When someone throws us a ball, we can anticipate its future trajectory, its speed and the time it will reach us. These expectations then inform the motor system to plan an appropriate action to catch it. Generating expectations and planning behavior accordingly builds on our ability to learn from past experiences and to encode the statistical regularities of the tasks we perform. At the core of this ability lies a continuous perception-action loop, initially proposed for sensorimotor systems (e.g. Wolpert et al., 2011), which is now at the heart of many leading theories of brain function including active inference (Friston et al., 2016), predictive coding (Huang and Rao, 2011) and reinforcement learning (Daw and Dayan, 2014).

Critically, the brain needs to balance three primary objectives to effectively guide behavior in a dynamic environment. First, it needs to capture the specific aspects of the task that inform the relevant behavior (e.g. the remaining time to catch the ball). Second, it needs to generalize from a limited set of examples to novel and noisy situations. This can be achieved by regularizing the currently encoded information based on past experiences (e.g. by inferring how fast previous balls flew on average). Third, the sensorimotor representations that guide the behavior need to be updated flexibly whenever feedback about our actions becomes available (e.g. when we catch or miss the ball), or when task demands change (e.g. when someone throws us a frisbee instead). Herein, we refer to these objectives as specificity, regularization, and flexibility. While these are all fundamental principles underlying human cognition broadly, how the brain forms and continuously updates sensorimotor representations that balance these three objectives remains unclear.

Here, we approach this question with a new perspective by bridging two parallel lines of research centered on sensorimotor timing and hippocampal-dependent cognitive mapping. Specifically, we test how the human hippocampus, an area often implicated in episodic-memory formation (Schiller et al., 2015; Eichenbaum, 2017), may support the flexible updating of sensorimotor representations in real time and in concert with other regions. Importantly, the hippocampus is not traditionally thought to support sensorimotor functions, and its contributions to memory formation are typically discussed for longer time scales (hours, days, weeks). Here, however, we characterize in detail the relationship between hippocampal activity and real-time behavioral performance in a fast-paced timing task, which is traditionally believed to be hippocampal-independent. We propose that the capacity of the hippocampus to encode statistical regularities of our environment (Doeller et al., 2005; Schapiro et al., 2017; Momennejad, 2020) situates it at the core of a brain-wide network balancing specificity vs. regularization in real time as the relevant behavior is performed.

An optimal behavioral domain to study these processes is sensorimotor timing (Gershman et al., 2014; Petter et al., 2018). This is because prior work suggested that timing estimates indeed rely on the statistics of prior experiences (Wolpert et al., 2011; Jazayeri and Shadlen, 2010; Acerbi et al., 2012; Chang and Jazayeri, 2018). Crucially, however, timing estimates are not always accurate. Instead, they directly reflect the trade-off between specificity and regularization, which is expressed in systematic behavioral biases. Estimated intervals regress towards the mean of the distribution of tested intervals (Jazayeri and Shadlen, 2010), a well-known effect that we will refer to as the regression effect (Petzschner et al., 2015). The regression effect suggests that the brain encodes a probability distribution of possible intervals rather than the exact information obtained in each trial (Wolpert et al., 2011). Timing estimates therefore depend not only on the interval tested in a trial, but also on the temporal context in which they were encountered (i.e. the intervals tested in all other trials). This likely helps to predict future scenarios, to adapt behavior flexibly and to generalize to novel or noisy situations (Jazayeri and Shadlen, 2010; Acerbi et al., 2012; Roach et al., 2017).

Importantly, the hippocampus proper codes for time and temporal context on various scales (Howard, 2017) and it has been shown to process behavioral feedback in decision-making tasks (Shohamy and Wagner, 2008), pointing to a role in feedback learning. Moreover, the hippocampal formation has been implicated in encoding the latent structure of a task along with the individual features that were tested (Kumaran, 2012; Schlichting and Preston, 2015; Schapiro et al., 2017; Wikenheiser et al., 2017; Behrens et al., 2018; Schuck and Niv, 2019; Whittington et al., 2020; Peer et al., 2021), providing a unified account for its many proposed roles in navigation (Burgess et al., 2002), memory (Schiller et al., 2015; Eichenbaum, 2017) and decision making (Kaplan et al., 2017; Vikbladh et al., 2019). We propose that a central function of the human hippocampus is to encode the temporal context of stimuli and behaviors rapidly, and that this process manifests as the behavioral regression effect observed in time estimation and other domains (Petzschner et al., 2015). This puts the hippocampus at the core of a brain-wide network solving the trade-off between specificity and regularization for flexible behavior by continuously updating sensorimotor representations in a feedback-dependent manner. Using functional magnetic resonance imaging (fMRI) and a sensorimotor timing task, we here test this proposal empirically.

In the following, we present our experiment and results in four steps. First, we introduce our task, which built on the estimation of the time-to-contact (TTC) between a moving fixation target and a visual boundary, as well as the behavioral and fMRI measurements we acquired. On a behavioral level, we show that participants’ timing estimates systematically regress towards the mean of the tested intervals. Second, we demonstrate that anterior hippocampal fMRI activity and functional connectivity tracks the behavioral feedback participants received in each trial, revealing a link between hippocampal processing and timing-task performance. Third, we show that this hippocampal feedback modulation reflects improvements in behavioral performance over trials. We interpret this activity to signal the updating of task-relevant sensorimotor representations in real time. Fourth, we show that these updating signals in the posterior hippocampus were independent of the specific interval that was tested and activity in the anterior hippocampus reflected the magnitude of the behavioral regression effect in each trial.

Notably, for each of the hippocampal main analyses, we also performed whole-brain voxel-wise analyses to uncover the larger brain network at play. We found that in addition to the hippocampus, regions typically associated with timing and reward processing signaled sensorimotor updating in our task, particularly the striatum. Follow-up analyses further revealed a striking distinction in TTC-specific and TTC-independent updating signals between striatal sub-regions. We conclude by discussing the potential neural underpinnings of these results and how the hippocampus may contribute to solving the trade-off between task specificity and regularization in concert with this larger brain network.

Time-to-contact (TTC) estimation task

We monitored whole-brain activity using fMRI with concurrent eye tracking in 34 participants performing a TTC task. This task offered a rich behavioral read-out and required sustained attention in every single trial. During scanning, participants visually tracked a fixation target, which moved on linear trajectories within a circular boundary. The target moved at one of four possible speed levels and in one of 24 possible directions (Figure 1A, similar to Nau et al., 2018a). The sequence of tested speeds was counterbalanced across trials. Whenever the target stopped moving, participants estimated when the target would have hit the boundary if it had continued moving. They did so while maintaining fixation, and they indicated the estimated TTC by pressing a button. Feedback about their performance was provided foveally and instantly with a colored cue. The received feedback depended on the timing error, that is the difference between objectively true and estimated TTC (Figure 1B), and it comprised three levels reflecting high, middle, and low accuracy (Figure 1C). Because timing judgements typically follow the Weber-Fechner law (Rakitin et al., 1998), the feedback levels were scaled relative to the ground-truth TTC of each trial. This ensured that participants were exposed to approximately the same distribution of feedback at all intervals tested (Figure 1C, Figure 1—figure supplement 1B). After a jittered inter-trial interval (ITI), the next trial began and the target moved into another direction at a given speed. The tested speeds of the fixation target were counterbalanced across trials to ensure a balanced sampling within each scanning run. Because the target always stopped moving at the same distance to the boundary, matching the boundary’s retinal eccentricity across trials, the different speeds led to four different TTCs: 0.55, 0.65, 0.86, and 1.2 s. Each participant performed a total of 768 trials. Please see Materials and methods for more details.

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Analyzing the behavioral responses revealed that participants performed the task well and that the estimated and true TTCs were tightly correlated (Figure 1B; Spearman’s $rho=0.91,p=2.2x{10}^{-16}$). However, participants’ responses were also systematically biased towards the grand mean of the TTC distribution (0.82 s), indicating that shorter durations tended to be overestimated and longer durations tended to be underestimated. We confirmed this in all participants by examining the slopes of linear regression lines fit to the behavioral responses (Figure 1—figure supplement 1D). These slopes differed from 1 (veridical performance; Figure 1B, diagonal dashed line; one-tailed one-sample t test, $t(33)=-19.26,p=2.2x{10}^{-16},d=-3.30,CI:[-4.22,-2.47]$) as well as from 0 (grand mean; Figure 1B, horizontal dashed line; one-tailed one-sample t test, $t(33)=21.62,p=2.2x{10}^{-16},d=3.71,CI:[2.79,4.72]$) and clustered at 0.5. Moreover, the slopes also correlated positively with behavioral accuracy across participants (Figure 1—figure supplement 1E; Spearman’s $rho=0.794,p=2.1x{10}^{-08}$), consistent with previous reports (Cicchini et al., 2012). Notably, the regression effect we observed in behavior has been argued to show that timing estimates indeed rely on the latent task regularities that our brain has encoded (e.g. Jazayeri and Shadlen, 2010; Roach et al., 2017). It may therefore reflect a key behavioral adaptation helping to regularize encoded intervals to optimally support both current task performance and generalization to future scenarios. In support of this, participants’ regression slopes converged over time towards the optimal value of 0.5, that is the slope value between veridical performance and the grand mean (Figure 1—figure supplement 1F; linear mixed-effects model with task segment as a predictor and participants as the error term, $F(1)=8.172,p=0.005,{ϵ}^2=0.08,CI:[0.01,0.18]$), and participants’ slope values became more similar (Figure 1—figure supplement 1G; linear regression with task segment as predictor, $F(1)=6.283,p=0.046,{ϵ}^2=0.43,CI:[0,1]$). Consequently, this also led to an improvement in task performance over time on group level (i.e. task accuracy and precision increased; Figure 1—figure supplement 1I), and the relationship between accuracy and precision became stronger (Figure 1—figure supplement 1H), linear mixed-effect model results for accuracy: ${\displaystyle F(1)=15.127,p=1.3x{10}^{-4}}$, ${\displaystyle {ϵ}^2=0.06,CI:[0.02,0.11]}$, precision: ${\displaystyle F(1)=20.189,p=6.1x{10}^{-5}}$, ${\displaystyle {ϵ}^2=0.32,CI:[0.13,1]}$, accuracy-precision relationship: $F(1)=8.288,p=0.036,{ϵ}^2=0.56,CI:[0,1]$, see methods for model details.

Behavioral feedback predicts hippocampal activity

Importantly, sensorimotor updating is expected to occur right after the value of the performed action became apparent, which is when participants received feedback. As a proxy, we therefore analyzed how activity in each voxel reflected the feedback participants received in the previous trial. Using a mass-univariate general linear model (GLM), we modeled the three feedback levels with one regressor each plus additional nuisance regressors (see Materials and methods for details). The three feedback levels (high, medium, and low accuracy) corresponded to small, medium and large timing errors, respectively. We then contrasted the beta weights estimated for low-accuracy vs. high-accuracy feedback and examined the effects on group-level averaged across runs. We performed both whole-brain voxel-wise analyses as well as regions-of-interest (ROI) analysis for anterior and posterior hippocampus separately, for which prior work suggested functional differences with respect to their contributions to memory-guided behavior (Poppenk et al., 2013; Strange et al., 2014).

In both our ROI analysis and a voxel-wise analysis, we found that hippocampal activity could indeed be predicted by the feedback participants received in the previous trial (Figure 2A, Figure 2B). Higher-accuracy feedback resulted in overall stronger activity in the anterior section of the hippocampus (Figure 2B, Figure 2—figure supplement 1A; two-tailed one-sample t tests: anterior HPC, $t(33)=-3.80,p=5.9x{10}^{-4},p_{fwe}=0.001,d=-0.65,CI:[-1.03,-0.28]$; posterior HPC, $t(33)=-1.60,p=0.119,p_{fwe}=0.237,d=-0.27,CI:[-0.62,0.07]$). Moreover, the voxel-wise analysis revealed feedback-related activity also in the thalamus and the striatum (Figure 2A), and in the hippocampus when the feedback of the current trial was modeled (Figure 2—figure supplement 2A).

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Note that these results were robust even when fewer nuisance regressors were included to control for model over-specification (Figure 2—figure supplement 3B; two-tailed one-sample t tests: anterior HPC, $t(33)=-3.65,p=8.9x{10}^{-4},p_{fwe}=0.002,d=-0.63,CI:[-1.01,-0.26]$; posterior HPC, $t(33)=-1.43,p=0.161,p_{fwe}=0.322,d=-0.25,CI:[-0.59,0.10]$), and when all three feedback levels were modeled with one parametric regressors (Figure 2—figure supplement 3C; two-tailed one-sample t tests: anterior HPC, $t(33)=-3.59,p=0.002,p_{fwe}=0.005,d=-0.56,CI:[-0.93,-0.20]$; posterior HPC, $t(33)=-0.99,p=0.329,p_{fwe}=0.659,d=-0.17,CI:[-0.51,0.17]$). In addition, hippocampal activity was higher for medium-accuracy feedback relative to low-accuracy feedback on voxel-wise and ROI level (Figure 2—figure supplement 4A; two-tailed one-sample t tests: anterior HPC, ${\displaystyle t(33)=-4.40,p=1.1\times {10}^{-4}}$, ${\displaystyle p_{fwe}=2.1\times 10-4}$, ${\displaystyle d=-0.76,CI:[-1.15,-0.37]}$; posterior HPC, ${\displaystyle t(33)=-3.62,p=9.8\times {10}^{-4}}$, ${\displaystyle p_{fwe}=0.002,d=-0.62}$, ${\displaystyle CI:[-1.00,-0.25]}$) and for high-accuracy feedback vs. medium-accuracy feedback on voxel-wise but not ROI level (Figure 2—figure supplement 4B; two-tailed one-sample t tests: anterior HPC, $t(33)=-0.08,p=0.933,p_{fwe}=1,d=-0.01,CI:[-0.36,0.33]$; posterior HPC, $t(33)=t=0.99,p=0.327,p_{fwe}=0.654,d=0.17,CI:[-0.17,0.52]$). Further, there was no systematic relationship between subsequent trials on a behavioral level (Figure 1—figure supplement 1A; two-tailed one-sample t test; $t(33)=1.03,p=0.312,d=0.18,CI:[-0.17,0.52]$; see Materials and methods for details) and that the direction of the effects differed across regions (Figure 2A), speaking against potential feedback-dependent biases in attention.

Hippocampal activity explains accuracy and biases in task performance

Two critical open questions remained. First, did the observed feedback modulation actually reflect improvements in behavioral performance over trials? Second, was the information that was learned specific to the interval that was tested in a given trial, likely serving task specificity, or was independent of the tested interval, potentially serving regularization? To answer these questions in one analysis, we used a GLM modeling activity not as a function of feedback received in the previous trial (Figure 2), but as a function of the difference in feedback between trials (Figure 3). Specifically, we modeled with two separate parametric regressors the improvements in TTC task performance across subsequent trials (regressor 1: TTC-independent updating) as well as the improvements over subsequent trials in which the same TTC interval was tested (regressor 2: TTC-specific updating). We again accounted for nuisance variance as before, and we contrasted trials in which participants had improved versus the ones in which they had not improved or got worse (see Materials and methods for details). Because stronger sensorimotor updating should lead to larger performance improvements, we predicted to find stronger activity for improvements vs. no improvements in these tests (one-tailed hypothesis).

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We found both TTC-specific and TTC-independent activity throughout cortical and subcortical regions. Distinct areas engaged in either one or in both of these processes (Figure 3A, Figure 3—figure supplement 1). Crucially, we found that hippocampal activity signaled behavioral improvements independent of the TTC intervals tested. This effect was localized to the posterior section of the hippocampus (Figure 3B, Figure 2—figure supplement 1A; one-tailed one-sample t tests; TTC-independent: anterior HPC, ${\displaystyle t(33)=0.36,p=0.360}$, ${\displaystyle p_{fwe}=1,d=0.06,CI:[-0.28,0.40]}$, posterior HPC, ${\displaystyle t(33)=2.81,p=0.004}$, ${\displaystyle p_{fwe}=0.017,d=0.48,CI:[0.12,0.85]}$; TTC-specific: anterior HPC, ${\displaystyle t(33)=0.57,p=0.285}$, ${\displaystyle p_{fwe}=1,d=0.10,CI:[-0.24,0.44]}$, posterior HPC, ${\displaystyle t(33)=1.29,p=0.103}$, ${\displaystyle p_{fwe}=0.413,d=0.22,CI:[-0.12,0.57]}$). We then again estimated the functional connectivity profile of the hippocampal main effect using a PPI analysis (sphere with 4 mm radius centered on the peak voxel at x=-30, y=-24, z=-18), revealing co-fluctuations in multiple regions including the putamen and the thalamus that were specific to behavioral improvements (Figure 3—figure supplement 2).

We reasoned that updating TTC-independent information may support generalization performance by means of regularizing the encoded intervals based on the temporal context in which they were encoded. In our task, an efficient way of regularizing the encoded information is to bias one’s TTC estimates towards the mean of the TTC distribution, which corresponds to the regression effect that we observed on a behavioral level (Figure 1B, Figure 1—figure supplement 1D). Given the hippocampal feedback modulation and updating activity we reported above, we hypothesized that hippocampal activity should therefore also reflect the magnitude of the regression effect in behavior. To test this in a final analysis, we modeled the activity in each trial parametrically either as a function of performance (i.e. the absolute difference between estimated and true TTC) or as a function of the strength of the regression effect in each trial (i.e. the absolute difference between the estimated TTC and the mean of the tested intervals). Voxel-wise weights for these two regressors were estimated in two independent GLMs (see Materials and methods for details).

Our analyses showed that trial-wise hippocampal activity increased with better TTC-task performance (Figure 4A, Figure 4B; two-tailed one-sample t tests; anterior HPC, ${\displaystyle t(33)=-4.85,p=2.9x{10}^{-5}}$, ${\displaystyle p_{fwe}=5.8x{10}^{-5}}$, ${\displaystyle d=-0.83,CI:[-1.24,-0.44]}$; posterior HPC, ${\displaystyle t(33)=-2.88,p=0.007}$, ${\displaystyle p_{fwe}=0.014,d=-0.49}$, ${\displaystyle CI:[-0.86,-0.14]}$), and consistently also with the valence of the feedback received in the current trial (Figure 2—figure supplement 2). In addition, however, and as predicted, it also reflected the trial-wise magnitude of the behavioral regression effect (Figure 4A, Figure 4B; two-tailed one-sample t tests; anterior HPC, $t(33)=-5.55,p=3.6x{10}^{-6},p_{fwe}=1.1x{10}^{-5},d=-0.95,CI:[-1.37,-0.55]$; posterior HPC, $t(33)=-1.06,p=0.295,p_{fwe}=0.886,d=-0.18,CI:[-0.53,0.16]$). Activity in the anterior hippocampus was stronger in trials in which participants’ TTC estimates were more biased towards the mean of the sampled intervals (indicated by a negative beta estimate). Notably, similar effects were observed in prefrontal and posterior cingulate areas (Figure 4A).

Figure 4

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This study investigated how the human brain flexibly updates sensorimotor representations in a feedback-dependent manner in the service of timing behavior. We specifically focused on the hippocampus, due to its known role in temporal coding and learning, asking how hippocampal processing may support behavioral flexibility, specificity, and regularization. Because anterior and posterior sections of the hippocampus differ in whole-brain connectivity as well as in their contributions to memory-guided behavior (Strange et al., 2014), we analyzed the two sections separately. Moreover, we explored the larger brain-wide network involved in balancing these objectives. To do so, we monitored human brain activity with fMRI while participants estimated the time-to-contact between a moving target and a visual boundary. This allowed us to analyze brain activity as a function of task performance and as a function of the improvements in performance over time. We found that anterior hippocampal activity as well as functional connectivity reflected the feedback participants received during this task, and its activity followed the performance improvements in a temporal-context-dependent manner. Its activity reflected trial-wise behavioral biases towards the mean of the sampled intervals, and activity in the posterior hippocampus signaled sensorimotor updating independent of the specific intervals tested. In what follows, we discuss our results in the context of prior work on timing behavior and on hippocampal spatiotemporal coding. Moreover, we elaborate on the domain-general nature of hippocampal-cortical interactions and on the sensorimotor updating mechanisms that potentially underlie the effects observed in this study.

Source data and analysis code are available at the following Open Science Framework repository: https://osf.io/cs8d6/. Pre-processed eye-tracker data can be found here: https://osf.io/mrhk9/. Raw fMRI data is available at the following G-Node Infrastructure repository: https://gin.g-node.org/ipolti/TTC_HPCF.git.

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Author details

1. Ignacio Polti

   1. Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer’s Disease, Norwegian University of Science and Technology, Trondheim, Norway
   2. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Matthias Nau

   For correspondence

   ignacio.polti@ntnu.no

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6631-4315

2. Matthias Nau

   1. Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer’s Disease, Norwegian University of Science and Technology, Trondheim, Norway
   2. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

   Contribution

   Conceptualization, Data curation, Supervision, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Ignacio Polti

   For correspondence

   matthias.nau@ntnu.no

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0956-7815

3. Raphael Kaplan

   1. Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer’s Disease, Norwegian University of Science and Technology, Trondheim, Norway
   2. Department of Basic Psychology, Clinical Psychology, and Psychobiology, Universitat Jaume I, Castellón de la Plana, Spain

   Contribution

   Supervision, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5023-1566

4. Virginie van Wassenhove

   CEA DRF/Joliot, NeuroSpin; INSERM, Cognitive Neuroimaging Unit; CNRS, Université Paris-Saclay, Gif-Sur-Yvette, France

   Contribution

   Supervision, Writing – review and editing

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-2569-5502

5. Christian F Doeller

   1. Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer’s Disease, Norwegian University of Science and Technology, Trondheim, Norway
   2. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   3. Wilhelm Wundt Institute of Psychology, Leipzig University, Leipzig, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

   For correspondence

   doeller@cbs.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4120-4600

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