A study in the cognition of individuals’ identity: Solving the problem of singular cognition in object and agent tracking

This article compares the ability to track individuals lacking mental states with the ability to track intentional agents. It explains why reference to individuals raises the problem of explaining how cognitive agents track unique individuals and in what sense reference is based on procedures of perceptual-motor and epistemic tracking. We suggest applying the notion of singular-files from theories in perception and semantics to the problem of tracking intentional agents. In order to elucidate the nature of agent-files, three views of the relation between object- and agent-tracking are distinguished: the Independence, Deflationary and Organism-Dependence Views. The correct view is argued to be the latter, which states that perceptual and epistemic tracking of a unique human organism requires tracking both its spatio-temporal object-properties and its agent-properties

Maps of Bounded Rationality

The work cited by the Nobel committee was done jointly with the late Amos Tversky (1937-1996) during a long and unusually close collaboration. Together, we explored the psychology of intuitive beliefs and choices and examined their bounded rationality. This essay presents a current perspective on the three major topics of our joint work: heuristics of judgment, risky choice, and framing effects. In all three domains we studied intuitions - thoughts and preferences that come to mind quickly and without much reflection. I review the older research and some recent developments in light of two ideas that have become central to social-cognitive psychology in the intervening decades: the notion that thoughts differ in a dimension of accessibility - some come to mind much more easily than others - and the distinction between intuitive and deliberate thought processes.behavioral economics; experimental economics

After-effects and the reach of perceptual content

In this paper, I discuss the use of after-effects as a criterion for showing that we can perceive high-level properties. According to this criterion, if a high-level property is susceptible to after-effects, this suggests that the property can be perceived, rather than cognized. The defenders of the criterion claim that, since after-effects are also present for low-level, uncontroversially perceptual properties, we can safely infer that high-level after-effects are perceptual as well. The critics of the criterion, on the other hand, assimilate it to superficially similar effects in cognition and argue that the after-effect criterion is a cognitive phenomenon rather than a perceptual one, and that as a result it is not a reliable guide for exploring the contents of perception. I argue against both of these views and show that high-level after-effects cannot be identified either with low-level after-effects or with cognitive biases. I suggest an intermediate position: high-level after-effects are not cognitive, but they are nonetheless not a good criterion for exploring the contents of perception

Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition

We outline a proposal for a research program leading to a new paradigm, architectural framework, and prototypical implementation, for the cognitively inspired anchoring of an agent’s learning, knowledge formation, and higher reasoning abilities in real-world interactions: Learning through interaction in real-time in a real environment triggers the incremental accumulation and repair of knowledge that leads to the formation of theories at a higher level of abstraction. The transformations at this higher level filter down and inform the learning process as part of a permanent cycle of learning through experience, higher-order deliberation, theory formation and revision. The envisioned framework will provide a precise computational theory, algorithmic descriptions, and an implementation in cyber-physical systems, addressing the lifting of action patterns from the subsymbolic to the symbolic knowledge level, effective methods for theory formation, adaptation, and evolution, the anchoring of knowledge-level objects, real-world interactions and manipulations, and the realization and evaluation of such a system in different scenarios. The expected results can provide new foundations for future agent architectures, multi-agent systems, robotics, and cognitive systems, and can facilitate a deeper understanding of the development and interaction in human-technological settings

On the Possibility of Hallucinations

Many take the possibility of hallucinations to imply that a relationalist account, according to which perceptual experiences are constituted by direct relations to ordinary mind-independent objects, is false. The common reaction among relationalists is to adopt a disjunctivist view that denies that hallucinations have the same nature as perceptual experiences. This paper proposes a non-disjunctivist response to the argument from hallucination by arguing that the alleged empirical and a priori evidence in support of the possibility of hallucinations is inconclusive. A corollary upshot of the article is that whether hallucinations are possible or not is still an open empirical question

Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems