13,386 research outputs found
Frontoparietal action-oriented codes support novel task set implementation
A key aspect of human cognitive flexibility concerns the ability to rapidly convert complex symbolic instructions into novel behaviors. Previous research proposes that this fast configuration is supported by two differentiated neurocognitive states, namely, an initial declarative maintenance of task knowledge, and a progressive transformation into a pragmatic, action-oriented state necessary for optimal task execution. Furthermore, current models predict a crucial role of frontal and parietal brain regions in this transformation. However, direct evidence for such frontoparietal formatting of novel task representations is still lacking. Here, we report the results of an fMRI experiment in which participants had to execute novel instructed stimulus-response associations. We then used a multivariate pattern-tracking procedure to quantify the degree of neural activation of instructions in declarative and procedural representational formats. This analysis revealed, for the first time, format-unique representations of relevant task sets in frontoparietal areas, prior to execution. Critically, the degree of procedural (but not declarative) activation predicted subsequent behavioral performance. Our results shed light on current debates on the architecture of cognitive control and working memory systems, suggesting a contribution of frontoparietal regions to output gating mechanisms that drive behavior
The cognitive neuroscience of visual working memory
Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain
Oscillatory Mechanisms of Preparing for Visual Distraction
Evidence shows that observers preactivate a target representation in preparation of a visual selection task. In this study, we addressed the question if and how preparing to ignore an anticipated distractor differs from preparing for an anticipated target. We measured EEG while participants memorized a laterally presented color, which was cued to be either a target or a distractor in two subsequent visual search tasks. Decoding the location of items in the search display from EOG channels revealed that, initially, the anticipated distractor attracted attention and could only be ignored later during the trial. This suggests that distractors could not be suppressed in advance but were represented in an active, attention-guiding format. Consistent with this, lateralized posterior alpha power did not dissociate between target and distractor templates during the delay periods, suggesting similar encoding and maintenance. However, distractor preparation did lead to relatively enhanced nonlateralized posterior alpha power, which appeared to gate sensory processing at search display onset to prevent attentional capture in general. Finally, anticipating distractors also led to enhanced midfrontal theta power during the delay period, a signal that was predictive of how strongly both target and distractor were represented in the search display. Together, our results speak against a distractor-specific advance inhibitory template, thus contrary to the preactivation of specific target templates. Rather, we demonstrate a general selection suppression mechanism, which serves to prevent initial involuntary capture by anticipated distracting input
Resonant Neural Dynamics of Speech Perception
What is the neural representation of a speech code as it evolves in time? How do listeners integrate temporally distributed phonemic information across hundreds of milliseconds, even backwards in time, into coherent representations of syllables and words? What sorts of brain mechanisms encode the correct temporal order, despite such backwards effects, during speech perception? How does the brain extract rate-invariant properties of variable-rate speech? This article describes an emerging neural model that suggests answers to these questions, while quantitatively simulating challenging data about audition, speech and word recognition. This model includes bottom-up filtering, horizontal competitive, and top-down attentional interactions between a working memory for short-term storage of phonetic items and a list categorization network for grouping sequences of items. The conscious speech and word recognition code is suggested to be a resonant wave of activation across such a network, and a percept of silence is proposed to be a temporal discontinuity in the rate with which such a resonant wave evolves. Properties of these resonant waves can be traced to the brain mechanisms whereby auditory, speech, and language representations are learned in a stable way through time. Because resonances are proposed to control stable learning, the model is called an Adaptive Resonance
Theory, or ART, model.Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-01-1-0624)
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Representational dynamics across multiple timescales in human cortical networks
Human cognition occurs at multiple timescales, including immediate processing of the ongoing experiences and slowly drifting higher-level thoughts. To understand how the brain selects and represents these various types of information to guide behavior, this thesis examined representational content within sensory regions, multiple demand (MD) network, and default mode network (DMN). Chapter 1 provides a background review of the current literature. It begins by reviewing experimental investigations of component visual processes that unfold over time. Next, the MD network is introduced as a collection of frontal and parietal regions involved in implementing cognitive control by assembling the required operations for task-relevant behavior. Finally, the DMN is introduced in the context of temporal processing hierarchies, with focus on its representation of situation models summarizing interactions among entities and the environment. The first experiment, presented in Chapter 2, used EEG/MEG to track multiple component processes of selective attention. Five distinct processing operations with different time-courses were quantified, including representation of visual display properties, target location, target identity, behavioral significance, and finally, possible reactivation of the attentional template. Chapter 3 used fMRI to examine neural representations of task episodes, which are temporally organized sequences of steps that occur within a given context. It was found that MD and visual regions showed sensitivity to the fine structure of the contents within a task. DMN regions showed gradual change throughout the entire task, with increased activation at the offset of the entire episode. Chapter 4 analyzed activation profiles of DMN regions using six diverse tasks to examine their functional convergence during social, episodic, and self-referential thought. Results supported proposals of separate subsystems, yet also suggest integration within the DMN. The final chapter, Chapter 5, provides an extended discussion of theoretical concepts related to the three experiments and proposes possible avenues for further research
Oscillatory Control over Representational States in Working Memory
In the visual world, attention is guided by perceptual goals activated in visual working memory (VWM). However, planning multiple-task sequences also requires VWM to store representations for future goals. These future goals need to be prevented from interfering with the current perceptual task. Recent findings have implicated neural oscillations as a control mechanism serving the implementation and switching of different states of prioritization of VWM representations. We review recent evidence that posterior alpha-band oscillations underlie the flexible activation and deactivation of VWM representations and that frontal delta-to-theta-band oscillations play a role in the executive control of this process. That is, frontal delta-to-theta appears to orchestrate posterior alpha through long-range oscillatory networks to flexibly set up and change VWM states during multitask sequences
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Investigation of the multiple-demand network at multiple spatial scales
This dissertation investigates the frontoparietal ‘multiple-demand’ (MD) network that is
involved in the processing of diverse cognitive demands. This network is active when the
task at hand is made more demanding, in a variety of different tasks including working
memory, task switching, inhibition, math, language etc.
While the different MD regions have partly different functions, they are highly
interconnected allowing them to function together as a network. The experiment in Chapter 2
looked at the interplay between functional differences as well as co-recruitment within this
multiple-demand network. Quantitative differences between regions were more prominent in
simple tasks. A strong co-recruitment was seen with increased challenge or incentive.
In Chapter 3, task preferences were studied at the voxel level. MD regions were equally well
localised in single-subjects using any of three task demands. Voxels localised by all three
tasks also captured the underlying neural representations to a similar level in a separate
criterion task.
Chapter 4 investigated if task representations, as measured by multi-voxel patterns, were
modified due to external motivation. The effect was limited to the cue phase and did not
extend to the stimulus processing phase where the stimulus is integrated with the cue to arrive
at the response.
Chapter 5 examined neural representations in frontal and parietal regions more directly
through single unit activity and local field potentials (LFPs), during a spatial working
memory task. While single neurons showed dynamic coding of target information rather than
persistent coding, LFPs held this information constant through time. The impact of reference
voltages on LFP data was further investigated.
Together, these results explore the functional differences between and within the MD
regions, and provide evidence for flexible task representations at the voxel and neuronal level.Funded by Gates Cambridg
Frontoparietal action-oriented codes support novel instruction implementation
A key aspect of human cognitive flexibility concerns the ability to convert complex symbolic instructions into novel behaviors. Previous research proposes that this transformation is supported by two neurocognitive states: an initial declarative maintenance of task knowledge, and an implementation state necessary for optimal task execution. Furthermore, current models predict a crucial role of frontal and parietal brain regions in this process. However, whether declarative and procedural signals independently contribute to implementation remains unknown. We report the results of an fMRI experiment in which participants executed novel instructed stimulus-response associations. We then used a pattern-tracking procedure to quantify the contribution of format-unique signals during instruction implementation. This revealed independent procedural and declarative representations of novel S-Rs in frontoparietal areas, prior to execution. Critically, the degree of procedural activation predicted subsequent behavioral performance. Altogether, our results suggest an important contribution of frontoparietal regions to the neural architecture that regulates cognitive flexibility
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