Is there any Influence of Variations in Context on Object-Affordance Effects in Schizophrenia? Perception of Property and Goals of Action

Motor Inhibition to Dangerous Objects: Electrophysiological Evidence for Task-dependent Aversive Affordances

Previous work suggests that perception of an object automatically facilitates actions related to object grasping and manipulation. Recently, the notion of automaticity has been challenged by behavioral studies suggesting that dangerous objects elicit aversive affordances that interfere with encoding of an object's motor properties; however, related EEG studies have provided little support for these claims. We sought EEG evidence that would support the operation of an inhibitory mechanism that interferes with the motor encoding of dangerous objects, and we investigated whether such mechanism would be modulated by the perceived distance of an object and the goal of a given task. EEGs were recorded by 24 participants who passively perceived dangerous and neutral objects in their peripersonal, boundary, or extrapersonal space and performed either a reachability judgment task or a categorization task. Our results showed that greater attention, reflected in the visual P1 potential, was drawn by dangerous and reachable objects. Crucially, a frontal N2 potential, associated with motor inhibition, was larger for dangerous objects only when participants performed a reachability judgment task. Furthermore, a larger parietal P3b potential for dangerous objects indicated the greater difficulty in linking a dangerous object to the appropriate response, especially when it was located in the participants' extrapersonal space. Taken together, our results show that perception of dangerous objects elicits aversive affordances in a task-dependent way and provides evidence for the operation of a neural mechanism that does not code affordances of dangerous objects automatically, but rather on the basis of contextual information.

Exploring the Role of Action Consequences in the Handle-Response Compatibility Effect

Previous research investigating handle-response compatibility effects with graspable objects used different categories of objects as stimuli, regardless of their specific, intrinsic characteristics. The current study explores whether different types of objects' characteristics may elicit different types of spatial compatibility, that is, handle-response and response-effect compatibility as well as their potential interaction. In Experiment 1, objects having a graspable handle opposite to either a visible functional component (i.e., handle-function objects: a teapot) or a latent functional component (handle-only objects: a pitcher lacking the spout) were presented separately in different blocks. Both the handle and the goal-directed functional components of these objects were located on the horizontal axis. In Experiment 2, handle-only objects had a handle located on the horizontal axis and a latent functional component located on the vertical axis (e.g., a cup). In both experiments, participants were required to judge the material (plastic and metal) the object was made of. Results showed that the handle-response compatibility effect was sensitive to whether the actions consequences of object manipulation took place on the horizontal rather than on the vertical axis.

Computing Bayes factors to measure evidence from experiments: An extension of the BIC approximation

Bayesian inference affords scientists powerful tools for testing hypotheses. One of these tools is the Bayes factor, which indexes the extent to which support for one hypothesis over another is updated after seeing the data. Part of the hesitance to adopt this approach may stem from an unfamiliarity with the computational tools necessary for computing Bayes factors. Previous work has shown that closed-form approximations of Bayes factors are relatively easy to obtain for between-groups methods, such as an analysis of variance or t-test. In this paper, I extend this approximation to develop a formula for the Bayes factor that directly uses information that is typically reported for ANOVAs (e.g., the F ratio and degrees of freedom). After giving two examples of its use, I report the results of simulations which show that even with minimal input, this approximate Bayes factor produces similar results to existing software solutions.