scholarly journals Prediction and information integration determine subtle anticipatory fixation biases

2018 ◽  
Author(s):  
Giuseppe Notaro ◽  
Wieske van Zoest ◽  
David Melcher ◽  
Uri Hasson
Keyword(s):  
Information Integration ◽  
Computational Models ◽  
Temporal Scales ◽  
Target Presentation ◽  
Stimulus Response ◽  
Environmental Statistics ◽  
Anticipatory Behavior ◽  
Predictive Processes ◽  
Target Locations ◽  
Fixation Bias

ABSTRACTA core question underlying neurobiological and computational models of behavior is how individuals learn environmental statistics and use them for making predictions. Treatment of this issue largely relies on reactive paradigms, where inferences about predictive processes are derived by modeling responses to stimuli that vary in likelihood. Here we deployed a novel proactive oculomotor metric to determine how input statistics impact anticipatory behavior, decoupled from stimulus-response. We implemented transition constraints between target locations, and quantified a subtle fixation bias (FB) discernible while individuals fixated a screen center awaiting target presentation. We show that FB is informative with respect the input statistics, reflects learning at different temporal scales, predicts saccade latencies on a trial level, and can be linked to fundamental oculomotor metrics. We also present an extension of this approach to a more complex paradigm. Our work demonstrates how learning impacts strictly predictive processes and presents a novel direction for studying learning and prediction.

scholarly journals Computational Models of Neuromodulation

1998 ◽  
Vol 10 (4) ◽  
pp. 771-805 ◽  
Author(s):  
Jean-Marc Fellous ◽  
Christiane Linster
Keyword(s):  
Sensory Processing ◽  
Information Integration ◽  
Computational Models ◽  
Neural Substrates ◽  
Pattern Generation ◽  
Neural Models ◽  
Functional Roles ◽  
Modeling Studies ◽  
And Control

Computational modeling of neural substrates provides an excellent theoretical framework for the understanding of the computational roles of neuromodulation. In this review, we illustrate, with a large number of modeling studies, the specific computations performed by neuromodulation in the context of various neural models of invertebrate and vertebrate preparations. We base our characterization of neuromodulations on their computational and functional roles rather than on anatomical or chemical criteria. We review the main framework in which neuromodulation has been studied theoretically (central pattern generation and oscillations, sensory processing, memory and information integration). Finally, we present a detailed mathematical overview of how neuromodulation has been implemented at the single cell and network levels in modeling studies. Overall, neuromodulation is found to increase and control computational complexity.

scholarly journals Cross-modal and non-monotonic representations of statistical regularity are encoded in local neural response patterns

2018 ◽  
Author(s):  
Samuel A. Nastase ◽  
Ben Davis ◽  
Uri Hasson
Keyword(s):  
Sensory Modality ◽  
Theoretical Development ◽  
Response Patterns ◽  
Neural Responses ◽  
Input Uncertainty ◽  
Environmental Statistics ◽  
Statistical Regularities ◽  
Cross Classification ◽  
Multivariate Pattern ◽  
Predictive Processes

AbstractCurrent neurobiological models assign a central role to predictive processes calibrated to environmental statistics. Neuroimaging studies examining the encoding of stimulus uncertainty have relied almost exclusively on manipulations in which stimuli were presented in a single sensory modality, and further assumed that neural responses vary monotonically with uncertainty. This has left a gap in theoretical development with respect to two core issues: i) are there cross-modal brain systems that encode input uncertainty in way that generalizes across sensory modalities, and ii) are there brain systems that track input uncertainty in a non-monotonic fashion? We used multivariate pattern analysis to address these two issues using auditory, visual and audiovisual inputs. We found signatures of cross-modal encoding in frontoparietal, orbitofrontal, and association cortices using a searchlight cross-classification analysis where classifiers trained to discriminate levels of uncertainty in one modality were tested in another modality. Additionally, we found widespread systems encoding uncertainty non-monotonically using classifiers trained to discriminate intermediate levels of uncertainty from both the highest and lowest uncertainty levels. These findings comprise the first comprehensive report of cross-modal and non-monotonic neural sensitivity to statistical regularities in the environment, and suggest that conventional paradigms testing for monotonic responses to uncertainty in a single sensory modality may have limited generalizability.

scholarly journals Hands Ahead in Mind and Motion: Active Inference in Peripersonal Hand Space

Vision ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 15
Author(s):  
Johannes Lohmann ◽  
Anna Belardinelli ◽  
Martin V. Butz
Keyword(s):  
Movement Control ◽  
Virtual Object ◽  
Active Inference ◽  
Behavior Control ◽  
Motor Mapping ◽  
Anticipatory Behavior ◽  
Predictive Processes ◽  
Object Interactions ◽  
Crossmodal Congruency Effect ◽  
Crossmodal Congruency

According to theories of anticipatory behavior control, actions are initiated by predicting their sensory outcomes. From the perspective of event-predictive cognition and active inference, predictive processes activate currently desired events and event boundaries, as well as the expected sensorimotor mappings necessary to realize them, dependent on the involved predicted uncertainties before actual motor control unfolds. Accordingly, we asked whether peripersonal hand space is remapped in an uncertainty anticipating manner while grasping and placing bottles in a virtual reality (VR) setup. To investigate, we combined the crossmodal congruency paradigm with virtual object interactions in two experiments. As expected, an anticipatory crossmodal congruency effect (aCCE) at the future finger position on the bottle was detected. Moreover, a manipulation of the visuo-motor mapping of the participants’ virtual hand while approaching the bottle selectively reduced the aCCE at movement onset. Our results support theories of event-predictive, anticipatory behavior control and active inference, showing that expected uncertainties in movement control indeed influence anticipatory stimulus processing.

Learning

2018 ◽  
Author(s):  
Clark Glymour
Keyword(s):  
Computational Models ◽  
Mental Representations ◽  
Causal Structure ◽  
Normative Theory ◽  
Neural Net ◽  
Stimulus Response ◽  
The World ◽  
Computational Procedures ◽  
Computational Systems ◽  
Learning Language

Learning is the acquisition of some true belief or skill through experience. Rationalist/idealist philosophers held that the very constitution of thought guarantees that fundamental laws hold of the world we experience, and that our understanding of these laws was therefore innate, not learned. The empiricist tradition, doubtful of these Rationalist claims, denied that much was innate, and held that learning occurred through associations of mental representations. This view was lent support by the nineteenth-century development of physiological psychology, which led to a view of learning as a system of adjustments in a network without any intervening representations, a perspective that led in turn, in the twentieth century, to behaviourist studies of stimulus–response associations, and eventually to contemporary neural net computational models. Empiricism, however, had also invited, especially with Hume, doubts that the correspondence between mental representations and the world could be known. Hume believed people learn, or at least form new habits, but he did not think there could be any normative theory of learning – any way of making it ‘rational’. His scepticism led to the development by Bayes and other statisticians of formal theories of how learning from evidence ought to be done. However, the standards that developed in the form of the theory of subjective probability proved impossible to apply until very fast digital computers became available. The digital computer in turn prompted both novel normative theories of learning not considered by the statistical tradition, and also attempts to describe human learning by computational procedures. At the same time, a revolution in linguistics held that humans have an innate, specialized algorithm for learning language. Applications of computation theory to learning led to an understanding of what computational systems – possibly including people – can and cannot reliably learn. Major issues remain concerning how people acquire the system of distinctions they use to describe the world, and how – and how well – they learn the causal structure of the everyday world.

Mechanisms of Priming by Masked Stimuli: Inferences From Event-Related Brain Potentials

1998 ◽  
Vol 9 (4) ◽  
pp. 263-269 ◽  
Author(s):  
Hartmut Leuthold ◽  
Bruno Kopp
Keyword(s):  
Priming Effect ◽  
Event Related Potentials ◽  
Brain Potentials ◽  
Sensorimotor Processing ◽  
Response Mapping ◽  
Stimulus Response ◽  
Motor Activation ◽  
Related Potentials ◽  
The Relationship ◽  
Target Locations

A metacontrast procedure was combined with the recording of event-related potentials (ERPs) to examine the mechanisms underlying the priming effect exerted by masked visual stimuli (primes) on target processing. Participants performed spatially arranged choice responses to stimulus locations. The relationship between prime and target locations (congruity) and the mapping between target and response locations (compatibility) were factorially manipulated. Although participants were unaware of prime locations, choice responses were faster for congruent than incongruent conditions irrespective of the mapping. Visual ERP components and the onset of the lateralized readiness potential (LRP), an index of specific motor activation, revealed that neither perceptual nor preselection processes contributed to the congruity effect. However, the LRP waveform indicated that primes activated responses that fit the stimulus-response mapping. These results support the view that sensorimotor processing of masked stimuli is functionally distinct from their conscious perception.

scholarly journals COALIA: a computational model of human EEG for consciousness research

2019 ◽  
Author(s):  
Siouar Bensaid ◽  
Julien Modolo ◽  
Isabelle Merlet ◽  
Fabrice Wendling ◽  
Pascal Benquet
Keyword(s):  
Computational Model ◽  
Long Range ◽  
Information Integration ◽  
Computational Models ◽  
Pyramidal Neurons ◽  
Clinical Results ◽  
Neuronal Dynamics ◽  
Human Cortex ◽  
Physiological Processes ◽  
States Of Consciousness

AbstractUnderstanding the origin of the main physiological processes involved in consciousness is a major challenge of contemporary neuroscience, with crucial implications for the study of Disorders of Consciousness (DOC). The difficulties in achieving this task include the considerable quantity of experimental data in this field, along with the non-intuitive, nonlinear nature of neuronal dynamics. One possibility of integrating the main results from the experimental literature into a cohesive framework, while accounting for nonlinear brain dynamics, is the use of physiologically-inspired computational models. In this study, we present a physiologically-grounded computational model, attempting to account for the main micro-circuits identified in the human cortex, while including the specificities of each neuronal type. More specifically, the model accounts for thalamo-cortical (vertical) regulation of cortico-cortical (horizontal) connectivity, which is a central mechanism for brain information integration and processing. The distinct neuronal assemblies communicate through feedforward and feedback excitatory and inhibitory synaptic connections implemented in a template brain accounting for long-range connectome. The EEG generated by this physiologically-based simulated brain is validated through comparison with brain rhythms recorded in humans in two states of consciousness (wakefulness, sleep). Using the model, it is possible to reproduce the local disynaptic disinhibition of basket cells (fast GABAergic inhibition) and glutamatergic pyramidal neurons through long-range activation of VIP interneurons that induced inhibition of SST interneurons. The model (COALIA) predicts that the strength and dynamics of the thalamic output on the cortex control the local and long-range cortical processing of information. Furthermore, the model reproduces and explains clinical results regarding the complexity of transcranial magnetic stimulation TMS-evoked EEG responses in DOC patients and healthy volunteers, through a modulation of thalamo-cortical connectivity that governs the level of cortico-cortical communication. This new model provides a quantitative framework to accelerate the study of the physiological mechanisms involved in the emergence, maintenance and disruption (sleep, anesthesia, DOC) of consciousness.

scholarly journals Auditory information-integration category learning in young children and adults

2018 ◽  
Author(s):  
Casey L Roark ◽  
Lori L. Holt
Keyword(s):  
Middle Childhood ◽  
Category Learning ◽  
Information Integration ◽  
Computational Models ◽  
Developmental Differences ◽  
Learning Ability ◽  
Auditory Information ◽  
Rule Based ◽  
Old Adults ◽  
Multiple Dimensions

Adults outperform children on category learning that requires selective attention to individual dimensions (rule-based categories) due to their more highly developed working memory abilities, but much less is known about developmental differences in learning categories that require integration across multiple dimensions (information-integration categories). The current study investigates auditory information-integration category learning in 5-7-year-old children (n = 34) and 18-25-year-old adults (n = 35). Adults generally outperformed children during learning. However, some children learned the categories well and used strategies similar to those of adults, assessed through decision bound computational models. The results demonstrate that information-integration learning ability continues to develop throughout at least middle childhood. These results have implications for the development of mechanisms that contribute to speech category learning.

scholarly journals Information Integration and Collective Motility in Phototactic Cyanobacteria

2019 ◽  
Author(s):  
S. N. Menon ◽  
P. Varuni ◽  
G I. Menon
Keyword(s):  
Computational Model ◽  
Information Integration ◽  
Cell Response ◽  
Large Scale ◽  
Individual Cell ◽  
Incident Light ◽  
Collective Behaviour ◽  
Light Sources ◽  
Temporal Scales ◽  
Light Regimes

AbstractCells in microbial colonies integrate information across multiple spatial and temporal scales while sensing environmental cues. A number of photosynthetic cyanobacteria respond in a directional manner to incident light, resulting in the phototaxis of individual cells. Colonies of such bacteria exhibit large-scale changes in morphology, arising from cell-cell interactions, during phototaxis. These interactions occur through type IV pili-mediated physical contacts between cells, as well as through the secretion of complex polysaccharides (‘slime’) that facilitates cell motion. Here, we describe a computational model for such collective behaviour in colonies of the cyanobacteriumSynechocystis. The model is designed to replicate observations from recent experiments on the emergent response of the colonies to varied light regimes. It predicts the complex colony morphologies that arise as a result. We ask if changes in colony morphology during phototaxis can be used to infer if cells integrate information from multiple light sources simultaneously, or respond to these light sources separately at each instant of time. We find that these two scenarios cannot be distinguished from the shapes of colonies alone. However, we show that tracking the trajectories of individual cyanobacteria provides a way of determining their mode of response. Our model allows us to address the emergent nature of this class of collective bacterial motion, linking individual cell response to the dynamics of colony shape.Statement of SignificanceMicrobial colonies in the wild often consist of large groups of heterogeneous cells that coordi-nate and integrate information across multiple spatio-temporal scales. We describe a computational model for one such collective behaviour, phototaxis, in colonies of the cyanobacteriumSynechocystisthat move in response to light. The model replicates experimental observations the response of cyanobacterial colonies to varied light regimes, and predicts the complex colony morphologies that arise as a result. The results suggest that tracking the trajectories of individual cyanobacteria may provide a way of determining their mode of information integration. Our model allows us to address the emergent nature of this class of collective bacterial motion, linking individual cell response to the large scale dynamics of the colony.

Automatic Retrieval of Newly Instructed Cue-Task Associations Seen in Task-Conflict Effects in the First Trial after Cue-Task Instructions

2017 ◽  
Vol 64 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Nachshon Meiran ◽  
Maayan Pereg
Keyword(s):  
Task Switching ◽  
Task Conflict ◽  
The Novel ◽  
Stimulus Response ◽  
Task Instructions ◽  
Automatic Retrieval ◽  
Target Locations ◽  
And Task ◽  
Three Phases

Abstract. Novel stimulus-response associations are retrieved automatically even without prior practice. Is this true for novel cue-task associations? The experiment involved miniblocks comprising three phases and task switching. In the INSTRUCTION phase, two new stimuli (or familiar cues) were arbitrarily assigned as cues for up-down/right-left tasks performed on placeholder locations. In the UNIVALENT phase, there was no task cue since placeholder’s location afforded one task but the placeholders were the stimuli that we assigned as task cues for the following BIVALENT phase (involving target locations affording both tasks). Thus, participants held the novel cue-task associations in memory while executing the UNIVALENT phase. Results show poorer performance in the first univalent trial when the placeholder was associated with the opposite task (incompatible) than when it was compatible, an effect that was numerically larger with newly instructed cues than with familiar cues. These results indicate automatic retrieval of newly instructed cue-task associations.

scholarly journals Toward a Computational Neuropsychology of Cognitive Flexibility

2020 ◽  
Vol 10 (12) ◽  
pp. 1000
Author(s):  
Alexander Steinke ◽  
Bruno Kopp
Keyword(s):  
Computational Modeling ◽  
Cognitive Flexibility ◽  
Computational Models ◽  
Neurological Diseases ◽  
Wisconsin Card Sorting Test ◽  
Future Research ◽  
Card Sorting ◽  
Stimulus Response ◽  
Cognitive Inflexibility ◽  
Sorting Test

Cognitive inflexibility is a well-documented, yet non-specific corollary of many neurological diseases. Computational modeling of covert cognitive processes supporting cognitive flexibility may provide progress toward nosologically specific aspects of cognitive inflexibility. We review computational models of the Wisconsin Card Sorting Test (WCST), which represents a gold standard for the clinical assessment of cognitive flexibility. A parallel reinforcement-learning (RL) model provides the best conceptualization of individual trial-by-trial WCST responses among all models considered. Clinical applications of the parallel RL model suggest that patients with Parkinson’s disease (PD) and patients with amyotrophic lateral sclerosis (ALS) share a non-specific covert cognitive symptom: bradyphrenia. Impaired stimulus-response learning appears to occur specifically in patients with PD, whereas haphazard responding seems to occur specifically in patients with ALS. Computational modeling hence possesses the potential to reveal nosologically specific profiles of covert cognitive symptoms, which remain undetectable by traditionally applied behavioral methods. The present review exemplifies how computational neuropsychology may advance the assessment of cognitive flexibility. We discuss implications for neuropsychological assessment and directions for future research.

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