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By reworking a theory first proposed by physicists in the 1920s, the researchers discovered a new way to predict important characteristics of a new material before it's been created. The new formula allows computers to model the properties of a material up to 100,000 times faster than previously possible and vastly expands the range of properties scientists can study. "The equation scientists were using before was inefficient and consumed huge amounts of computing power, so we were limited to modeling only a few hundred atoms of a perfect material," said Emily Carter, the engineering professor who led the project. "But most materials aren't perfect," said Carter, the Arthur W. Marks '19 Professor of Mechanical and Aerospace Engineering and Applied and Computational Mathematics. "Important properties are actually determined by the flaws, but to understand those you need to look at thousands or tens of thousands of atoms so the defects are included. Using this new equation, we've been able to model up to a million atoms, so we get closer to the real properties of a substance." By offering a panoramic view of how substances behave in the real world, the theory gives scientists a tool for developing materials that can be used for designing new technologies. Car frames made from lighter, strong metal alloys, for instance, might make vehicles more energy efficient, and smaller, faster electronic devices might be produced using nanowires with diameters tens of thousands of times smaller than that of a human hair.

Statistical validation of results, as Shaffer described it, simply involves testing the null hypothesis: that the pattern you detect in your data occurs at random. If you can reject the null hypothesis—and science and medicine have settled on rejecting it when there's only a five percent or less chance that it occurred at random—then you accept that your actual finding is significant. The problem now is that we're rapidly expanding our ability to do tests. Various speakers pointed to data sources as diverse as gene expression chips and the Sloan Digital Sky Survey, which provide tens of thousands of individual data points to analyze. At the same time, the growth of computing power has meant that we can ask many questions of these large data sets at once, and each one of these tests increases the prospects than an error will occur in a study; as Shaffer put it, "every decision increases your error prospects." She pointed out that dividing data into subgroups, which can often identify susceptible subpopulations, is also a decision, and increases the chances of a spurious error. Smaller populations are also more prone to random associations. In the end, Young noted, by the time you reach 61 tests, there's a 95 percent chance that you'll get a significant result at random. And, let's face it—researchers want to see a significant result, so there's a strong, unintentional bias towards trying different tests until something pops out. Young went on to describe a study, published in JAMA, that was a multiple testing train wreck: exposures to 275 chemicals were considered, 32 health outcomes were tracked, and 10 demographic variables were used as controls. That was about 8,800 different tests, and as many as 9 million ways of looking at the data once the demographics were considered.

To explore how such memories are recorded, the researchers showed ten volunteers three short films and asked them to memorise what they saw. The films were very simple, sharing a number of similar features - all included a woman carrying out an everyday task in a typical urban street, and each film was the same length, seven seconds long. For example, one film showed a woman drinking coffee from a paper cup in the street before discarding the cup in a litter bin; another film showed a (different) woman posting a letter. The volunteers were then asked to recall each of the films in turn whilst inside an fMRI scanner, which records brain activity by measuring changes in blood flow within the brain. A computer algorithm then studied the patterns and had to identify which film the volunteer was recalling purely by looking at the pattern of their brain activity. The results are published in the journal Current Biology. "The algorithm was able to predict correctly which of the three films the volunteer was recalling significantly above what would be expected by chance," explains Martin Chadwick, lead author of the study. "This suggests that our memories are recorded in a regular pattern." Although a whole network of brain areas support memory, the researchers focused their study on the medial temporal lobe, an area deep within the brain believed to be most heavily involved in episodic memory. It includes the hippocampus - an area which Professor Maguire and colleagues have studied extensively in the past. They found that the key areas involved in recording the memories were the hippocampus and its immediate neighbours. However, the computer algorithm performed best when analysing activity in the hippocampus itself, suggesting that this is the most important region for recording episodic memories. In particular, three areas of the hippocampus - the rear right and the front left and front right areas - seemed to be involved consistently across all participants. The rear right area had been implicated in the earlier study, further enforcing the idea that this is where spatial information is recorded. However, it is still not clear what role the front two regions play.