The Human Cerebellum as a Hub of the Predictive Brain

Although the cerebellum has long been believed to be involved uniquely in sensorimotor processes, recent research works pointed to its participation in a wide range of cognitive predictive functions. Here, we review the available evidence supporting a generalized role of the cerebellum in predictive computation. We then discuss the anatomo-physiological properties that make the cerebellum the ideal hub of the predictive brain. We further argue that cerebellar involvement in cognition may follow a continuous gradient, with higher cerebellar activity occurring for tasks relying more on predictive processes, and outline the empirical scenarios to probe this hypothesis.

Repetition Probability Effects for Words Depend on Language Knowledge

The magnitude of repetition suppression (RS), measured by fMRI, is modulated by the probability of repetitions (P(rep)) for various sensory stimulus categories. It has been suggested that for visually presented simple letters this P(rep) effect depends on the prior practices of the participants with the stimuli. Here we tested further if previous experiences affect the neural mechanisms of RS, leading to the modulatory effects of stimulus P(rep), for more complex lexical stimuli as well. We measured the BOLD signal in the Visual Word Form Area (VWFA) of native Chinese and German participants and estimated the P(rep) effects for Chinese characters and German words. The results showed a significant P(rep) effect for stimuli of the mother tongue in both participant groups. Interestingly, Chinese participants, learning German as a second language, also showed a significant P(rep) modulation of RS for German words while the German participants who had no prior experiences with the Chinese characters showed no such effects. Our findings suggest that P(rep) effects on RS are manifest for visual word processing as well, but only for words of a language with which the participants have prior experiences. These results support further the idea that predictive processes, estimated by P(rep) modulations of RS, require prior experiences.

Cognitive Liminalism

Beckett’s fictional minds are pensive and tensive cognitive agents. If rumination feels to many of them a task to be performed or a “pensum to discharge” (U, 304), it is the way they think, however, that sparks a sustained and unsolvable cognitive differential or tension: a state of liminality due to the fact that they are not yet, or not anymore, endowed with what it takes to navigate the world effortlessly and meaningfully. The twilight atmosphere of Beckett’s boundary storyworlds or innerscapes therefore exponentially resonates with the wavering cognitive processes of what this chapter will define as liminal minds. After an overture section reinforcing how liminality is a structural principle that applies to many of Beckett’s storyworlds on several domains, the chapter heads on to the cognitive functioning of Beckett’s fictional minds. The second section focuses on Beckett’s alteration of the enactive scaffolding co-operation of language, narrative, and motility in human development. The third section analyzes his lesioning of human teleological dispositions on the motivational and emotional level, as well as the malfunctioning of predictive processes. In the final section, it addresses what kind of readerly experience results from engaging with cognitive liminalism, where liminal minds are constantly occupied by the activity of sense-making without the functional possibility of making sense.

The Illusions of Time Passage: Why Time Passage is Real

Philosophers and scientists alike often endorse the view that the passage of time is an illusion. Here we instead account for the phenomenology of passage as a real psycho-biological phenomenon. We argue that the experience of time passage has a real and measurable basis as it arises from an internal generative model for anticipating upcoming events. The experience of passage is not representation by a passive recipient of sensory stimulation but is generated by predictive processes of the brain and proactive sensorimotor activity of the whole body. The biological basis of the passage of time has not been examined in the metaphysics of time or the epistemology of time perception from a scientific perspective. This paper proposes to remedy this omission.

Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction

Over the past few years, hierarchical Bayesian models have been extensively used for modeling the joint spatial and temporal dependence of big spatio-temporal data which commonly involves a large number of missing observations. This article represented, assessed, and compared some recently proposed Bayesian and non-Bayesian models for predicting the daily average particulate matter with a diameter of less than 10 (PM10) measured in Qatar during the years 2016–2019. The disaggregating technique with a Markov chain Monte Carlo method with Gibbs sampler are used to handle the missing data. Based on the obtained results, we conclude that the Gaussian predictive processes with autoregressive terms of the latent underlying space-time process model is the best, compared with the Bayesian Gaussian processes and non-Bayesian generalized additive models.

Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling

Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.

Referential Chains Reveal Predictive Processes and Form-to-Function Mapping: An Electroencephalographic Study Using Naturalistic Story Stimuli

In discourse pragmatics, different referential forms are claimed to be indicative of the cognitive status of a referent in the current discourse. Referential expressions thereby possess a double function: They point back to an (existing) referent (form-to-function mapping), and they are used to derive predictions about a referent’s subsequent recurrence in discourse. Existing event-related potential (ERP) research has mainly focused on the form-to-function mapping of referential expression. In the present ERP study, we explore the relationship of form-to-function mapping and prediction derived from the antecedent of referential expressions in naturalistic auditory language comprehension. Specifically, the study investigates the relationship between the form of a referential expression (pronoun vs. noun) and the form of its antecedent (pronoun vs. noun); i.e., it examines the influence of the interplay of predictions derived from an antecedent (forward-looking function) and the form-to-function mapping of an anaphor (backward-looking function) on the ERPs time-locked to anaphoric expressions. The results in the time range of the P300 and N400 allow for a dissociation of these two functions during online language comprehension.

Prediction Under Uncertainty: Dissociating Sensory from Cognitive Expectations in Highly Uncertain Musical Contexts

Predictive models in the brain rely on the continuous extraction of regularities from the environment. These models are thought to be updated by novel information, as reflected in prediction error responses such as the mismatch negativity (MMN). However, although in real life individuals often face situations in which uncertainty prevails, it remains unclear whether and how predictive models emerge in high-uncertainty contexts. Recent research suggests that uncertainty affects the magnitude of MMN responses in the context of music listening. However, musical predictions are typically studied with MMN stimulation paradigms based on Western tonal music, which are characterized by relatively high predictability. Hence, we developed an MMN paradigm to investigate how the high uncertainty of atonal music modulates predictive processes as indexed by the MMN and behavior. Using MEG in a group of 20 subjects without musical training, we demonstrate that the magnetic MMN in response to pitch, intensity, timbre, and location deviants is evoked in both tonal and atonal melodies, with no significant differences between conditions. In contrast, in a separate behavioral experiment involving 39 non-musicians, participants detected pitch deviants more accurately and rated confidence higher in the tonal than in the atonal musical context. These results indicate that contextual tonal uncertainty modulates processing stages in which conscious awareness is involved, although deviants robustly elicit low-level pre-attentive responses such as the MMN. The achievement of robust MMN responses, despite high tonal uncertainty, is relevant for future studies comparing groups of listeners' MMN responses to increasingly ecological music stimuli.

Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing

The human brain has the astonishing capacity of integrating streams of sensory information from the environment and forming predictions about future events in an automatic way. Despite being initially developed for visual processing, the bulk of predictive coding research has subsequently focused on auditory processing, with the famous mismatch negativity signal as possibly the most studied signature of a surprise or prediction error (PE) signal. Auditory PEs are present during various consciousness states. Intriguingly, their presence and characteristics have been linked with residual levels of consciousness and return of awareness. In this review we first give an overview of the neural substrates of predictive processes in the auditory modality and their relation to consciousness. Then, we focus on different states of consciousness - wakefulness, sleep, anesthesia, coma, meditation, and hypnosis - and on what mysteries predictive processing has been able to disclose about brain functioning in such states. We review studies investigating how the neural signatures of auditory predictions are modulated by states of reduced or lacking consciousness. As a future outlook, we propose the combination of electrophysiological and computational techniques that will allow investigation of which facets of sensory predictive processes are maintained when consciousness fades away.

CLASSIFICATION OF THE COMORBID SYMPTOMATIC GROUPS ON AUTISM SPECTRUM DISORDER DIAGNOSIS

A total of 126 people with a nuclear diagnosis of autism spectrum disorder (ASD) participated in this study, corresponding to Galicia Community (Spain), found through survey regarding significantly more common symptoms related this disorder nuclear diagnosis. Hence, main aim is delimiting symptoms symptomatic groups that co-occur to each other, regarding basic diagnosis of ASD, in order elaborate predictive processes of comorbid recurrence along ASD diagnosis and develop the related psycho- educational approach. Data analysis, achieved throughout CLUSTER K-MEDIAS test of SPSS statistic, 23 version, allowed conclude there´s an interaction of symptoms recurrent themselves, which let conclude a classification of 3 symptomatic groups that make up basic comorbidity of ASD diagnosis: 1) group I, formed by epilepsy (2.00) and severe cognitive deficit (1.86) interaction, 2) II group, with significant interrelated scores in schizotypal features (.82) and anxiety processes (.77), and 3) III group, characterized by interaction between motor tics (1.92), cognitive deficit (1.54), hypersensitivity (1.23) and severe behavior problems (1.38). It´s possible conclude these symptomatic groups are predictors variables of comorbidity associated with ASD to carry out effective psycho- social- educational implementation to people with ASD.