scholarly journals Perceptual learning evidence for an interval- and modality-invariant representation of subsecond time

2019 ◽  
Author(s):  
Ying-Zi Xiong ◽  
Shu-Chen Guan ◽  
Cong Yu

AbstractA central theme in time perception research is whether subsecond timing relies on a dedicated centralized clock, or on distributed neural temporal dynamics. A fundamental constraint is the interval- and modality-specificity in perceptual learning of temporal interval discrimination (TID), which argues against a dedicated centralized clock, but is more consistent with multiple distributed mechanisms. Here we demonstrated an abstract, interval- and modality-invariant, representation of subsecond time in the brain. Participants practiced TID at a specific interval (100 ms), and received exposure to a transfer interval (200 ms), or to a different auditory/visual modality, through training of an orthogonal task. This double training enabled complete transfer of TID learning to the untrained interval, and mutual complete transfer between visual and auditory modalities. These results demonstrate an interval- and modality-invariant representation of subsecond time, which resembles a centralized clock, on top of the known distributed timing mechanisms and their readout and integration.

2010 ◽  
Author(s):  
Matthew G. Wisniewski ◽  
Barbara A. Church ◽  
Estella H. Liu ◽  
Eduardo Mercado

2019 ◽  
Vol 121 (5) ◽  
pp. 1588-1590 ◽  
Author(s):  
Luca Casartelli

Neural, oscillatory, and computational counterparts of multisensory processing remain a crucial challenge for neuroscientists. Converging evidence underlines a certain efficiency in balancing stability and flexibility of sensory sampling, supporting the general idea that multiple parallel and hierarchically organized processing stages in the brain contribute to our understanding of the (sensory/perceptual) world. Intriguingly, how temporal dynamics impact and modulate multisensory processes in our brain can be investigated benefiting from studies on perceptual illusions.


2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.


2019 ◽  
Author(s):  
Ulrik Beierholm ◽  
Tim Rohe ◽  
Ambra Ferrari ◽  
Oliver Stegle ◽  
Uta Noppeney

AbstractTo form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discountingOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.


2010 ◽  
Author(s):  
Αικατερίνη Χαραλαμποπούλου

In this study I have attempted to present a linguistic investigation into the nature and structure of time, based on proposals developed in Evans (2004). Accordingly, as linguistic structure and particularly patterns of elaboration reflect conceptual structure conventionalized into a format encodable in language, this study presents an examination of the human conceptual system for time. Indeed, an examination of the ways in which language lexicalizes time provides important insights into the nature and organization of time. That is, given the widely held assumption that semantic structure derives from and reflects, at least partially, conceptual structure, language offers a direct way of investigating the human conceptual system. However, how time is realized at the conceptual level, that is, how we represent time as revealed by the way temporal concepts are encoded in language, does not tell the whole story, if we are to uncover the nature and structure of time. Research in cognitive science suggests that phenomenological experience and the nature of the external world of sensory experience to which subjective experience constitutes a response, give rise to our pre- conceptual experience of time. In other words, as Evans (2004) says, time is not restricted to one particular layer of experience but it rather “constitutes a complex range of phenomena and processes which relate to different levels and kinds of experience” (ibid.: 5). Accordingly, while my focus in this study is on the temporal structure, which is to say the organization and structuring of temporal concepts, at the conceptual level, I have also attempted to present an examination of the nature of temporal experience at the pre-conceptual level (prior to representation in conceptual structure). In this regard, I have examined the results of research from neuroscience, cognitive psychology and social psychology. More specifically and with respect to evidence from neuroscience, it is suggested that temporal experience is ultimately grounded in neurological mechanisms necessary for regulating and facilitating perception (e.g., Pöppel 1994). That is, perceptual processing is underpinned by the occurrence of neurologically instantiated temporal intervals, the perceptual moments, which facilitate the integration of sensory information into coherent percepts. As we have seen, there is no single place in the brain where perceptual input derived from different modalities, or even information from within the same modality, can be integrated. In other words, there is no one place where spatially distributed sensory information associated with the distinct perceptual processing areas of the brain, are integrated in order to produce a coherent percept. Rather, what seems to be the case is that the integration of sensory information into coherent percepts is enabled by the phenomena of periodic perceptual moments. Such a mechanism enables us to perceive, in that the nature of our percepts are in an important sense ‘constructed’. Put another way, perception is a kind of constructive process which updates successive perceptual information to which an organism has access. The updating occurs by virtue of innate timing mechanisms, the perceptual moments, which occur at all levels of neurological processing and range from a fraction of second up to an outer limit of about three seconds. It is these timing mechanisms which form the basis of our temporal experience. As Gell says, “perception is intrinsically time-perception, and conversely, time-perception, or internal time-consciousness, is just perception itself...That is to say, time is not something we encounter as a feature of contingent reality, as if it lay outside us, waiting to be perceived along with tables and chairs and the rest of the perceptible contents of the universe. Instead, subjective time arises as inescapable feature of the perceptual process itself, which enters into the perception of anything whatsoever” (1992: 231). In other words, our experience of time is a consequence of the various innate ‘timing mechanisms' in the brain which give rise to a range of perceptual moments, which are in turn necessary for and underpin perceptual processing. In this way, time exists into the experience of everything as it is fundamental to the way in which perceptual process operates. […]


2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.


2019 ◽  
Vol 31 (11) ◽  
pp. 2177-2211 ◽  
Author(s):  
Saurabh Bhaskar Shaw ◽  
Kiret Dhindsa ◽  
James P. Reilly ◽  
Suzanna Becker

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.


2020 ◽  
Vol 32 (2) ◽  
pp. 201-211 ◽  
Author(s):  
Qiaoli Huang ◽  
Huan Luo

Objects, shown explicitly or held in mind internally, compete for limited processing resources. Recent studies have demonstrated that attention samples locations and objects rhythmically. Interestingly, periodic sampling not only operates over objects in the same scene but also occurs for multiple perceptual predictions that are held in attention for incoming inputs. However, how the brain coordinates perceptual predictions that are endowed with different levels of bottom–up saliency information remains unclear. To address the issue, we used a fine-grained behavioral measurement to investigate the temporal dynamics of processing of high- and low-salient visual stimuli, which have equal possibility to occur within experimental blocks. We demonstrate that perceptual predictions associated with different levels of saliency are organized via a theta-band rhythmic course and are optimally processed in different phases within each theta-band cycle. Meanwhile, when the high- and low-salient stimuli are presented in separate blocks and thus not competing with each other, the periodic behavioral profile is no longer present. In summary, our findings suggest that attention samples and coordinates multiple perceptual predictions through a theta-band rhythm according to their relative saliency. Our results, in combination with previous studies, advocate the rhythmic nature of attentional process.


2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.


2017 ◽  
Vol 114 (48) ◽  
pp. 12827-12832 ◽  
Author(s):  
Diego Vidaurre ◽  
Stephen M. Smith ◽  
Mark W. Woolrich

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.


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