Bayesian statistics: relevant for the brain?

Cumulative multisensory discrepancies shape the ventriloquism aftereffect but not the ventriloquism bias

Multisensory integration and recalibration are two processes by which perception deals with discrepant signals. Both are often studied in the spatial ventriloquism paradigm. There, integration is probed by the presentation of discrepant audio-visual stimuli, while recalibration manifests as an aftereffect in subsequent judgements of unisensory sounds. Both biases are typically quantified against the degree of audio-visual discrepancy, reflecting the possibility that both may arise from common underlying multisensory principles. We tested a specific prediction of this: that both processes should also scale similarly with the history of multisensory discrepancies, i.e. the sequence of discrepancies in several preceding audio-visual trials. Analyzing data from ten experiments with randomly varying spatial discrepancies we confirmed the expected dependency of each bias on the immediately presented discrepancy. And in line with the aftereffect being a cumulative process, this scaled with the discrepancies presented in at least three preceding audio-visual trials. However, the ventriloquism bias did not depend on this three-trial history of multisensory discrepancies and also did not depend on the aftereffect biases in previous trials - making these two multisensory processes experimentally dissociable. These findings support the notion that the ventriloquism bias and the aftereffect reflect distinct functions, with integration maintaining a stable percept by reducing immediate sensory discrepancies and recalibration maintaining an accurate percept by accounting for consistent discrepancies.

Targeted Modulation of Human Brain Interregional Effective Connectivity With Spike-Timing Dependent Plasticity

OBJECTIVE

The ability to selectively up- or downregulate interregional brain connectivity would be useful for research and clinical purposes. Toward this aim, cortico-cortical paired associative stimulation (ccPAS) protocols have been developed in which two areas are repeatedly stimulated with a millisecond-level asynchrony. However, ccPAS results in humans using bifocal transcranial magnetic stimulation (TMS) have been variable, and the mechanisms remain unproven. In this study, our goal was to test whether ccPAS mechanism is spike-timing-dependent plasticity (STDP).

MATERIALS AND METHODS

Eleven healthy participants received ccPAS to the left primary motor cortex (M1) → right M1 with three different asynchronies (5 milliseconds shorter, equal to, or 5 milliseconds longer than the 9-millisecond transcallosal conduction delay) in separate sessions. To observe the neurophysiological effects, single-pulse TMS was delivered to the left M1 before and after ccPAS while cortico-cortical evoked responses were extracted from the contralateral M1 using source-resolved electroencephalography.

RESULTS

Consistent with STDP mechanisms, the effects on synaptic strengths flipped depending on the asynchrony. Further implicating STDP, control experiments suggested that the effects were unidirectional and selective to the targeted connection.

CONCLUSION

The results support the idea that ccPAS induces STDP and may selectively up- or downregulate effective connectivity between targeted regions in the human brain.

Bayes' Theorem in Neurocritical Care: Principles and Practice

Patients with critical neurological illness are diverse. As a result of the heterogeneity of this patient population, standardized approaches to patient management might not confer benefit. A precision medicine approach to neurocritical care is therefore urgently needed to improve our understanding of neurocritical illness and the care provided to this vulnerable cohort. Research designs and approaches based on Bayesian models have the potential to meet this need, as they are specifically designed to evolve with emerging evidence. This adaptability provides a benefit over the popular frequentist statistical approach, as it provides a way of adjusting hypotheses and trial procedures to maximize efficacy. This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care. We review the basic principles underlying Bayes' theorem, compare the use of Bayesian versus frequentist statistics in medicine, and discuss the relevance of Bayesian statistics to the field of neuroscience and to clinical research. Finally, we explore the potential benefits of employing Bayesian methods within the field of neurocritical care as a steppingstone toward implementing precision medicine approaches to improve patient outcomes for complex, heterogeneous disorders.

Rethinking minority stress: A social safety perspective on the health effects of stigma in sexually-diverse and gender-diverse populations

For over two decades, the minority stress model has guided research on the health of sexually-diverse individuals (those who are not exclusively heterosexual) and gender-diverse individuals (those whose gender identity/expression differs from their birth-assigned sex/gender). According to this model, the cumulative stress caused by stigma and social marginalization fosters stress-related health problems. Yet studies linking minority stress to physical health outcomes have yielded mixed results, suggesting that something is missing from our understanding of stigma and health. Social safety may be the missing piece. Social safety refers to reliable social connection, inclusion, and protection, which are core human needs that are imperiled by stigma. The absence of social safety is just as health-consequential for stigmatized individuals as the presence of minority stress, because the chronic threat-vigilance fostered by insufficient safety has negative long-term effects on cognitive, emotional, and immunological functioning, even when exposure to minority stress is low. We argue that insufficient social safety is a primary cause of stigma-related health disparities and a key target for intervention.

Changes in Error-Correction Behavior According to Visuomotor Maps in Goal-Directed Projection Tasks

Humans can move objects to target positions, out of their reach with certain accuracy by throwing or hitting them with tools. However, the outcome - the final object position - after the same movement varies due to various internal and external factors. Therefore, to improve outcome accuracy, humans correct their movements in the following trial as necessary by estimating the relationship between movement and visual outcome (visuomotor map). In the present study, we compared participants' error-correction behaviors to visual errors under three conditions, wherein the relationship between joystick movement direction and cursor projection direction on the monitor covertly differed. This allowed us to examine whether the error-correction behavior changed depending on the visuomotor map. Moreover, to determine whether participants maintain the visuomotor map regardless of the visual error size (cursor projection) and proprioceptive errors (joystick movement), we for the first time focused on whether temporary visual errors deviating from the conventional relationship between joystick movement direction and cursor projection direction (i.e., visual perturbation) are ignored. The visual information was occasionally perturbed in two ways to create a situation wherein the visual error was larger or smaller than the proprioceptive error. We found that participants changed their error-correction behaviors according to the conditions and could ignore visual perturbations. This suggests that humans can be implicitly aware of differences in visuomotor maps and adapt accordingly to visual errors.

Quantitative skills in undergraduate neuroscience education in the age of big data

For over a half-Century, the mathematics requirement for graduation at most undergraduate colleges and universities has been one year of calculus and a semester of statistics. Many universities and colleges offer a neuroscience major that may or may not add additional mathematics, statistics, or data science requirements. Today in the age of Big Data and Systems Neuroscience, many students are ill-equipped for the future without the tools of computational competency that are necessary to tackle the large data sets generated by contemporary neuroscience research. Required courses in statistics still focus on parametric statistics based on the normal distribution and do not provide the computational tools required to analyze big data sets. Undergraduates in STEM fields including neuroscience need to enroll in the Data Science courses that are required in the social sciences (e.g., economics, political science and psychology). Contemporary systems neuroscience is routinely done by interdisciplinary research teams of statisticians, engineers, and physical scientists. Emerging "NeuroX-omics" such as connectomics have emerged along with genomics, proteomics, and transcriptomics, all of which deploy systems analysis techniques based on mathematical graph theory. Connectomics is the 21st Century's functional neuroanatomy. Whole brain connectome research appears almost monthly in the Drosphila, zebra fish, and mouse literature, and human brain connectomics is not far behind. The techniques for connectomics rely on the tools of data science. Undergraduate neuroscience students are already squeezed for credit hours given the high-prescribed science curriculum for biology majors and premedical students, in addition to required courses in social sciences and humanities. However, additional training in mathematics, statistics, computer science, and/or data science is urgently needed for undergraduate neuroscience majors just to understand the contemporary research literature. Undoubtedly, the faculty who teach neuroscience courses are acutely aware of the problem and most of them freely acknowledge the importance of quantitative analytical skills for their students. However, some faculty members may feel that their own math and statistics knowledge or other analytical skills have atrophied beyond recall or were never fulfilled in the first place. In this commentary I suggest that this problem can be ameliorated, though not solved, through organizing workshops, journal clubs, or independent studies courses in which the students and the instructors learn and teach each other in short-course format. In addition, web-available teaching materials such as targeted video clips are plentifully available on the internet. To attract and maintain student interest, qauntitative instruction and learning should occur in neuroscience context.

Probing the structure-function relationship with neural networks constructed by solving a system of linear equations

Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure–function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.

Global and local interference effects in ensemble encoding are best explained by interactions between summary representations of the mean and the range

Through ensemble encoding, the visual system compresses redundant statistical properties from multiple items into a single summary metric (e.g., average size). Numerous studies have shown that global summary information is extracted quickly, does not require access to single-item representations, and often interferes with reports of single items from the set. Yet a thorough understanding of ensemble processing would benefit from a more extensive investigation at the local level. Thus, the purpose of this study was to provide a more critical inspection of global-local processing in ensemble perception. Taking inspiration from Navon (Cognitive Psychology, 9(3), 353-383, 1977), we employed a novel paradigm that independently manipulates the degree of interference at the global (mean) or local (single item) level of the ensemble. Initial results were consistent with reciprocal interference between global and local ensemble processing. However, further testing revealed that local interference effects were better explained by interference from another summary statistic, the range of the set. Furthermore, participants were unable to disambiguate single items from the ensemble display from other items that were within the ensemble range but, critically, were not actually present in the ensemble. Thus, it appears that local item values are likely inferred based on their relationship to higher-order summary statistics such as the range and the mean. These results conflict with claims that local information is captured alongside global information in summary representations. In such studies, successful identification of set members was not compared with misidentification of items within the range, but which were nevertheless not presented within the set.

The Consumer Contextual Decision-Making Model

Consumers can have difficulty expressing their buying intentions on an explicit level. The most common explanation for this intention-action gap is that consumers have many cognitive biases that interfere with rational decision-making. The current resource-rational approach to understanding human cognition, however, suggests that brain environment interactions lead consumers to minimize the expenditure of cognitive energy according to the principle of Occam’s Razor. This means that the consumer seeks as simple of a solution as possible for a problem requiring decision-making. In addition, this resource-rational approach to decision-making emphasizes the role of inductive inference and Bayesian reasoning. Together, the principle of Occam’s Razor, inductive inference, and Bayesian reasoning illuminate the dynamic human-environment relationship. This paper analyzes these concepts from a contextual perspective and introduces the Consumer Contextual Decision-Making Model (CCDMM). Based on the CCDMM, two hypothetical strategies of consumer decision-making will be presented. First, the SIMilarity-Strategy (SIMS) is one in which most of a consumer’s decisions in a real-life context are based on prior beliefs about the role of a commodities specific to real-life situation being encountered. Because beliefs are based on previous experiences, consumers are already aware of the most likely consequences of their actions. At the same time, they do not waste time on developing contingencies for what, based on previous experience, is unlikely to happen. Second, the What-is-Out-there-in-the-World-Strategy (WOWS) is one in which prior beliefs do not work in a real-life situation, requiring consumers to update their beliefs. The principle argument being made is that most experimental consumer research describes decision-making based on the WOWS, when participants cannot apply their previous knowledge and situation-based strategy to problems. The article analyzes sensory and cognitive biases described by behavioral economists from a CCDMM perspective, followed by a description and explanation of the typical intention-action gap based on the model. Prior to a section dedicated to discussion, the neuroeconomic approach will be described along with the valuation network of the brain, which has evolved to solve problems that the human has previously encountered in an information-rich environment. The principles of brain function will also be compared to CCDMM. Finally, different approaches and the future direction of consumer research from a contextual point of view will be presented.

The Psychology of Reaching: Action Selection, Movement Implementation, and Sensorimotor Learning

The study of motor planning and learning in humans has undergone a dramatic transformation in the 20 years since this journal's last review of this topic. The behavioral analysis of movement, the foundational approach for psychology, has been complemented by ideas from control theory, computer science, statistics, and, most notably, neuroscience. The result of this interdisciplinary approach has been a focus on the computational level of analysis, leading to the development of mechanistic models at the psychological level to explain how humans plan, execute, and consolidate skilled reaching movements. This review emphasizes new perspectives on action selection and motor planning, research that stands in contrast to the previously dominant representation-based perspective of motor programming, as well as an emerging literature highlighting the convergent operation of multiple processes in sensorimotor learning.

Hallucinations in posttraumatic stress disorder: Insights from predictive coding

Although hallucinations are not one of the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) criteria for posttraumatic stress disorder (PTSD), they are increasingly documented in PTSD. They are noted in the absence of clear delusions, formal thought disorganization, disorganized speech, or behavior, ruling out a comorbid psychotic disorder like schizophrenia as a better explanation for these hallucinations. Hallucinations in both PTSD and schizophrenia share phenomenological features. We propose that hallucinations in PTSD, like those in schizophrenia, might be explained in terms of aberrant predictive coding, specifically the misapplication of strong prior beliefs that vitiate perceptual inference. This approach highlights the broader relationship between trauma and psychosis. Under predictive coding, the nervous system organizes past sensory data into an internal model of the world. Under stress, the brain prioritizes speed over accurate encoding. However, memories for traumatic experiences are typically strongly consolidated, to avoid similar experiences in future. In PTSD, this could lead to a world model comprised of inaccurate but overly precise prior beliefs, that can be triggered by stimuli tangentially related to the index trauma, resulting in hallucinations. Crucially, this evidence accumulation depends upon the relative precision of prior beliefs and sensory evidence (supplied in the form of prediction errors). Our basic argument is that stressful situations induce belief updating, in terms of precise prior beliefs, that are difficult to undo. These unduly precise, trauma-related beliefs then constitute perceptual hypotheses, memories, or narratives that bias subsequent experience. This prior bias may be so severe that sensory evidence is effectively ignored; that is, treated as very imprecise, in relation to prior beliefs. Such an account may lead to cognitive therapies for hallucinations aimed at strong prior beliefs, and the exciting prospect of combining such therapies with drugs that modulate neuroplasticity and enhance the adaptive consolidation of more appropriate priors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Neural implementation of Bayesian inference in a sensorimotor behavior

Actions are guided by a Bayesian-like interaction between priors based on experience and current sensory evidence. Here, we unveil a complete neural implementation of Bayesian-like behavior, including adaptation of a prior. We recorded the spiking of single neurons in the smooth eye movement region of the frontal eye fields (FEF_SEM), a region that is causally involved in smooth pursuit eye movements. Monkeys tracked moving targets in contexts that set different priors for target speed. Before the onset of target motion, preparatory activity encodes and adapts in parallel with the behavioral adaptation of the prior. During the initiation of pursuit, FEF_SEM output encodes a maximum a posteriori estimate of target speed based on a reliability-weighted combination of the prior and sensory evidence. FEF_SEM responses during pursuit are sufficient both to adapt a prior that may be stored in FEF_SEM and, through known downstream pathways, to cause Bayesian-like behavior in pursuit.

Feasibility Theory Reconciles and Informs Alternative Approaches to Neuromuscular Control

We present Feasibility Theory, a conceptual and computational framework to unify today's theories of neuromuscular control. We begin by describing how the musculoskeletal anatomy of the limb, the need to control individual tendons, and the physics of a motor task uniquely specify the family of all valid muscle activations that accomplish it (its ‘feasible activation space’). For our example of producing static force with a finger driven by seven muscles, computational geometry characterizes—in a complete way—the structure of feasible activation spaces as 3-dimensional polytopes embedded in 7-D. The feasible activation space for a given task is the landscape where all neuromuscular learning, control, and performance must occur. This approach unifies current theories of neuromuscular control because the structure of feasible activation spaces can be separately approximated as either low-dimensional basis functions (synergies), high-dimensional joint probability distributions (Bayesian priors), or fitness landscapes (to optimize cost functions).

The spatio-temporal profile of multisensory integration

Task-irrelevant visual stimuli can enhance auditory perception. However, while there is some neurophysiological evidence for mechanisms that underlie the phenomenon, the neural basis of visually induced effects on auditory perception remains unknown. Combining fMRI and EEG with psychophysical measurements in two independent studies, we identified the neural underpinnings and temporal dynamics of visually induced auditory enhancement. Lower- and higher-intensity sounds were paired with a non-informative visual stimulus, while participants performed an auditory detection task. Behaviourally, visual co-stimulation enhanced auditory sensitivity. Using fMRI, enhanced BOLD signals were observed in primary auditory cortex for low-intensity audiovisual stimuli which scaled with subject-specific enhancement in perceptual sensitivity. Concordantly, a modulation of event-related potentials could already be observed over frontal electrodes at an early latency (30-80 ms), which again scaled with subject-specific behavioural benefits. Later modulations starting around 280 ms, that is in the time range of the P3, did not fit this pattern of brain-behaviour correspondence. Hence, the latency of the corresponding fMRI-EEG brain-behaviour modulation points at an early interplay of visual and auditory signals in low-level auditory cortex, potentially mediated by crosstalk at the level of the thalamus. However, fMRI signals in primary auditory cortex, auditory thalamus and the P50 for higher-intensity auditory stimuli were also elevated by visual co-stimulation (in the absence of any behavioural effect) suggesting a general, intensity-independent integration mechanism. We propose that this automatic interaction occurs at the level of the thalamus and might signify a first step of audiovisual interplay necessary for visually induced perceptual enhancement of auditory perception.

Mentalizing regions represent distributed, continuous, and abstract dimensions of others' beliefs

The human capacity to reason about others' minds includes making causal inferences about intentions, beliefs, values, and goals. Previous fMRI research has suggested that a network of brain regions, including bilateral temporo-parietal junction (TPJ), superior temporal sulcus (STS), and medial prefrontal-cortex (MPFC), are reliably recruited for mental state reasoning. Here, in two fMRI experiments, we investigate the representational content of these regions. Building on existing computational and neural evidence, we hypothesized that social brain regions contain at least two functionally and spatially distinct components: one that represents information related to others' motivations and values, and another that represents information about others' beliefs and knowledge. Using multi-voxel pattern analysis, we find evidence that motivational versus epistemic features are independently represented by theory of mind (ToM) regions: RTPJ contains information about the justification of the belief, bilateral TPJ represents the modality of the source of knowledge, and VMPFC represents the valence of the resulting emotion. These representations are found only in regions implicated in social cognition and predict behavioral responses at the level of single items. We argue that cortical regions implicated in mental state inference contain complementary, but distinct, representations of epistemic and motivational features of others' beliefs, and that, mirroring the processes observed in sensory systems, social stimuli are represented in distinct and distributed formats across the human brain.

Control of the strength of visual-motor transmission as the mechanism of rapid adaptation of priors for Bayesian inference in smooth pursuit eye movements

Bayesian inference provides a cogent account of how the brain combines sensory information with "priors" based on past experience to guide many behaviors, including smooth pursuit eye movements. We now demonstrate very rapid adaptation of the pursuit system's priors for target direction and speed. We go on to leverage that adaptation to outline possible neural mechanisms that could cause pursuit to show features consistent with Bayesian inference. Adaptation of the prior causes changes in the eye speed and direction at the initiation of pursuit. The adaptation appears after a single trial and accumulates over repeated exposure to a given history of target speeds and directions. The influence of the priors depends on the reliability of visual motion signals: priors are more effective against the visual motion signals provided by low-contrast vs. high-contrast targets. Adaptation of the direction prior generalizes to eye speed and vice versa, suggesting that both priors could be controlled by a single neural mechanism. We conclude that the pursuit system can learn the statistics of visual motion rapidly and use those statistics to guide future behavior. Furthermore, a model that adjusts the gain of visual-motor transmission predicts the effects of recent experience on pursuit direction and speed, as well as the specifics of the generalization between the priors for speed and direction. We suggest that Bayesian inference in pursuit behavior is implemented by distinctly non-Bayesian internal mechanisms that use the smooth eye movement region of the frontal eye fields to control of the gain of visual-motor transmission.NEW & NOTEWORTHY Bayesian inference can account for the interaction between sensory data and past experience in many behaviors. Here, we show, using smooth pursuit eye movements, that the priors based on past experience can be adapted over a very short time frame. We also show that a single model based on direction-specific adaptation of the strength of visual-motor transmission can explain the implementation and adaptation of priors for both target direction and target speed.

Does the sensorimotor system minimize prediction error or select the most likely prediction during object lifting?

The human sensorimotor system is routinely capable of making accurate predictions about an object's weight, which allows for energetically efficient lifts and prevents objects from being dropped. Often, however, poor predictions arise when the weight of an object can vary and sensory cues about object weight are sparse (e.g., picking up an opaque water bottle). The question arises, what strategies does the sensorimotor system use to make weight predictions when one is dealing with an object whose weight may vary? For example, does the sensorimotor system use a strategy that minimizes prediction error (minimal squared error) or one that selects the weight that is most likely to be correct (maximum a posteriori)? In this study we dissociated the predictions of these two strategies by having participants lift an object whose weight varied according to a skewed probability distribution. We found, using a small range of weight uncertainty, that four indexes of sensorimotor prediction (grip force rate, grip force, load force rate, and load force) were consistent with a feedforward strategy that minimizes the square of prediction errors. These findings match research in the visuomotor system, suggesting parallels in underlying processes. We interpret our findings within a Bayesian framework and discuss the potential benefits of using a minimal squared error strategy.

NEW & NOTEWORTHY

Using a novel experimental model of object lifting, we tested whether the sensorimotor system models the weight of objects by minimizing lifting errors or by selecting the statistically most likely weight. We found that the sensorimotor system minimizes the square of prediction errors for object lifting. This parallels the results of studies that investigated visually guided reaching, suggesting an overlap in the underlying mechanisms between tasks that involve different sensory systems.

The Cluster Variation Method: A Primer for Neuroscientists

Effective Brain-Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.

Optimal cue integration in ants

In situations with redundant or competing sensory information, humans have been shown to perform cue integration, weighting different cues according to their certainty in a quantifiably optimal manner. Ants have been shown to merge the directional information available from their path integration (PI) and visual memory, but as yet it is not clear that they do so in a way that reflects the relative certainty of the cues. In this study, we manipulate the variance of the PI home vector by allowing ants (Cataglyphis velox) to run different distances and testing their directional choice when the PI vector direction is put in competition with visual memory. Ants show progressively stronger weighting of their PI direction as PI length increases. The weighting is quantitatively predicted by modelling the expected directional variance of home vectors of different lengths and assuming optimal cue integration. However, a subsequent experiment suggests ants may not actually compute an internal estimate of the PI certainty, but are using the PI home vector length as a proxy.

Neurología de la anticipación y sus implicaciones en el deporte

<p>El movimiento es una acción que involucra interconexiones complejas, por lo cual se requiere profundizar en los procesos de adaptación, predicción y anticipación que permiten entender la importancia de estos aspectos desde sus bases filogenéticas y ontogenéticas hasta su implicación en movimientos complejos. Parte de la optimización de los procesos descritos se haya en la calidad de información aferente, la cual permite la relación con el entorno —especialmente la entrada visual— que reconoce un flujo de imágenes y una proyección al contexto en el que se está inmerso. Las estructuras e interconexiones implicadas en la anticipación y predicción de movimientos son descritas de modo que se evidencia la congruencia y continuidad del flujo de información que caracteriza esta especialidad neuromecánica de movimiento. Por otro lado, se aborda la integración de centros puntuales del sistema nervioso central y redes neuronales que permiten el entramado de procesos de aprendizaje por observación, además de proveer equilibrio y eficiencia al sistema en la recepción de estímulos y su relación con la generación de eferencias motoras que cumplan con objetivos específicos. En el ámbito deportivo estos procesos favorecen la eficiencia del gesto optimizando el movimiento.</p>

Perception and Reality: Why a Wholly Empirical Paradigm is Needed to Understand Vision

A central puzzle in vision science is how perceptions that are routinely at odds with physical measurements of real world properties can arise from neural responses that nonetheless lead to effective behaviors. Here we argue that the solution depends on: (1) rejecting the assumption that the goal of vision is to recover, however imperfectly, properties of the world; and (2) replacing it with a paradigm in which perceptions reflect biological utility based on past experience rather than objective features of the environment. Present evidence is consistent with the conclusion that conceiving vision in wholly empirical terms provides a plausible way to understand what we see and why.

Will understanding vision require a wholly empirical paradigm?

Based on electrophysiological and anatomical studies, a prevalent conception is that the visual system recovers features of the world from retinal images to generate perceptions and guide behavior. This paradigm, however, is unable to explain why visual perceptions differ from physical measurements, or how behavior could routinely succeed on this basis. An alternative is that vision does not recover features of the world, but assigns perceptual qualities empirically by associating frequently occurring stimulus patterns with useful responses on the basis of survival and reproductive success. The purpose of the present article is to briefly describe this strategy of vision and the evidence for it.

Presence, objecthood, and the phenomenology of predictive perception

Can perceptual presence be explained by counterfactually-rich predictive models linking perception and action? Considering an unusually rich range of responses to this idea has led me to (1) re-emphasize the core conceptual commitment of "predictive processing of sensorimotor contingencies" (PPSMC) to predictive model-based perception, (2) reconsider the relationship between presence and objecthood, and (3) refine the phenomenological target by differentiating between perceptual presence and the phenomenology of absence-of-presence, or "phenomenal unreality." It turns out that this requires blue-sky thinking.

Computational neuroscience: beyond the local circuit

Computational neuroscience has focused largely on the dynamics and function of local circuits of neuronal populations dedicated to a common task, such as processing a common sensory input, storing its features in working memory, choosing between a set of options dictated by controlled experimental settings or generating the appropriate actions. Most of current circuit models suggest mechanisms for computations that can be captured by networks of simplified neurons connected via simple synaptic weights. In this article I review the progress of this approach and its limitations. It is argued that new experimental techniques will yield data that might challenge the present paradigms in that they will (1) demonstrate the computational importance of microscopic structural and physiological complexity and specificity; (2) highlight the importance of models of large brain structures engaged in a variety of tasks; and (3) reveal the necessity of coupling the neuronal networks to chemical and environmental variables.