Online research in philosophy

As Paul Griffiths [2002] puts it, “innateness” is associated with different clusters of related ideas where each cluster depends on different historical, cultural and intellectual contexts. In psychology innateness is typically opposed to learning while the biological opposite of innate is ‘acquired’. ‘Acquired’ and ‘learned’ have different extensions. Learning is one way to acquire a character but there are others. Cuts and scratches are unlearned yet acquired; if we could acquire languages by popping a pill, then languages would be unlearned yet acquired according to the wide biological application of the term [Sober, 1998]. Further, in psychology and philosophy innateness is often associated with both “universality” (or species-specificity), and, relatedely, innate traits are often thought to be “fixed” or “unmodifiable”. But, biologists recognize a range of developmental patterns that a specific trait may take. Some are universal, but others are not, as in the case of innate diseases. Some are “fixed” in the sense that once we develop them we have them for the rest of our lives; some innate diseases are like this, but others, are modifiable. Sober [1998] cites a case of an Egyptian vulture that when first confronted with an ostrich egg and a stone, will break the egg with the stone, but if the vulture repeatedly comes to find broken eggs to be empty, it will eventually stop breaking eggs. These examples lend support to Griffiths’s thesis, since the concept of innateness in psychology appears to be in several ways distinct from the concept of innateness in biology.

The philosophical innateness debate has long relied onpsychological evidence. For a century, however, a parallel debate hastaken place within neuroscience. In this paper, I consider theimplications of this neuroscience debate for the philosophicalinnateness debate. By combining the tools of theoretical neurobiologyand learning theory, I introduce the ``problem of development'' that alladaptive systems must solve, and suggest how responses to this problemcan demarcate a number of innateness proposals. From this perspective, Isuggest that the majority of natural systems are in fact innate. Lastly,I consider the acquistion strategies implemented by the human brain andsuggest that there is a rigorous way of characterizing these ``neuralconstructivist'' strategies as not being strongly innate. Alternatives toinnateness are thus both rigorously definable and empirically supported.

Machine learning and statistical methods have yielded impressive results in a wide variety of natural language processing tasks. These advances have generally been regarded as engineering achievements. In fact it is possible to argue that the success of machine learning methods is significant for our understanding of the cognitive basis of language acquisition and processing. Recent work in unsupervised grammar induction is particularly relevant to this issue. It suggests that knowledge of language can be achieved through general learning procedures, and that a richly articulated language faculty is not required to explain its acquisition.

Computational learning theory explores the limits of learnability. Studying language acquisition from this perspective involves identifying classes of languages that are learnable from the available data, within the limits of time and computational resources available to the learner. Different models of learning can yield radically different learnability results, where these depend on the assumptions of the model about the nature of the learning process, and the data, time, and resources that learners have access to. To the extent that such assumptions accurately reflect human language learning, a model that invokes them can offer important insights into the formal properties of natural languages, and the way in which their representations might be efficiently acquired. In this chapter we consider several computational learning models that have been applied to the language learning task. Some of these have yielded results that suggest that the class of natural languages cannot be efficiently learned from the primary linguistic data (PLD) available to children, through..

Controversies over the innateness of cognitive processes, mechanisms, and structures play a persistent role in driving research in philosophy as well as the cognitive sciences, but the appropriate way to understand the category of the innate remains subject to dispute. One venerable approach in philosophy and cognitive science merely contrasts innate features with those that are learned. In fact, Jerry Fodor has recently suggested that this remains our best handle on innateness.

Controversies over the innateness of cognitive processes, mechanisms, and structures play a persistent role in driving research in philosophy as well as the cognitive sciences, but the appropriate way to understand the category of the innate remains subject to dispute. One venerable approach in philosophy and cognitive science merely contrasts innate features with those that are learned. In fact, Jerry Fodor has recently suggested that this remains our best handle on innateness.

In LOT 2: The Language of Thought Revisited , Jerry Fodor argues that concept learning of any kind—even for complex concepts—is simply impossible. In order to avoid the conclusion that all concepts, primitive and complex, are innate, he argues that concept acquisition depends on purely biological processes. In this paper, we show (1) that Fodor fails to establish that concept learning is impossible, (2) that his own biological account of concept acquisition is unworkable, and (3) that there are in fact many promising general models for explaining how concepts are learned.

In LOT 2: The Language of Thought Revisited, Jerry Fodor argues that concept learning of any kind—even for complex concepts—is simply impossible. In order to avoid the conclusion that all concepts, primitive and complex, are innate, he argues that concept acquisition depends on purely noncognitive biological processes. In this paper, we show (1) that Fodor fails to establish that concept learning is impossible, (2) that his own biological account of concept acquisition is unworkable, and (3) that there are in fact many promising general models for explaining how concepts are learned.

Arguments from the Logical Problem of Language Acquisition suggest that since linguistic experience provides few negative data that would falsify overgeneral grammatical hypotheses, innate knowledge of the principles of Universal Grammar must constrain learners hypothesis formulation. Although this argument indicates a need for domain-specific constraints, it does not support their innateness. Learning from mostly positive data proceeds unproblematically in virtually all domains. Since not every domain can plausibly be accorded its own special faculty, the probative value of the argument in the linguistic case is dubious. In ignoring the holistic and probablistic nature of theory construction, the argument underestimates the extent to which positive data can supply negative evidence and hence overestimates the intractability of language learning in the absence of a dedicated faculty. While nativism about language remains compelling, the alleged Logical Problem contributes nothing to its plausibility and the emphasis on the Problem in the recent acquisition literature has been a mistake.