Speed-Accuracy Trade-off in Value-Driven Attentional Capture

Attention is traditionally divided into two types: voluntary, goal-directed attention and involuntary, stimulus-driven attention (Corbetta & Shulman, 2002; Theeuwes, 2010). Seminal work on value-driven attentional capture (VDAC) has shown that stimuli associated with reward during a reward learning phase slowed reaction time (RT) in a test phase even when task-irrelevant and non-salient (Anderson, Laurent, & Yantis, 2011). However, performance-contingent reward and a response deadline impose additional constraints in the VDAC paradigm: responding too quickly decreases reward likelihood and responding too late drops the reward probability to zero. Thus, to maximize reward, participants must carefully decide when to respond, potentially altering the strategic balancing of speed and accuracy and confounding attentional effects with decisional ones. We replicated the VDAC paradigm to address the influence of different response strategies. Using maximum likelihood estimation, RT distributions were fitted with an exGaussian model. We found that RT variability (σ) was significantly greater in the experimental group (p\u3c0.05), suggesting that reward learning produced a less stable strategy. Further, RT variability positively correlated with error rate (r=0.51, p\u3c0.001), reflecting a behavioral cost with greater RT variability. These results call into question the validity of the baseline trials used in the VDAC paradigm, as reward learning altered the response strategy even after the reward was removed

Post-Frog Pond: Cultural Variations in Hiring Decisions

Honors (Bachelor's)PsychologyUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/147376/1/blackerl.pd

Genetic Analysis of Prostate Cancer with Computer Science Methods

Metastatic prostate cancer is one of the most common cancers in men. In the advanced stages of prostate cancer, tumours can metastasise to other tissues in the body, which is fatal. In this thesis, we performed a genetic analysis of prostate cancer tumours at different metastatic sites using data science, machine learning and topological network analysis methods. We presented a general procedure for pre-processing gene expression datasets and pre-filtering significant genes by analytical methods. We then used machine learning models for further key gene filtering and secondary site tumour classification. Finally, we performed gene co-expression network analysis and community detection on samples from different prostate cancer secondary site types. In this work, 13 of the 14,379 genes were selected as the most metastatic prostate cancer related genes, achieving approximately 92% accuracy under cross-validation. In addition, we provide preliminary insights into the co-expression patterns of genes in gene co-expression networks. Project code is available at https://github.com/zcablii/Master_cancer_project