6 research outputs found
Model-based analysis of time-dependent consolidation
Reactivation and replay in both biological and artificial agents offer significant computational
advantages. Research has demonstrated that these processes can lead to
accelerated learning, reduced forgetting, and the reorganization or augmentation of
experiences, further supporting planning and generalization. These reactivations can
transpire during both online and offline periods. Specifically, online reactivation pertains
to the immediate reactivation of neural activity patterns during wakefulness.
In contrast, offline reactivation takes place during rest or sleep intervals, when the
brain is disengaged from external tasks. In these moments, the brain revisits and
reactivates neural activity patterns initially established during wakeful states.
A significant gap exists in our understanding however of how the brain determines
which information to reactivate during its limited offline periods. Specifically, the conditions
under which offline reactivation contributes to adaptive generalization remain
ambiguous, especially as recent reviews have highlighted the limitations of the extent
of offline benefits (Cordi & Rasch, 2021; Lerner & Gluck, 2019a). This uncertainty is
not trivial; given the pivotal role offline periods play in memory consolidation, comprehending
the mechanisms underlying adaptive consolidation and generalization is
paramount. Such understanding can offer insights into optimizing learning strategies
and determining the best times to take breaks. Furthermore, it can guide the
development of artificial agents designed for continuous learning and might on day
serve as reliable assistants in our daily activities. Additionally, a deeper grasp of
healthy memory consolidation processes can pave the way for identifying early indicators
of consolidation breakdowns, whether due to aging, mental health conditions,
or other factors. This knowledge could also usher in innovative applications, such as
facilitating learning or unlearning during rest periods, where memory might be more
malleable than during wake.
The central aim of this dissertation is to investigate the merit of a model-based
analysis of offline reactivation-dependent consolidation following episodic learning.
This exploration seeks to understand the potential mechanistic contributions of hippocampal
offline reactivations to the generalization observed in animals when engaged
in episodic and serial learning tasks. Another pivotal objective is to identify moderating
variables that can account for why extended post-learning retention intervals,
which encompass “offline reactivations,” sometimes result in noticeable generalization
benefits, while in other instances they either have no effect or even lead to decreased
performance upon delayed retrieval. Augmenting this empirical research with a quantitative
meta-analysis can further validate claims regarding these moderator variables.
With a deeper understanding of these conditions, it is anticipated that advancements
in pattern analysis of generalization, combined with neuroimaging techniques like
fMRI, can be employed to pinpoint not only the “loci” of generalization but also the
specific nature and evolution of generalization over time.
This dissertation, at its core, encapsulates my journey in learning diverse methodologies
and approaches with the overarching aim of producing research that is both
reproducible and replicable. The significance of this work extends beyond its immediate
findings, offering broader implications for the field at large. The cognitive
modeling of ostensibly simple tasks, such as transitive inference, holds promise. It
not only facilitates precise communication through mathematical paradigms among
researchers from disparate disciplines but also engenders interdisciplinary adversarial
collaborations. Such collaborations can catalyze the design of experiments situated
at the intersection of contending formal theories, thereby fostering incremental advancements
in the field. The adoption of online experimentation, particularly within
the domain of time and sleep-dependent consolidation, represents a relatively nascent
approach. Our endeavors in this realm are anticipated to pave the way for future
studies, characterized by enhanced statistical robustness. In tandem with this, our
concise meta-analysis of the extant datasets serves as a precursor to more expansive
meta-analytical endeavors, poised to elucidate the moderators of generalization and
provide direction for subsequent research. Furthermore, this dissertation endeavors
to illuminate an efficacious methodology for examining representational shifts over
time. This is achieved through a within-subject multi-session design that combines
remote learning conditions with in-scanner retrieval, employing a localizer task to
scrutinize the representational geometry underpinning inference. While each of these
methodological innovations might not be unprecedented in isolation, their confluence
within this research area is rare. Such a synthesis holds the potential to augment the
current state of sleep and memory research.
However, it’s important to note certain limitations that might influence the interpretation
or generalizability of the findings. The scope of this dissertation is defined
by its concentrated focus on theories of generalization discussed in Chapter 1, particularly
emphasizing reactivation as the primary underlying mechanism. In terms of
tasks, the research is primarily centered on the phenomena of associative inference,
with a specific emphasis on transitive inference throughout. Additionally, while the
main emphasis is on offline consolidation, this research does not include any direct
physiological measures of reactivation during the offline period. The analysis is anchored
solely in recall performance, with only minimal measures following immediate
recall. Regrettably, none of the experiments undertaken involve measuring immediate
post-learning rest or sleep physiology, which could provide deeper insights into the
process of offline consolidation.
For this research, a multifaceted methodological approach was adopted to delve
into the intricacies of reactivation-dependent generalization. A vector-based memory
models were crafted using both MATLAB and Python, facilitating the simulation of
specific experiments. These simulations were useful in shedding light on reactivationdependent
generalization, resonating with the overarching goals of this dissertation.
On the behavioral analysis front, the methodology predominantly hinges on logistic
mixed-models and employs remote web-based experimentation to dissect timedependent
consolidation. Furthermore, univariate random-effects models have been
employed for a meta-analysis of the transitive inference findings, as well as a metaregression
analysis of moderator variables. To enhance the depth of the research,
a follow-up experiment was analyzed using model-based representational similarity
analysis, complemented by fMRI data.
The dissertation is structured to provide a rigorous exploration of the topic at
hand. Chapter 1 commences with a theoretical exposition, delineating both the
classical and more recent paradigms that inform our understanding of human generalization.
As the discourse advances, attention is directed towards cognitive models
of generalization, with an emphasis on those that can produce offline generalization
phenomena. In Chapter 2, a methodical comparative analysis is undertaken, juxtaposing
two salient cognitive models: REMERGE and MINERVA2. This chapter
underscores the efficacy of model-based methodologies in the study of generalization,
positing MINERVA2 as an exemplar baseline model. Its value lies in its capacity to account
for an array of time-dependent findings with a parsimonious set of parameters.
Chapter 3 presents a triad of empirical investigations centered on the details of time
and sleep-dependent generalization as they manifest following the transitive inference
task. Of these, one is a reanalysis of an extant dataset, while the subsequent two
collected as part of this dissertation, adopt divergent design paradigms: the former
adhering to the conventional between-subject design and the latter a novel withinsubject
approach. Transitioning to Chapter 4, the narrative engages in a reevaluation
of two secondary datasets published in the realm of transitive inference. This chapter
ends with a meta-analytical synthesis, amalgamating insights from our three primary
experiments, the reinterpreted datasets, and additional published research, thereby
facilitating a comprehensive examination of time and sleep-dependent effects. Concluding
the dissertation, Chapter 5 introduces the final empirical endeavor, which
harnesses neuroimaging techniques to probe the representational geometry underpinning
successful inference at delayed test, building upon the previously piloted
within-subject study of transitive inference