424 research outputs found
Mismatch responses: Probing probabilistic inference in the brain
Sensory signals are governed by statistical regularities and carry valuable information about the unfolding of environmental events. The brain is thought to capitalize on the probabilistic nature of sequential inputs to infer on the underlying (hidden) dynamics driving sensory stimulation. Mis-match responses (MMRs) such as the mismatch negativity (MMN) and the P3 constitute prominent neuronal signatures which are increasingly interpreted as reflecting a mismatch between the current sensory input and the brainâs generative model of incoming stimuli. As such, MMRs might be viewed as signatures of probabilistic inference in the brain and their response dynamics can provide insights into the underlying computational principles. However, given the dominance of the auditory modality in MMR research, the specifics of brain responses to probabilistic sequences across sensory modalities and especially in the somatosensory domain are not well characterized.
The work presented here investigates MMRs across the auditory, visual and somatosensory modality by means of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We designed probabilistic stimulus sequences to elicit and characterize MMRs and employed computational modeling of response dynamics to inspect different aspects of the brainâs generative model of the sensory environment. In the first study, we used a volatile roving stimulus paradigm to elicit somatosensory MMRs and performed single-trial modeling of EEG signals in sensor and source space. Model comparison suggested that responses reflect Bayesian inference based on the estimation of transition probability and limited information integration of the recent past in order to adapt to a changing environment. The results indicated that somatosensory MMRs reflect an initial mismatch between sensory input and model beliefs represented by confidence-corrected surprise (CS) followed by model adjustment dynamics represented by Bayesian surprise (BS). For the second and third study we designed a tri-modal roving stimulus paradigm to delineate modality specific and modality general features of mismatch processing. Computational modeling of EEG signals in study 2 suggested that single-trial dynamics reflect Bayesian inference based on estimation of uni-modal transition probabilities as well as cross-modal conditional dependencies. While early mismatch processing around the MMN tended to reflect CS, later MMRs around the P3 rather reflect BS, in correspondence to the somatosensory study. Finally, the fMRI results of study 3 showed that MMRs are generated by an interaction of modality specific regions in higher order sensory cortices and a modality general fronto-parietal network. Inferior parietal regions in particular were sensitive to expectation violations with respect to the cross-modal contingencies in the stimulus sequences. Overall, our results indicate that MMRs across the senses reflect processes of probabilistic inference in a complex and inherently multi-modal environment.Sensorische Signale sind durch statistische RegularitĂ€ten bestimmt und beinhalten wertvolle Informationen ĂŒber die Entwicklung von Umweltereignissen. Es wird angenommen, dass das Gehirn die Wahrscheinlichkeitseigenschaften sequenzieller Reize nutzt um auf die zugrundeliegenden (verborgenen) Dynamiken zu schlieĂen, welche sensorische Stimulation verursachen. Diskrepanz-Reaktionen ("Mismatch responses"; MMRs) wie die "mismatch negativity" (MMN) und die P3 sind bekannte neuronale Signaturen die vermehrt als Signale einer Diskrepanz zwischen der momentanen sensorischen Einspeisung und dem generativen Modell, welches das Gehirn von den eingehenden Reizen erstellt angesehen werden. Als solche können MMRs als Signaturen von wahrscheinlichkeitsbasierter Inferenz im Gehirn betrachtet werden und ihre Reaktionsdynamiken können Einblicke in die zugrundeliegenden komputationalen Prinzipien geben. Angesichts der Dominanz der auditorischen ModalitĂ€t in der MMR-Forschung, sind allerdings die spezifischen Eigenschaften von Hirn-Reaktionen auf Wahrscheinlichkeitssequenzen ĂŒber sensorische ModalitĂ€ten hinweg und vor allem in der somatosensorischen ModalitĂ€t nicht gut charakterisiert.
Die hier vorgestellte Arbeit untersucht MMRs ĂŒber die auditorische, visuelle und somatosensorische ModalitĂ€t hinweg anhand von Elektroenzephalographie (EEG) und funktioneller Magnetresonanztomographie (fMRT). Wir gestalteten wahrscheinlichkeitsbasierte Reizsequenzen, um MMRs auszulösen und zu charakterisieren und verwendeten komputationale Modellierung der Reaktionsdynamiken, um verschiedene Aspekte des generativen Modells des Gehirns von der sensorischen Umwelt zu untersuchen. In der ersten Studie verwendeten wir ein volatiles "Roving-Stimulus"-Paradigma, um somatosensorische MMRs auszulösen und modellierten die Einzel-Proben der EEG-Signale im sensorischen und Quell-Raum. Modellvergleiche legten nahe, dass die Reaktionen Bayesâsche Inferenz abbilden, basierend auf der SchĂ€tzung von Transitionswahrscheinlichkeiten und limitierter Integration von Information der jĂŒngsten Vergangenheit, welche eine Anpassung an UmweltĂ€nderungen ermöglicht. Die Ergebnisse legen nahe, dass somatosen-sorische MMRs eine initiale Diskrepanz zwischen sensorischer Einspeisung und ModellĂŒberzeugung reflektieren welche durch "confidence-corrected surprise" (CS) reprĂ€sentiert ist, gefolgt von Modelanpassungsdynamiken reprĂ€sentiert von "Bayesian surprise" (BS). FĂŒr die zweite und dritte Studie haben wir ein Tri-Modales "Roving-Stimulus"-Paradigma gestaltet, um modalitĂ€tsspezifische und modalitĂ€tsĂŒbergreifende Eigenschaften von Diskrepanzprozessierung zu umreiĂen. Komputationale Modellierung von EEG-Signalen in Studie 2 legte nahe, dass Einzel-Proben Dynamiken Bayesâsche Inferenz abbilden, basierend auf der SchĂ€tzung von unimodalen Transitionswahrscheinlichkeiten sowie modalitĂ€tsĂŒbergreifenden bedingten AbhĂ€ngigkeiten. WĂ€hrend frĂŒhe Diskrepanzprozessierung um die MMN dazu tendierten CS zu reflektieren, so reflektierten spĂ€tere MMRs um die P3 eher BS, in Ăbereinstimmung mit der somatosensorischen Studie. AbschlieĂend zeigten die fMRT-Ergebnisse der Studie 3 dass MMRs durch eine Interaktion von modalitĂ€tsspezifischen Regionen in sensorischen Kortizes höherer Ordnung mit einem modalitĂ€tsĂŒbergreifenden fronto-parietalen Netzwerk generiert werden. Inferior parietale Regionen im Speziellen waren sensitiv gegenĂŒber ErwartungsverstoĂ in Bezug auf die modalitĂ€tsĂŒbergreifenden Wahrscheinlichkeiten in den Reizsequenzen. Insgesamt weisen unsere Ergebnisse darauf hin, dass MMRs ĂŒber die Sinne hinweg Prozesse von wahrscheinlichkeitsbasierter Inferenz in einer komplexen und inhĂ€rent multi-modalen Umwelt darstellen
String Covering: A Survey
The study of strings is an important combinatorial field that precedes the
digital computer. Strings can be very long, trillions of letters, so it is
important to find compact representations. Here we first survey various forms
of one potential compaction methodology, the cover of a given string x,
initially proposed in a simple form in 1990, but increasingly of interest as
more sophisticated variants have been discovered. We then consider covering by
a seed; that is, a cover of a superstring of x. We conclude with many proposals
for research directions that could make significant contributions to string
processing in future
Evaluating the neurophysiological evidence for predictive processing as a model of perception
For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the longâstanding orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP
Great expectations: Is there evidence for predictive coding in auditory cortex?
Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. Whilst proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modelling studies of auditory pattern processing. For the first two assumptions - that neural responses are shaped by expectations and that these expectations are hierarchically organised - animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding - and, as such, help determine whether this popular grand theory can fulfil its expectations
Acoustic sequences in non-human animals: a tutorial review and prospectus.
Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise - let alone understand - the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, 'Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.This review was developed at an investigative workshop, âAnalyzing Animal Vocal Communication Sequencesâ that took place on October 21â23 2013 in Knoxville, Tennessee, sponsored by the National Institute for Mathematical and Biological Synthesis (NIMBioS). NIMBioS is an Institute sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Awards #EF-0832858 and #DBI-1300426, with additional support from The University of Tennessee, Knoxville. In addition to the authors, Vincent Janik participated in the workshop. D.T.B.âs research is currently supported by NSF DEB-1119660. M.A.B.âs research is currently supported by NSF IOS-0842759 and NIH R01DC009582. M.A.R.âs research is supported by ONR N0001411IP20086 and NOPP (ONR/BOEM) N00014-11-1-0697. S.L.DeR.âs research is supported by the U.S. Office of Naval Research. R.F.-i-C.âs research was supported by the grant BASMATI (TIN2011-27479-C04-03) from the Spanish Ministry of Science and Innovation. E.C.G.âs research is currently supported by a National Research Council postdoctoral fellowship. E.E.V.âs research is supported by CONACYT, Mexico, award number I010/214/2012.This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1111/brv.1216
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The role of predictive processing in conscious access and regularity learning across sensory domains
To increase fitness for survival, organisms not only passively react to environmental changes but also actively predict future events to prepare for potential hazards within their environment. Accumulating evidence indicates that the human brain is a remarkable predictive machine which constantly models causal relationships and predicts future events. This âpredictive processingâ framework, a prediction-based form of Bayesian inference, states that the brain continuously generates and updates predictions about incoming sensory signals. This framework has been showing notable explanatory power in understanding the mechanisms behind both human behaviour and neurophysiological data and elegantly specifies the underlying computational principles of the neural system. However, even though predictive processing has the potential to provide a unified theory of the brain (Karl Friston, 2010), we still have a limited understanding about fundamental aspects of this model, such as how it deals with different types of information, learns statistical regularities and perhaps most fundamentally of all what its relationship to conscious experience is. This thesis aims to investigate the major gaps in our current understanding of the predictive processing framework via a series of studies. Study 1 investigated the fundamental relationship between unconscious statistical inference reflected by predictive processing and conscious access. It demonstrated that predictions that are in line with sensory evidence accelerate conscious access. Study 2 investigated how low level information within the sensory hierarchy is dealt with by predictive processing and regularity learning mechanisms through âperceptual echoâ in which the cross-correlation between a sequence of randomly fluctuating luminance values and occipital electrophysiological (EEG) signals exhibits a long-lasting periodic (~100ms cycle) reverberation of the input stimulus. This study identified a new form of regularity learning and the results demonstrate that the perceptual echo may reflect an iterative learning process, governed by predictive processing. Study 3 investigated how supra-modal predictive processing is capable of
learning regularities of temporal duration and also temporal predictions about future events. This study revealed a supramodal temporal prediction mechanism which processes auditory and visual temporal information and integrates information from the duration and rhythmic structures of events. Together these studies provide a global picture of predictive processing and regularity learning across differing types of predictive information
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
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