Biology subjects

Rowe, K.

Publications and source records attributed to Rowe, K..

3 recordsLinked to original sources

bioRxiv2018

Influence of learning strategy on response time during complex value-based learning and choice

Measurements of response time (RT) have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning). Alternatively, they could learn reward values of options features (e.g. color, shape) and combine these values to estimate reward values for individual options (feature-based learning). We found that RT was slower when the difference between subjects estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach) adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

neuroscience↗

bioRxiv2017

Identification of Novel Breast Tumor Expression Dimensions within the PAM50 and Discovery of a Breast Cancer Susceptibility Locus at 12q15

It is well-known that breast tumors exhibit different expression patterns that can be used to assign intrinsic subtypes - the PAM50 assay, for example, categorizes tumors into: Luminal A, Luminal B, HER2-enriched and Basal-like - yet tumors are often more complex than categorization can describe. We used 911 sporadic breast tumors to reparameterize expression from the PAM50 genes to five orthogonal tumor dimensions using principal components (PC). Three dimensions captured intrinsic subtype, two dimensions were novel, and all replicated in 945 TCGA tumors. By definition dimensions are independent, an important attribute for inclusion in downstream studies exploring effects of tumor diversity. One application where tumor subtyping has failed to provide impact is susceptibility genetics. Germline genetic heterogeneity reduces power for gene-finding. The identification of heritable tumor characteristics has potential to increase homogeneity. We compared 238 breast tumors from high-risk pedigrees not attributable to BRCA1 or BRCA2 to 911 sporadic breast tumors. Two PC dimensions were significantly enriched in the pedigrees (intrinsic subtypes were not). We performed proof-of-concept gene-mapping in one enriched pedigree and identified a 0.5 Mb genomewide significant region at 12q15 that segregated to the 8 breast cancer cases with the most extreme PC tumors through 32 meioses (p=2.6x10-8). In conclusion, our study: suggests a new approach to describe tumor diversity; supports the hypothesis that tumor characteristics are heritable providing new avenues for germline studies; and proposes a new breast cancer locus. Reparameterization of expression patterns may similarly inform other studies attempting to model the effects of tumor heterogeneity.

genetics↗

bioRxiv2017

Your favorite color makes learning more adaptable and precise

Learning from reward feedback is essential for survival but can become extremely challenging with myriad choice options. Here, we propose that learning reward values of individual features can provide a heuristic for estimating reward values of choice options in dynamic, multidimensional environments. We hypothesized that this feature-based learning occurs not just because it can reduce dimensionality, but more importantly because it can increase adaptability without compromising precision of learning. We experimentally tested this hypothesis and found that in dynamic environments, human subjects adopted feature-based learning even when this approach does not reduce dimensionality. Even in static, low-dimensional environments, subjects initially adopted feature-based learning and gradually switched to learning reward values of individual options, depending on how accurately objects values can be predicted by combining feature values. Our computational models reproduced these results and highlight the importance of neurons coding feature values for parallel learning of values for features and objects.

neuroscience↗