Medial prefrontal cortex and hippocampal activity differentially contribute to ordinal and temporal context retrieval during sequence memory

Remembering sequences of events defines episodic memory, but retrieval can be driven by both ordinality and temporal contexts. Whether these modes of retrieval operate at the same time or not remains unclear. Theoretically, medial prefrontal cortex (mPFC) confers ordinality, while the hippocampus (HC) associates events in gradually changing temporal contexts. Here, we looked for evidence of each with BOLD fMRI in a sequence task that taxes both retrieval modes. To test ordinal modes, items were transferred between sequences but retained their position (e.g., AB3). Ordinal modes activated mPFC, but not HC. To test temporal contexts, we examined items that skipped ahead across lag distances (e.g., ABD). HC, but not mPFC, tracked temporal contexts. There was a mPFC and HC by retrieval mode interaction. These current results suggest that the mPFC and HC are concurrently engaged in different retrieval modes in support of remembering when an event occurred.

Memory for sequences of events is a fundamental component of episodic memory (Tulving 1984, 2002; Allen and Fortin 2013; Howard and Eichenbaum 2013; Eichenbaum 2017). While different experiences share overlapping elements, the sequence of events is unique. Remembering the order of events allows us to disambiguate episodes with similar content and make detailed predictions supporting decision-making.At least two complementary memory processes contribute to the retrieval of events in the correct sequence: ordinal (Orlov et al. 2002) and temporal context (Howard and Kahana 2002) retrieval modes. Whether these disparate retrieval modes operate coincidently or not remains an open question with consequences for understanding basic mechanisms of how we remember the events that unfold throughout our day. According to an ordinal retrieval mode, items are remembered by their position within an event sequence (DuBrow and Davachi 2013; Allen et al. 2014; Long and Kahana 2019), providing sequential memory through well-established semantic or abstracted relationships (first, second, third, etc.). While for a temporal context retrieval mode, events are remembered through a gradually changing temporal context within which specific items have been associated. According to temporal contexts, when an element of a sequence is presented or retrieved (e.g., “C” in ABCDEF), items that are more proximal in the sequence (e.g., the “D” in the sequence) have a higher retrieval rate compared with items that are further away (e.g., the “F” in the sequence). These temporal contexts result from item associations that are dependent on time varying neural activity (e.g., Eichenbaum 2014), and contribute to sequence memory through the reactivation of neighboring items during retrieval (DuBrow and Davachi 2013; Long and Kahana 2019).The medial prefrontal cortex (mPFC) and hippocampus (HC) are thought to contribute to sequence memory through ordinal representations and temporal contexts, respectively (Agster et al. 2002; Fortin et al. 2002; Kesner et al. 2002; DeVito and Eichenbaum 2011; Allen et al. 2016; Jenkins and Ranganath 2016). In rodents, mPFC disruptions impair sequence memory (DeVito and Eichenbaum 2011; Jayachandran et al. 2019), mPFC “time cells” are evident (Tiganj et al. 2017), and positions within a sequence can be the main determinant of differential activity in mPFC neurons during spatial sequences (Euston and McNaughton 2006). In humans, mPFC activation is sensitive to temporal order memory (Preston and Eichenbaum 2013), and codes for information about temporal positions within image sequences regardless of the image itself (Hsieh and Ranganath 2015). HC activations are also generally associated with temporal order memory (Kumaran and Maguire 2006; Ekstrom and Bookheimer 2007; Lehn et al. 2009; Ross et al. 2009; Jenkins and Ranganath 2010; Tubridy and Davachi 2011; Kalm et al. 2013; Hsieh et al. 2014; Goyal et al. 2018). Prior evidence further shows that the medial temporal lobe, specifically the HC formation, plays a critical role in the use of a TCM retrieval mode in the brain (Manns et al. 2007; Hsieh et al. 2014; Bladon et al. 2019). The HC binds events within temporal contexts (Eichenbaum et al. 2007; DuBrow and Davachi 2013; Bladon et al. 2019) through a gradually changing neural context (Manns et al. 2007; Mankin et al. 2012). Similarly, medial temporal lobe neuronal and BOLD activations in humans have demonstrated evidence for gradually evolving temporal contexts (Howard et al. 2012; Kalm et al. 2013; Kragel et al. 2015).Here we tested the contributions of the mPFC and HC during a visual sequence memory task that provides behavioral evidence of both ordinal and temporal context retrieval modes (see Fig. 1A; task modified from Allen et al. 2014). Briefly, participants first memorized six visual sequences (six images each) in a single passive viewing phase, and then were instructed to make judgments as to whether individual items were subsequently presented in sequence (InSeq) or out of sequence (OutSeq) over 240 self-paced presentations of each of the six items from each sequence. In the task, the two retrieval modes are parsed using probe trials that place conflicting demands on ordinal (Orlov et al. 2000; Allen et al. 2014, 2015) and temporal context modes (Jayachandran et al. 2019). We first evaluated ordinal retrieval modes using items that were transferred from one sequence to another while retaining their ordinal position (Ordinal Transfers) (Fig. 1B). Evidence for an ordinal-based retrieval mode occurs when these probes are identified as in sequence, because they occur in the same ordinal position as their original sequence. mPFC activations (but not HC) was strongest for these ordinal retrievals. Second, we evaluated a temporal context retrieval mode using items that skipped ahead (Skips) (Fig. 1B) with shorter lag distances (ABCFEF) compared with larger lag distances (AFCDEF). Skips should be most difficult to detect on the shortest lag distances because proximal items in a sequence are more likely to be retrieved (Howard and Kahana 2002; Kragel et al. 2015) and thus judged as InSeq. HC activations (but not mPFC) tracked with lag distance, providing evidence the HC is more reflective of a temporal context-based retrieval mode. Importantly, a significant interaction was observed such that mPFC and HC differentially activated for ordinal and temporal context retrievals. Altogether, our data show that sequence memory involves both retrieval modes. In line with these results, we suggest that understanding episodic memory requires more insight into the neurobiology of ordinal processing, in addition to the more often studied temporal contexts, in the mPFC and HC system.Open in a separate windowFigure 1.Sequence memory task and overall performance levels. Participants were tested on a sequence memory task that differentially burdens different retrieval modes using different out of sequence probe trial types. (A) An example sequence set that included six sequences. Two sequences were low memory demand sequences and four were high memory demand sequences. (B) There were three out of sequence probe trial types: items that were repeated in the sequence (Repeats), items that were presented too early in the sequence (Skips), and items that transferred from one sequence to another, while remaining in their ordinal position (Ordinal Transfers). Repeats and Skips occurred throughout the whole task, whereas Ordinal Transfers occurred during the second half only. (C) Accuracy throughout the task (error bars = ±1SD). Participants performed best on Repeats, then Skips, and poorest on Ordinal Transfers. (D,E) Distributions of response times for all InSeq trials (D, gray bars) and for all OutSeq trials (E, gray bars) for all participants with a fitted two-term Gaussian curve (black line). (F) A bimodal Gaussian curve fit better than a unimodal curve for InSeq and OutSeq trials. A trimodal curve did not improve the fit and increased the root mean squared error (not shown), suggesting distinct decisions decision-making between two decisions. It was rare to observe responses outside of the two distributions.     

The Impact of Text-Structure Strategy Instruction on the Text Recall and Eye-Movement Patterns of Second Language English Readers

Previous studies have demonstrated the efficacy of the Text Structure Strategy for improving text recall. The strategy emphasizes the identification of text structure for encoding and recalling information. Traditionally, the efficacy of this strategy has been measured through free recall. The present study examined whether recall and eye-movement patterns of second language English readers would benefit from training on the strategy. Participants’ free recall and eye-movement patterns were measured before and after training. There was a significant increase in recall at posttest and a change in eye-movement patterns, reflecting additional processing time of phrases and words signaling the text structure.     

Motives,mechanisms, and emotions

Abstract

Intelligent animals are solutions to a design problem posed by: the varying requirements of individuals, the more permanent requirements of species and social groups, the constraints of the environment, and the available biological mechanisms. Analysis of this design problem, especially the implications of limited knowledge and a continuous flow of information in a rapidly changing environment, leads to a theory of how new motives are processed in an intelligent system. The need for speed leads to architectures and algorithms that are fallible in ways that explain why intelligent agents are susceptible to emotions and errors. This holds also for intelligent robots. A study of such mechanisms and processes is a step towards a computational theory of emotions, attitudes, moods, character traits, and other aspects of mind so far not studied in Artificial Intelligence. In particular, it turns out that no special emotional subsystem is required. This framework clarifies and refines ordinary concepts of mental processes, and suggests a computational approach to psychotherapy.