Affective value in the predictive mind

Although affective value is fundamental in explanations of behavior, it is still a somewhat alien concept in cognitive science. It implies a normativity or directionality that mere information processing models cannot seem to provide. In this paper we trace how affective value can emerge from information processing in the brain, as described by predictive processing. We explain the grounding of predictive processing in homeostasis, and articulate the implications this has for the concept of reward and motivation. However, at first sight, this new conceptualization creates a strong tension with conventional ideas on reward and affective experience. We propose this tension can be resolved by realizing that valence, a core component of all emotions, might be the reflection of a specific aspect of predictive information processing, namely the dynamics in prediction errors across time and the expectations we, in turn, form about these dynamics. Specifically, positive affect seems to be caused by positive rates of prediction error reduction, while negative affect is induced by a shift in a state with lower prediction errors to one with higher prediction errors (i.e., a negative rate of error reduction). We also consider how intense emotional episodes might be related to unexpected changes in prediction errors, suggesting that we also build (meta)predictions on error reduction rates. Hence in this account emotions appear as the continuous non-conceptual feedback on evolving —increasing or decreasing—uncertainties relative to our predictions. The upshot of this view is that the various emotions, from “basic” ones to the non-typical ones such as humor, curiosity and aesthetic affects, can be shown to follow a single underlying logic. Our analysis takes several cues from existing emotion theories but deviates from them in revealing ways. The account on offer does not just specify the interactions between emotion and cognition, rather it entails a deep integration of the two

MELODI : Semantic Similarity of Words and Compositional Phrases using Latent Vector Weighting

International audienceIn this paper we present our system for the SemEval 2013 Task 5a on semantic similar- ity of words and compositional phrases. Our system uses a dependency-based vector space model, in combination with a technique called latent vector weighting. The system computes the similarity between a particular noun in- stance and the head noun of a particular noun phrase, which was weighted according to the semantics of the modifier. The system is en- tirely unsupervised; one single parameter, the similarity threshold, was tuned using the train- ing data

Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model

The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.Comment: This paper is accepted the 24th International Conference On Neural Information Processing (ICONIP 2017). The previous submission to arXiv is replaced by this version because there was an error in Equation

A Tensor-based Factorization Model of Semantic Compositionality

International audienceIn this paper, we present a novel method for the computation of compositionality within a distributional framework. The key idea is that compositionality is modeled as a multi-way interaction between latent factors, which are automatically constructed from corpus data. We use our method to model the composition of subject verb object triples. The method consists of two steps. First, we compute a latent factor model for nouns from standard co-occurrence data. Next, the latent factors are used to induce a latent model of three-way subject verb object interactions. Our model has been evaluated on a similarity task for transitive phrases, in which it exceeds the state of the art

MELODI : A Supervised Distributional Approach for Free Paraphrasing of Noun Compounds

National audienceThis paper describes the system submitted by the MELODI team for the SemEval-2013 Task 4 : Free Paraphrases of Noun Compounds (Hendrickx et al., 2013). Our approach combines the strength of an unsupervised distributional word space model with a supervised maximum-entropy classification model; the distributional model yields a feature representation for a particular compound noun, which is subsequently used by the classifier to induce a number of appropriate paraphrases

Discourse-Based Evaluation of Language Understanding

We introduce DiscEval, a compilation of $11$ evaluation datasets with a focus on discourse, that can be used for evaluation of English Natural Language Understanding when considering meaning as use. We make the case that evaluation with discourse tasks is overlooked and that Natural Language Inference (NLI) pretraining may not lead to the learning really universal representations. DiscEval can also be used as supplementary training data for multi-task learning-based systems, and is publicly available, alongside the code for gathering and preprocessing the datasets

A Quantitative Evaluation of Global Word Sense Induction

International audienceWord sense induction (WSI) is the task aimed at automatically identifying the senses of words in texts, without the need for handcrafted resources or annotated data. Up till now, most WSI algorithms extract the different senses of a word 'locally' on a per-word basis, i.e. the different senses for each word are determined separately. In this paper, we compare the performance of such algorithms to an algorithm that uses a 'global' approach, i.e. the different senses of a particular word are determined by comparing them to, and demarcating them from, the senses of other words in a full-blown word space model. We adopt the evaluation framework proposed in the SemEval-2010 Word Sense Induction \& Disambiguation task. All systems that participated in this task use a local scheme for determining the different senses of a word. We compare their results to the ones obtained by the global approach, and discuss the advantages and weaknesses of both approaches