Exploring the Cognitive Foundations of the Shared Attention Mechanism: Evidence for a Relationship Between Self-Categorization and Shared Attention Across the Autism Spectrum.

Published onlineJournal ArticleThis is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.The social difficulties of autism spectrum disorder (ASD) are typically explained as a disruption in the Shared Attention Mechanism (SAM) sub-component of the theory of mind (ToM) system. In the current paper, we explore the hypothesis that SAM's capacity to construct the self-other-object relations necessary for shared-attention arises from a self-categorization process, which is weaker among those with more autistic-like traits. We present participants with self-categorization and shared-attention tasks, and measure their autism-spectrum quotient (AQ). Results reveal a negative relationship between AQ and shared-attention, via self-categorization, suggesting a role for self-categorization in the disruption in SAM seen in ASD. Implications for intervention, and for a ToM model in which weak central coherence plays a role are discussed.This research was supported by the Australian Research Council (FLFL110100199) and the Canadian Institute for Advanced Research (Social Interactions Identity and Well-Being Program)

Assessing the speed and ease of extracting group and person information from faces

This research was supported by the Australian Research Council (FLFL110100199) and the Canadian Institute for Advanced Research (Social Interactions Identity and Well-Being Program).The human face is a key source of social information. In particular, it communicates a target's personal identity and some of their group memberships. Different models of social perception posit distinct stages at which this group-level and person-level information is extracted from the face, with divergent downstream consequences for cognition and behavior. This paper presents four experiments that explore the time-course of extracting group and person information from faces. In Experiments 1 and 2, we explore the effect of chunked versus unchunked processing on the speed of extracting group versus person information, as well as the impact of familiarity in Experiment 2. In Experiment 3, we examine the effect of the availability of a diagnostic cue on these same judgments. In Experiment 4, we explore the effect of both group-level and person-level prototypicality of face exemplars. Across all four experiments, we find no evidence for the perceptual primacy of either group or person information. Instead, we find that chunked processing, featural processing based on a single diagnostic cue, familiarity, and the prototypicality of face exemplars all result in a processing speed advantage for both group-level and person-level judgments equivalently. These results have important implications for influential models of impression formation and can inform, and be integrated with, an understanding of the process of social categorization more broadly.PostprintPeer reviewe

Smoke signals: The decline of brand identity predicts reduced smoking behaviour following the introduction of plain packaging

This study tests a social identity based mechanism for the effectiveness of plain tobacco packaging legislation, introduced in Australia in December 2012, to reduce cigarette smoking. 178 Australian smokers rated their sense of identification with fellow smokers of their brand, positive brand stereotypes, quitting behaviours and intentions, and smoking intensity, both before and seven months after the policy change. Mediation analyses showed that smokers, especially those who initially identified strongly with their brand, experienced a significant decrease in their brand identity following the introduction of plain packaging and this was associated with lower smoking behaviours and increased intentions to quit. The findings provide the first quantitative evidence that brand identities may help maintain smoking behaviour, and suggest the role of social-psychological processes in the effectiveness of public health policy

Correction to: Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions

The original version of the article (Manning et al. 2018) contained an error in the funding section and name of an author. The correction funding note should be This project was funded by the Economic & Social Research Council grant (ESRC Reference: ES/L007223/1) titled ‘University Consortium for Evidence-Based Crime Reduction’, the Australian National University’s Cross College Grant and the Jill Dando Institute of Security and Crime Science. The author name was spelt incorrectly as Cristen instead of Christen. The original article has been corrected. The original article can be found online at https://doi.org/10.1186/s40163-018-0086-4

Towards a 'smart' cost-benefit tool: using machine learning to predict the costs of criminal justice policy interventions

BACKGROUND: The Manning Cost–Benefit Tool (MCBT) was developed to assist criminal justice policymakers, policing organisations and crime prevention practitioners to assess the benefits of different interventions for reducing crime and to select those strategies that represent the greatest economic return on investment. DISCUSSION: A challenge with the MCBT and other cost–benefit tools is that users need to input, manually, a considerable amount of point-in-time data, a process that is time consuming, relies on subjective expert opinion, and introduces the potential for data-input error. In this paper, we present and discuss a conceptual model for a ‘smart’ MCBT that utilises machine learning techniques. SUMMARY: We argue that the Smart MCBT outlined in this paper will overcome the shortcomings of existing cost–benefit tools. It does this by reintegrating individual cost–benefit analysis (CBA) projects using a database system that securely stores and de-identifies project data, and redeploys it using a range of machine learning and data science techniques. In addition, the question of what works is respecified by the Smart MCBT tool as a data science pipeline, which serves to enhance CBA and reconfigure the policy making process in the paradigm of open data and data analytics.This project was funded by the Economic & Social Research Council grant (ESRC Reference: ES/L007223/1) titled ‘University Consortium for EvidenceBased Crime Reduction’, the Australian National University’s Cross College Grant and the Jill Dando Institute of Security and Crime Science

Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions

BACKGROUND: The Manning Cost–Benefit Tool (MCBT) was developed to assist criminal justice policymakers, policing organisations and crime prevention practitioners to assess the benefits of different interventions for reducing crime and to select those strategies that represent the greatest economic return on investment. DISCUSSION: A challenge with the MCBT and other cost–benefit tools is that users need to input, manually, a considerable amount of point-in-time data, a process that is time consuming, relies on subjective expert opinion, and introduces the potential for data-input error. In this paper, we present and discuss a conceptual model for a ‘smart’ MCBT that utilises machine learning techniques. SUMMARY: We argue that the Smart MCBT outlined in this paper will overcome the shortcomings of existing cost–benefit tools. It does this by reintegrating individual cost–benefit analysis (CBA) projects using a database system that securely stores and de-identifies project data, and redeploys it using a range of machine learning and data science techniques. In addition, the question of what works is respecified by the Smart MCBT tool as a data science pipeline, which serves to enhance CBA and reconfigure the policy making process in the paradigm of open data and data analytics

Twenty years of stereotype threat research: A review of psychological mediators

This systematic literature review appraises critically the mediating variables of stereotype threat. A bibliographic search was conducted across electronic databases between 1995 and 2015. The search identified 45 experiments from 38 articles and 17 unique proposed mediators that were categorized into affective/subjective (n = 6), cognitive (n = 7) and motivational mechanisms (n = 4). Empirical support was accrued for mediators such as anxiety, negative thinking, and mind-wandering, which are suggested to co-opt working memory resources under stereotype threat. Other research points to the assertion that stereotype threatened individuals may be motivated to disconfirm negative stereotypes, which can have a paradoxical effect of hampering performance. However, stereotype threat appears to affect diverse social groups in different ways, with no one mediator providing unequivocal empirical support. Underpinned by the multi-threat framework, the discussion postulates that different forms of stereotype threat may be mediated by distinct mechanisms