Emotion, Meaning, and Appraisal Theory

According to psychological emotion theories referred to as appraisal theory, emotions are caused by appraisals (evaluative judgments). Borrowing a term from Jan Smedslund, it is the contention of this article that psychological appraisal theory is “pseudoempirical” (i.e., misleadingly or incorrectly empirical). In the article I outline what makes some scientific psychology “pseudoempirical,” distinguish my view on this from Jan Smedslund’s, and then go on to show why paying heed to the ordinary meanings of emotion terms is relevant to psychology, and how appraisal theory is methodologically off the mark by employing experiments, questionnaires, and the like, to investigate what follows from the ordinary meanings of words. The overarching argument of the article is that the scientific research program of appraisal theory is fundamentally misguided and that a more philosophical approach is needed to address the kinds of questions it seeks to answer

Geometrical organization of solutions to random linear Boolean equations

The random XORSAT problem deals with large random linear systems of Boolean variables. The difficulty of such problems is controlled by the ratio of number of equations to number of variables. It is known that in some range of values of this parameter, the space of solutions breaks into many disconnected clusters. Here we study precisely the corresponding geometrical organization. In particular, the distribution of distances between these clusters is computed by the cavity method. This allows to study the `x-satisfiability' threshold, the critical density of equations where there exist two solutions at a given distance.Comment: 20 page

Quantitative localized proton-promoted dissolution kinetics of calcite using scanning electrochemical microscopy (SECM)

Scanning electrochemical microscopy (SECM) has been used to determine quantitatively the kinetics of proton-promoted dissolution of the calcite (101̅4) cleavage surface (from natural “Iceland Spar”) at the microscopic scale. By working under conditions where the probe size is much less than the characteristic dislocation spacing (as revealed from etching), it has been possible to measure kinetics mainly in regions of the surface which are free from dislocations, for the first time. To clearly reveal the locations of measurements, studies focused on cleaved “mirror” surfaces, where one of the two faces produced by cleavage was etched freely to reveal defects intersecting the surface, while the other (mirror) face was etched locally (and quantitatively) using SECM to generate high proton fluxes with a 25 μm diameter Pt disk ultramicroelectrode (UME) positioned at a defined (known) distance from a crystal surface. The etch pits formed at various etch times were measured using white light interferometry to ascertain pit dimensions. To determine quantitative dissolution kinetics, a moving boundary finite element model was formulated in which experimental time-dependent pit expansion data formed the input for simulations, from which solution and interfacial concentrations of key chemical species, and interfacial fluxes, could then be determined and visualized. This novel analysis allowed the rate constant for proton attack on calcite, and the order of the reaction with respect to the interfacial proton concentration, to be determined unambiguously. The process was found to be first order in terms of interfacial proton concentration with a rate constant k = 6.3 (± 1.3) × 10–4 m s–1. Significantly, this value is similar to previous macroscopic rate measurements of calcite dissolution which averaged over large areas and many dislocation sites, and where such sites provided a continuous source of steps for dissolution. Since the local measurements reported herein are mainly made in regions without dislocations, this study demonstrates that dislocations and steps that arise from such sites are not needed for fast proton-promoted calcite dissolution. Other sites, such as point defects, which are naturally abundant in calcite, are likely to be key reaction sites

Tensors and compositionality in neural systems

Neither neurobiological nor process models of meaning composition specify the operator through which constituent parts are bound together into compositional structures. In this paper, we argue that a neurophysiological computation system cannot achieve the compositionality exhibited in human thought and language if it were to rely on a multiplicative operator to perform binding, as the tensor product (TP)-based systems that have been widely adopted in cognitive science, neuroscience and artificial intelligence do. We show via simulation and two behavioural experiments that TPs violate variable-value independence, but human behaviour does not. Specifically, TPs fail to capture that in the statements fuzzy cactus and fuzzy penguin, both cactus and penguin are predicated by fuzzy(x) and belong to the set of fuzzy things, rendering these arguments similar to each other. Consistent with that thesis, people judged arguments that shared the same role to be similar, even when those arguments themselves (e.g., cacti and penguins) were judged to be dissimilar when in isolation. By contrast, the similarity of the TPs representing fuzzy(cactus) and fuzzy(penguin) was determined by the similarity of the arguments, which in this case approaches zero. Based on these results, we argue that neural systems that use TPs for binding cannot approximate how the human mind and brain represent compositional information during processing. We describe a contrasting binding mechanism that any physiological or artificial neural system could use to maintain independence between a role and its argument, a prerequisite for compositionality and, thus, for instantiating the expressive power of human thought and language in a neural system

Learning object relationships which determine the outcome of actions

Peer reviewedPublisher PD

Reasons and Means to Model Preferences as Incomplete

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review some of the reasons that have been put forward to justify more complex modeling, and review some of the techniques that have been proposed to obtain models of such preferences