampus. These manifestations might be underlie affective burden in the condition.

Anonymised data and code for replicating the main results in the manuscript have been deposited on the Open Science Framework platform: https://osf.io/7ysqu/.

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Decision letter after peer review:

Thank you for submitting your article "Hypersensitivity to uncertainty is key feature of subjective cognitive impairment" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by Valentin Wyart as Reviewing Editor and Christian Büchel as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Sander Nieuwenhuis (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. As you will see, both reviewers have found that your study provides important new clues about the psychological and neural manifestations of subjective cognitive impairment (SCI), and we agree with this evaluation. However, the two reviewers have also identified a number of essential revisions, detailed below, that they agreed should be addressed in a point-by-point response letter, submitted together with the revised manuscript. Individual reviews are provided at the bottom of this message for your information, they do not require point-by-point responses. We hope that you will be able to address these different points in a revised version of your manuscript.

Essential revisions:

1) The test of overestimation or over-use of uncertainty in the passive task is limited by its reliance on self-report measures. Indeed, it is established that self-report confidence does not necessarily map neatly onto a single internal uncertainty variable that is used to inform decision-making (see, e.g., Navajas, 2017, Nature Human Behaviour). This means that it is not possible using the used task design to rule out alterations in uncertainty estimation in SCI. If the authors do not have an empirical way to deal with this limitation of the task design, they should at least mention this limitation explicitly in a paragraph of the Discussion section.

2) The use of the term 'hypersensitivity' throughout the manuscript to characterize the main finding is problematic. First, if the SCI group was hypersensitive to uncertainty, then why did they not differ from controls in how they estimated and valued uncertainty in the 'passive' condition? In other places in the manuscript, the authors summarize the same key findings as the SCI group being 'hyperreactive' to uncertainty. Given the importance of agency and active sampling, the term 'hyperreactivity' seems far more appropriate than 'hypersensitivity' to characterize the main finding. Second, the term 'hypersensitivity' lacks a level of explanation – what exactly is 'hypersensitivity'? And why should it specifically result in faster and more exhaustive information? The authors should either clarify more explicitly upfront in the manuscript what is meant by 'hypersensitivity', or even better adjust their terminology by switching to 'hyperreactivity', including in the title of the paper.

3) Important information about psychometrics is missing in the current version of the manuscript. First, while the use of PCA to generate a measure of general affective burden is sensible, it ignores potential differential effects of anxiety and depression. The authors should therefore investigate and report the effects of each separately to clarify whether behavioral tendencies are indeed related to a common affective component rather than to specific symptoms. Second, were subjects assessed for clinical anxiety and depression? If so, this could be mentioned. Third, was the level of SCI quantified by any means? And if so, did it relate to anxiety/depression or sampling behavior? And last, was there any correlation between affective burden and confidence or acceptance probability in the passive task? This should also be reported in the revised manuscript.

4) Alternative accounts of the main finding are missing. Even if the proposed interpretation of the main finding is appealing, there are alternative explanations that are hard to exclude conclusively, in particular related to subjective costs (regardless of objective values in the task). For example, it may be that the subjective cost of not finding the circle (e.g., the personal cost of failing at the task) is exaggerated in SCI, which could explain the more extensive sampling, or potentially a lower subjective cost of sampling. Such alternative accounts should at least be discussed explicitly in a dedicated paragraph in the Discussion section.

5) Cross-subject correlations between a number of variables that differ between the two groups are highly problematic when pooling across the two groups. If you take two variables that both significantly differ between groups and then correlate the two variables across all study participants, you are almost bound to find a significant correlation, even when within each group there is no significant correlation. This problem is illustrated by the classic statistical textbook example of a spurious correlation between two random point clouds that are separated from each other along both axes. These correlations (gray numbers in Figures 5C and 7) should therefore not be reported because they probably reflect little more than the significant main effects of the group on the two variables that were already reported in earlier parts of the Results section. The fact that the correlations between measures of hyperreactivity and affective burden (purple numbers in Figure 5C) are also significant when only the SCI participants are included is reassuring. The same is true for the correlation between hyperreactivity and insular-hippocampal connectivity (purple numbers in Figure 7). The problem is with the correlation between affective burden and insular-hippocampal connectivity (Figure 7): this correlation is significant when all participants are included but is clearly not present when only the SCI participants (or only the controls) are considered. This means that it cannot be concluded that these measures are related to each other. The same line of argument also invalidates the mediation analysis that includes all three variables (and corresponding conclusions). If the authors want to report a mediation analysis in the revised manuscript, they should base it only on the SCI participants. Reviewer #1 has noted that the authors acknowledge that "Caution should be exercised when interpreting these [correlation] results as these models were performed with data from both groups included. This was done to take advantage of the larger sample size for this type of analysis, while also trying to explore inter-individual differences regardless of group assignment." However, based on the statistical issue raised above, this is not sufficient. These between-subject correlation analyses including all participants should be removed from the manuscript, and the authors should adjust the Results and Discussion sections accordingly. As stressed by Reviewer #1, there are plenty of remaining valid results that are interesting and novel once the problematic analyses have been removed from the paper.

6) Were there any group differences in motion parameters from the imaging data? Or any correlations with symptoms or behavior in the task? These analyses would be helpful to confirm that there are no issues with motion that could explain the connectivity results.

7) How does one determine the optimal number of samples? This quantification should be unpacked much more explicitly in the revised manuscript.

8) Have there been any previous fMRI studies of functional connectivity in SCI? If so, then their results should be summarized somewhere in the Discussion section, and compared to the current results.

9) The authors should either make explicit what the voxel-wise seed-based functional connectivity analysis adds to the whole-brain analysis or remove it from the revised manuscript. Otherwise, readers may see this additional analysis as a form of 'double-dipping'.

Essential revisions:

1) The test of overestimation or over-use of uncertainty in the passive task is limited by its reliance on self-report measures. Indeed, it is established that self-report confidence does not necessarily map neatly onto a single internal uncertainty variable that is used to inform decision-making (see, e.g., Navajas, 2017, Nature Human Behaviour). This means that it is not possible using the used task design to rule out alterations in uncertainty estimation in SCI. If the authors do not have an empirical way to deal with this limitation of the task design, they should at least mention this limitation explicitly in a paragraph of the Discussion section.

We agree that estimation of uncertainty in the passive task is limited to self-reported confidence. This is now addressed in the Discussion section as follows:

“It should also be noted that the measure of subjective uncertainty based on self-reports in the passive may map imperfectly onto actual internal uncertainty (Navajas et al., 2017; Peters, 2022). Self-reports of confidence might be contaminated by other factors such as perceptual uncertainty (e.g., meaning of perceived stimuli on the task), memory (e.g., remembering task instructions or previous estimations) and decisions (e.g., confidence in estimation). The contribution of these metacognitive dimensions could be explored in future studies, especially given that SCI – by definition – is characterised by inconsistency between self-report estimates and objective measures (Jessen et al., 2020). Similarly, SCI might involve different cognitive domains (e.g., memory, attention, language, etc.) with different levels of severity (Diaz-Galvan et al., 2021; Jessen et al., 2020; Miebach et al., 2019; Si et al., 2020; Smart et al., 2014; Si et al., 2020; Diaz-Galvan et al., 2021; Miebach et al., 2019). How such differences relate to uncertainty processing and affective burden will require a fine-grained approach aimed specifically at characterising SCI cognitive complaints in-depth to disentangle the dimensions involved. ”

However, we also note that there is an important distinction between over-estimation and over-use of uncertainty, and the test of the latter is not totally reliant on self-report of uncertainty. Use (valuation) of uncertainty was measured by analysing the decisions requiring a trade-off between uncertainty and reward (passive task). Here, the term ‘sensitivity’ might be appropriate (see below – reviewers’ comment 2) as we are interested in how SCI participants change their acceptance based on changes in expected error (EE) and reward on offer. Participants with a steeper slope of acceptance as a function of changes in EE (or reward) are considered to have higher sensitivity to changes in uncertainty (or reward) compared to participants with flatter slopes. In the generalised mixed effects model used to analyse the data, this is equal to an interaction term between group and EE/reward (group X EE/reward), which was not significant – visually evident by almost identical slopes of offer acceptance as function of reward and EE. Author response image 1 displays another visualisation of these sensitivity measures extracted from the model. Note that there was no significant difference between SCI individuals and controls, indicating similar incentivisation by both attributes when making decisions (see Figure 3c. and Table in Supplementary file 5). Thus, there was no evidence of over-use of uncertainty in SCI participants based on these objective measures extracted from participants’ choices.

Author response image 1

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2) The use of the term 'hypersensitivity' throughout the manuscript to characterize the main finding is problematic. First, if the SCI group was hypersensitive to uncertainty, then why did they not differ from controls in how they estimated and valued uncertainty in the 'passive' condition? In other places in the manuscript, the authors summarize the same key findings as the SCI group being 'hyperreactive' to uncertainty. Given the importance of agency and active sampling, the term 'hyperreactivity' seems far more appropriate than 'hypersensitivity' to characterize the main finding. Second, the term 'hypersensitivity' lacks a level of explanation – what exactly is 'hypersensitivity'? And why should it specifically result in faster and more exhaustive information? The authors should either clarify more explicitly upfront in the manuscript what is meant by 'hypersensitivity', or even better adjust their terminology by switching to 'hyperreactivity', including in the title of the paper.

We have changed the term ‘hypersensitivity’ to ‘hyperreactivity’ throughout the manuscript, including the title.

3) Important information about psychometrics is missing in the current version of the manuscript. First, while the use of PCA to generate a measure of general affective burden is sensible, it ignores potential differential effects of anxiety and depression. The authors should therefore investigate and report the effects of each separately to clarify whether behavioral tendencies are indeed related to a common affective component rather than to specific symptoms.

We have now added the results for anxiety and depression separately to the revised manuscript.

“To assess the individual contribution of anxiety and depression to these findings in SCI, the same analysis was performed separately for each affective component. This showed that BDI-II score (depression) correlated with faster sampling (R² = 0.17, p = 0.039) but not over-sampling (p = 0.15) in SCI. The HADS score (anxiety) on the other hand correlated with over-sampling (R² = 0.17, p = 0.038) but not speed (p = 0.16). However, these effects became insignificant (p = 0.12 for both) when the two affective components partialled out each other (partial correlations), suggesting that while there might be distinctive effects of depression and anxiety, these effects are better explained by the general affective burden imposed by these two dimensions given their association.”

Second, were subjects assessed for clinical anxiety and depression? If so, this could be mentioned.

During the clinical interview, all participants were assessed for anxiety and depression but not by a psychiatrist with expertise in making such diagnoses, so we report simply objective questionnaire scores.

Third, was the level of SCI quantified by any means? And if so, did it relate to anxiety/depression or sampling behavior?

No. level of SCI was not quantified. This is an important question that should be investigated in future research. We highlighted this in the revised discussion:

“Similarly, SCI might involve different cognitive domains (e.g., memory, attention, language, etc.) with different levels of severity (Diaz-Galvan et al., 2021; Jessen et al., 2020; Miebach et al., 2019; Si et al., 2020; Smart et al., 2014; Si et al., 2020; Diaz-Galvan et al., 2021; Miebach et al., 2019). How such differences relate to uncertainty processing and affective burden will require a fine-grained approach aimed specifically at characterising SCI cognitive complaints in-depth to disentangle the dimensions involved.”

“Additionally, because of the hippocampus’ well-established mnemonic function, insular-hippocampal processing of uncertainty might also affect one’s awareness of memory performance, giving rise to subjective cognitive complaints characterising SCI. Testing such a hypothesis will require future research with a detailed examination of the nature of subjective complaints, their severity, and association with uncertainty and expression of concerns.”

And last, was there any correlation between affective burden and confidence or acceptance probability in the passive task? This should also be reported in the revised manuscript.

We investigated this effect using two additional LMMs for confidence and decisions. There was no significant effect of affective burden in these two models. These results have now been added to the revised manuscripts and tables with summary statistics are added to supplementary tables (Tables in Supplementary files 4 and 5).

“Finally, the correlation between affective burden and passive task performance was investigated using LMMs. The results showed that affective burden had no significant effect (main or interaction) on subjective uncertainty or acceptance probability (Main effect of affective burden on subjective uncertainty: β = −0.003, t(5394) = −0.27, p = 0.79; Main effect of affective burden on offer acceptance: β = −0.151, t(5390) = −1.96, p = 0.05; See Supplementary files 4 and 5 for full statistical details including interactions)”

4) Alternative accounts of the main finding are missing. Even if the proposed interpretation of the main finding is appealing, there are alternative explanations that are hard to exclude conclusively, in particular related to subjective costs (regardless of objective values in the task). For example, it may be that the subjective cost of not finding the circle (e.g., the personal cost of failing at the task) is exaggerated in SCI, which could explain the more extensive sampling, or potentially a lower subjective cost of sampling. Such alternative accounts should at least be discussed explicitly in a dedicated paragraph in the Discussion section.

Alternative accounts of the main finding are now developed more extensively in the discussion (see text below). In one of these paragraphs, we highlight the computational model used to capture some of the agent-related costs in active sampling. Results from this model are included in Supplementary information. We chose not to add these results to the main text for the sake of simplicity as they represent alternative formalisation of the results already reported (relying on harder concepts to grasp for the reader). Interested readers can consult Appendix 1 for full report.

“One might argue that rather than suggesting that SCI individuals are hyperreactive to uncertainty in the active task, our findings might alternatively be explained by a lower reactivity to reward – as participants lost more credits to obtain the extra information. However, some observations in the study suggest that this might not be the case. First, the influence of economic constraints (R₀ , η_s ) on sampling behaviour and speed did not significantly differ between individuals with SCI and controls. If SCI participants were less reactive to reward, then one would expect economic constraints to affect sampling behaviour in SCI participants to a lesser degree than age- and gender-matched controls. Instead, the group effect (i.e., acquiring more samples in individuals with SCI) was equally manifested in all experimental conditions, regardless of the current cost-benefit structure. Second, acquiring more samples indeed allowed SCI participants to achieve lower uncertainty levels prior to decision, suggesting that these additional samples carried instrumental utility and were not merely reflective of wasteful sampling behaviour driven by insensitivity to reward.

Another account of SCI individuals’ extensive sampling behaviour might involve subjective costs that are not directly specified in economic terms in the task. Namely, SCI individuals and controls might differ in the subjective cost they assign to sampling speed and efficiency. As demonstrated previously, these additional agent-related factors contribute to the utility of the samples being acquired and thus influence the extensiveness of sampling (Petitet et al., 2021). Given that SCI participants sampled faster but as efficiently as controls, these subjective costs appear to be lower in this population (see Appendix 1 for a formal demonstration using a computational model). As a result, samples carry higher utility overall, which promotes more extended data gathering. Potentially, there are several other subjective costs that people might be considering when gathering information. For example, SCI participants might have exaggerated cost linked to bad performance or inaccurate placements of the circle. We aimed to limit this specific cost by not displaying the hidden circle as feedback and showing the credits won or lost instead (Figure 1). In addition, since the task depends on touching the screen to obtain clues, the cost of these motor actions might have influenced sampling behaviour (Carland et al., 2019; Cisek and Kalaska, 2010; Morel et al., 2017; Pierrieau et al., 2021; Rangel and Hare, 2010; Scott, 2012). It is possible, for example, that SCI participants might be regarding reaching and touching the screen as less effortful than controls or assigning lower costs for motor precision. While we tried to limit the influence of the motor aspects on performance by strictly controlling the settings (e.g., fixed distance from the screen, using only index finger of dominant hand), the contribution of motor elements to the observed behaviour cannot be entirely determined or excluded and might pose a limitation. Such inter-individual differences could be in part captured by the intercept term in the computational model used to fit active information sampling data (w₀), which was not significantly different between the two groups (see Appendix 1).

In a similar vein, while the results from the passive task suggest that SCI individuals have intact estimation of uncertainty, this might not necessarily apply to a dynamic situation such as the active task. The distinct contexts of the two tasks (passive viewing vs. active gathering) may impose different computations involved in uncertainty estimation. For example, SCI individuals and controls might specifically differ in how they judge self-generated configurations of uncertainty (active task) vs. externally predefined ones (passive task) (Acosta, 1982; Gaschler et al., 2014; Kemper et al., 2012; Acosta, 1982;; Kemper and Gaschler, 2017). People might also differ in the reference points (temporal or spatial) they compare updated environments with as more information is added (Koop and Johnson, 2012; O’Donoghue and Sprenger, 2018; Sher and McKenzie, 2006; Tversky and Kahneman, 1986). It is also possible that these computations might be cognitively effortful, influencing how frequently or/and efficiently people re-estimate and update uncertainty during their search (Bhui et al., 2021; Lieder and Griffiths, 2019). All these factors might have contributed with different degrees to the pattern of the results observed in the study and might be a limitation to active information sampling investigations in general.”

5) Cross-subject correlations between a number of variables that differ between the two groups are highly problematic when pooling across the two groups. If you take two variables that both significantly differ between groups and then correlate the two variables across all study participants, you are almost bound to find a significant correlation, even when within each group there is no significant correlation. This problem is illustrated by the classic statistical textbook example of a spurious correlation between two random point clouds that are separated from each other along both axes. These correlations (gray numbers in Figures 5C and 7) should therefore not be reported because they probably reflect little more than the significant main effects of the group on the two variables that were already reported in earlier parts of the Results section. The fact that the correlations between measures of hyperreactivity and affective burden (purple numbers in Figure 5C) are also significant when only the SCI participants are included is reassuring. The same is true for the correlation between hyperreactivity and insular-hippocampal connectivity (purple numbers in Figure 7). The problem is with the correlation between affective burden and insular-hippocampal connectivity (Figure 7): this correlation is significant when all participants are included but is clearly not present when only the SCI participants (or only the controls) are considered. This means that it cannot be concluded that these measures are related to each other. The same line of argument also invalidates the mediation analysis that includes all three variables (and corresponding conclusions). If the authors want to report a mediation analysis in the revised manuscript, they should base it only on the SCI participants. Reviewer #1 has noted that the authors acknowledge that "Caution should be exercised when interpreting these [correlation] results as these models were performed with data from both groups included. This was done to take advantage of the larger sample size for this type of analysis, while also trying to explore inter-individual differences regardless of group assignment." However, based on the statistical issue raised above, this is not sufficient. These between-subject correlation analyses including all participants should be removed from the manuscript, and the authors should adjust the Results and Discussion sections accordingly. As stressed by Reviewer #1, there are plenty of remaining valid results that are interesting and novel once the problematic analyses have been removed from the paper.

We removed all cross-subject correlations reported in the manuscript and made the necessary changes to all sections of the revised manuscript.

6) Were there any group differences in motion parameters from the imaging data? Or any correlations with symptoms or behavior in the task? These analyses would be helpful to confirm that there are no issues with motion that could explain the connectivity results.

Six motion parameters were used during realignment procedure for rfMRI processing. These correspond to six timeseries containing three transnational and three rotational parameters over time for each subject. None of these parameters was significantly different between SCI and controls groups (all p_corr > 0.61). Five quality control estimates were used during reprocessing of neuroimaging data. These included number of valid scans after scrubbing procedure, mean and maximum motion (extracted from the six motion parameters), mean and max global signal change (Whitfield-Gabrieli and Nieto-Castanon, 2012). None of these parameters was significantly different between the two groups (all p_corr > 0.16). Based on mean motion and global signal changes, four potential outliers (three SCI participants and one control) were identified. A second version of the neuroimaging analysis was performed with these participants excluded. There were no changes to the results or conclusions made in the paper. These findings suggest that rfMRI differences between SCI participants and controls are unlikely due to motion artifacts. These analyses are now added to supplementary information in the revised manuscript (Supplementary files 10 and 11)

We also investigated correlations between mean motion or mean global signal change and ISI, over-sampling and affective burden. No significant correlation was found (all p_corr = 1). Specifically, no correlation between ISI and these quality control measures (mean motion and mean GS change) across SCI participants (p = 0.13 and p = 0.49, respectively). These control analyses have been added to supplementary information in the revised manuscript (Supplementary file 12).

7) How does one determine the optimal number of samples? This quantification should be unpacked much more explicitly in the revised manuscript.

We included more details about this in the methods section. As recommended by reviewer #1 we moved the equations for the quantification of uncertainty, efficiency, and optimal solution to the main method section in the revised manuscript.

The paragraph below in the revised manuscript explains quantification of optimal solution and deviation from optimality. We also summarised these predictions in Figure 2—figure supplement 2 in the revised manuscript.

“Quantifying deviation from optimality.

Behaving rationally in the active task means stopping sampling when the expected value, EV, is maximal. […] We quantified deviations from optimality as the difference between the number of samples participants actually acquired and s^⋆.

8) Have there been any previous fMRI studies of functional connectivity in SCI? If so, then their results should be summarized somewhere in the Discussion section, and compared to the current results.

We have now included this in the Discussion section.

“These neuroimaging findings are consistent with previous fMRI studies (task-related and resting) in SCI showing altered connectivity between temporal brain regions (e.g., hippocampus and para-hippocampus) and several other areas and networks including default mode and salience networks (Cai et al., 2020; Dillen et al., 2017; Hafkemeijer et al., 2013;Hu et al., 2017; Rodda et al., 2009; Verfaillie et al., 2018; Viviano and Damoiseaux, 2020). […] Further validation of our neuroimaging results might be needed using other techniques (e.g., task-related MRI, lesion studies) as well as replication efforts, ideally with larger samples.”

9) The authors should either make explicit what the voxel-wise seed-based functional connectivity analysis adds to the whole-brain analysis or remove it from the revised manuscript. Otherwise, readers may see this additional analysis as a form of 'double-dipping'.

This analysis was performed for visualisation purposes only. To avoid double-dipping, we removed it from the revised manuscript.

Author details

1. Bahaaeddin Attaallah

   Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing - original draft

   For correspondence

   Bahaaeddin.Attaallah@ndcn.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7842-7974

2. Pierre Petitet

   Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1422-5326

3. Elista Slavkova

   Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation

   Competing interests

   No competing interests declared

4. Vicky Turner

   Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation

   Competing interests

   No competing interests declared

5. Youssuf Saleh

   Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation

   Competing interests

   No competing interests declared

6. Sanjay G Manohar

   1. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
   2. Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

   Contribution

   Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

7. Masud Husain

   1. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
   2. Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Resources, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

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