I am grateful to the twelve commentators on my article “A Computational Foundation for the Study of Cognition”. The commentaries reflect a great deal of thought by authors many of whom have thought about this topic in more depth than I have in recent years. In many cases, they raise issues that I did not anticipate when I wrote the article and that require me to rethink various aspects of the account I gave there.1 Publication of this symposium, almost twenty years after writing the paper, has encouraged me to dig into my records to determine the article’s history. The roots of the article lie in a lengthy e-mail discussion on the topic of “What is Computation?”, organized by Stevan Harnad in 1992. I was a graduate student in Doug Hofstadter’s AI laboratory at Indiana University at that point and vigorously advocated what I took to be a computationalist position against skeptics. Harnad suggested that the various participants in that discussion write up “position papers” to be considered for publication in the journal Minds and Machines. I wrote a first draft of the article in December 1992 and revised it after reviewer comments in March 1993. I decided to send a much shorter article on implementation to Minds and Machines and to submit a further revised version of the full article to Behavioral and Brain Sciences in April 1994. I received encouraging reports from BBS later that year, but for some reason (perhaps because I was finishing a book and then moving from St. Louis to Santa Cruz) I never revised or resubmitted the article. It was the early days of the web, and perhaps I had the idea that web publication was almost as good as journal publication. Over the years, the article has been discussed here and there, and as it turns out it has been cited about as much as the shorter published article. I have always viewed this article as the more important of the two, and it is good to have it finally published. Over the last two decades the discussion of computation has continued apace and has become much more sophisticated, but as 1This article was published in Journal of Cognitive Science 13: 211-48, 2012. The papers I am replying to are available at http://j-cs.org (issues 12:4, 13:1, 13:2). I would like to thank Gualtiero Piccinini for conceiving and organizing the symposium and also for his comments on a draft of this reply.

When memory became portable in the course of evolution, the information content of memory became replicable as memes. Propagation of memes started with imitation, but with the advent of human language, memes thrived and caused enhanced brain capacity for meme processing through cognition. Culture comprises of endemic memes that enter young brains and may be protective or pathogenic. Childhood nurturance and stress enter the brain as memes and cause epigenetic changes in genes for future resilience or vulnerability. Personal development is the process of interaction among the epigenetically determined brain, resident memes, and newly introduced memes. The selfplex, the sense of self, represents a dominant memeplex formed through development, and consists of several memeplexes usually known as roles. In certain persons, there may be conflicting selfplexes, one dominant and the other(s) repressed. Mental health is attained when there is a memetic democracy, where the selfplexes are tolerated, and may shift and adapt. Stress attenuates resident memes including selfplex and introduces stress memes that may awaken/ strengthen repressed memes, resulting in a memetic anarchy and a final common pathway psychiatric syndrome. Treatment of such syndromes should be geared toward both the brain state and suppression of pathologic memes. Broad spectrum and specific meme-directed therapies should be considered.

In this article, after presenting the basic idea of causal accounts of implementation and the problems they are supposed to solve, I sketch the model of computation preferred by Chalmers and argue that it is too limited to do full justice to computational theories in cognitive science. I also argue that it does not suffice to replace Chalmers’ favorite model with a better abstract model of computation; it is necessary to acknowledge the causal structure of physical computers that is not accommodated by the models used in computability theory. Additionally, an alternative mechanistic proposal is outlined.

In this paper we present a methodology for creating and populating a lexical knowledge base (LKB) to be used in an onomasiological dictionary. The purpose of this methodology is to automatize the creation of specialized onomasiological dictionaries, which help to solve the “tip of the tongue” problem and assist authors in the active linguistic state (encoding). This article includes information about the main architecture of the LKB as well as our proposed dictionary. The methodology presented in this article allows the LKB to be populated with a wide variety of definitions from both colloquial and normative sources in such a way that by employing this LKB these specialized onomasiological dictionaries are able to handle users’ queries in natural languages.

This paper is concerned with one possible way of having quantification in
\na language without determiners, taking Japanese as a case study. After starting
\nwith data that suggests deep connections between the morphosyntax
\nemployed for constituent questions and for quantification, we present a formal
\naccount that initially considers sentences without any quantification to see how
\nargument linking is nevertheless achieved when case marking is added to noun
\nphrases. We then look at how quantification can subsequently be added to the
\nsystem with the aid of indeterminate pronouns (e.g., dare, nani, dono) and particles
\n(ka and mo). We end by considering the kinds of grammatical effects that
\narise from this integration.