We investigated the role of attention and executive control in rhythmic timing, using a dual-task paradigm. The main task was a finger tapping task in which participants were asked to tap their index finger in time with metronome sequences. The tempo of the sequences ranged from 600 ms to 3000 ms between each beat. The distractor task, chosen so as to engage executive control processes, was a novel covert n-back task. When the tempo was slow, simultaneous performance of the tapping and n-back tasks resulted in significant performance degradation in both tasks. There was also some dual-task interference at the fast tempo levels, however, the magnitude of the interference was much smaller in comparison. The results suggests that, when the tempo is sufficiently slow, performing rhythmic timing demands attentional resources and executive control. This accords with models of time perception that assume that different timing mechanisms are recruited at different time scales. It also accords with models that assume a dedicated mechanism for rhythm perception and where rhythm perception is assumed to have a slower limit. Like visual perception can be divided into such subcategories as color perception, motion perception, and depth perception, so too can time perception. Some aspects of time perception are interval timing, temporal motor coordination, rhythm perception, and meter perception. It is possible to further subdivide time perception by modality and time scale. Much debated is whether all aspects of time perception share a common mechanism and, if not, what aspects of which mechanisms they do share (for a review see Ivry & Schlerf 2008). One influential class of models assumes that timing is governed by a pacemaker-accumulator type mechanism (Ivry, 1996), while more recent theoretical development are dynamical systems models that assume that timing and rhythm perception depend on oscillatory neural circuits (Large & Jones, 1999;Large, 2010). The former have been used successfully to model interval timing but has not proven a good model of responses to more complex stimuli such as musical rhythms, while the latter have been used to model rhythm and meter perception but have not been applied to interval timing (Grondin, 2010). The two mechanisms -pacemaker-accumulator type and oscillatory based -need not stand in opposition; models exist that incorporate both (Teki et al., 2012). KeywordsSome have suggested that time perception relies on different mechanisms, depending on time scale. Lewis and Miall (2006) report evidence that different neural mechanism are responsible for timing intervals shorter versus longer than one second. The timing of sub-second intervals has been termed automatic timing and that of supra-second intervals has been termed cognitive timing. These terms reflect that automatic timing recruits circuits within the motor system and auditory cortex, while cognitive timing depends more on circuits within the prefrontal and parietal cortices (Lewis & Miall, 2003). Interval timing is but one

We introduce a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of arousal systems that are relevant for consciousness. The three central components are an identification network, a localization network, and a working memory network. Attention serves as the interface between the inner and the external world. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception. It also compares external sensations to internal top-down expectations. The model is tested in a number of computer simulations that illustrate how it can operate as a component in various cognitive tasks including perception, the A-not-B test, delayed matching to sample, episodic recall, and vicarious trial and error.

We show how a multi-resolution network can model the development of acuity and coarse-to-fine processing in the mammalian visual cortex. The network adapts to input statistics in an unsupervised manner, and learns a coarse-to-fine representation by using cumulative inhibition of nodes within a network layer. We show that a system of such layers can represent input by hierarchically composing larger parts from smaller components. It can also model aspects of top-down processes, such as image regeneration.

Epi is a humanoid robot developed by Lund University Cognitive Science Robotics Group. It was designed to be used in experiments in developmental robotics and has proportions to give a childlike impression while still being decidedly robotic. The robot head has two degrees of freedom in the neck and each eye can independently move laterally. There is a camera in each eye to make stereovision possible. The arms are designed to resemble those of a human. Each arm has five degrees of freedom, three in the shoulder, one in the elbow and one in the wrist. The hands have four movable fingers and a stationary thumb. A force distribution mechanism inside the hand connect a single servo to the movable fingers and makes sure the hand closes around an object regardless of its shape. The rigid parts of the hands are 3D printed in PLA and HIPS while the flexible parts, including the joints and the tendons, are made from polyurethane rubber. The control system for Epi is based on neurophysiological data and is implemented using the Ikaros system. Most of the sensory and motor processing is done at 40 Hz to allow smooth movements. The irises of the eyes can change colour and the pupils can dilate and contract. There is also a grid of LEDs that resembles a mouth that can be animated by changing colour and intensity.

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