Prediction of collision events: An EEG coherence analysis
Highlights
► Collision judgment involves widespread information processing across both hemispheres. ► Besides visual cues, cognitive/strategic strategies are used in collision judgment. ► Distractor object processing is managed at different levels of control.
Introduction
The ability to deal with collision events is a sophisticated skill that involves perceptual decision-making; a function that uses various information processing sources for guiding behaviour (Regan and Gray, 2000). Important in making an appropriate prediction regarding a potential collision is dealing with the combination of spatial and temporal information, processing that associates with distinct neural correlates (Coull and Nobre, 1998, Marshall and Fink, 2001, Schubotz and von Cramon, 2001, Coull et al., 2004). In particular, Lux et al. (2003) observed that judging spatial congruence increased activity in the right hemisphere whereas evaluating temporal synchrony activated a left hemisphere circuit. Furthermore, when attending simultaneously to spatial locations and temporal intervals, hemispheric activities preferentially implicated the right and left parietal regions, respectively (Coull and Nobre, 1998). However, when collision judgment is required, and temporal information needs to be used in conjunction with spatial information in order to extrapolate trajectory changes over time of the moving objects, increased activity in the left parietal cortex becomes dominant, underlining its involvement in perceptual spatio-temporal integration (Assmus et al., 2003). As the left parietal cortex is also involved in skilled actions and gesture discrimination (Hanna-Pladdy et al., 2001, Hermsdörfer et al., 2001, Buxbaum et al., 2003), the premise has been made that similar neural circuitry is used for achieving perceptual and motor predictions for which events in time are established (Schubotz, 2007). This hypothesis suggests that successful decision-making requires the coupling of task-independent regions of prediction with specialized task-dependent sites. During decision-making, it is also important that distracting information from the environment is ignored or inhibited as much as possible, as distractors interfere with processing of the target task (Ruff and Driver, 2006). In order to cope with a distractor situation, visual processing helps to focus attention on the relevant task characteristics and filter out the distracting irrelevant ones (Friedman-Hill et al., 2003). This implies that attentional control enables to modulate competition between the task relevant and irrelevant information.
To examine the process of perceptual decision-making, the present study assesses the neural and behavioural correlates as well as eye movements that are associated with a collision judgment task of moving objects. In addition, the influence of a distractor object upon the task processing demands is evaluated. For assessment of the neural dynamics and identification of higher-order decision-making processes, we use EEG methods and focus on coherence analysis, which expresses functional communication between brain areas. The hypothesis is made that the collision task would involve distributed information processing, with additional resources in the presence of a distractor. It is further hypothesized that behavioural success of decision-making as well as directed eye movements would be affected by the complexity of the collision task.
Section snippets
Subjects
Thirteen participants (seven female, age: 22.8 ± 1.4 years) took part in the experiment. They were right-handed as determined by the Edinburgh handedness inventory (Oldfield, 1971). In accordance with the declaration of Helsinki, all gave informed consent to participate in the study, which was approved by the local ethics committee. The data from one participant was excluded from analysis due to excessive EEG artefacts.
Task and procedure
The participants were asked to perform a decision-making task that required
Behavioural data and eye movements
The behavioural data and eye movement measurements are presented in Table 1. With respect to the behavioural data, decision time revealed a significant effect due to distractor presence whereas decision accuracy was not affected. Furthermore, the eye movement measures showed that fixation times of the moving objects and mask changed significantly when the distractor was present during collision judgment. In addition, the type of collision (target hit vs. target miss) additionally influenced the
Discussion
Perceptual decision-making is an essential function that integrates various sources of information in view of a behavioural response (Schall, 2003, Gold and Shadlen, 2007). Although this process enables the decision-making network to link the decision with the preparation of the response (Heekeren et al., 2008), it is hypothesized that similar neural circuitry supports the formulation of perceptual and motor predictions (Schubotz, 2007). In the present study, participants performed a
Conclusions
Decision-making as required during collision judgment involves widespread information communication across both hemispheres. This underlines that besides visual cues, cognitive and strategic strategies are required to establish a decision of events in time. When distracting information is introduced into the collision judgment process, it is managed at different processing levels and supported by distinct neural correlates. Overall, these data shed light on the regulatory mechanisms that
Acknowledgments
This research was supported by the Biotechnology and Biological Sciences Research Council (Grant BB/F012454/1) to DJS.
References (35)
- et al.
Left inferior parietal cortex integrates time and space during collision judgments
Neuroimage
(2003) - et al.
Difficulty of perceptual spatiotemporal integration modulates the neural activity of left inferior parietal cortex
Neuroscience
(2005) - et al.
Cognitive representations of hand posture in ideomotor apraxia
Neuropsychologia
(2003) - et al.
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
J Neurosci Methods
(2004) - et al.
Do changes in coherence always reflect changes in functional coupling?
Electroencephalogr Clin Neurophysiol
(1998) - et al.
A neural representation of categorization uncertainty in the human brain
Neuron
(2006) - et al.
Cortical correlates of gesture processing: clues to the cerebral mechanisms underlying apraxia during the imitation of meaningless gestures
Neuroimage
(2001) - et al.
Neural mechanisms associated with attention to temporal synchrony versus spatial orientation: an fMRI study
Neuroimage
(2003) - et al.
Spatial cognition: where we were and where we are
Neuroimage
(2001) The assessment and analysis of handedness: the Edinburgh inventory
Neuropsychologia
(1971)
A comparative study of different references for EEG default mode network: The use of the infinity reference
Clin Neurophysiol
Visually guided collision avoidance and collision achievement
Trends Cogn Sci
Action sets and decisions in the medial frontal cortex
Trends Cogn Sci
Neural correlates of decision processes: neural and mental chronometry
Curr Opin Neurobiol
Prediction of external events with our motor system: towards a new framework
Trends Cogn Sci
Functional organization of the lateral premotor cortex: fMRI reveals different regions activated by anticipation of object properties, location and speed
Brain Res Cogn Brain Res
Interregional synchrony of visuomotor tracking: perturbation effects and individual differences
Behav Brain Res
Cited by (5)
Space, time and number: common coding mechanisms and interactions between domains
2022, Psychological ResearchMotor timing and covariation with time perception: Investigating the role of handedness
2017, Frontiers in Behavioral NeuroscienceOnline prediction of driver distraction based on brain activity patterns
2015, IEEE Transactions on Intelligent Transportation SystemsTraffic displays for visual flight indicating track and priority cues
2014, IEEE Transactions on Human-Machine SystemsBrain function connectivity analysis for recognizing different relation of social emotion in virtual reality
2013, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)