N-back Performance: Comparing Assessment and Training Performance

N-back Performance: Comparing Assessment and Training Performance J. Isaiah Harbison (jiharb@umd.edu) Center for Advanced Study of Language and Department of Psychology, University of Maryland 7005 52 nd Avenue, College Park, MD 27642 USA Sharona M. Atkins (smatkins@umd.edu) Neuroscience & Cognitive Science Program and Department of Psychology, University of Maryland Biology/Psychology Building, College Park, MD 27642 USA Michael R. Dougherty (mdougherty@psyc.umd.edu) Department of Psychology and Center for Advanced Study of Language, University of Maryland Biology/Psychology Building, College Park, MD 27642 USA Abstract Despite its frequent use, much is unknown about how the n- back task is performed and how it relates to working memory. We conducted a detailed analysis of the accuracy and reaction time data from a 4-back version of the task and compared the results with previous results from an adaptive training version of the task. The experiment was also designed to test the novel predictions of a computational model of n-back performance. The assessment results were largely consistent with both the training data and the model predictions. Keywords: working memory; executive functioning; n-back; working memory training; computational model. N-back and Cognition The n-back task is used both to measure (Owen et al., 2005) and improve (Jaeggi et al., 2008) working memory (WM). It is considered a memory updating task, and updating is thought to be a core component of working memory (Miyake et al., 2000). However, the task is not consistently or strongly correlated with performance on complex working memory span tasks, such as operation span or reading span (Kane et al., 2007). Furthermore, despite transfer to measures of fluid intelligence, n-back training has not been found to transfer to other measures of WM (Jaeggi et al., 2008; Li et al., 2008). To better understand n-back performance and its relation to WM, the present study provides a detailed analysis of 4- back data. This study builds on a previous analysis of an n- back training task (Harbison, Atkins, & Dougherty 2011) by testing if the results from an adaptive, training version of the n-back task are replicated in a non-adaptive, assessment version of the task. The present study also tests new predictions made by the computational model of n-back performance based on that training data (Harbison et al., The N-back Task In the n-back task participants are presented with a sequence of stimuli (e.g., letters). As each stimulus is presented, participants are asked to compare the current stimulus with the stimulus that occurred n items prior in the sequence. For example, in the 4-back version of the task, participants might be presented with the letter sequence “H-G-S-M-L-T- …”. If the next letter in the sequence is “S” then participants should respond “target” as the current letter matches the letter occurring four letters prior. If the next letter is anything else, then the correct response is “non-target”. Not all non-matching letters are the same in terms of difficulty. Lures, stimuli that match an item near to but not at the target location, are more difficult than fillers (stimuli that are neither lures nor targets). Participants are less accurate and take longer to respond to lures relative to fillers (Gray, Chabris, & Braver, 2003; Harbison et al., 2011; Kane et al., 2007; McCabe & Hartmen, 2008; Oberauer, 2005). From the example, the letters “H”, “G”, “M”, and “L” are lures. They match the 6 th , 5 th , 3 rd , and 2 nd letter back, respectively, but not the 4 th letter back. Letters such as “F”, “P”, and “R” are fillers. In the training version of the n-back task the level of n varies as a function of participant performance. The n level is increased when participants perform well and decreased when participants perform poorly at their current n level. In contrast, assessment versions of the task are non-adaptive; participants are given a set number of trials at predetermined levels of n. Previous Results Performance on the n-back task is not often the focus of the experiments in which the task is used. Instead, the n-back task is either used to measure or to improve WM. Therefore, despite its frequent use, there remains a lack of detailed data on n-back task performance (for exceptions see Gray et al., 2003; Kane et al., 2007; McCabe & Hartmen, 2008; Oberauer, 2005). Previously, we (Harbison et al., 2011) identified four results that characterize n-back training task performance. First, accuracy for target trials varies as a function of serial position. Figure 1a shows the results for sequences of 4- back from the training data; participants demonstrated primacy for target trials whereas this effect was weak to non-existent for lure and filler trials. Here the lures were one position away from the target, so they matched either the 3 rd or 5 th back stimuli. Second, in the reaction time (RT) data, we found that participants were faster making correct than