Lack of privileged access to awareness for rewarding social scenes in Autism Spectrum Disorder

Reduced social motivation is hypothesised to underlie social behavioural symptoms of Autism Spectrum Disorder (ASD). The extent to which rewarding social stimuli are granted privileged access to awareness in ASD is currently unknown. We use continuous flash suppression to investigate whether individuals with and without ASD show privileged access to awareness for social over nonsocial rewarding scenes that are closely matched for stimulus features. Strong evidence for a privileged access to awareness for rewarding social over nonsocial scenes was observed in neurotypical adults. No such privileged access was seen in ASD individuals, and moderate support for the null model was noted. These results suggest that the purported deficits in social motivation in ASD may extend to early processing mechanisms

Acute hunger does not always undermine prosociality

This is the final version. Available on open access from Nature Research via the DOI in this recordData Availability: The data that support the findings of this paper are available on the OSF website (https://osf.io/zexd7/?view_only=480593713c904397a033e751a6da7a69).It has been argued that, when they are acutely hungry, people act in self-protective ways by keeping resources to themselves rather than sharing them. In four studies, using experimental, quasi-experimental, and correlational designs (total N = 795), we examine the effects of acute hunger on prosociality in a wide variety of non-interdependent tasks (e.g. dictator game) and interdependent tasks (e.g. public goods games). While our procedures successfully increase subjective hunger and decrease blood glucose, we do not find significant effects of hunger on prosociality. This is true for both decisions incentivized with money and with food. Metaanalysis across all tasks reveals a very small effect of hunger on prosociality in noninterdependent tasks (d = .108), and a non-significant effect in interdependent tasks (d = -0.076). In study five (N = 197), we show that, in stark contrast to our empirical findings, people hold strong lay theories that hunger undermines prosociality.Volkswagen Foundatio

On the interpretation of removable interactions: A survey of the field 33 years after Loftus

In a classic 1978 Memory &Cognition article, Geoff Loftus explained why noncrossover interactions are removable. These removable interactions are tied to the scale of measurement for the dependent variable and therefore do not allow unambiguous conclusions about latent psychological processes. In the present article, we present concrete examples of how this insight helps prevent experimental psychologists from drawing incorrect conclusions about the effects of forgetting and aging. In addition, we extend the Loftus classification scheme for interactions to include those on the cusp between removable and nonremovable. Finally, we use various methods (i.e., a study of citation histories, a questionnaire for psychology students and faculty members, an analysis of statistical textbooks, and a review of articles published in the 2008 issue of Psychology andAging) to show that experimental psychologists have remained generally unaware of the concept of removable interactions. We conclude that there is more to interactions in a 2 × 2 design than meets the eye

The ROC Toolbox: A toolbox for analyzing receiver-operating characteristics derived from confidence ratings

Signal-detection theory, and the analysis of receiver-operating characteristics (ROCs), has played a critical role in the development of theories of episodic memory and perception. The purpose of the current paper is to present the ROC Toolbox. This toolbox is a set of functions written in the Matlab programming language that can be used to fit various common signal detection models to ROC data obtained from confidence rating experiments. The goals for developing the ROC Toolbox were to create a tool (1) that is easy to use and easy for researchers to implement with their own data, (2) that can flexibly define models based on varying study parameters, such as the number of response options (e.g. confidence ratings) and experimental conditions, and (3) that provides optimal routines (e.g., Maximum Likelihood estimation) to obtain parameter estimates and numerous goodness-of-fit measures.The ROC toolbox allows for various different confidence scales and currently includes the models commonly used in recognition memory and perception: (1) the unequal variance signal detection (UVSD) model, (2) the dual process signal detection (DPSD) model, and (3) the mixture signal detection (MSD) model. For each model fit to a given data set the ROC toolbox plots summary information about the best fitting model parameters and various goodness-of-fit measures. Here, we present an overview of the ROC Toolbox, illustrate how it can be used to input and analyse real data, and finish with a brief discussion on features that can be added to the toolbox