An integrative review

In the neuroscience of language, phonemes are frequently described as multimodal units whose neuronal representations are distributed across perisylvian cortical regions, including auditory and sensorimotor areas. A different position views phonemes primarily as acoustic entities with posterior temporal localization, which are functionally independent from frontoparietal articulatory programs. To address this current controversy, we here discuss experimental results from neuroimaging (fMRI) as well as transcranial magnetic stimulation (TMS) studies. On first glance, a mixed picture emerges, with earlier research documenting neurofunctional distinctions between phonemes in both temporal and frontoparietal sensorimotor systems, but some recent work seemingly failing to replicate the latter. Detailed analysis of methodological differences between studies reveals that the way experiments are set up explains whether sensorimotor cortex maps phonological information during speech perception or not. In particular, acoustic noise during the experiment and ‘motor noise’ caused by button press tasks work against the frontoparietal manifestation of phonemes. We highlight recent studies using sparse imaging and passive speech perception tasks along with multivariate pattern analysis (MVPA) and especially representational similarity analysis (RSA), which succeeded in separating acoustic-phonological from general-acoustic processes and in mapping specific phonological information on temporal and frontoparietal regions. The question about a causal role of sensorimotor cortex on speech perception and understanding is addressed by reviewing recent TMS studies. We conclude that frontoparietal cortices, including ventral motor and somatosensory areas, reflect phonological information during speech perception and exert a causal influence on understanding

Modelling concrete and abstract concepts using brain-constrained deep neural networks

A neurobiologically constrained deep neural network mimicking cortical areas relevant for sensorimotor, linguistic and conceptual processing was used to investigate the putative biological mechanisms underlying conceptual category formation and semantic feature extraction. Networks were trained to learn neural patterns representing specific objects and actions relevant to semantically ‘ground’ concrete and abstract concepts. Grounding sets consisted of three grounding patterns with neurons representing specific perceptual or action-related features; neurons were either unique to one pattern or shared between patterns of the same set. Concrete categories were modelled as pattern triplets overlapping in their ‘shared neurons’, thus implementing semantic feature sharing of all instances of a category. In contrast, abstract concepts had partially shared feature neurons common to only pairs of category instances, thus, exhibiting family resemblance, but lacking full feature overlap. Stimulation with concrete and abstract conceptual patterns and biologically realistic unsupervised learning caused formation of strongly connected cell assemblies (CAs) specific to individual grounding patterns, whose neurons were spread out across all areas of the deep network. After learning, the shared neurons of the instances of concrete concepts were more prominent in central areas when compared with peripheral sensorimotor ones, whereas for abstract concepts the converse pattern of results was observed, with central areas exhibiting relatively fewer neurons shared between pairs of category members. We interpret these results in light of the current knowledge about the relative difficulty children show when learning abstract words. Implications for future neurocomputational modelling experiments as well as neurobiological theories of semantic representation are discussed

Influence of language on concept formation and perception in a brain-constrained deep neural network model

Whether language influences perception and thought remains a subject of intense debate (1, 2). We address this question in a brain-constrained neurocomputational model (3) of fronto-occipital (extrasylvian) and fronto-temporal (perisylvian) cortex including spiking neurons. The unsupervised neural network was simultaneously presented with word forms (phonological patterns, “labels”) in perisylvian areas and semantic grounding information (sensory-motor patterns, “percepts”) in extrasylvian areas representing either concrete or abstract concepts. Following the approach used in a previous simulation (4), each to-be-learned concept was modeled as a triplet of partly overlapping percepts; the models were trained under two conditions: each instance of a perceptual triplet (patterns in extrasylvian areas) was repeatedly paired with patterns in perisylvian areas consisting of either (a) a corresponding word form (label condition), or (b) noise (no-label condition). We quantified the emergence of neuronal representations for the conceptually-related percepts using dissimilarity (Euclidean distance) of neuronal activation vectors during perceptual stimulation. Category learning was measured as the difference between within- and between concept dissimilarity values (DissimDiff) of perceptual activation patterns. A repeated-measures ANOVA with factors SemanticType (concrete/abstract) and Labelling showed main effects of both SemanticType and Label, and a significant interaction. We also quantified the “label effect” in percentage change from NoLabel to Label conditions, separately for between- and within-category dissimilarities. This showed that the label effect was mainly driven by changes in between-category dissimilarity, was significantly larger for abstract than concrete concepts, and became even larger in the “deeper” layers of the model. Providing a referential verbal label during the acquisition of a new concept significantly improves the cortex’ ability to develop distinct semantic-category representations from partly-overlapping (and non-overlapping) perceptual instances. Crucially, this effect is most pronounced in higher order semantic-hub areas of the network. These results provide the first neurocomputational evidence for a “Whorfian” effect of language on perception and concept formation

Influence of verbal labels on concept formation and perception in a deep unsupervised neural network model

OBJECTIVES/RESEARCH QUESTION: Whether language influences perception and thought remains a subject of intense debate. Would the presence or absence of a linguistic label facilitate or hinder the acquisition of new concepts? We here address this question in a neurocomputational model. METHODS: We used a computational brain model of fronto-occipital (extrasylvian) and fronto-temporal (perisylvian) cortex including spiking neurons. With Hebbian learning, the network was trained to associate word forms (phonological patterns, or “labels”) in perisylvian areas with semantic grounding information (sensory-motor patterns, or “percepts”) in extrasylvian areas. To study the effects of labels on the network’s ability to spontaneously develop distinct semantic representations from the multiple perceptual instances of a concept, we modelled each to-be-learned concept as a triplet of partly overlapping percepts and trained the model under two conditions: each instance of a perceptual triplet (patterns in extrasylvian areas) was repeatedly paired with patterns in perisylvian areas consisting of either (1) a corresponding word form (label condition), or (2) white noise (no-label condition). To quantify the emergence of neuronal representations for the conceptually-related percepts, we measured the dissimilarity (Euclidean distance) of neuronal activation vectors during perceptual stimulation. Category learning performance was measured as the difference between within- and between-concept dissimilarity values (DissimDiff) of perceptual activation patterns. RESULTS: The presence or absence of a linguistic label had a significant main effect on category learning (F=2476, p<0.0001, DissimDiff with labels m=0.92, SD=0.32; no-labels m=0.36, SD=0.21). DissimDiff values were also significantly larger in areas most important for semantic processing, so-called semantic-hubs, than in sensorimotor areas (main effect of centrality, F=2535, p<0.0001). Finally, a significant interaction between centrality and label type (F=711, p<0.0001) revealed that the label-related learning advantage was most pronounced in semantic hubs. CONCLUSION: These results suggest that providing a referential verbal label during the acquisition of a new concept significantly improves the cortex’ ability to develop distinct semantic-category representations from partly-overlapping (and non-overlapping) perceptual instances. Crucially, this effect is most pronounced in higher-order semantic-hub areas of the network. In sum, our results provide the first neurocomputational evidence for a “Whorfian” effect of language on perception and concept formation

Paying for Green?: Payment for Ecosystem Services in Practice - Successful Examples of PES from Germany, the United Kingdom and the United States.

Diverse studies have shown that despite various efforts the state of our natural resources as well as the development of biodiversity and climate change are still a cause for concern. This is the case at the global level as well as at the level of individual countries and regions. In the industrialized countries in particular, they have been trying to solve environmental problems by regulatory means for many decades. And still the problems are increasing. It is not surprising, therefore, that different and complementary means of exerting influence have repeatedly been sought. Against this background, the attention given to economic instruments to resolve environmental problems has increased worldwide in recent years. In the wake of large international studies such as the "Millennium Ecosystem Assessment" of the UN and the international as well as national TEEB studies on the economic value of ecosystem services and biodiversity, there is growing interest in particular in Payments for Ecosystem Services, PES for short. How can this interest be explained, and what is the distinguishing feature of PES? The increased attention given to PES is closely related to the establishment of the ecosystem services approach, whereby a social and economic value is attached to nature. This is the basis of PES reasoning: When such a value is ascribed to an ecosystem service, then this value can be realized specifically at the moment when that service is scarce. Someone should be ready to pay money for a scarce ecosystem service. Hence the users of ecosystem services are the starting point of the discourse: Who uses clean drinking water? Who enjoys a scenice landscape? Who benefits when our rivers and lakes are less nutrient-rich? If we carry this further we can conclude that when the benefits decline ("we have an environmental problem!") those users would in their own self-interest pay to have the benefits restored or continued

The quick and the dead: when reaction beats intention

Everyday behaviour involves a trade-off between planned actions and reaction to environmental events.Evidence from neurophysiology, neurology and functional brain imaging suggests different neural bases for the control of different movement types. Here we develop a behavioural paradigm to test movement dynamics for intentional versus reaction movements and provide evidence for a ‘reactive advantage’ in movement execution, whereby the same action is executed faster in reaction to an opponent. We placed pairs of participants in competition with each other to make a series of button presses. Within subject analysis of movement times revealed a 10 per cent benefit for reactive actions. This was maintained when opponents performed dissimilar actions, and when participants competed against a computer, suggesting that the effect is not related to facilitation produced by action observation. Rather, faster ballistic movements may be a general property of reactive motor control, potentially providing a useful means of promoting survival

Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex

The human brain sets itself apart from that of its primate relatives by specific neuroanatomical features, especially the strong linkage of left perisylvian language areas (frontal and temporal cortex) by way of the arcuate fasciculus (AF). AF connectivity has been shown to correlate with verbal working memory—a specifically human trait providing the foundation for language abilities— but a mechanistic explanation of any related causal link between anatomical structure and cognitive function is still missing. Here, we provide a possible explanation and link, by using neurocomputational simulations in neuroanatomically structured models of the perisylvian language cortex. We compare networks mimicking key features of cortical connectivity in monkeys and humans, specifically the presence of relatively stronger higher-order “jumping links” between nonadjacent perisylvian cortical areas in the latter, and demonstrate that the emergence of working memory for syllables and word forms is a functional consequence of this structural evolutionary change. We also show that a mere increase of learning time is not sufficient, but that this specific structural feature, which entails higher connectivity degree of relevant areas and shorter sensorimotor path length, is crucial. These results offer a better understanding of specifically human anatomical features underlying the language faculty and their evolutionary selection advantage

Biological constraints on neural network models of cognitive function

Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. Here, we discuss different types of neural models, including localist, auto-associative and hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning, to implementation of inhibition and control, along with neuroanatomical properties including area structure and local and long-range connectivity. We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, based on these brain-constrained neural models, hitherto unaddressed issues regarding the nature, localization and ontogenetic and phylogenetic development of higher brain functions. In closing, we point to possible future clinical applications of brain-constrained modelling

Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods

In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational and corpus linguistics, analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and computational corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity

Payments for ecosystem services in the tropics: a closer look at effectiveness and equity

We undertake a review of academic literature that examines the effectiveness and equity-related performance of PES initiatives targeting biodiversity conservation in tropical and sub-tropical countries. We investigate the key features of such analyses as regards their analytical and methodological approach and we identify emerging lessons from PES practice, leading to a new suggested research agenda. Our results indicate that analyses of PES effectiveness have to date focused on either ecosystem service provision or habitat proxies, with only half of them making explicit assessment of additionality and most describing that payments have been beneficial for land cover and biodiversity. Studies evaluating the impact of PES on livelihoods suggest more negative outcomes, with an uneven treatment of the procedural and distributive considerations of scheme design and payment distribution, and a large heterogeneity of evaluative frameworks. We propose an agenda for future PES research based on the emerging interest in assessing environmental outcomes more rigorously and documenting social impacts in a more comparative and contextually situated form