Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning

Abstract

Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individual’s estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low-dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.

Curiosity as filling, compressing, and reconfiguring knowledge networks

“…Second, we assume that knowledge networks are embedded in Euclidean space, where they possess several degrees of freedom. Both assumptions are predicated on how humans encode spatial and abstract knowledge (Garvert et al, 2017; Peer et al, 2021; Stiso et al, 2022; Warren, 2019). Evidence from spatial navigation studies demonstrates that mental representations of space take the form of labeled cognitive graphs.…”

“…One such model that has shown promise is a network model where knowledge is composed of discrete and yet interconnected concepts (Chrastil and Warren, 2014, 2015; Ericson and Warren, 2020; Peer et al, 2021; Schapiro et al, 2016; Stiso et al, 2022; Warren et al, 2017). In graph learning studies, volunteers are shown sequences of images on a screen, where, unbeknownst to the volunteers, each image corresponds to a node in an underlying network (Lynn and Bassett, 2020).…”

Curiosity as filling, compressing, and reconfiguring knowledge networks

“…Second, we assume that knowledge networks are embedded in Euclidean space, where they possess several degrees of freedom. Both assumptions are predicated on how humans encode spatial and abstract knowledge [23,24,27,54]. Evidence from spatial navigation studies demonstrates that mental representations of space take the form of labeled cognitive graphs.…”

“…One such model that has shown promise is a network model where knowledge is composed of discrete and yet interconnected concepts [18,19,20,21,22,23,24]. In graph learning studies, volunteers are shown sequences of images on a screen, where, unbeknownst to the volunteers, each image corresponds to a node in an underlying network [25].…”

Route sequence knowledge supports the formation of cognitive maps

“…For navigation‐based sequence learning in rodents, the hippocampus has been shown to be involved in learning sequences of places (Foster & Knierim, 2012), and is recruited during sequence‐based navigation, but not during exploration behavior (Babayan et al, 2017). Further, a recent study using human intercranial electroencephalographic recordings showed that neural activity elicited by non‐spatial sequence learning closely resembled that predicted by neural models of cognitive map formation, most strongly in the hippocampus (Stiso et al, 2022). In fact, aging adults (both healthy and those showing cognitive decline) often experience marked volumetric decline and structural changes to the hippocampus (Bettio et al, 2017), which coincides with difficulties in spatial sequence learning and cognitive map‐reliant navigation in these populations (Lester et al, 2017).…”