Tag: Caitlin Bowman

Presenter(s): Rennie Kendrick

Faculty Mentor(s): Dasa Zeithamova & Caitlin Bowman

Poster 119

Session: Social Sciences & Humanities

Memory allows us to link across multiple experiences to derive new information. For example, if we see a person, person 1, walking a Dalmatian, and later see another person, person 2, walking the same Dalmatian, we may infer that person 1 and 2 live in the same household. This linking of experiences to derive new information is called associative inference, and my research asks which conditions lead to the best associative inference. Participants are trained and tested on object pairs that each share an object in common with another pair. Half of the participants see object pairs in blocked format and the other half see the object pairs in interleaved format. In the blocked condition, participants have strongly established prior knowledge before encountering overlapping new information. In the interleaved condition, participants encounter a new overlapping episode before the first is strongly established. For the associative inference test, participants must infer that two objects that were never directly paired together, but paired with the same object, are indirectly related. I found that participants in the blocked condition performed significantly better on associative inference and directly-paired object tests compared to interleaved condition participants. Thus, strong memory for the first episode before encountering the second, overlapping episode enhanced associative inference ability and memory for both individual episodes. One possibility for this effect is that strongly established prior knowledge prevents interference from overlapping, but distinct episodes. Further investigation into the effect of blocked versus interleaved training on learning could lead to enhanced teaching methods.

Presenter(s): Takako Iwashita

Faculty Mentor(s): Dasa Zeithamova & Caitlin Bowman

Poster 128

Session: Social Sciences & Humanities

Concept learning involves linking related pieces of information to a shared label, like learning that furry creatures that bark are called ‘dogs.’ People vary is how well they learn concepts and apply them to new situations (generalization). What factors drive these individual differences? In the present study, we tested whether stable aspects of intelligence or transient activations in the brain best predicted concept generalization abilities. To measure aspects of intelligence, subjects underwent an assessment that included measures of working memory, processing speed, perceptual reasoning, and verbal comprehension, which could be combined into an overall IQ. Subjects also completed a concept generalization task while undergoing fMRI, allowing us to measure activations in brain regions that are part of the explicit rule-learning system (hippocampus, prefrontal cortex) or part of an implicit system that learns without awareness (caudate, posterior visual cortex). To elucidate the shared or dissociable roles of behavioral and neural predictors in concept generalization, we tested the relationship between accuracy in concept generalization and individual differences in measures of intelligence and activation in each brain region of interest. Behaviorally, we found that overall IQ, but not its subcomponents, predicted concept generalization abilities. Neurally, we found that only the activation in the hippocampus predicted concept generalization abilities. Finally, we found that IQ and hippocampal activation each predicted concept generalization above-and-beyond each other. These results show dissociable contributions of behavioral and neural predictors of concept generalization, suggesting that both stable cognitive abilities and transient brain states influence the ability to learn new concepts.

Presenter(s): Wenjia Cao

Faculty Mentor(s): Dasa Zeithamova-Demircan & Caitlin Bowman

Poster 132

Session: Social Sciences & Humanities

Concept learning involves linking pieces of information to a shared category label, like learning that furry creatures that live with humans and bark are called dogs. What factors affect how well people learn new categories? Prior research suggests that people can learn categories either by memorizing individual category members or by averaging across category members to form an abstract representation of the perfect category member, known as the prototype. We reasoned that if people learn categories through memorization, then they should learn better from small training sets, but set size should not affect prototype learning. We also reasoned that if people learn categories through memorization, then they should learn better from training sets where items are distinct from one another (i.e., atypical), but that prototype learning would be facilitated by training on typical items. To test our hypotheses, separate groups of participants underwent category training that varied in the number and typicality of category members. During training, participants saw category members one at a time, guessed which category they belonged to, and were told whether they were right or wrong. Following training, participants were tested on their ability to categorize the training items as well as new examples that they had never seen. We found that people who trained with more typical items learned more quickly and were better at categorizing new examples. Training size did not have a significant influence on learning rate or categorization accuracy. Therefore, our results support the idea that the typicality of training items greatly influences category learning, which is likely because it promotes formation of abstract, prototype category representations.

Studies show that emotion enhances memory for individual items but weakens memory for associations between items. One explanation for this associative memory impairment is that emotional stimuli capture attention, causing enhanced encoding of the emotional item but reduced encoding of the surrounding environment. This hypothesis generates the prediction that emotional information always impairs associative memory. Alternatively, it may be that emotion orients attention towards threats in the environment. For example, seeing an angry face constitutes a direct threat, potentially capturing attention and reducing memory for associated information. In contrast, seeing a fearful face indicates a threat elsewhere in the environment, potentially enhancing encoding of associated information. In the present study, I tested whether perception of anger and fear have different effects on memory for associated information. I hypothesized that associative memory would be worse for all emotional faces, both angry and fearful ones, compared to neutral faces. I also predicted that associative memory would be better for fearful faces than angry faces. To test these hypotheses, subjects studied sets of three images, consisting of two objects and a face with either a neutral, angry, or fearful expression. Subjects were later tested on their memory for the associations between the three items. Supporting our first hypothesis, memory for both angry and fearful associations was worse than memory for neutral associations. Contrary to our second hypothesis, there were no differences in memory for angry versus fearful associations. Thus, emotional information itself seems to capture attention, weakening memory for related information.