Volitional learning promotes theta phase coding in the human hippocampus

Electrophysiological studies in rodents show that active navigation enhances hippocampal theta oscillations (4–12 Hz), providing a temporal framework for stimulus-related neural codes. Here we show that active learning promotes a similar phase coding regime in humans, although in a lower frequency range (3–8 Hz). We analyzed intracranial electroencephalography (iEEG) from epilepsy patients who studied images under either volitional or passive learning conditions. Active learning increased memory performance and hippocampal theta oscillations and promoted a more accurate reactivation of stimulus-specific information during memory retrieval. Representational signals were clustered to opposite phases of the theta cycle during encoding and retrieval. Critically, during active but not passive learning, the temporal structure of intracycle reactivations in theta reflected the semantic similarity of stimuli, segregating conceptually similar items into more distant theta phases. Taken together, these results demonstrate a multilayered mechanism by which active learning improves memory via a phylogenetically old phase coding scheme.

Volitionally controlled—or “active”—learning has become a crucial topic in education, psychology, and neuroscience (1, 2). Behavioral studies show that memory benefits from voluntary action (3–5), putatively through a distinct modulation of attention, motivation, and cognitive control (2, 6). While these functions depend on widespread frontoparietal networks (7), a critical role of the hippocampus in coordinating volitional learning has been demonstrated in both humans (8) and rodents (9) (for a review see ref. 10). However, the mechanisms by which volition improves learning and memory are not well understood. Rodent recordings suggest that hippocampal theta oscillations (usually occurring between 4 and 12 Hz) might play a critical role, because they increase during voluntary movement (11) and active sensing (12). Consistently, human studies have shown volition-related theta power increases, although in a lower frequency range (typically between 3 and 8 Hz), during navigation in virtual (13, 14) and physical (15, 16) environments. It is believed that theta oscillations facilitate mnemonic processing by providing a temporal framework for the organization of stimulus-related neural codes (17). This is observed in the phenomenon of phase precession, where spatial locations represented by place cells in the rodent hippocampus are sequentially reactivated at distinct phases of theta oscillations (18). A similar phase coding mechanism underlies the representation of possible future scenarios in rats performing a spatial decision-making task, with early and late hippocampal theta phases representing current and prospective scenarios, respectively (19). It has been proposed (17) that these forms of neural phase coding support a range of cognitive processes, including multi-item working memory (20), episodic memory (21, 22), and mental time travel (23). In humans, this proposal has received empirical support from phase-amplitude coupling studies looking at the relationship between the amplitude of high-frequency activity and the phase of activity at a lower frequency, in particular theta (24–26). However, these analyses are agnostic to the specific content that is coupled to the theta phase and thus do not reflect “phase coding” in the narrower sense. Recent studies used multivariate analysis techniques to identify stimulus-specific representational signals at the high temporal resolution provided by human intracranial electroencephalography data (iEEG, see refs. 27, 28 for review). These analyses demonstrated the relevance of theta oscillations for hippocampal reinstatement of item-context associations (29), for the orchestration of content-specific representations of goal locations (30), and for word-object associations (31). However, it is unclear whether this mechanism is recruited when learning is volitionally controlled.Building on these empirical findings and methodological advances, we aimed to elucidate whether the improved memory performance typically observed in human active learning paradigms can be traced back to a hippocampal theta phase code. In particular, we hypothesized that during active learning, this theta phase code organizes and structures stimulus-specific memory representations. We analyzed electrophysiological activity from the hippocampus and widespread neocortical regions in epilepsy patients (n = 13, age = 33.5 ± 9.32) implanted with iEEG electrodes (total number of electrodes = 392; Fig. 1F) who performed a virtual reality (VR)-based navigation and memory task. Subjects navigated in a square virtual arena (Fig. 1A) and were asked to remember images of specific objects presented at distinct spatial locations indicated by red “boxes” located on the ground (Fig. 1B). Images were only visible when participants visited the red boxes and were hidden otherwise. Navigation occurred under two conditions: active (A) and passive (P) (Fig. 1B). In the active condition, participants could freely control their movements in visiting the stimulus sites while in the passive condition, they were exposed to the navigation path and order of image presentation generated by another participant (yoked design; Fig. 1 C and D). At the end of the experiment, the recognition memory for both the actively and passively learned items was tested (Fig. 1E). We predicted that active learning would enhance memory by promoting hippocampal theta phase coding of stimulus-specific memory representations.Open in a separate windowFig. 1.Experimental procedure, electrode implantation, and behavioral results. (A) Participants studied images presented at specific locations, indicated by red boxes located on the ground, in a square virtual environment (here shown from a bird’s eye perspective). (B) Stimulus presentation during the encoding phase of the experiment as seen by a participant. (C) Schematic timeline showing the main blocks of the experiment (A = active, P = passive, counterbalanced). (D) Detailed timeline of an example-encoding block. Participants freely determined the timings and materials of study in the active condition and were exposed to the trajectory of a different subject in the passive condition. (E) Timeline of the experiment at retrieval. (F) All electrodes included in the analyses (n = 392, MNI space), color coded by participant identity. (G) Receiver operating characteristic (ROC) curves for each subject (gray) and grand average (red). (H) Proportion of correct items for all stimuli as a function of confidence. (I) Proportion of remembered items (Left) and of high-confidence remembered items (Right) for active and passive conditions. *P < 0.05; ***P < 0.001.