Memory encoding and retrieval: The role of attention, representations and networks

Episodic memory, as a cognitive construct, exists only in relation to those other cognitive constructs that reference it. It is, as Ribot suggests: the tactile, the muscular, the auditory and so forth. And it is even more than this, extending to a breadth of cognitive operations, including, for example, attention and cognitive control, both of which are generally believed to facilitate episodic memory encoding and episodic memory retrieval. Without these types of sensory and cognitive referents, episodic memory does not exist. Accordingly, these types of referents are critical to an understanding of episodic memory. Therefore, in this dissertation I examine how different cognitive constructs serve to facilitate episodic memory.

Chapter 2 examines attention-related subsequent memory effects. Many studies of subsequent memory rely upon a reverse inference, i.e. increased activity in attention-related networks during memory encoding is related to better subsequent memory, ergo increased attention predicts better memory. However, it is only through direct manipulation of attentional states and the examination of specific neural markers that this claim can be strongly established. Additionally, attention is a multifaceted process, and claims that attention in general facilitates memory ignore the fact that attention consists of a set of rapidly enfolding processes. To address these issues, I designed a modified visual-search EEG experiment with a subsequent long-term memory test. The utilization of a visual-search paradigm has advantages, as the search process evokes a series of independent and well-established attention-related EEG markers which can be linked to subsequent memory. All of the attentional effects examined were found to also predict subsequent memory, suggesting that these attentional processes associated with visual search, aid long-term memory formation as well.

Chapter 3 examines how large-scale network dynamics affect long-term memory retrieval. Until now, all studies of long-term memory have focused on individual regions, pair-wise connections between regions, or, very rarely, complex interactions between a small subset of regions. In a pair of fMRI studies, I use mathematical concepts from network science to examine the large-scale brain networks associated with successful remembering and forgetting. In doing so, I demonstrate that the hippocampus increases its integration with the rest brain when individuals successfully remember an item as compared to when they do not.

Chapter 4 examines how individual items are represented in the brain with machine-learning techniques and fMRI data. Studies of episodic memory often focus on things that are common across a set of items, while ignoring the uniqueness of individual events. However, an event’s uniqueness is what defines it as being episodic with respect to memory. A primary reason unique events are not often studied is the difficulty of decoding brain states associated with individual events. In Chapter 4, I develop a machine-learning framework, utilizing cross-subject single-item decoding, to predict what image or word a left-out subject is viewing. This establishes a robust way to detect individual events which could be used in service of better understanding episodic memory.

By examining long-term memory from these perspectives, I provide evidence of how different cognitive constructs facilitate episodic memory. In Chapter 2, I focus on the role of attentional processes with respect to episodic memory encoding, in Chapter 3, I focus on how large-scale network interactions facilitate episodic memory retrieval, and in Chapter 4 I focus on the representational nature of unique events. In all cases, the examination centers on how diverse processes coordinate in order to facilitate episodic memory.