Megan Peters

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

Higher-order representations are neural or computational states that are “about” first-order representations, encoding information not about the external world per se but about the agent’s own representational processes – such as the reliability, source, or structure of a first-order representation. These higher-order representations appear critical to metacognition, learning, and even consciousness by some accounts, yet their dimensionality, construction, and neural substrates remain poorly understood. Here, we propose that metacognitive estimates of uncertainty or noise reflect a read-out of higher-order “posteriors” from a Bayesian perspective. We then discuss how these higher-order posteriors reflect a combination of higher-order “likelihoods” (current uncertainty evidence, conditioned on true first-order uncertainty) and “priors” – learned distributions over expected first-order uncertainty – and how various emerging engineering and theory-based analytical approaches may be employed to examine the estimation processes and neural correlates associated with these highly under-explored components of our experienced uncertainty.

Perception is the process by which an agent with a mind comes to represent and experience the structure of the world. Cognitive science describes perception as an inference process – often assumed to be Bayesian – in which the agent combines prior expectations with sensory information to produce a percept: a “read out” of the “most likely” state of the environment. How does belief come into play? In most cases, perception seems to be veridical, producing perceptual experiences that match the state of the world – the locations and movements of the objects around you, for example. But sometimes, perception produces experiences where we know our eyes and ears to be deceiving us. In the well-known Müller-Lyer illusion, for example, we see two lines with physically identical length as different due to the different arrowheads placed on their ends. So we can cognitively believe (or not) that what we are perceiving is actually the true state of the world. But this cognitive, doxastic belief is only one variety of belief, tacked on after perceptual experience is constructed. In this entry we explore beyond such doxastic beliefs in the output of the perceptual processes, focusing on what kinds of beliefs may contribute to generating perceptual experience to begin with. These perceptual-type beliefs might concern the location and length of each Müller-Lyer line in isolation, or the source of sensory signals you’re processing – either “really out there” somewhere in the world, or somehow generated within your own nervous system. Together, these non-doxastic beliefs produce the complex perceptual content we experience – whether we cognitively believe our eyes or not.

Which systems/organisms are conscious? New tests for consciousness (‘C-tests’) are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology. Although a number of C-tests have been proposed in recent years, most are of limited use, and currently we have no C-tests for many of the populations for which they are most critical. Here, we identify challenges facing any attempt to develop C-tests, propose a multidimensional classification of such tests, and identify strategies that might be used to validate them.

The episode title “One Angry Veronica” calls attention to how often Veronica's anger becomes a source of conflict, as well as a force in the service of justice. All along the way, she provides us with a wonderful resource for thinking critically about feminist issues in contemporary society. More than most other characters on the show, Veronica is attuned to how issues of gender, class, and race intersect within the corrupt world of Neptune. Veronica's appreciation of the fluidity of gender roles helps her to steer the expectations of those around her to serve her purposes. Veronica can be seen as a representative of a new type of feminism, which remedies the limitations of the earlier waves. She doesn't dismiss femininity, she has a positive attitude toward sex, and she approaches most issues on a personal rather than just on an outwardly political level.

The scientific study of consciousness emerged as an organized field of research only a few decades ago. As empirical results have begun to enhance our understanding of consciousness, it is important to find out whether other factors, such as funding for consciousness research and status of consciousness scientists, provide a suitable environment for the field to grow and develop sustainably. We conducted an online survey on people’s views regarding various aspects of the scientific study of consciousness as a field of research. 249 participants completed the survey, among which 80% were in academia, and around 40% were experts in consciousness research. Topics covered include the progress made by the field, funding for consciousness research, job opportunities for consciousness researchers, and the scientific rigor of the work done by researchers in the field. The majority of respondents (78%) indicated that scientific research on consciousness has been making progress. However, most participants perceived obtaining funding and getting a job in the field of consciousness research as more difficult than in other subfields of neuroscience. Overall, work done in consciousness research was perceived to be less rigorous than other neuroscience subfields, but this perceived lack of rigor was not related to the perceived difficulty in finding jobs and obtaining funding. Lastly, we found that, overall, the global workspace theory was perceived to be the most promising (around 28%), while most non-expert researchers (around 22% of non-experts) found the integrated information theory (IIT) most promising. We believe the survey results provide an interesting picture of current opinions from scientists and researchers about the progresses made and the challenges faced by consciousness research as an independent field. They will inspire collective reflection on the future directions regarding funding and job opportunities for the field.

Having a confirmation bias sometimes leads us to hold inaccurate beliefs. So, the puzzle goes: why do we have it? According to the influential argumentative theory of reasoning, confirmation bias emerges because the primary function of reason is not to form accurate beliefs, but to convince others that we’re right. A crucial prediction of the theory, then, is that confirmation bias should be found only in the reasoning domain. In this article, we argue that there is evidence that confirmation bias does exist outside the reasoning domain. This undermines the main evidential basis for the argumentative theory of reasoning. In presenting the relevant evidence, we explore why having such confirmation bias may not be maladaptive.

Confidence can dissociate from perceptual accuracy, suggesting distinct computational and neural processes underlie these psychological functions. Recent investigations have therefore sought to experimentally isolate metacognitive processes by creating conditions where perceptual sensitivity is matched but confidence differs (“matched-performance / different-confidence”; MPDC). Despite these endeavors’ success, much remains unknown about MPDC effects and how to best harness them in experimental settings. Here we developed a principled approach to comprehensively characterizing MPDC effects through analyzing metaperceptual (i.e., type 2 psychometric) functions relating objective performance to subjective confidence across widely varying performance levels and experimental manipulations. We found that MPDC effect magnitude depends on stimulus properties, observers’ sensitivity level, and critically on trial type order (blocked or interleaved across stimulus property variations). Our findings provide the first comprehensive exploration of MPDC effects, offer a prescriptive guide to metaperceptual analysis, and suggest optimal experimental paradigms for experimentally isolating metacognition and awareness in future studies.