Evidence from aphasia is considered that leads to a distinction between abstract and concrete thought processes and hence for a distinction between rules and similarity. It is argued that perceptual classification is inherently a rule-following procedure and these rules are unable to be followed when a patient has difficulty with name comprehension and retrieval.

Starting from Dennett's distinction between personal and sub-personal levels of description, I consider the relationships amongst three levels: the personal level, the level of information-processing mechanisms, and the level of neurobiology. I defend a conception of the relationship between the personal level and the sub-personal level of information-processing mechanisms as interaction without reduction . Even given a nonreductionist conception of persons, philosophical theorizing sometimes supports downward inferences from the personal to the sub-personal level. An example of a downward inference is (...) provided and an objection is considered. (shrink)

[This is an earlier (1997), much longer and more detailed version of my entry on LOTH in the _Stanford Encyclopedia of Philosophy_] The Language of Thought Hypothesis (LOTH) is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that (...) has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)

A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in (...) syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)

Connectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, as it (...) offered a way of counteracting the conceptual ties to the philosophical traditions of atomism, rationalism, logic, nativism, rule realism and a concern with the role symbols play in human cognitive functioning, which was prevalent as a consequence of artificial intelligence research. The surge in philosophical interest waned, possibly in part due to the efforts of some traditionalists and the so-called black box problem. Most recently, what may be thought of as a third phase of connectionist research, based on so-called deep learning methods, is beginning to show some signs of again exciting philosophical interest. (shrink)

In this article, I argue that if tacit knowledge of grammar is analyzable in functional‐computational terms, then it cannot ground linguistic meaning, structure, or sound. If to know or cognize a grammar is to be in a certain computational state playing a certain functional role, there can be no unique grammar cognized. Satisfying the functional conditions for cognizing a grammar G entails satisfying those for cognizing many grammars disagreeing with G about expressions' semantic, phonetic, and syntactic values. This threatens the (...) Chomskyan view that expressions have such values for speakers because they cognize grammars assigning them those values. For if this is true, semantics, syntax, and phonology must be indeterminate, thanks to the indeterminacy of grammar‐cognizing (qua functional‐computational state). So, the fact that a speaker cognizes a grammar cannot explain the determinate character of their language. (shrink)

The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be (...) described on the basis of separate models. In the present article, I assume that the rules versus similarity distinction can be understood in the same way in learning, reasoning, categorization, and language, and that a unified model for rules and similarity is appropriate. A rules process is considered to be a similarity one where only a single or a small subset of an object's properties are involved. Hence, rules and overall similarity operations are extremes in a single continuum of similarity operations. It is argued that this viewpoint allows adequate coverage of theory and empirical findings in learning, reasoning, categorization, and language, and also a reassessment of the objectives in research on rules versus similarity. Key Words: categorization; cognitive explanation; language; learning; reasoning; rules; similarity. (shrink)

In this chapter, I shall be examining an argument for the language of thought hypothesis.

As AI systems become increasingly complex it may become unclear, even to the designer of a system, why exactly a system does what it does. This leads to a lack of trust in AI systems. To solve this, the field of explainable AI has been working on ways to produce explanations of these systems’ behaviors. Many methods in explainable AI, such as LIME, offer only a statistical argument for the validity of their explanations. However, some methods instead study the internal (...) structure of the system and try to find components which can be assigned an interpretation. I believe that these methods provide more valuable explanations than those statistical in nature. I will try to identify which explanations can be considered internal to the system using the Chomskyan notion of tacit knowledge. I argue that each explanation expresses a rule, and through the localization of this rule in the system internals, we can take a system to have tacit knowledge of the rule. I conclude that the only methods which are able to sufficiently establish this tacit knowledge are those along the lines of Olah : 4901–4911, 2017), and therefore they provide explanations with unique strengths. (shrink)

In this paper, I argue for a modified version of what Devitt calls the Representational Thesis. According to RT, syntactic rules or principles are psychologically real, in the sense that they are represented in the mind/brain of every linguistically competent speaker/hearer. I present a range of behavioral and neurophysiological evidence for the claim that the human sentence processing mechanism constructs mental representations of the syntactic properties of linguistic stimuli. I then survey a range of psychologically plausible computational models of comprehension (...) and show that they are all committed to RT. I go on to sketch a framework for thinking about the nature of the representations involved in sentence processing. My claim is that these are best characterized not as propositional attitudes but, rather, as subpersonal states whose representational properties are determined by their functional role. Finally, I distinguish between explicit and implicit representations and argue that the latter can be drawn on as data by the algorithms that constitute our sentence processing routines. I conclude that skepticism concerning the psychological reality of grammars cannot be sustained. (shrink)

According to the theory theory of folk psychology, our engagement in the folk psychological practices of prediction, interpretation and explanation draws on a rich body of knowledge about psychological matters. According to the simulation theory, in apparent contrast, a fundamental role is played by our ability to identify with another person in imagination and to replicate or re-enact aspects of the other person’s mental life. But amongst theory theorists, and amongst simulation theorists, there are significant differences of approach.

Thinking is special. There is nothing quite like it. Thinking.

It is increasingly common in cognitive science and philosophy of perception to regard perceptual processing as a probabilistic engine, taking into account uncertainty in computing representations of the distal environment. Models of this kind often postulate probabilistic representations, or what we will call probabilistic states,. These are states that in some sense mark or represent information about the probabilities of distal conditions. It has also been argued that perceptual experience itself in some sense represents uncertainty (Morrison _Analytic Philosophy_ 57 (1): (...) 15 48, 2016 ). In this article, we will first consider three models of sensory activity from perceptual neuroscience, namely signal detection theory (SDT), probabilistic population codes (PPC), and sampling. We will then reflect on the sense in which the probabilistic states introduced in these models are probabilistic representations. To sharpen this discussion, we will compare and contrast these probabilistic states to credences as they are understood in epistemology. We will suggest that probabilistic representation, in an appropriately robust sense, can be understood as a form of analog representation. In the last part of the paper, we apply this to the issue of whether conscious experience represents uncertainty—we will interpret this as the claim that there are phenomenal features of experience that serve as analog probabilistic representations. (shrink)