Two auditory lexical decision experiments document for morphologically complex words two points at which the probability of a target word given the evidence shifts dramatically. The first point is reached when morphologically unrelated competitors are no longer compatible with the evidence. Adapting terminology from Marslen-Wilson (1984), we refer to this as the word's initial uniqueness point (UP1). The second point is the complex uniqueness point (CUP) introduced by Balling and Baayen (2008), at which morphologically related competitors become incompatible with the input. Later initial as well as complex uniqueness points predict longer response latencies. We argue that the effects of these uniqueness points arise due to the large surprisal (Levy, 2008) carried by the phonemes at these uniqueness points, and provide independent evidence that how cumulative surprisal builds up in the course of the word co-determines response latencies. The presence of effects of surprisal, both at the initial uniqueness point of complex words, and cumulatively throughout the word, challenges the Shortlist B model of Norris and McQueen (2008), and suggests that a Bayesian approach to auditory comprehension requires complementation from information theory in order to do justice to the cognitive cost of updating probability distributions over lexical candidates.Keywords: spoken word recognition; Shortlist B; morphological processing; uniqueness points; neighbourhood measures; (cumulative) surprisal; Kullback-Leibler divergence; morphological family size. The Shortlist B model proposed by Norris and McQueen (2008) is the most comprehensive computational theory of auditory comprehension available to date. The model computes that sequence of words that is most likely to represent the lexical parse of the utterance heard, given the input, a stream of phonemes coming in over time. For instance, given the Dutch sequence of spoken words kar personen, the final state of the model is one in which kar ('cart') and personen ('persons') have probability 1, whereas competitors such as karper ('carp') and persoon ('persons') have probability 0.