ResumenEl presente trabajo tiene como finalidad determinar la utilidad del Inventario de Gestión e Intervención para Jóvenes (IGI-J), que se emplea tanto en la predicción de la conducta delictiva reiterada en jóvenes infractores, como en la implementación de programas de intervención apropiados para dicha población. Se realizó un estudio retrospectivo con una muestra de 258 delincuentes juveniles que se hallaban cumpliendo una medida judicial. El instrumento que evaluó la predicción de la reincidencia es el Youth Level of Service/Case Management Inventory (Hoge y Andrews, 2002), cuyo resultado de su adaptación al castellano es el IGI-J (Garrido, López y Silva, 2004). Los resultados indican que el IGI-J identifica correctamente el 66.7% de los reincidentes y el 68.8% de los no reincidentes, con un valor alpha de fiabilidad de 0,904. Respecto a la validez retrospectiva, el Área Bajo la Curva fue de .717, por lo que puede considerarse adecuada. Al comparar a sujetos que delinquieron únicamente antes de habérseles administrado el IGI-J con los no reincidentes; es decir, delincuentes primarios, hallamos que los reincidentes retrospectivos presentan un riesgo mayor en las ocho áreas del instrumento, con una probabilidad de significación muy elevadas (mayoritariamente p<0,000). Considerando sus limitaciones, el IGI-J es un buen instrumento para predecir la reincidencia de los jóvenes delincuentes. Finalmente, se discute el uso del instrumento para delimitar las metas factibles de un programa de intervención.Palabras clave: IGI-J, juicio clínico estructurado, delincuentes juveniles, predicción de la reincidencia, programas de intervención.

Nonrigid materials, such as jelly, rubber, or sponge move and deform in distinctive ways depending on their stiffness. Which cues do we use to infer stiffness? We simulated cubes of varying stiffness and optical appearance (e.g., wood, metal, wax, jelly) being subjected to two kinds of deformation: (a) a rigid cylinder pushing downwards into the cube to various extents (shape change, but little motion: shape dominant), (b) a rigid cylinder retracting rapidly from the cube (same initial shapes, differences in motion: motion dominant). Observers rated the apparent softness/hardness of the cubes. In the shape-dominant condition, ratings mainly depended on how deeply the rod penetrated the cube and were almost unaffected by the cube's intrinsic physical properties. In contrast, in the motion-dominant condition, ratings varied systematically with the cube's intrinsic stiffness, and were less influenced by the extent of the perturbation. We find that both results are well predicted by the absolute magnitude of deformation, suggesting that when asked to judge stiffness, observers resort to simple heuristics based on the amount of deformation. Softness ratings for static, unperturbed cubes varied substantially and systematically depending on the optical properties. However, when animated, the ratings were again dominated by the extent of the deformation, and the effect of optical appearance was negligible. Together, our results suggest that to estimate stiffness, the visual system strongly relies on measures of the extent to which an object changes shape in response to forces.

The concurrent and predictive validity of the Psychopathy Checklist: Youth Version (PCL:YV) were examined across gender and ethnicity using multiple outcome measures on a community-based sample of 130 adjudicated youths. The PCL:YV demonstrated concurrent validity with externalizing behavior problems but, it is important to note, was also associated with internalizing measures of negative affect. With a mean follow-up period of 3 years, the PCL:YV was found to predict general and violent recidivism in male, Native Canadian, and Caucasian youths. However, the PCL:YV demonstrated weaker concurrent and predictive validity with girls and failed to predict nonviolent recidivism in all subgroups. Implications of the findings for clinical practice are discussed.

Visually inferring the stiffness of objects is important for many tasks but is challenging because, unlike optical properties (e.g., gloss), mechanical properties do not directly affect image values. Stiffness must be inferred either (a) by recognizing materials and recalling their properties (associative approach) or (b) from shape and motion cues when the material is deformed (estimation approach). Here, we investigated interactions between these two inference types. Participants viewed renderings of unfamiliar shapes with 28 materials (e.g., nickel, wax, cork). In Experiment 1, they viewed nondeformed, static versions of the objects and rated 11 material attributes (e.g., soft, fragile, heavy). The results confirm that the optical materials elicited a wide range of apparent properties. In Experiment 2, using a blue plastic material with intermediate apparent softness, the objects were subjected to physical simulations of 12 shape-transforming processes (e.g., twisting, crushing, stretching). Participants rated softness and extent of deformation. Both correlated with the physical magnitude of deformation. Experiment 3 combined variations in optical cues with shape cues. We find that optical cues completely dominate. Experiment 4 included the entire motion sequence of the deformation, yielding significant contributions of optical as well as motion cues. Our findings suggest participants integrate shape, motion, and optical cues to infer stiffness, with optical cues playing a major role for our range of stimuli.

Response priming is a well-understood but sparsely employed paradigm in cognitive science. The method is powerful and well-suited for exploring early visuomotor processing in a wide range of tasks and research fields. Moreover, response priming can be dissociated from visual awareness, possibly because it is based on the first sweep of feedforward processing of primes and targets. This makes it a theoretically interesting device for separating conscious and unconscious vision. We discuss the major opportunities of the paradigm and give specific recommendations (e.g., tracing the time-course of priming in parametric experiments). Also, we point out typical confounds, design flaws, and data processing artifacts.