Environmental and genetic influences on neurocognitive development: the importance of multiple methodologies and time-dependent intervention

Genetic mutations and environmental factors dynamically influence gene expression and developmental trajectories at the neural, cognitive, and behavioral levels. The examples in this article cover different periods of neurocognitive development—early childhood, adolescence, and adulthood—and focus on studies in which researchers have used a variety of methodologies to illustrate the early effects of socioeconomic status and stress on brain function, as well as how allelic differences explain why some individuals respond to intervention and others do not. These studies highlight how similar behaviors can be driven by different underlying neural processes and show how a neurocomputational model of early development can account for neurodevelopmental syndromes, such as autism spectrum disorders, with novel implications for intervention. Finally, these studies illustrate the importance of the timing of environmental and genetic factors on development, consistent with our view that phenotypes are emergent, not predetermined

Semantic similarity dissociates shortfrom long-term recency effects: testing a neurocomputational model of list memory

The finding that recency effects can occur not only in immediate free recall (i.e., short-term recency) but also in the continuous-distractor task (i.e., long-term recency) has led many theorists to reject the distinction between short- and long-term memory stores. Recently, we have argued that long-term recency effects do not undermine the concept of a short-term store, and we have presented a neurocomputational model that accounts for both short- and long-term recency and for a series of dissociations between these two effects. Here, we present a new dissociation between short- and long-term recency based on semantic similarity, which is predicted by our model. This dissociation is due to the mutual support between associated items in the short-term store, which takes place in immediate free recall and delayed free recall but not in continuous-distractor free recall

Establishing, versus Maintaining, Brain Function: A Neuro-computational Model of Cortical Reorganization after Injury to the Immature Brain

The effect of age at injury on outcome after acquired brain injury (ABI) has been the subject of much debate. Many argue that young brains are relatively tolerant of injury. A contrasting viewpoint due to Hebb argues that greater system integrity may be required for the initial establishment of a function than for preservation of an already-established function. A neuro-computational model of cortical map formation was adapted to examine effects of focal and distributed injury at various stages of development. This neural network model requires a period of training during which it self-organizes to establish cortical maps. Injuries were simulated by lesioning the model at various stages of this process and network function was monitored as "development" progressed to completion. Lesion effects are greater for larger, earlier, and distributed (multifocal) lesions. The mature system is relatively robust, particularly to focal injury. Activities in recovering systems injured at an early stage show changes that emerge after an asymptomatic interval. Early injuries cause qualitative changes in system behavior that emerge after a delay during which the effects of the injury are latent. Functions that are incompletely established at the time of injury may be vulnerable particularly to multifocal injury

Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration

Are face and object recognition abilities independent? Although it is commonly believed that they are, Gauthier et al.(2014) recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing (The Model, TM). Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a "spreading transform" for faces that generalizes to objects that must be individuated. Interestingly, when the task of the network is basic level categorization, no increase in the correlation between domains is observed. Hence, our model predicts that it is the type of experience that matters and that the source of the correlation is in the fusiform face area, rather than in cortical areas that subserve basic level categorization. This result is consistent with our previous modeling elucidating why the FFA is recruited for novel domains of expertise (Tong et al., 2008)

A neurocomputational model for intrinsic reward

Standard economic indicators provide an incomplete picture of what we value both as individuals and as a society. Furthermore, canonical macroeconomic measures, such as GDP, do not account for non-market activities (e.g., cooking, childcare) that nevertheless impact well-being. Here, we introduce a computational tool that measures the affective value of experiences (e.g., playing a musical instrument without errors). We go on to validate this tool with neural data, using fMRI to measure neural activity in male and female human subjects performing a reinforcement learning task that incorporated periodic ratings of subjective affective state. Learning performance determined level of payment (i.e., extrinsic reward). Crucially, the task also incorporated a skilled performance component (i.e., intrinsic reward) which did not influence payment. Both extrinsic and intrinsic rewards influenced affective dynamics, and their relative influence could be captured in our computational model. Individuals for whom intrinsic rewards had a greater influence on affective state than extrinsic rewards had greater ventromedial prefrontal cortex (vmPFC) activity for intrinsic than extrinsic rewards. Thus, we show that computational modelling of affective dynamics can index the subjective value of intrinsic relative to extrinsic rewards, a 'computational hedonometer' that reflects both behavior and neural activity that quantifies the affective value of experience.SIGNIFICANCE STATEMENTTraditional economic indicators are increasingly recognized to provide an incomplete picture of what we value as a society. Standard economic approaches struggle to accurately assign values to non-market activities that nevertheless may be intrinsically rewarding, prompting a need for new tools to measure what really matters to individuals. Using a combination of neuroimaging and computational modeling, we show that despite their lack of instrumental value, intrinsic rewards influence subjective affective state and ventromedial prefrontal cortex activity. The relative degree to which extrinsic and intrinsic rewards influence affective state is predictive of their relative impacts on neural activity, confirming the utility of our approach for measuring the affective value of experiences and other non-market activities in individuals

LaDIVA: A neurocomputational model providing laryngeal motor control for speech acquisition and production

Many voice disorders are the result of intricate neural and/or biomechanical impairments that are poorly understood. The limited knowledge of their etiological and pathophysiological mechanisms hampers effective clinical management. Behavioral studies have been used concurrently with computational models to better understand typical and pathological laryngeal motor control. Thus far, however, a unified computational framework that quantitatively integrates physiologically relevant models of phonation with the neural control of speech has not been developed. Here, we introduce LaDIVA, a novel neurocomputational model with physiologically based laryngeal motor control. We combined the DIVA model (an established neural network model of speech motor control) with the extended body-cover model (a physics-based vocal fold model). The resulting integrated model, LaDIVA, was validated by comparing its model simulations with behavioral responses to perturbations of auditory vocal fundamental frequency (fo) feedback in adults with typical speech. LaDIVA demonstrated capability to simulate different modes of laryngeal motor control, ranging from short-term (i.e., reflexive) and long-term (i.e., adaptive) auditory feedback paradigms, to generating prosodic contours in speech. Simulations showed that LaDIVA’s laryngeal motor control displays properties of motor equivalence, i.e., LaDIVA could robustly generate compensatory responses to reflexive vocal fo perturbations with varying initial laryngeal muscle activation levels leading to the same output. The model can also generate prosodic contours for studying laryngeal motor control in running speech. LaDIVA can expand the understanding of the physiology of human phonation to enable, for the first time, the investigation of causal effects of neural motor control in the fine structure of the vocal signal.Fil: Weerathunge, Hasini R.. Boston University; Estados UnidosFil: Alzamendi, Gabriel Alejandro. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; ArgentinaFil: Cler, Gabriel J.. University of Washington; Estados UnidosFil: Guenther, Frank H.. Boston University; Estados UnidosFil: Stepp, Cara E.. Boston University; Estados UnidosFil: Zañartu, Matías. Universidad Técnica Federico Santa María; Chil

The Electrophysiology of Language Comprehension:A Neurocomputational Model

Ongeveer tien jaar geleden stuitten onderzoekers die gebruik maken vanelektro-encefalografie (EEG) op resultaten die leken te wijzen op een"Semantische Illusie" bij het begrijpen van taal: Zinnen die qua betekenisvreemd zijn, maar verder grammaticaal correct, hadden geen effect op de aanbetekenis gerelateerde "N400 component" in het EEG signaal, maar juist op deaan structuur gerelateerde "P600 component". Op basis van deze bevindingenzijn er maar liefst vijf nieuwe modellen van taalverwerking voorgesteld. Aldeze modellen claimen dat er niet een enkele verwerkingsroute, maar twee ofmeer verwerkingroutes zijn binnen het taalverwerkingsysteem, waarvan bij éénroute de structuur van een zin volstrekt niet van belang is, maar enkel debetekenis van de woorden waaruit de zin bestaat. In dit proefschrift laat ikzien dat er een veel simpelere uitleg is voor de 'problematische'bevindingen en toon ik met computersimulaties aan dat deze alternatieveuitleg alle relevante data kan verklaren.One decade ago, researchers using event-related brain potential (ERP)measurements stumbled upon what looked like a Semantic Illusion in languagecomprehension: Semantically anomalous, but otherwise well-formed sentencesdid not affect the meaning-related N400 component, but instead increased theamplitude of the structure-related P600 component. This finding spawned fivenew models of language comprehension, all of which claim that instead ofa single comprehension process, there are two or even more separateprocessing streams, one of which is not driven by structure, but by wordmeaning alone. In this thesis, I argue that there is a much simpler way toaccount for these data, and present evidence from neurocomputionalsimulations showing that this alternative explanation is able to predict allrelevant ERP patterns found in the literature