Abstract:In computational systems for visuo-haptic object recognition, vision and haptics are often modeled as separate processes. But this is far from what really happens in the human brain, where cross- as well as multimodal interactions take place between the two sensory modalities. Generally, three main principles can be identified as underlying the processing of the visual and haptic object-related stimuli in the brain: (1) hierarchical processing, (2) the divergence of the processing onto substreams for object sh… Show more
“…While other studies were seeking how motor responses were done differently for every participant in a decision-making task [ 10 ] and how the reference frame is important for decisions in hand choice [ 11 ]. Even more, on goal-driven tasks behavioural motor answers that used greater auditory processing suppress responses in the auditory cortex [ 12 ] and recent report has shown motor-dependent changes in auditory cortical dynamics were driven by a subset of neurons in the secondary motor cortex that innervate the auditory cortex and are active during movement [ 13 ]. These studies have led to the uncertainty of how the auditory stimulus is affecting motor responses in a kind of self-induced motor signal [ 14 ].…”
In this study, the relationship of orienting of attention, motor control and the Stimulus- (SDN) and Goal-Driven Networks (GDN) was explored through an innovative method for fMRI analysis considering all voxels in four experimental conditions: standard target (Goal; G), novel (N), neutral (Z) and noisy target (NG). First, average reaction times (RTs) for each condition were calculated. In the second-level analysis, ‘distracted’ participants, as indicated by slower RTs, evoked brain activations and differences in both hemispheres’ neural networks for selective attention, while the participants, as a whole, demonstrated mainly left cortical and subcortical activations. A context analysis was run in the behaviourally distracted participant group contrasting the trials immediately prior to the G trials, namely one of the Z, N or NG conditions, i.e. Z.G, N.G, NG.G. Results showed different prefrontal activations dependent on prior context in the auditory modality, recruiting between 1 to 10 prefrontal areas. The higher the motor response and influence of the previous novel stimulus, the more prefrontal areas were engaged, which extends the findings of hierarchical studies of prefrontal control of attention and better explains how auditory processing interferes with movement. Also, the current study addressed how subcortical loops and models of previous motor response affected the signal processing of the novel stimulus, when this was presented laterally or simultaneously with the target. This multitasking model could enhance our understanding on how an auditory stimulus is affecting motor responses in a way that is self-induced, by taking into account prior context, as demonstrated in the standard condition and as supported by Pulvinar activations complementing visual findings. Moreover, current BCI works address some multimodal stimulus-driven systems.
“…While other studies were seeking how motor responses were done differently for every participant in a decision-making task [ 10 ] and how the reference frame is important for decisions in hand choice [ 11 ]. Even more, on goal-driven tasks behavioural motor answers that used greater auditory processing suppress responses in the auditory cortex [ 12 ] and recent report has shown motor-dependent changes in auditory cortical dynamics were driven by a subset of neurons in the secondary motor cortex that innervate the auditory cortex and are active during movement [ 13 ]. These studies have led to the uncertainty of how the auditory stimulus is affecting motor responses in a kind of self-induced motor signal [ 14 ].…”
In this study, the relationship of orienting of attention, motor control and the Stimulus- (SDN) and Goal-Driven Networks (GDN) was explored through an innovative method for fMRI analysis considering all voxels in four experimental conditions: standard target (Goal; G), novel (N), neutral (Z) and noisy target (NG). First, average reaction times (RTs) for each condition were calculated. In the second-level analysis, ‘distracted’ participants, as indicated by slower RTs, evoked brain activations and differences in both hemispheres’ neural networks for selective attention, while the participants, as a whole, demonstrated mainly left cortical and subcortical activations. A context analysis was run in the behaviourally distracted participant group contrasting the trials immediately prior to the G trials, namely one of the Z, N or NG conditions, i.e. Z.G, N.G, NG.G. Results showed different prefrontal activations dependent on prior context in the auditory modality, recruiting between 1 to 10 prefrontal areas. The higher the motor response and influence of the previous novel stimulus, the more prefrontal areas were engaged, which extends the findings of hierarchical studies of prefrontal control of attention and better explains how auditory processing interferes with movement. Also, the current study addressed how subcortical loops and models of previous motor response affected the signal processing of the novel stimulus, when this was presented laterally or simultaneously with the target. This multitasking model could enhance our understanding on how an auditory stimulus is affecting motor responses in a way that is self-induced, by taking into account prior context, as demonstrated in the standard condition and as supported by Pulvinar activations complementing visual findings. Moreover, current BCI works address some multimodal stimulus-driven systems.
“…Emulating the functionality and/or structuralism of the natural system would intrinsically address unmet needs in current digital systems 4 – 6 . Although bioinspired systems using silicon-based circuits and software have realized some complicated and dexterous sensorimotor functions 7 – 9 , their efficiency still suffers when data size increases due to centralized and sequential operation. In contrast, the biological systems are essentially run on distributed computing paradigm, whose superior fault tolerance and power efficiency are inherent in the adaptive, plastic, and event-driven network of sensory neurons 10 , 11 .…”
Section: Introductionmentioning
confidence: 99%
“…The neurons in this cortex area subsume both the macrospace of vision and the microspace encompassed by the hand, to avoid misjudgment from environmental complexity such as variances in the object’s pose 9 . The behavioral and psychological experiments also indicate a higher precision of appreciating an object when combining the two cues 9 , 15 . Fig.…”
Human behaviors are extremely sophisticated, relying on the adaptive, plastic and event-driven network of sensory neurons. Such neuronal system analyzes multiple sensory cues efficiently to establish accurate depiction of the environment. Here, we develop a bimodal artificial sensory neuron to implement the sensory fusion processes. Such a bimodal artificial sensory neuron collects optic and pressure information from the photodetector and pressure sensors respectively, transmits the bimodal information through an ionic cable, and integrates them into post-synaptic currents by a synaptic transistor. The sensory neuron can be excited in multiple levels by synchronizing the two sensory cues, which enables the manipulating of skeletal myotubes and a robotic hand. Furthermore, enhanced recognition capability achieved on fused visual/haptic cues is confirmed by simulation of a multi-transparency pattern recognition task. Our biomimetic design has the potential to advance technologies in cyborg and neuromorphic systems by endowing them with supramodal perceptual capabilities.
“…While other studies were seeking how motor responses were done differently for every participant in a decision-making task [10] and how the reference frame is important for decisions in hand choice [11]. Even more, on goal-driven tasks behavioural motor answers that used greater auditory processing suppress responses in the auditory cortex [12] and recent report has shown motor-dependent changes in auditory cortical dynamics were driven by a subset of neurons in the secondary motor cortex that innervate the auditory cortex and are active during movement [13]. These studies have led to the uncertainty of how auditory stimulus is changing motor responses in a kind of self-induced motor signal [15].…”
In this study, the relationship of orienting of attention, motor control and the Stimulus- (SDN) and Goal-Driven Network (GDN) was explored through an innovative method for fMRI analysis considering all voxels after every type condition. The task consisted of four conditions: standard target (G), novel (N), neutral (Z) and noisy target (NG). First running average reaction times of each condition was made. In the second level analysis, ‘distracted’ participants evoke brain activations and differences in both hemispheres neural network for selective attention as previously reported while the participants, as a whole, demonstrated mainly left cortical and subcortical activations. A context analysis was run in the behaviourally distracted participant group contrasting the trials immediately prior to the G trials, namely one of the Z, N or NG conditions. Results showed different prefrontal activations were evoked dependent on prior context in auditory modality with recruiting from 1 to 10 brain prefrontal areas. The more motor response and influence of the previous novel stimulus the more prefrontal areas, which extend hierarchical studies of prefrontal control of attention and explain better how the auditory processing interferes with movement. Also, it was addressed how subcortical loops and model of previous motor response affected the signal processing the novel stimulus, lateralized or simultaneously, with the Target. This model makes the first grasp to understand how auditory stimulus is changing motor responses in a kind of self-induced motor response. Moreover, current BCI works address some multimodal stimulus-driven systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
