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In Western tonal music, voice leading (VL) and harmony are two central concepts influencing whether a musical sequence is perceived as well-formed. However, experimental studies have primarily focused on the effect of harmony on the cognitive processing of polyphonic music. The additional effect of VL remains unknown, despite music theory suggesting VL to be tightly connected to harmony. Therefore, the aim of this study was to investigate and compare the effects of both VL and harmony on listener expectations. Using a priming paradigm and a choice reaction time task, participants (N = 34) were asked to indicate whether the final chord in a sequence had a different timbre than the preceding ones (cover task), with the experimental conditions being good and poor VL or harmony, respectively. An analysis with generalised mixed effects models revealed a significant influence of both VL and harmony on reaction times (RTs). Moreover, pairwise comparison showed significantly faster RTs when VL was good as compared to both VL and harmony being poor, which was not the case when only harmony was good. This study thus provides evidence for the additional importance of VL for the processing of Western polyphonic music. [ABSTRACT FROM AUTHOR]

Partial observability poses a major challenge for a reinforcement learning agent since the complete history of observations may be relevant for predicting and acting optimally. This is especially true in the general case where the underlying state space and dynamics are unknown. Existing approaches either try to learn a latent state representation or use decision trees based on the history of observations. In this paper we present a method for explicitly identifying relevant features of the observation history. These temporally extended features can be discovered using our P ulse algorithm and used to learn a compact model of the environment. Temporally extended features reveal the temporal structure of the environment while empirically outperforming other history-based approaches. [ABSTRACT FROM AUTHOR]

Vocal signalling systems, as used by humans and various non-human animals, exhibit discrete and continuous properties that can naturally be used to express discrete and continuous information, such as distinct words to denote objects in the world and prosodic features to convey the emotions of the speaker. However, continuous aspects are not always expressed with the continuous properties of an utterance but are frequently categorised into discrete symbols. While the existence of symbols in communication is self-evident, the emergence of discretisation from a continuous space is not well understood. In this paper, we investigate the emergence of discrete symbols in regions with a continuous semantics by simulating the learning process of two agents that acquire a shared signalling system. The task is formalised as a reinforcement learning problem with a continuous form and meaning space. We identify two causes for the emergence of discretisation that do not originate in discrete semantics: 1) premature convergence to sub-optimal signalling conventions and 2) topological mismatch between the continuous form space and the continuous semantic space. The insights presented in this paper shed light on the origins of discrete symbols, whose existence is assumed by a large body of research concerned with the emergence of syntactic structures and meaning in language. [ABSTRACT FROM AUTHOR]