This article addresses contemporary art as a means to investigate how, and to what extent, financial logic impacts upon the socio-cultural sphere. Its contribution is twofold: on the one hand, the article shows that contemporary art's valuation practices increasingly reflect the logic of capitalization; on the other hand, it assesses the emancipatory potential of blockchain technology for the cultural sphere. In relation to the latter I argue that, in spite of the technological novelty of blockchain-based art projects, these nonetheless fail to challenge a received logic of finance. This exposes the limitations to technological determinism as a means of countering financial power in the socio-cultural sphere, and points to new problems for art's valuation methods in relation to the liquid logic of algorithmic finance.

This article presents a speculative philosophical account of money as a computational machine. It does so by leveraging a computational and machinic framework, drawing primarily from the work of Philip Mirowski and Jean Cartelier. The argument is focused on a specific level of abstraction, i.e., the monetary operations involved in the creation and transfer of units of account, asking whether it is possible to view these operations as computations that mediate economic relations. As the primary function of such a machine would be one of social coordination, the article also highlights the political consequences of its implementation across society.

This lecture was given by Per Martin-Löf at Leiden University on August 25, 2001 at the invitation by Göran Sundholm to address the topic mentioned in the title and to reflect on Dummett’s earlier effort of almost a decade before (published in this journal). The lecture was part of a three-day conference on Gottlob Frege. Sundholm arranged for the lecture to be recorded and commissioned Bjørn Jespersen to make a transcript. The information in footnote 1, which Sundholm provided, has been independently confirmed by Thomas Ricketts in an email to the author. The present version has been edited by Ansten Klev. Following the displayed text (Int-id) there is a lacuna in the original transcript corresponding to a pause in the recording when the tape was changed. The continuous text of the present version is the result of a few additions to the original transcript suggested by Klev and agreed to by the author.

We describe a method to estimate background noise in atom probe tomography (APT) mass spectra and to use this information to enhance both background correction and quantification. Our approach is mathematically general in form for any detector exhibiting Poisson noise with a fixed data acquisition time window, at voltages varying through the experiment. We show that this accurately estimates the background observed in real experiments. The method requires, as a minimum, the z-coordinate and mass-to-charge-state data as input and can be applied retrospectively. Further improvements are obtained with additional information such as acquisition voltage. Using this method allows for improved estimation of variance in the background, and more robust quantification, with quantified count limits at parts-per-million concentrations. To demonstrate applications, we show a simple peak detection implementation, which quantitatively suppresses false positives arising from random noise sources. We additionally quantify the detectability of 121-Sb in a standardized-doped Si microtip as (1.5 × 10−5, 3.8 × 10−5) atomic fraction, α = 0.95. This technique is applicable to all modes of APT data acquisition and is highly general in nature, ultimately allowing for improvements in analyzing low ionic count species in datasets.

Social kinds are heterogeneous. As a consequence of this diversity, some authors have sought to identify and analyse different kinds of social kinds. One distinct kind of social kinds, however, has not yet received sufficient attention. I propose that there exists a class of social-computation-supporting kinds, or SCS-kinds for short. These SCS-kinds are united by the function of enabling computations implemented by social groups. Examples of such SCS-kinds are reimbursement form, US dollar bill, chair of the board. I will analyse SCS-kinds, contrast my analysis with theories of institutional kinds, and discuss the benefits of investigating SCS-kinds.

I argue that acceptance of realist intentional explanations of cognitive behaviour inescapably lead to a commitment to the language of thought (LOT) and that this is, therefore, a widely held commitment of philosophers of mind. In the course of the discussion, I offer a succinct and precise statement of the hypothesis and analyze a representative series of examples of pro-LOT argumentation. After examining two cases of resistance to this line of reasoning, I show, by way of conclusion, that the commitment to LOT is an empirically substantial one in spite of the flexibility and incomplete character of the hypothesis.

Minimizing the costs that others impose upon oneself and upon those in whom one has a fitness stake, such as kin and allies, is a key adaptive problem for many organisms. Our ancestors regularly faced such adaptive problems (including homicide, bodily harm, theft, mate poaching, cuckoldry, reputational damage, sexual aggression, and the infliction of these costs on one's offspring, mates, coalition partners, or friends). One solution to this problem is to impose retaliatory costs on an aggressor so that the aggressor and other observers will lower their estimates of the net benefits to be gained from exploiting the retaliator in the future. We posit that humans have an evolved cognitive system that implements this strategy – deterrence – which we conceptualize as a revenge system. The revenge system produces a second adaptive problem: losing downstream gains from the individual on whom retaliatory costs have been imposed. We posit, consequently, a subsidiary computational system designed to restore particular relationships after cost-imposing interactions by inhibiting revenge and motivating behaviors that signal benevolence for the harmdoer. The operation of these systems depends on estimating the risk of future exploitation by the harmdoer and the expected future value of the relationship with the harmdoer. We review empirical evidence regarding the operation of these systems, discuss the causes of cultural and individual differences in their outputs, and sketch their computational architecture.

Different explanations of color vision favor different philosophical positions: Computational vision is more compatible with objectivism (the color is in the object), psychophysics and neurophysiology with subjectivism (the color is in the head). Comparative research suggests that an explanation of color must be both experientialist (unlike objectivism) and ecological (unlike subjectivism). Computational vision's emphasis on optimally “recovering” prespecified features of the environment (i.e., distal properties, independent of the sensory-motor capacities of the animal) is unsatisfactory. Conceiving of visual perception instead as the visual guidance of activity in an environment that is determined largely by that very activity suggests new directions for research.

Cognitive science typically postulates unconscious mental phenomena, computational or otherwise, to explain cognitive capacities. The mental phenomena in question are supposed to be inaccessible in principle to consciousness. I try to show that this is a mistake, because all unconscious intentionality must be accessible in principle to consciousness; we have no notion of intrinsic intentionality except in terms of its accessibility to consciousness. I call this claim the “Connection Principle.” The argument for it proceeds in six steps. The essential point is that intrinsic intentionality has aspectual shape: Our mental representations represent the world under specific aspects, and these aspectual features are essential to a mental state's being the state that it is.

Once we recognize the Connection Principle, we see that it is necessary to perform an inversion on the explanatory models of cognitive science, an inversion analogous to the one evolutionary biology imposes on preDarwinian animistic modes of explanation. In place of the original intentionalistic explanations we have a combination of hardware and functional explanations. This radically alters the structure of explanation, because instead of a mental representation (such as a rule) causing the pattern of behavior it represents (such as rule-governed behavior), there is a neurophysiological cause of a pattern (such as a pattern of behavior), and the pattern plays a functional role in the life of the organism. What we mistakenly thought were descriptions of underlying mental principles in, for example, theories of vision and language were in fact descriptions of functional aspects of systems, which will have to be explained by underlying neurophysiological mechanisms. In such cases, what looks like mentalistic psychology is sometimes better construed as speculative neurophysiology. The moral is that the big mistake in cognitive science is not the overestimation of the computer metaphor (though that is indeed a mistake) but the neglect of consciousness.