Understanding Cognition through Synthesis and Analysis

This article discusses the interdisciplinary research area of cognitive science. It presents methods and approaches to investigating cognitive processes. Spatial cognition as a subfield of cognitive science is used to show how mental processes are connected with the physical realm through perception and action. These bridges between physical and mental permit a variety of rather distinct approaches that contribute to the investigation of the cognitive processes involved. Particular attention is given to the role of artificial intelligence approaches to help discovering cognitive processing principles. The article discusses the interplay between synthetic and analytic approaches to cognitive science and emphasizes the complementary strengths of these approaches. It shows how the environment, the context of the specific situation, and the task can reduce the cognitive effort to be taken. The trade-off between general abstract representations and specific concrete representations is shown. The power of virtual environments for testing and understanding models of cognition is demonstrated. Numerous examples are given.     

Information Compression by Multiple Alignment, Unification and Search as a Unifying Principle in Computing and Cognition

This article presents an overview ofthe idea that information compression bymultiple alignment, unification and search(ICMAUS) may serve as a unifying principle incomputing (including mathematics and logic) andin such aspects of human cognition as theanalysis and production of natural language,fuzzy pattern recognition and best-matchinformation retrieval, concept hierarchies withinheritance of attributes, probabilisticreasoning, and unsupervised inductive learning.The ICMAUS concepts are described together withan outline of the SP61 software model in whichthe ICMAUS concepts are currently realised. Arange of examples is presented, illustratedwith output from the SP61 model.     

Scheme-Based Alethic Realism: Agency,the Environment,and Truthmaking

This paper presents a position called Scheme-based Alethic Realism, which reconciles a realist position on the nature of truth with a pluralistic Kantian perspective that allows for multiple environments in which truthmaking relationships are established. We argue that truthmaking functions are constrained by a stable phenomenal world and a stable cognitive architecture. This account takes truth as normatively distinct from epistemic justification while relativizing the truth conditions of our statements to what we call Frameworks. The pluralistic aspect allows that these stable elements, while constraining representational and linguistic schemes, do not define a single framework for truthmaking relations. We strengthen this position by considering themes on situated rational agency from cognitive science and artificial intelligence, arguing that whatever enables or supports rational action within a particular environment must figure into some account of truth and truthmaking, and vice versa.     

The Chinese Room Argument Reconsidered: Essentialism,Indeterminacy, and Strong AI

I argue that John Searle's (1980) influential Chinese room argument (CRA) against computationalism and strong AI survives existing objections, including Block's (1998) internalized systems reply, Fodor's (1991b) deviant causal chain reply, and Hauser's (1997) unconscious content reply. However, a new ``essentialist' reply I construct shows that the CRA as presented by Searle is an unsound argument that relies on a question-begging appeal to intuition. My diagnosis of the CRA relies on an interpretation of computationalism as a scientific theory about the essential nature of intentional content; such theories often yield non-intuitive results in non-standard cases, and so cannot be judged by such intuitions. However, I further argue that the CRA can be transformed into a potentially valid argument against computationalism simply by reinterpreting it as an indeterminacy argument that shows that computationalism cannot explain the ordinary distinction between semantic content and sheer syntactic manipulation, and thus cannot be an adequate account of content. This conclusion admittedly rests on the arguable but plausible assumption that thought content is interestingly determinate. I conclude that the viability of computationalism and strong AI depends on their addressing the indeterminacy objection, but that it is currently unclear how this objection can be successfully addressed.     

Minds, Machines and Turing

Turing's celebrated 1950 paper proposes a very generalmethodological criterion for modelling mental function: total functionalequivalence and indistinguishability. His criterion gives rise to ahierarchy of Turing Tests, from subtotal (toy) fragments of ourfunctions (t1), to total symbolic (pen-pal) function (T2 – the standardTuring Test), to total external sensorimotor (robotic) function (T3), tototal internal microfunction (T4), to total indistinguishability inevery empirically discernible respect (T5). This is areverse-engineering hierarchy of (decreasing) empiricalunderdetermination of the theory by the data. Level t1 is clearly toounderdetermined, T2 is vulnerable to a counterexample (Searle's ChineseRoom Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3is the appropriate target level for cognitive science. When it isreached, however, there will still remain more unanswerable questionsthan when Physics reaches its Grand Unified Theory of Everything (GUTE),because of the mind/body problem and the other-minds problem, both ofwhich are inherent in this empirical domain, even though Turing hardlymentions them.