Aaron S. W. Wong

Theta frontoparietal connectivity associated with proactive and reactive cognitive control processes

Cognitive control involves both proactive and reactive processes. Paradigms that rely on reactive control have shown that frontoparietal oscillatory synchronization in the theta frequency band is associated with interference control. This study examines whether proactive control is also associated with connectivity in the same frontoparietal theta network or involves a distinct neural signature. A task-switching paradigm was used to differentiate between proactive and reactive control processes, involved in preparing to switch or repeat a task and resolving post-target interference, respectively. We confirm that reactive control is associated with frontoparietal theta connectivity. Importantly, we show that proactive control is also associated with theta band oscillatory synchronization but in a different frontoparietal network. These findings support the existence of distinct proactive and reactive cognitive control processes that activate different theta frontoparietal oscillatory networks.

Frontoparietal theta oscillations during proactive control are associated with goal-updating and reduced behavioral variability

Low frequency oscillations in the theta range (4-8Hz) are increasingly recognized as having a crucial role in flexible cognition. Such evidence is typically derived from studies in the context of reactive (stimulus-driven) control processes. However, little research has explored the role of theta oscillations in preparatory control processes. In the current study, we explored the extent of theta oscillations during proactive cognitive control and determined if these oscillations were associated with behavior. Results supported a general role of theta oscillations during proactive cognitive control, with increased power and phase coherence during the preparatory cue interval. Further, theta oscillations across frontoparietal electrodes were also modulated by proactive control demands, with increased theta phase synchrony and power for cues signaling the need for goal updating. Finally, we present novel evidence of negative associations between behavioral variability and both power and phase synchrony across many of these frontoparietal electrodes that were associated with the need for goal updating. In particular, greater consistency in frontoparietal theta oscillations, indicated by increased theta phase and power during mixed-task blocks, resulted in more consistent task-switching performance. Together, these findings provide new insight into the temporal dynamics and functional relevance of theta oscillations during proactive cognitive control.

Support vector clustering of time series data with alignment kernels

Abstract Time series clustering is an important data mining topic and a challenging task due to the sequences’ potentially very complex structures. In the present study we experimentally investigate the combination of support vector clustering with a triangular alignment kernel by evaluating it on an artificial time series benchmark dataset. The experiments lead to meaningful segmentations of the data, thereby providing an example that clustering time series with specific kernels is possible without pre-processing of the data. We compare our approach and the results and learn that the clustering quality is competitive when compared to other approaches.

Visual gaze analysis of robotic pedestrians moving in urban space

This study is founded on the idea that an analysis of the visual gaze dynamics of pedestrians can increase our understanding of how important architectural features in urban environments are perceived by pedestrians. The results of such an analysis can lead to improvements in urban design. However, a technical challenge arises when trying to determine the gaze direction of pedestrians recorded on video. High ‘noise’ levels and the subtlety of human gaze dynamics hamper precise calculations. However, as robots can be programmed and analysed more efficiently than humans, this study uses them for developing and training a gaze analysis system with the aim to later apply it to human video data using the machine learning technique of manifold alignment. For this study, a laboratory was set up to become a model street scene in which autonomous humanoid robots of approximately 55 cm in height simulate the behaviour of human pedestrians. The experiments compare the inputs from several cameras as the robot walks down the model street and changes its behaviour upon encountering ‘visually attractive objects’. Overhead recordings and the robots internal joint signals are analysed after filtering to provide ‘true’ data against which the recorded data can be compared for accuracy testing. A central component of the research is the calculation of a torus-like manifold that represents all the different three-dimensional (3D) head directions of a robot head and allows the ordering of extracted 3D gaze vectors obtained from video sequences. We briefly describe how the obtained multidimensional trajectory data can be analysed by using a temporal behaviour analysis technique based on support vector machines that was developed separately.

The Age-ility Project (Phase 1): Structural and functional imaging and electrophysiological data repository

Our understanding of the complex interplay between structural and functional organisation of brain networks is being advanced by the development of novel multi-modal analyses approaches. The Age-ility Project (Phase 1) data repository offers open access to structural MRI, diffusion MRI, and resting-state fMRI scans, as well as resting-state EEG recorded from the same community participants (n=131, 15-35 y, 66 male). Raw imaging and electrophysiological data as well as essential demographics are made available via the NITRC website. All data have been reviewed for artifacts using a rigorous quality control protocol and detailed case notes are provided.

Towards visualisation of sound-scapes through dimensionality reduction

Sound-scapes are useful for understanding our surrounding environments in applications such as security, source tracking or understanding human computer interaction. Accurate position or localisation information from sound-scape samples consists of many channels of high dimensional acoustic data. In this paper we demonstrate how to obtain a visual representation of sound-scapes by applying dimensionality reduction techniques to a range of artificially generated sound-scape datasets. Linear and non-linear dimensionality techniques were compared including principle component analysis (PCA), multi-dimensional scaling (MDS), locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Results obtained by applying the dimensionality reduction techniques led to visual representations of affine positions of the sound source on its sound-scape manifold. These displayed clearly the order relationships of angles and intensities of the generated sound-scape samples. In a simple classification task with the artificial sound data, the successful combination of dimensionality reduction and classifier methods are demonstrated.

Robot emotions generated and modulated by visual features of the environment

Emotions are generated and modulated by many factors in the ever-changing surrounding environment. A new and challenging task is to emulate emotional responses on a robot that are caused by visual stimuli, such that the robots responses mirror that of the human user. This paper presents the initial stage of an affective system that has been trained on-line using reinforcement learning to generate and modulate emotions. The inputs of the system comprise a subset of emotionally relevant visual features extracted from the environment: colours, fractal dimension, and facial pareidolia. These inputs are mapped onto an output that expresses the associated emotion in terms of language. Pilot experiments demonstrate how a humanoid robot tries to learn through interaction with a human companion to express emotions associated with different environmental scenes in a (near) human-like manner.

Event-Related Potential Responses to Task Switching Are Sensitive to Choice of Spatial Filter

Event-related potential (ERP) studies using the task-switching paradigm show that multiple ERP components are modulated by activation of proactive control processes involved in preparing to repeat or switch task and reactive control processes involved in implementation of the current or new task. Our understanding of the functional significance of these ERP components has been hampered by variability in their robustness, as well as their temporal and scalp distribution across studies. The aim of this study is to examine the effect of choice of reference electrode or spatial filter on the number, timing and scalp distribution of ERP elicited during task-switching. We compared four configurations, including the two most common (i.e., average mastoid reference and common average reference) and two novel ones that aim to reduce volume conduction (i.e., reference electrode standardization technique (REST) and surface Laplacian) on mixing cost and switch cost effects in cue-locked and target-locked ERP waveforms in 201 healthy participants. All four spatial filters showed the same well-characterized ERP components that are typically seen in task-switching paradigms: the cue-locked switch positivity and target-locked N2/P3 effect. However, both the number of ERP effects associated with mixing and switch cost, and their temporal and spatial resolution were greater with the surface Laplacian transformation which revealed rapid temporal adjustments that were not identifiable with other spatial filters. We conclude that the surface Laplacian transformation may be more suited to characterize EEG signatures of complex spatiotemporal networks involved in cognitive control.