The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through (...) these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models.Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition. (shrink)

Chomskyian claims of a genetically hard-wired and cognitively autonomous “universal grammar” are being promoted by generative linguistics as facts about language to the present day. The related doctrine of an evolutionary discontinuity in language emergence, however, is based on misconceptions about the notions of homology and preadaptation. The obvious lack of equivalence between symbolic communicative capacities in existing nonhuman primates and human language does not preclude common roots. Normal and disordered language development is strongly influenced by the genome, but there (...) is no evidence for the existence of specific genes underlying “universal grammar.” In the mature brain, stages of language processing can be distinguished and “first-pass” syntactic analyses appear to precede semantic decoding. However, this partial seriality – as well as behavioral and clinical dissociations between lexical and functional categories – can be best described, not in terms of serially activated and discrete modules, but in terms of classes of cell assemblies that differ in their distributional properties. Whereas cell assemblies involved in semantic interpretation (“content word” assemblies) are widely distributed and are generally less vulnerable to focal lesion, those involved in structural decoding (functor assemblies) are primarily distributed within the left perisylvian cortices and are selectively vulnerable to left perisylvian lesion. These distributional differences are explained in terms of the perceptuomotor components involved in the acquisition of relevant representations. The emerging “motivated” or “toposemantic” brain regional specificity can only be accommodated with “soft” and maturational versions of modularity. The failure to reproduce double dissociations in current connectionist models is due to overly simple neuroscientific assumptions, notably that of overall equipotentiality. Linguistic models should not be expected to be “implemented” in the brain, but need to be constrained by neuroscientific evidence on how biological brains function. (shrink)

Contrary to Müller's claims, and in support of modular theories, genetic factors play a substantial and significant role in language. The finding that some children with specific language impairment (SLI) have nonlinguistic impairments may reflect improper diagnosis of SLI or impairments that are secondary to linguistic impairments. Thus, such findings do not argue against the modularity thesis. The lexical/functional distinction appears to be innate and specifically linguistic and could be instantiated in either symbolic or connectionist systems.

The belief that syntax is an innate, autonomous, species-specific module is highly questionable. Syntax demonstrates the mosaic nature of evolutionary change, in that it made use of (and led to the enhancement of) numerous preexisting neurocognitive features. It is best understood as an emergent characteristic of the explosion of semantic complexity that occurred during hominid evolution.

Müller's target article aims to summarize approaches to the question of how language elements (phonemes, morphemes, etc.) and rules are laid down in the brain. However, it suffers from being too vague about basic assumptions and empirical predictions of neurobiological models, and the empirical evidence available to test the models is not appropriately evaluated. (1) In a neuroscientific model of language, different cortical localizations of words can only be based on biological principles. These need to be made explicit. (2) Evidence (...) for and against word class differences could be evaluated more rigorously. (3) All (and only) humans are able to learn languages with complex syntactic structures; it is, therefore, not appropriate to deny innateness and universality of syntactic principles. The real question appears to be the following: Which neurobiological principles are the linguistic principles based on? (shrink)

I enlarge on the theme that the brain mechanisms required for languageand other aspects of the human mind evolved through selective changes in the regulatory genes governing growth. Extension of the period of postnatal growth increases the role of the environment in structuring the brain, and spatiotemporal programming (heterochrony) ofgrowth might explain hierarchical representation, hemispheric specialization, and perhaps sex differences.

The concepts of the innateness, universality, species-specificity, and autonomy of the human language capacity have had an extreme impact on the psycholinguistic debate for over thirty years. These concepts are evaluated from several neurobiological perspectives, with an emphasis on the emergence of language and its decay due to brain lesion and progressive brain disease.Evidence of perceptuomotor homologies and preadaptations for human language in nonhuman primates suggests a gradual emergence of language during hominid evolution. Regarding ontogeny, the innate component of language (...) capacity is likely to be polygenic and shared with other developmental domains. Dissociations between verbal and nonverbal development are probably rooted in the perceptuomotor specializations of neural substrates rather than the autonomy of a grammar module. Aphasiologicaldata often assumed to suggest modular linguistic subsystems can be accounted for in terms of a neurofunctional model incorporating perceptuomotor-based regional specializationsand distributivity of representations. Thus, dissociations between grammatical functors and content words are due to different conditions of acquisition and resulting differences in neural representation. Human brains are characterized by multifactorial interindividual variability, and strict universality of functional organization is biologically unrealistic.A theoretical alternative is proposed according to which (1) linguistic specialization of brain areas is due to epigenetic and probabilistic maturational events, not to genetic ”hard-wiring,” and (2) linguistic knowledge is neurally represented in distributed cell assemblies whose topography reflects the perceptuomotor modalities involved in the acquisition and use of a given item of knowledge. (shrink)

Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...) Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realise biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biologically and cognitive accuracy of artificial systems in order project and predict their explanatory power with respect to the natural systems taken as source of inspiration. -/- Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science. (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)

Amit's Hebbian model risks being overexplanatory, since it does not depend on specific physiological modelling of cortical ANNs, but concentrates on those phenomena which are modelled by a large class of ANNs. While offering a strong demonstration of the presence of Hebb's “cell assemblies,” it does not offer an equal account of Hebb's “phase sequence” concept.

In “Truth by Convention” W.V. Quine gave an influential argument against logical conventionalism. Even today his argument is often taken to decisively refute logical conventionalism. Here I break Quine’s arguments into two— the super-task argument and the regress argument—and argue that while these arguments together refute implausible explicit versions of conventionalism, they cannot be successfully mounted against a more plausible implicit version of conventionalism. Unlike some of his modern followers, Quine himself recognized this, but argued that implicit conventionalism was explanatorily (...) idle. Against this I show that pace Quine’s claim that implicit conventionalism has no content beyond the claim that logic is firmly accepted, implicit rules of inference can be used to distinguish the firmly accepted from the conventional. As part of my case, I argue that positing syntactic rules of inference as part of our linguistic competence follows from the same methodology that leads contemporary linguists and cognitive scientists to posit rules of phonology, morphology, and grammar. The upshot of my discussion is a diagnosis of the fallacy in Quine’s master critique of logical conventionalism and a re-opening of possibilities for an attractive conventionalist theory of logic. (shrink)

One can disagree with Müller that it is neurobiologically questionable to suppose that human language is innate, specialized, and species-specific, yet agree that the precise brain mechanisms controlling language in any individual will be influenced by epigenesis and genetic variability, and that the interplay between inherited and acquired aspects of linguistic capacity deserves to be investigated.

Critics of the computational connectionism of the last decade suggest that it shares undesirable features with earlier empiricist or associationist approaches, and with behaviourist theories of learning. To assess the accuracy of this charge the works of earlier writers are examined for the presence of such features, and brief accounts of those found are given for Herbert Spencer, William James and the learning theorists Thorndike, Pavlov and Hull. The idea that cognition depends on associative connections among large networks of neurons (...) is indeed one with precedents, although the implications of this for psychological issues have been interpreted variously — not all versions of connectionism are alike. (shrink)

A central controversy in cognitive science concerns the roles of rules versus similarity. To gain some leverage on this problem, we propose that rule‐ versus similarity‐based processes can be characterized as extremes in a multidimensional space that is composed of at least two dimensions: the number of features (Pothos, 2005) and the physical presence of features. The transition of similarity‐ to rule‐based processing is conceptualized as a transition in this space. To illustrate this, we show how a neural network model (...) uses input features (and in this sense produces similarity‐based responses) when it has a low learning rate or in the early phases of training, but it switches to using self‐generated, more abstract features (and in this sense produces rule‐based responses) when it has a higher learning rate or is in the later phases of training. Relations with categorization and the psychology of learning are pointed out. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition. (shrink)

Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can (...) be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models. (shrink)

Regularly inflected forms often behave differently in language production than irregular forms. These differences are often used to argue that irregular forms are listed in the lexicon but regular forms are produced by rule. Using an experimental speech production task with adults, it is shown that overtensing errors, where a tensed verb is used in place of an infinitive, predominantly involve irregular forms, but that the differences may be due to phonological confounds, not to regularity per se. Errors involve vowel‐changing (...) irregular forms more than suffixing inflected forms, with at best a small difference between regular ‐ed and irregular ‐en. Frequency effects on overtensing errors require a model in which the past‐tense and base forms of the verb are in competition and in which activation functions are nonlinear, and rule out models with specialized subnetworks for past‐tense forms. Implications for theories of language production are discussed. (shrink)

Although debates continue, studies of cognition in infancy suggest that knowledge begins to emerge early in life and constitutes part of humans' innate endowment. Early-developing knowledge appears to be both domain-specific and task-specific, it appears to capture fundamental constraints on ecologically important classes of entities in the child's environment, and it appears to remain central to the commonsense knowledge systems of adults.

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...) have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization–Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. (shrink)