Context-sensitive Fashion Research Articles

Donkey disjunctions and overlapping updates

This paper is devoted to an analysis of anaphoric dependencies in disjunctive sentences, and consequences for the understanding of the ∃/∀ ambiguity observed with donkey anaphora. The primary focus is on donkey disjunctions, which are sentences where a (negated) existential in an initial disjunct appears to bind a pronoun in a later disjunct, such as "Either there's no bathroom, or its upstairs". The main empirical focus is that donkey disjunctions, like donkey anaphora, exhibit the ∃/∀ ambiguity, and more generally oscillate between homogeneous and heterogeneous readings in a context-sensitive fashion. The paper then proceeds in two steps: first, a principled analysis of donkey disjunctions is developed in the context of a Bilateral Update Semantics (BUS). BUS, by default, generates heterogeneous readings for donkey anaphora/donkey disjunctions (i.e., ∃ readings, in a positive context). In order to account of homogeneous readings, the conjecture is that sentences may be interpreted exhaustively relative to their negations. This has non-trivial consequences due to the non-classicality of BUS — specifically, a failure of the Law of Non-Contradiction.

What if? Counterfactual (Hi)Stories of International Law

AbstractThis paper proposes to think counterfactually about international law: How could it have been otherwise? Asking that question has the benefit of, first, exposing contingencies in international law’s development that are otherwise glossed over in the rush towards making sense of what happened. Second, counterfactual thinking supports the understanding of what actually happened in a context-sensitive fashion. Third, it forms part of comparative moral assessments and exposes blind spots. Counterfactual thinking may thus contribute to the freedom from necessity, from grand theory, and from reality. The paper draws the contours of what writing counterfactual (hi)stories of international law is about, discusses its merits as well as drawbacks, offers guidance on how to do it, and then focuses on two probing examples: What if the International Trade Organization had been established around 1949? What if garment workers were seals and the European Commission prohibited the importation of certain textiles?

What If? Counterfactual (Hi)Stories of International Law

The article proposes to think counterfactually about international law. For example, what if the ICJ had decided South West Africa on the merits or had it not done so in Nicaragua? What if a different conception of the sources of international law had emerged in the drafting of the Statute of the PCIJ or during decolonization in the 1960s? Writing counterfactual (hi)stories of international law, the article submits, has the potential of, first, exposing the contingency of outcomes that is glossed over in the rush towards making sense of what actually happened. Second, it supports the understanding of what actually happened in a context-sensitive fashion that complements grand theories’ emphasis on systemic variables. Third, counterfactual thinking forms part of comparative assessments and exposes normative blind spots. Counterfactual thinking thus contributes to the freedom from necessity, from grand theory, and from reality. In short, by asking how it could be otherwise, counterfactual thinking questions the present shape of international law. Inspired by thriving practices of counterfactual thinking in history, social science and critical theory, the article develops a nuanced argument in favour of thinking counterfactually about international law. It draws the contours of what counterfactual thinking is about, discusses merits, offers guidance on how to do it, and provides at least one more detailed example — what if the International Trade Organization had been established around 1949?

Layered sparse associative network for soft pattern classification and contextual pattern completion

Event Abstract Back to Event Layered sparse associative network for soft pattern classification and contextual pattern completion Evan Ehrenberg1*, Pentti Kanerva1 and Friedrich Sommer1 1 University of California at Berkeley, Redwood Center for Theoretical Neuroscience, United States Traditional models of associative networks [1, 5] have impressive pattern completion capabilities. They feature high storage capacity and can reconstruct incomplete sensory input from stored memory in a context-sensitive fashion. However, associative networks are not useful as pattern classifiers beyond simple toy examples where the classes form well-separated clusters in pattern space. Conversely, multi-layer feed-forward neural networks [4] can be trained to solve challenging classification tasks, but they cannot perform pattern completion. Here we tested the ability of sparse two-layer associative networks to learn to 1) recognize patterns in real world data and 2) use memory content to reconstruct noisy input in a context-sensitive fashion. Specifically, we used the memory models with supervised learning on handwritten characters (NIST database [3]) and assessed how well the memory could predict the class of unknown input and reconstruct the input pattern. We started with the Kanerva memory model [2], which has a two-layer structure. The first neural layer has fixed random synapses and contains many more neurons than input fibers, which are sparsely activated. This stage maps the input space into a high-dimensional space of sparse patterns. The synapses of second layer neurons store associations between the high-dimensional sparse patterns and desired output patterns via Hebbian plasticity. We trained the model with the NIST data by storing for each input pattern the given digit interpretation as well as the input pattern in autoassociative fashion. When cross-validated with (noisy) inputs, the model frequently performed pattern recognition and reconstruction successfully. However, ambiguous inputs could not be classified, and the reconstruction was a mixture pattern not corresponding to a valid handwritten digit. To overcome these limitations, we constructed a new model with a two-layer structure similar to the described Kanerva model but with two important new features, enabling soft classification and pattern completion in two subsequent phases. One new feature is labeling first layer neurons during supervised training according to how their activity is correlated with the occurrence of certain classes. Thus, in the classification phase when a new input drives first layer neurons, estimates of class membership are encoded in active populations of first layer cells. The second feature is a phase of selective pattern completion performed after classification. In this phase first layer neurons are also influenced by lateral interactions and feedback from the second layer. This competitive dynamical process accomplishes pattern completion that is contingent on a specific interpretation rather than creating a mixture of the entire memory content, even if the input is ambiguous. We compare the model to current methods of classifying the NIST data. We found that the model exhibits competitive classification performance and adds a valuable explanatory component not provided by ordinary classification algorithms. Finally, we discuss how the proposed memory architecture would map onto dentate gyrus and CA3 of the hippocampus.

ATTRACTORS IN SONG

This paper summarizes our recent attempts to integrate action and perception within a single optimization framework. We start with a statistical formulation of Helmholtz's ideas about neural energy to furnish a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring the causes of our sensory inputs and learning regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organization and responses. We will demonstrate the brain-like dynamics that this scheme entails by using models of birdsongs that are based on chaotic attractors with autonomous dynamics. This provides a nice example of how non-linear dynamics can be exploited by the brain to represent and predict dynamics in the environment.

A context-sensitive framework for lexical ontologies

AbstractHuman categorization is neither a binary nor a context-free process. Rather, the criteria that govern the use and recognition of certain concepts may be satisfied to different degrees in different contexts. In light of this reality, the idealized, static structure of a lexical-ontology like WordNet appears both excessively rigid and unduly fragile when faced with real texts that draw upon different contexts to communicate different world-views. In this paper we describe a syntagmatic, corpus-based approach to redefining the concepts of a lexical-ontology like WordNet in a functional, gradable and context-sensitive fashion. We describe how the most diagnostic properties of concepts, on which these functional definitions are based, can be automatically acquired from the Web, and demonstrate how these properties are more predictive of how concepts are actually used and perceived than properties derived from other sources (such as WordNet itself).

Free-energy and the brain.

If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory input and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses.In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure.

A free energy principle for the brain

By formulating Helmholtz’s ideas about perception, in terms of modern-day theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts: using constructs from statistical physics, the problems of inferring the causes of sensory input and learning the causal structure of their generation can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organisation and responses. In this paper, we show these perceptual processes are just one aspect of emergent behaviours of systems that conform to a free energy principle. The free energy considered here measures the difference between the probability distribution of environmental quantities that act on the system and an arbitrary distribution encoded by its configuration. The system can minimise free energy by changing its configuration to affect the way it samples the environment or change the distribution it encodes. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment assumes that the system’s state and structure encode an implicit and probabilistic model of the environment. We will look at the models entailed by the brain and how minimisation of its free energy can explain its dynamics and structure.

A theory of cortical responses.

This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts.It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain's attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.

Generating context-sensitive responses to object-related misconceptions

A study of transcripts of users interacting with database or expert systems reveals that users often exhibit misconceptions about the objects modeled by the systems. Simply ignoring these misconceptions may not only mislead the user, but may seriously hamper the ability of the system. This paper discusses a method for responding to such misconceptions in a domain-independent and context-sensitive fashion. The method calls for reasoning about possible sources of the misconception using a model of the user and generating a response based on this reasoning. The process is made context-sensitive by augmenting the user model with domain perspective which provides shifting highlights on the user model.