Transfer-appropriate processing (TAP) proposes that retrieval success is based on the match between processing at encoding and retrieval. We propose that the processing described by TAP determines the contextual cues that are encoded with an event. At retrieval, the presence or absence of contextual cues matching the encoding cues will influence success. To implement these principles as a strategy to improve memory, the nature of future retrieval processing or cues must be known during encoding. As this is unlikely in real-world memory function, we propose that increased encoding variability – increasing the range of encoded cues – increases the likelihood of TAP when the retrieval scenario is unknown. The larger the set of encoded cues, the more likely those cues will recur during retrieval and therefore achieve TAP. Preliminary research in our lab (Diana, unpublished data) has found that increased encoding variability improves memory for item information in a novel retrieval context. To test whether this benefit to memory is due to the increased likelihood of TAP, the current experiment compared the effects of encoding variability under conditions that emphasize TAP to conditions that reduce TAP. We found main effects of encoding variability and TAP, but no interaction between the two. Planned comparisons between high and low variability encoding contexts within matching and non-matching retrieval contexts did not produce a significant difference between high and low variability when encoding-retrieval processing matched. We conclude that further studies are necessary to determine whether encoding variability has mechanisms that benefit memory beyond TAP. / M.S. / It is well accepted within the episodic memory literature that successful memory retrieval is often driven by context cues. Specifically, the cues that are stored with the memory of the event. To develop a better understanding of how episodic memory works, we must understand how manipulating context cues changes memory performance. One way to investigate the effects of context manipulation is using encoding variability, which refers to the amount of variability (i.e., change) in context cues from one repetition of an item or event, to the next. Preliminary research in our lab (Diana, unpublished data) has found that increased encoding variability improves memory retrieval in a novel context, but it is unclear why this is the case. We proposed that the mental processing described by transfer-appropriate processing (TAP) – a principle stating that memory retrieval success is determined by the match, or overlap, between the mental processing at encoding (i.e., memory formation) and memory retrieval – determines the contextual cues that are stored with the memory at encoding. We hypothesized that encoding variability works even when TAP has already been achieved by matching the processing and cues at encoding to those at retrieval. Alternatively, we hypothesized that encoding variability works by specifically achieving TAP, so that encoding variability is only helpful when the encoding and retrieval contexts do not match. Results indicated partial support for the alternative hypothesis, suggesting that encoding variability works by achieving TAP. However, these results were not sufficiently conclusive, and it is likely that there are other mechanisms that allow for encoding variability to improve memory. This study establishes the groundwork for future work examining encoding variability and its effects on memory.

Episodic memories are neurally coded records of personally experienced events across a lifetime. These records are encoded via medial temporal lobe structures in the brain, including the hippocampus, and are commonly called "representations" or "memory traces". Existing studies indicate that information about the neural signal corresponding to a memory representation can be found in functional magnetic resonance imaging (fMRI) data when the pattern across its smallest units (voxels, often 3mm3 sections of the brain) is measured. Many prior studies have measured these voxel patterns in response to stimuli as if they are a spontaneous brain function, regardless of cognitive factors. These studies sometimes find that similarity in the voxel patterns across repetition of a to-be-remembered event predicts later memory retrieval, but the results are inconsistent. The current fMRI study investigated the possibility that cognitive goals during encoding affect the type of neural representation (voxel pattern) that will later support memory retrieval. This seems likely because prior behavioral studies indicate that cognitive variability across repetitions of an event benefits later memory retrieval, which is difficult to reconcile with the common finding that voxel pattern variability across repetitions of an event harms later memory. We tested this hypothesis by comparing voxel patterns that support later memory retrieval to those associated with forgotten items in the medial temporal lobe, including the hippocampus, and lateral occipital cortex. Overall, as previously demonstrated, the behavioral results showed that exposure to variable cognitive goals across repetition of events during encoding benefited subsequent memory retrieval. Voxel patterns in the hippocampus indicated a significant interaction between cognitive goals (variable vs. consistent) and memory (remembered vs. forgotten) such that less voxel pattern similarity for the repeated events with variable cognitive goals, but not consistent cognitive goals, supported later memory success. In other words, variable hippocampal neural activations for the same events under different cognitive goals predicted better later memory performance. However, there was no significant interaction in neural pattern similarity between cognitive goals and memory success in medial temporal cortices or lateral occipital lobe. Instead, higher similarity in voxel patterns in right medial temporal cortices was associated with later memory retrieval, regardless of cognitive goals. In the lateral occipital lobe, the main effects of cognitive goals, hemisphere, and memory success were found but no interactions. In conclusion, we found that the relationship between pattern similarity and memory success in the hippocampus (but not the medial temporal lobe cortex) changes when the cognitive goal during encoding does or does not vary across repetitions of the event. / Master of Science / Episodic memory is a long-term memory of personal experiences which are encoded via the medial temporal lobe in the brain, primarily in the hippocampus. The records of personal experiences in these areas are commonly called "patterns", "representations", or "memory traces". Prior investigations indicate that the way of measuring the neural signals corresponding to personal events is functional magnetic resonance imaging (fMRI). The brain images taken by an fMRI scanner represent the patterns of the smallest unit (voxels, often 3mm3 sections of the brain). Many prior investigations of episodic memory used the voxel patterns but showed mixed results in whether similarity in the voxel patterns across repetition of a repeated event leads to subsequent memory retrieval. One of the possible explanations for mixed results is that the cognitive factors during encoding were neglected. Therefore, the current fMRI study examined how cognitive goals during encoding influence the voxel patterns that later support memory retrieval. During encoding, participants were shown an image repeated with the same or different questions and answered the question on the screen in an fMRI scanner. After 10 days, they were invited to the item memory test on the images that they were given during the encoding phase. The voxel patterns in the medial temporal lobe, including the hippocampus, and the lateral occipital lobe were compared across the repetitions of each image. The behavioral results showed that variable cognitive goals across repeated events during encoding benefited later memory retrieval. Furthermore, less similar voxel patterns in the hippocampus for the images repeated with different questions, but not the same questions, during encoding predicted better later memory success. In the right medial temporal cortices, higher similarity in voxel patterns was significantly associated with later memory retrieval, regardless of cognitive goals. In the lateral occipital lobe, higher voxel pattern similarity was found in the right hemisphere, for images repeated with the same question, and for images successfully retrieved later. In conclusion, we found that the relationship between voxel pattern similarity and memory success in the hippocampus (but not the medial temporal lobe cortex) changes when the cognitive goal during encoding does or does not vary across repetitions of the event.

In this dissertation we lay the groundwork of automated analysis of extended feature models with constraint programming. Among different proposals, feature modeling has proven to be very effective for modeling and managing variability in Software Product Lines. However, industrial experiences showed that feature models often grow too large with hundreds of features and complex cross-tree relationships, which necessitates automated analysis support. To address this issue we present a mapping from extended feature models, which may include complex feature-feature, feature-attribute and attribute-attribute cross-tree relationships as well as global constraints, to constraint logic programming over finite domains. Then, we discuss the effects of including complex feature attribute relationships on various analysis operations defined on the feature models. As new types of variability emerge due to the inclusion of feature attributes in cross-tree relationships, we discuss the necessity of reformulation of some of the analysis operations and suggest a revised understanding for some other. We also propose new analysis operations arising due to the nature of the new variability introduced. Then we propose a transformation from extended feature models to basic/cardinality-based feature models that may be applied under certain circumstances and enables using SAT or BDD solvers in automated analysis of extended feature models. Finally, we discuss the role of the context information in feature modeling, and propose to use context information in staged configuration of feature-models.