The invention of the airplane spans a period of 110 years from 1799 when Cayley first described the design of fixed-wing aircraft to 1909 when practical craft were flown at the Reims Air Show. At least 100 different designs were built and tested during this period, often at great expense, and occasionally at the cost of the pilot's life. With the exception of the Wright Brothers, progress was slow and sporadic. The Wrights needed only four years to develop their first airplane. Their efficiency is unique: N o other inventor was able to duplicate the steady rapid progress of the Wright Brothers or duplicate their results until details of the Wright craft became available in 1906.

To understand and aa upon new experiences, people may draw on specific past experiences. Analogical transfer models suggest that past experiences are used following the encoding of the new information. Research on reminding s suggests that specific experiences may be accessed during encoding, and text comprehension research suggests available information may be used to interpret incoming material. Together, these findings suggest a more dynamic organization of interpretation processes, where past experiences may be accessed and used during encoding. This paper repeats on two experiments that explore the relationship between reminding and interpretation. In particular, the question was whether remindings that occur early during encoding might bias later interpretation. The first experiment used a postprocessing measure of interpretation, while the second measured on-line sentence reading times. In contrast to most contemporary models of analogical transfer, we found that remindings m a y influence interpretation during encoding.

There are individual differences in complex task performance that can be attributed to the strategies people use and howthey adapt their strategies to task demands (Schunn & Reder, 2001). It is unclear if people are aware of the strategythey use and how this affects adaptation of their strategy. The present study assessed participants awareness of their ownstrategy while performing a complex task. Part of the task required participants to select which objects to sort based ondifferent object features that affect their score. Using a participants selections, their selection strategy was inferred usingautomated techniques and compared to their reported strategy. Participants reported using more of the object featuresin their strategies than what was inferred based on their choices. The features in the inferred strategy only partiallyoverlapped with the features participants reported. In addition, greater awareness of ones strategy was associated withbetter task performance.

In complex tasks, high performers often have better strategies than low performers even with similar practice. Relativelylittle research has examined how people form and modify strategies in tasks that permit a large set of possible strategies.One challenge with such research is determining strategies based on behavior. Three algorithms were developed to trackthe task features people employ in their strategies while performing a complex task. An ACT-R model that performs thetask was created to collect simulated data with a range of known strategies. The performance of the three algorithms iscompared, and a decision tree classification algorithm yielded the best performance across the test cases. Summary datafrom applying the algorithms to human data on the tasks is also presented and highlights potential challenges for futurework. However, this approach to tracking strategy exploration may enable further development of theories about howpeople search for good strategies.

Adapting ones strategy involves two steps: assessing and then modifying a strategy in a problem-solving environmentbased on performance. Schunn and Reder demonstrated a positive correlation between working memory and strategyadaptivity measured with the Air Traffic Control Task, though Schunn, Lovett, and Reder found no relationship betweenworking memory and adaptivity measured with the Building Sticks Task (BST). We explored this discrepancy by admin-istering a battery of individual differences measures, including BST adaptivity, fluid intelligence, working memory span,and a new measure of set effects based on the BST, administered to 109 Mississippi State undergraduate participants. FluidIntelligence and BST adaptivity were positively correlated, though the relationship was weak. Our measure of set-effectadaptivity exhibited internal consistency and obvious individual differences, but was uncorrelated with other tasks. Thusstrategy adaptivity may not rely heavily upon working memory and may draw upon distinct cognitive resources, dependingon the underlying task

The B cell receptor (BCR) signals together with a multi-component co-receptor complex to initiate B cell activation in response to antigen binding. Here, we take advantage of peroxidase-catalyzed proximity labeling combined with quantitative mass spectrometry to track co-receptor signaling dynamics in Raji cells from 10 s to 2 h after BCR stimulation. This approach enables tracking of 2,814 proximity-labeled proteins and 1,394 phosphosites and provides an unbiased and quantitative molecular map of proteins recruited to the vicinity of CD19, the signaling subunit of the co-receptor complex. We detail the recruitment kinetics of signaling effectors to CD19 and identify previously uncharacterized mediators of B cell activation. We show that the glutamate transporter SLC1A1 is responsible for mediating rapid metabolic reprogramming and for maintaining redox homeostasis during B cell activation. This study provides a comprehensive map of BCR signaling and a rich resource for uncovering the complex signaling networks that regulate activation.

A multi-session experiment explored the relationship between individual differences and the development of strate-gies in a complex task environment. In the first session, participants completed measures of working memory and adaptivity.Participants then performed 4.5 hours of a multitasking activity that involved prioritizing, selecting, and sorting objects intobins under time pressure. The analyses reported here focus on how participants prioritized objects in a queue of objects andselected objects from that queue for sorting. Priority selection strategies were automatically extracted using machine learningmethods. Differences in strategy use were related to measures of working memory and adaptivity. Strategy use and strategychange mediated the relationship between task performance and individual differences. A hierarchical clustering analysis re-vealed patterns of strategy shifts that distinguished between participants who improved and those who did not. These resultsprovide a basis for examining strategy training geared toward individuals’ cognitive abilities.