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When people speak about consciousness, they distinguish various types and different levels, and they argue for different concepts of cognition. This complicates the discussion about artificial or machine consciousness. Here we take a bottom-up approach to this question by presenting a family of robot experiments that invite us to think about consciousness in the context of artificial agents. The experiments are based on a computational model of sensorimotor contingencies. It has been suggested that these regularities in the sensorimotor flow of an agent can explain raw feels and perceptual consciousness in biological agents. We discuss the validity of the model with respect to sensorimotor contingency theory and consider whether a robot that is controlled by knowledge of its sensorimotor contingencies could have any form of consciousness. We propose that consciousness does not require higher-order thought or higher-order representations. Rather, we argue that consciousness starts when (i) an agent actively (endogenously triggered) uses its knowledge of sensorimotor contingencies to issue predictions and (ii) when it deploys this capability to structure subsequent action. [ABSTRACT FROM AUTHOR]

One of the main assertions of sensorimotor contingency theory is that sensory experience is not generated by activating an internal representation of the outside world through sensory signals, but corresponds to a mode of exploration and hence is an active process. Perception and sensory awareness emerge from using the structure of changes in the sensory input resulting from these exploratory actions, called sensorimotor contingencies (SMCs), for planning, reasoning, and goal achievement. Using a previously developed computational model of SMCs we show how an artificial agent can plan ahead with SMCs and use them for action guidance. Our main assumption is that SMCs are associated with a utility for the agent, and that the agent selects actions that maximize this utility. We analyze the properties of the resulting actions in a robot that is endowed with several sensory modalities and controlled by our model in a simple environment. The results demonstrate that its actions avoid aversive events, and that it can achieve a low-level form of spatial awareness that is resilient to the complete loss of a sensory modality. [ABSTRACT FROM AUTHOR]

In cognitive science, we are currently witnessing a ‘pragmatic turn’, away from the traditional representation-centered framework towards a paradigm that focuses on understanding cognition as ‘enactive’, as skillful activity that involves ongoing interaction with the external world. The key premise of this view is that cognition should not be understood as providing models of the world, but as subserving action and being grounded in sensorimotor coupling. Accordingly, cognitive processes and their underlying neural activity patterns should be studied primarily with respect to their role in action generation. We suggest that such an action-oriented paradigm is not only conceptually viable, but already supported by much experimental evidence. Numerous findings either overtly demonstrate the action-relatedness of cognition or can be re-interpreted in this new framework. We argue that new vistas on the functional relevance and the presumed ‘representational’ nature of neural processes are likely to emerge from this paradigm. [Copyright &y& Elsevier]

Gestalt-based feature binding becomes problematic if different objects overlap in their positional configuration and/or feature space, or if features vary over the spatial extent of an object. If synchronization is to be a viable mechanism for binding the responses of disparate feature selective neurons in the brain, it must cope with resulting ambiguities. In this article the synchronization properties of an oscillator network for multidimensional feature binding are investigated. For non-uniform feature distributions in a stimulus, its components are adequately represented by the eigenmodes of the oscillatory dynamics. The significance of the eigenmodes corresponds to the salience of different stimulus interpretations. [Copyright &y& Elsevier]

The correlated activity of neurons is being regarded as candidate mechanism for binding the responses of different feature selective cells. Binding, however, is rarely unique, especially at levels beyond feature detection. In this article the dynamical properties of a network of coupled oscillators as a model for a mental representation of visual stimuli are investigated. Oscillator networks have been analyzed previously, but here a new coupling scheme and a biologically more relevant transfer function are used. A principal mode analysis of the activity shows that the network sensibly handles ambiguous segmentations of a visual stimulus. [Copyright &y& Elsevier]

Compared to the different brain signals used in brain–computer interface (BCI) paradigms, the s teady-state visually evoked potential (SSVEP) features a high signal to noise ratio, enabling reliable and fast classification of neural activity patterns without extensive training requirements. In this paper, we present methods to further increase the information transfer rates (ITRs) of SSVEP-based BCIs. Starting with stimulus parameter optimizations methods, we develop an improved approach for the use of Canonical correlation analysis and analyze properties of the SSVEP when the user fixates a target and during transitions between targets. These transitions show a negative effect on the system's ITR which we trace back to delays and dead times of the SSVEP. Using two classifier types adapted to continuous and transient SSVEPs and two control modes (fast feedback and fast input), we present a simulated online BCI implementation which addresses the challenges introduced by transient SSVEPs. The resulting system reaches an average ITR of 181 Bits/min and peak ITR values of up to 295 Bits/min for individual users. [ABSTRACT FROM AUTHOR]

Abstract: Presentation of two targets in close temporal succession often results in an impairment of conscious perception for the second stimulus. Previous studies have identified several electrophysiological correlates for this so-called ‘attentional blink’. Components of the event-related potential (ERP) such as the N2 and the P3, but also oscillatory brain signals have been shown to distinguish between detected and missed stimuli, and thus, conscious perception. Here we investigate oscillatory responses that specifically relate to conscious stimulus processing together with potential ERP predictors. Our results show that successful target detection is associated with enhanced coherence in the low beta frequency range, but a decrease in alpha coherence before and during target presentation. In addition, we find an inverse relation between the P3 amplitudes associated with the first and second target. We conclude that the resources allocated to first and second target processing are directly mirrored by the P3 component and, moreover, that brain states before and during stimulus presentation, as reflected by oscillatory brain activity, strongly determine the access to consciousness. Thus, becoming aware of a stimulus seems to depend on the dynamic interaction between a number of widely distributed neural processes, rather than on the modulation of one single process or component. [Copyright &y& Elsevier]

The phase of neural oscillatory signals aligns to the predicted onset of upcoming stimulation. Whether such phase alignments represent phase resets of underlying neural oscillations or just rhythmically evoked activity, and whether they can be observed in a rhythm-free visual context, however, remains unclear. Here, we recorded the magnetoencephalogram while participants were engaged in a temporal prediction task, judging the visual or tactile reappearance of a uniformly moving stimulus. The prediction conditions were contrasted with a control condition to dissociate phase adjustments of neural oscillations from stimulus-driven activity. We observed stronger delta band inter-trial phase consistency (ITPC) in a network of sensory, parietal and frontal brain areas, but no power increase reflecting stimulus-driven or prediction-related evoked activity. Delta ITPC further correlated with prediction performance in the cerebellum and visual cortex. Our results provide evidence that phase alignments of low-frequency neural oscillations underlie temporal predictions in a non-rhythmic visual and crossmodal context. [ABSTRACT FROM AUTHOR]

Objective: We present a new type of brain-computer interface (BCI) that utilizes the retinotopic mapping of motion-onset visual evoked potentials (mVEP) to accomplish four control channels using a single motion stimulus. Methods: Participants selected a BCI command by fixating one of four target locations around a centrally presented visual motion stimulus. A template-matching method was employed to recognize the users’ intention by decoding the position of the motion stimulus in the peripheral visual field, and classification performances were evaluated in an offline manner. The motion stimulus eccentricity between the targets and the visual motion stimulus varied among 5.1°, 6.7°, 9.8°, and 13.0°. Results: Distinct N200 spatial patterns were elicited when participants directed attention overtly to the target locations. A four-class classification accuracy of 72.2 ± 5.05% was achieved with a distance of 5.1° visual angle between the targets and the visual motion stimulus. The classification accuracies decreased with increasing motion stimulus eccentricities but remained separable well above the chance level at 13.0° (47.3 ± 8.54%). Conclusion: Our results support the feasibility of a single-stimulus, multitarget mVEP BCI. Significance: The proposed system can simplify the visual stimulation of mVEP BCIs, improve user experience and pave the way for simple yet efficient BCI communication. [ABSTRACT FROM AUTHOR]

A promising approach for brain-computer interfaces (BCIs) employs the steady-state visual evoked potential (SSVEP) for extracting control information. Main advantages of these SSVEP BCIs are a simple and low-cost setup, little effort to adjust the system parameters to the user and comparatively high information transfer rates (ITR). However, traditional frequency-coded SSVEP BCIs require the user to gaze directly at the selected flicker stimulus, which is liable to cause fatigue or even photic epileptic seizures. The spatially coded SSVEP BCI we present in this article addresses this issue. It uses a single flicker stimulus that appears always in the extrafoveal field of view, yet it allows the user to control four control channels. We demonstrate the embedding of this novel SSVEP stimulation paradigm in the user interface of an online BCI for navigating a 2-dimensional computer game. Offline analysis of the training data reveals an average classification accuracy of 96.9±1.64%, corresponding to an information transfer rate of 30.1±1.8 bits/min. In online mode, the average classification accuracy reached 87.9±11.4%, which resulted in an ITR of 23.8±6.75 bits/min. We did not observe a strong relation between a subject’s offline and online performance. Analysis of the online performance over time shows that users can reliably control the new BCI paradigm with stable performance over at least 30 minutes of continuous operation. [ABSTRACT FROM AUTHOR]