Selective attention

…humans navigate this complexity by selectively focusing on relevant information, making real-world, multidimensional reinforcement learning (RL) tractable. Neuroimaging has identified correlates of attention and value learning in the lateral prefrontal (LPFC) and orbitofrontal (OFC) cortex, respectively, yet these methods fall short in elucidating the physiological mechanisms by which these regions coordinate during multidimensio

…Selective attention is a cognitive process that resolves this competition by filtering out irrelevant sensory information while enhancing the neural representation of relevant stimuli. This phenomenon is well-explained by the theory of biased competition, which posits that attention biases neural processing towards behaviorally relevant stimuli when multiple objects are present. However, the mechanism of attention remains under debate. We show how biased competition can be so

Selective attention allows us to focus on relevant information while filtering out distracting signals. Research on the neural mechanisms of selective attention to speech has primarily explored low-frequency responses to amplitude modulations in the auditory cortex. EEG studies revealed, in addition, attentional modulation of subcortical neural responses at the fundamental frequency of speech (speech-FFR). Recent MEG studies have identified cortical contributions to the speech-FFR (Coffey et al. (2017), Kulasingham et al. (2020), Schüller et al. (2024)), but it remains unclear if these cortica

Previous research indicated that neural responses to acoustic signals can differ in musicians and non-musicians. For instance, subcortical responses to speech sounds are stronger in musicians (Anderson et al. 2012). These neural responses include the frequency-following response at the fundamental frequency of the speech signal (speech-FFR). However, the speech-FFR also includes a cortical contribution with potential dependence on musical experience which is not yet known.We measured the cortical contribution to the speech-FFR via MEG recordings in 42 participants with different levels of musi

Aims: Recent research showed that selective attention to speech can be decoded from non-invasive EEG recordings [1]. This effect could potentially be applied in neuro-steered CIs, which could aid the wearer in noisy environments. Here we examine linear and non-linear decoding methods for selective attention, specifically for bimodal CI users, in a competing speaker scenario.Methods: EEG data was collected from 23 bimodal CI patients exposed to two competing speech stimuli emanating from spatially separated speakers. Patients were directed to focus on one speech stream during a given segment. D

…visual cognition that requires selective attention, yet the specific neural processes affected remain unclear. We hypothesized that tDCS over the PPC influenced the distribution of visuospatial attention by mediating the way selective attention wrap visuocortical processing. We tested this hypothesis using unilateral transcranial direct-current stimulation (tDCS) to the PPC of male and female human volunteers before we recorded electroencephalography (EEG) and collected psychophysical data.

…region in macaque monkeys, in selectively allocating attention to a visual target from among distractors based on previously learned rules; that is, if stimulus A is presented, select response X, but if stimulus B is presented, select response Y. Recent functional neuroimaging in healthy human participants has only examined this cognitive ability to guide decisions based on internalized rules when the behavioural output has a spatial component. The aim of the present

…Selective attention is a cognitive process that resolves this competition by filtering out irrelevant sensory information while enhancing the neural representation of relevant stimuli. This phenomenon is well-explained by the theory of biased competition, which posits that attention biases neural processing towards relevant stimuli when multiple objects are present. However, the mechanism of attention remains under debate. We show how biased competition can be solved in a bio

Hypnosis is a natural psychophysiological state of consciousness characterized by selective attention and elevated responsiveness to suggestions aimed at modifying perception and behavior. Induction of hypnosis typically leads to phenomenological changes, including feeling of mental absorption, dissociation, and alterations of agentivity. In investigating associated brain mechanisms, researchers have focused primarily on perceptual-cognitive suggestions. Yet, ideo-motor responses to suggestions, like limb rigidity, provide a relevant opportunity to examine behavioral responses dissociated from

Selective attention is a crucial cognitive function facilitates the relevant-sensory-input processing (i.e., targets) and filters out irrelevant information (i.e., distractors). Prior studies illustrated the ability to selectively process behaviorally sensory information declines with age, especially in older adults who exhibit Mild Cognitive Impairment (MCI), a transitional stage from healthy aging to dementia, e.g. Alzheimer’s disease. Less is known regarding how neural mechanisms help eliminating distractors go awry in MCI. We hypothesized the fronto-parietal attentional network, which is i

…in the visuocortical system supporting selective attention remains intact in late adulthood, it substantially diminishes in individuals with mild cognitive impairment (MCI), a transitional stage preceding dementia. However, it remains unclear to what extent neural substrates underlying attentional deficits vary across various MCI subtypes, commonly classified based on Montreal Cognitive Assessment (MoCA) performance. Here, employing an inverted encoding model (IEM) approach, we reconstructed spatially selective attentio

…– highlighting, amongst others, how selective attention plays a vital role in prioritising internal contents for behaviour, and the bi-directional links between visual

…One example is selective attention in the visual system, which allows to preferentially route signals from behaviourally relevant stimuli to downstream visual areas, while suppressing irrelevant visual information. It is an open question which neural mechanisms realize selective routing, and how this process is controlled. Here we propose that spontaneous synchronization in recurrent networks is the key mechanism for selective processin

Selective attention implements the preferential routing of attended stimuli, likely through increasing the gain of the respective synaptic inputs to higher-area neurons. As the synaptic inputs conveying competing stimuli converge and intermingle on postsynaptic neurons, impeding selective attentional addressing, presynaptic circuits might be the best target of top-down signals from higher areas of attentional control. If those signals enabled presynaptic circuits to selectively entrain postsynaptic neurons, this would be a mechanism for selective routing in accordance with the Communication-th

…shown to modulate the mechanisms of selective attention in both children and adults. Yet the nature of these adaptive changes is still not entirely clear. One possibility is that bilingualism boosts the capacity for selective attention; another is that it leads to a different distribution of this finite resource, aimed at supporting optimal performance under the increased processing demands. I will present a series of studies investigating the nature of modifications of selective attention i

…more than a few items at a time, selective attention is used to prioritize items for processing. Selective attention to an item allows its features to be bound together into a representation that can be matched to a target template in memory or rejected as a distractor. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 msec/item. Since selection occurs more fre

Understanding speech in noisy backgrounds requires selective attention to a particular speaker. Humans excel at this challenging task, while current speech recognition technology still struggles when background noise is loud. The neural mechanisms by which we process speech remain, however, poorly understood, not least due to the complexity of natural speech. Here we describe recent progress obtained through applying machine-learning to neuroimaging data of humans listening to speech in different types of background noise. In particular, we develop statistical models to relate characteristic f

…more than a few items at a time, selective attention is used to prioritize items for processing. Selective attention to an item allows its features to be bound together into a representation that can be matched to a target template in memory or rejected as a distractor. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 msec/item. Since selection occurs more frequently than that, it follo