1. Distribution of Tau Distances Assessing Episodic and Semantic Contributions in Serial Recall Pernille Hemmer, Brent Miller & Mark Steyvers University of California, Irvine A Wisdom of Crowds Analysis Can we reconstruct the original order on the basis of the recalled orders of a group of individuals? How can memory models help us in this reconstruction process? The goal here is to show that the reconstructed order from a group of individuals is better than the recalled order from any particular individual – a wisdom of crowds effect. We developed a variant of the perturbation model (Estes et al., 1972) that can explain a number of memory errors in serial recall. In this model, items are originally encoded in the correct temporal sequence. Errors occur by locally perturbing the temporal encoding. In our variant of the model, we build in individual differences and item differences – assuming that some people have better memories (sigma parameter), and that some items are easier to rank (e.g., because they stand out temporally) Prior knowledge and expectations about events are known to influence episodic memory. Researchers often assume that prior knowledge leads to errors in recall. We investigate recall for the order of sequences of events, focusing on the potential benefit of prior knowledge. First, we assess the prior knowledge that people bring to the memory task. Second, we utilize memory tasks that are ecologically valid. The stimulus materials to be remembered have the same statistical regularities as can be found in the natural environment. The memory task involves a rank-ordering task in which people have to remember the study order of pictures of stereotyped and random event sequences. We expect to show that combining our knowledge of the regularities of the environment with these noisy memory representations improves the overall accuracy in episodic memory Introduction The Tasks Ten images were drawn from 6 different video clips. Three videos depicted stereotyped events sequences (getting up in the morning, a wedding, getting on the school bus) for which people have strong prior expectations. Three videos depicted more random event sequences (yogurt, pizza, claymation) for which people have weak prior expectations. Prior knowledge condition: Participants would order the randomized sequence of images based on their prior expectation and with out having previously viewed the images Memory condition: Participants would first study the correct sequence of images. They would then order the sequence from memory. probability for individual j that the item at the source position i gets perturbed to the destination position k. Original order Recalled order ABCDEFGH D Note: the mean tau expected for random sequences is 22.5 (=10*9/4) Median Tau to Truth Performance was measures using Kendall’s Tau: The number of adjacent pair-wise swaps between recalled and true order. = 1 = 1+1 Ordering by Individual ABECD True Order ABCDE C D E ABAB ABCDEABCDE = 2 Measuring Performance Data for Morning Sequence (see top banner) Inferred Model Parameters Example Random Sequences Example Stereotyped Event Sequence Best individualAverage IndividualInferred Item AccuracyCalibration of Individuals Participant number (first row) Tau score for each participant (second row) Tau scores indicate high accuracy from guessing with prior knowledge for stereotyped events For random events prior knowledge performance is similar to chance Taus are significantly lower for stereotyped (prior) than for random (no prior) events Taus in the memory task (study) are significantly lower than in the prior knowledge task (no study) Even when participants did not previously study the events, they performed better for all three stereotyped events Semantic and Episodic Contributions priorserial recall A B CD EFG H IJ AB CD EFG H IJ Data for Pizza Sequence (see bottom banner) priorserial recall Morning Sequence Wedding Sequence Pizza Sequence Clay Sequence Mean  Mean Performance of Individuals and Model(s) Note: the Thurstonian and Borda count model are two alternative models that were applied to this data