Object representations from convolutional neural network (CNN) models of computer vision (LeCun, Bengio, & Hinton, 2015) were used to drive a cognitive model of decision making, the linear ballistic accumulator (LBA) model (Brown & Heathcote, 2008), to predict errors and response times (RTs) in a novel object recognition task in humans. CNNs have become very successful at visual tasks like classifying objects in real-world images (e.g., He, Zhang, Ren, & Sun, 2015; Krizhevsky, Sutskever, & Hinton, 2012). We asked whether object representations learned by CNNs previously trained on a large corpus of natural images could be used to predict performance recognizing novel objects the network has never been trained on; we used novel "Greebles," "Ziggerins," and "Sheinbugs" that have been used in a number of previous object recognition studies. We specifically investigated whether a model combining high-level CNN representations of these novel objects could be used to drive an LBA model of decision making to account for errors and RTs in a same-different matching task (from Richler et al., 2019). Combining linearly transformed CNN object representations with the LBA provided reasonable accounts of performance not only on average, but at the individual-participant level and the item level as well. We frame the findings in the context of growing interest in using CNN models to understand visual object representations and the promise of using CNN representations to extend cognitive models to explain more complex aspects of human behavior.