On the Poverty of Scientism, or: The Ineluctable Roughness of Rationality

If there is one rationality there must be a plurality of them. This conclusion follows, I argue, partly from the extreme and ineradicable vagueness of the fundamental concepts that every would-be rational explanation must presuppose. Logicistic/scientistic assaults on this vagueness are doomed to fail partly because they are unable to acknowledge the imaginative dimension of rational thought. Being limited to the play of "outward appearances," scientific investigations are also dependent on "inward imaginings" on their speculative side. The upshot is that schools of philosophy should be characterized by the kind of imaginary they adopt rather than by their logics. In which case, every attempt to get and tell something right about the world is bound to incorporate mythopoeic elements in its explanations.     

试析联结主义范式对婴儿发展的解释

用联结主义范式作为解释婴儿发展的机制已成为当前认知发展研究的新趋势。本文以婴儿分类和客体永久性为范例,分析了这一范式在解释婴儿发展方面的理论潜力及不足。     

Learning to attend: a connectionist model of situated language comprehension

Evidence from numerous studies using the visual world paradigm has revealed both that spoken language can rapidly guide attention in a related visual scene and that scene information can immediately influence comprehension processes. These findings motivated the coordinated interplay account ( Knoeferle & Crocker, 2006 ) of situated comprehension, which claims that utterance-mediated attention crucially underlies this closely coordinated interaction of language and scene processing. We present a recurrent sigma-pi neural network that models the rapid use of scene information, exploiting an utterance-mediated attentional mechanism that directly instantiates the CIA. The model is shown to achieve high levels of performance (both with and without scene contexts), while also exhibiting hallmark behaviors of situated comprehension, such as incremental processing, anticipation of appropriate role fillers, as well as the immediate use, and priority, of depicted event information through the coordinated use of utterance-mediated attention to the scene.     

Abductive reasoning in neural-symbolic systems

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration.

Modelling the effects of semantic ambiguity in word recognition

Most words in English are ambiguous between different interpretations; words can mean different things in different contexts. We investigate the implications of different types of semantic ambiguity for connectionist models of word recognition. We present a model in which there is competition to activate distributed semantic representations. The model performs well on the task of retrieving the different meanings of ambiguous words, and is able to simulate data reported by Rodd, Gaskell, and Marslen-Wilson [J. Mem. Lang. 46 (2002) 245] on how semantic ambiguity affects lexical decision performance. In particular, the network shows a disadvantage for words with multiple unrelated meanings (e.g., bark) that coexists with a benefit for words with multiple related word senses (e.g., twist). The ambiguity disadvantage arises because of interference between the different meanings, while the sense benefit arises because of differences in the structure of the attractor basins formed during learning. Words with few senses develop deep, narrow attractor basins, while words with many senses develop shallow, broad basins. We conclude that the mental representations of word meanings can be modelled as stable states within a high-dimensional semantic space, and that variations in the meanings of words shape the landscape of this space.