Francesc S. Beltran

Spanish Family Quality of Life Scales: Under and over 18 years old

Abstract Background Researchers, professionals, and families have shown increasing concern with the family quality of life (FQoL) of people with intellectual disability (ID) and their families. The goals of this research were (a) to explore how Spanish families understand FQoL by developing 2 different measurement tools for families with a member with ID under and over 18 years old, and (b) to provide 2 diagnostic instruments that will be useful for designing action plans. Method The study comprised 4 stages: (a) focus groups, (b) expert assessment, (c) pilot study, and (d) normalisation and standardisation. The data were collected in 5 regions in Spain, and 1,205 families with a member with ID took part in the normalisation and standardisation of the scales. Results Both FQoL scales were consistent and have valid psychometric characteristics. Conclusions The scales have a diagnostic purpose for use in designing action plans aimed at producing significant changes in families’ lives.

Determining shoal membership: A comparison between momentary and trajectory-based methods

Miller and Gerlai proposed two methods for determining shoal membership in Danio rerio, one based on momentary mean inter-individual distances and the other on post hoc analysis of the trajectories of nearest-neighbor distances. We propose a method based on momentary nearest-neighbor distances and compare the three methods using simulation. In general, our method yielded results that were more similar to their second method than their first one, and is computationally simpler.

Determining shoal membership using affinity propagation

We propose using the affinity propagation (AP) clustering algorithm for detecting multiple disjoint shoals, and we present an extension of AP, denoted by STAP, that can be applied to shoals that fusion and fission across time. STAP incorporates into AP a soft temporal constraint that takes cluster dynamics into account, encouraging partitions obtained at successive time steps to be consistent with each other. We explore how STAP performs under different settings of its parameters (strength of the temporal constraint, preferences, and distance metric) by applying the algorithm to simulated sequences of collective coordinated motion. We study the validity of STAP by comparing its results to partitioning of the same data obtained from human observers in a controlled experiment. We observe that, under specific circumstances, AP yields partitions that agree quite closely with the ones made by human observers. We conclude that using the STAP algorithm with appropriate parameter settings is an appealing approach for detecting shoal fusion-fission dynamics.

P-SPACE: a program for simulating spatial behavior in small groups.

P-SPACE is a computer program that simulates spatial behavior in a small group of individuals. The program describes how interpersonal distances change through time as a result of changes in microlevel features, such as the minimization of local dissatisfaction. Agents are located in a two-dimensional lattice and can move some discrete space units at each discrete time unit within their neighborhood. A nonsymmetrical matrix of ideal distances between agents must be specified. Agents move in order to minimize their dissatisfaction, defined as a function of the discrepancy between possible future distances and ideal distances between agents. At each iteration, agents will move to those cells in their neighborhoods for which the function is minimized. Depending on the specific values in the ideal-distance matrix, different kinds of social dynamics can be simulated.

A method for resolving occlusions when multitracking individuals in a shoal

Studying the collective behavior of fishes often requires tracking a great number of individuals. When many fishes move together, it is common for individuals to move so close to each other that some fishes superimpose themselves on others during one or several units of time, which impacts on tracking accuracy (i.e., loss of fish trajectories, interchange of fish identities). Type 1 occlusions arise when two fishes swim so near each other that they look like one long fish, whereas type 2 occlusions occur when the fishes’ trajectories cross to create a T- or X-shaped individual. We propose an image processing method for resolving these types of occlusions when multitracking shoals in two dimensions. We assessed processing effectiveness after videorecording shoals of 20 and 40 individuals of two species that exhibit different shoal styles: zebrafish (Danio rerio) and black neon tetras (Hyphessobrycon herbertaxelrodi). Results show that, although the number of occlusions depended on both the number of individuals and the species, the method is able to effectively resolve a great deal of occlusions, irrespective of the species and the number of individuals. It also produces images that can be used in a multitracking system to detect individual fish trajectories. Compared to other methods, our approach makes it possible to study shoals with water depths similar to those seen in the natural conditions of the two species studied.

Contextual Categorization: A Mechanism Linking Perception and Knowledge in Modeling and Simulating Perceived Events as Actions

The specific objective of this paper is to introduce the computer model ACACIA (Action by Contextually Automated Categorizing Interactive Agents) capable of simulating the way in which context is taken into account for the interpretation of perceived actions elaborated by a number of autonomous moving agents in a bidimensional space. With this in mind, we will examine some different modeling approaches in Artificial Intelligence and Artificial Life and emphasize the strong and weak points of each approach in relation to the set of issues addressed by our theory based on Contextual Categorization. Second, we provide a theoretical explanation of how contextual categorization accounts for temporal and environmental context to interpret ongoing situations in terms of perceived action. Finally, we describe the computer implementation of ACACIA, and we propose a preliminary simulation of a simple situation using StarLogo software.

An Index for Quantifying Flocking Behavior

One of the classic research topics in adaptive behavior is the collective displacement of groups of organisms such as flocks of birds, schools of fish, herds of mammals, and crowds of people. However, most agent-based simulations of group behavior do not provide a quantitative index for determining the point at which the flock emerges. An index was developed of the aggregation of moving individuals in a flock and an example was provided of how it can be used to quantify the degree to which a group of moving individuals actually forms a flock.

Reasoning based on categorisation for interpreting and acting: a first approach

Taking a detour to reach a goal is intelligent behavior based on making inferences. The main purpose of the present research is to show how such apparently complex behavior can emerge from basic mechanisms such as contextual categorisation and goal attribution when perceiving people. We presentacacia (Action by Contextually Automated Categorising Interactive Agents), a computer model implemented using StarLogo software, grounded in the principles of Artificial Life (Al), capable of simulating the behavior of a group of agents with a goal (for instance, to find a “treasure” in a “treasure hunt”) in an environment where obstacles mask the goal site. The results of the simulations show that agents reach the goal the fastest when they follow each other and take detours. We argue that these results indicate that intelligent adaptive behavior is based on the contextual categorisation of environmental constrainst (that is, obstacles and other agents).

Measuring the Typicality of Objects Included in Environmental Scenes: A Logistic Model for Atypicality

Some empirical studies have stated that people usually categorize scenes according to the suitability of their elements. This paper proposes a method of measuring the typicality of naturalistic objects contained in environmental scenes. 517 subjects gave a score of suitability for 110 object-scene pairs. We used a logistic model for the measurements which enabled us to obtain two indexes, atypicality and discrimination. Analysis showed that the objects could be arranged on a numerical scale according to their typicality in a scene, and from this we concluded that logistic models are a useful and powerful method of measuring typicality.

Measuring the Typicality of Objects Included in Environmental Scenes: A First Scale

Many researchers have been using the concept of “typicality” to explain the influence of knowledge structures on processing visual stimuli. In this paper we try to establish a preliminary method for measuring the typicality of natural objects contained in environmental scenes. The results are discussed in the context of research on how objects in scenes are identified. We also suggest that scales of typicality continue to be developed.