Over the past two decades, society has seen incredible advances in digital technology, resulting in the wide availability of cheap and easy-to-use software for creating highly sophisticated fake visual content. This democratisation of creating such content, paired with the ease of sharing it via social media, means that ill-intended fake images and videos pose a significant threat to society. To minimise this threat, it is necessary to be able to distinguish between real and fake content; to date, however, human perceptual research indicates that people have an extremely limited ability to do so. Generally, computational techniques fair better in these tasks, yet remain imperfect. What's more, this challenge is best considered as an arms race – as scientists improve detection techniques, fraudsters find novel ways to deceive. We believe that it is crucial to continue to raise awareness of the visual forgeries afforded by new technology and to examine both human and computational ability to sort the real from the fake. In this article, we outline three considerations for how society deals with future technological developments that aim to help secure the benefits of that technology while minimising its possible threats. We hope these considerations will encourage interdisciplinary discussion and collaboration that ultimately goes some way to limit the proliferation of harmful content and help to restore trust online.

Communicating evidence that a policy is effective can increase public support although the effects are small. In the context of policies to increase healthier eating in out-of-home restaurants, we investigate two ways of presenting evidence for a policy's effectiveness: (i) visualising and (ii) re-expressing evidence into a more interpretable form. We conducted an online experiment in which participants were randomly allocated to one of five groups. We used a 2 (text only vs visualisation) × 2 (no re-expression vs re-expression) design with one control group. Participants (n = 4500) representative of the English population were recruited. The primary outcome was perceived effectiveness and the secondary outcome was public support. Evidence of effectiveness increased perceptions of effectiveness (d = 0.14, p < 0.001). There was no evidence that visualising, or re-expressing, changed perceptions of effectiveness (respectively, d = 0.02, p = 0.605; d = −0.02, p = 0.507). Policy support increased with evidence but this was not statistically significant after Bonferroni adjustment (d = 0.08, p = 0.034, α = 0.006). In conclusion, communicating evidence of policy effectiveness increased perceptions that the policy was effective. Neither visualising nor re-expressing evidence increased perceived effectiveness of policies more than merely stating in text that the policy was effective.

Digital CBT refers to the use of digital tools, platforms or devices to deliver or enhance cognitive behavioural therapy assessment, formulation, treatment, training and supervision. The ‘Advances in Digital CBT’ special issue aimed to document examples of innovative digital CBT practice in this rapidly developing field. In this paper, we have briefly summarised and synthesised the advances demonstrated in this group of articles. These include developments in our understanding of mental health apps, the use of digital tools as an adjunct to therapy, the effectiveness of remotely delivered CBT in routine clinical practice, our understanding of user experiences and involvement, and in digital CBT research methods. We consider the extent of current knowledge in these areas and identify where gaps in evidence lie and how the field could be taken forward to address these. Lastly, we reflect on the broader digital CBT picture and offer our suggestions of six key directions for future research: using robust study designs to evaluate and optimise digital tools; translating and culturally adapting digital tools and practices; understanding and addressing digital exclusion; exploring, reporting and addressing possible negative effects; improving user involvement in design and evaluation; and addressing the implementation gap for digital tools. We suggest that further advances in these areas would be of particular benefit to the digital CBT field.

Digital image correlation (DIC) techniques were used to evaluate strain distributions along tensile gage lengths immediately after yielding of a medium manganese steel (7 wt% Mn) in samples cold rolled in the range of 1–6 pct. With an increase in cold work, DIC confirmed that the yielding behavior transitioned from nucleation and propagation of a single localized deformation zone (Lüders band) to uniform deformation, that is, no evidence of strain localization. At intermediate amounts of cold work, a unique yielding behavior was evident where the initially-low positive strain hardening rate increased with tensile strain until conventional strain hardening (i.e., decrease in strain hardening rate with strain). The intermediate yielding behavior was associated with the development of multiple non‑propagating regions of strain localization, an observation not previously evident without the use of DIC.

Imagery rescripting (ImRs) is a therapy technique that, unlike traditional re-living techniques, focuses less on exposure and verbal challenging of cognitions and instead encourages patients to directly transform the intrusive imagery to change the depicted course of events in a more desired direction. However, a comprehensive account of how and in what circumstances ImRs brings about therapeutic change is required if treatment is to be optimised, and this is yet to be developed. The present study reports on the development of a coding scheme of ImRs psychotherapy elements identified in the literature as potential ImRs mechanisms. The codes were assessed in relation to short-term outcomes of 27 individuals undergoing ImRs for post-traumatic stress disorder. The timing of the change in the image, degree of activation of the new image and associated cognitive, emotional and physiological processes, self-guided rescripting, rescript believability, narrative coherence and cognitive and emotional shift were identified as being related to symptom change and so are potentially important factors for the re-scripting process.

Ownership is universal and ubiquitous in human societies, yet the psychology underpinning ownership intuitions is generally not described in a coherent and computationally tractable manner. Ownership intuitions are commonly assumed to derive from culturally transmitted social norms, or from a mentally represented implicit theory. While the social norms account is entirely ad hoc, the mental theory requires prior assumptions about possession and ownership that must be explained. Here I propose such an explanation, arguing that the intuitions result from the interaction of two cognitive systems. One of these handles competitive interactions for the possession of resources observed in many species including humans. The other handles mutually beneficial cooperation between agents, as observed in communal sharing, collective action and trade. Together, these systems attend to specific cues in the environment, and produce definite intuitions such as “this is hers,” “that is not mine.” This computational model provides an explanation for ownership intuitions, not just in straightforward cases of property, but also in disputed ownership (squatters, indigenous rights), historical changes (abolition of slavery), as well as apparently marginal cases, such as the questions, whether people own their seats on the bus, or their places in a queue, and how people understand “cultural appropriation” and slavery. In contrast to some previous theories, the model is empirically testable and free of ad hoc stipulations.

An ideography is a general-purpose code made of pictures that do not encode language, which can be used autonomously – not just as a mnemonic prop – to encode information on a broad range of topics. Why are viable ideographies so hard to find? I contend that self-sufficient graphic codes need to be narrowly specialized. Writing systems are only an apparent exception: At their core, they are notations of a spoken language. Even if they also encode nonlinguistic information, they are useless to someone who lacks linguistic competence in the encoded language or a related one. The versatility of writing is thus vicarious: Writing borrows it from spoken language. Why is it so difficult to build a fully generalist graphic code? The most widespread answer points to a learnability problem. We possess specialized cognitive resources for learning spoken language, but lack them for graphic codes. I argue in favor of a different account: What is difficult about graphic codes is not so much learning or teaching them as getting every user to learn and teach the same code. This standardization problem does not affect spoken or signed languages as much. Those are based on cheap and transient signals, allowing for easy online repairing of miscommunication, and require face-to-face interactions where the advantages of common ground are maximized. Graphic codes lack these advantages, which makes them smaller in size and more specialized.