Inferential Communication: Bridging the Gap Between Intentional and Ostensive Communication in Non-human Primates

Communication, when defined as an act intended to affect the psychological state of another individual, demands the use of inference. Either the signaler, the recipient, or both must make leaps of understanding which surpass the semantic information available and draw from pragmatic clues to fully imbue and interpret meaning. While research into human communication and the evolution of language has long been comfortable with mentalistic interpretations of communicative exchanges, including rich attributions of mental state, research into animal communication has balked at theoretical models which describe mentalized cognitive mechanisms. We submit a new theoretical perspective on animal communication: the model of inferential communication. For use when existing proximate models of animal communication are not sufficient to fully explain the complex, flexible, and intentional communication documented in certain species, specifically non-human primates, we present our model as a bridge between shallower, less cognitive descriptions of communicative behavior and the perhaps otherwise inaccessible mentalistic interpretations of communication found in theoretical considerations of human language. Inferential communication is a framework that builds on existing evidence of referentiality, intentionality, and social inference in primates. It allows that they might be capable of applying social inferences to a communicative setting, which could explain some of the cognitive processes that enable the complexity and flexibility of primate communication systems. While historical models of animal communication focus on the means-ends process of behavior and apparent cognitive outcomes, inferential communication invites consideration of the mentalistic processes that must underlie those outcomes. We propose a mentalized approach to questions, investigations, and interpretations of non-human primate communication. We include an overview of both ultimate and proximate models of animal communication, which contextualize the role and utility of our inferential communication model, and provide a detailed breakdown of the possible levels of cognitive complexity which could be investigated using this framework. Finally, we present some possible applications of inferential communication in the field of non-human primate communication and highlight the role it could play in advancing progress toward an increasingly precise understanding of the cognitive capabilities of our closest living relatives.

Are Robots Sociopaths? A Neuroscientific Approach to the Alignment Problem

Artificial intelligence (AI) is expanding into every niche of human life, organizing our activity, expanding our agency and interacting with us to an exponentially increasing extent. At the same time, AI’s efficiency, complexity and refinement are growing at an accelerating speed. An expanding, ubiquitous intelligence that does not have a means to care about us poses a species-level risk. Justifiably, there is a growing concern with the immediate problem of how to engineer an AI that is aligned with human interests. Computational approaches to the alignment problem currently focus on engineering AI systems to (i) parameterize human values such as harm and flourishing, and (ii) avoid overly drastic solutions, even if these are seemingly optimal. In parallel, ongoing work in applied AI (caregiving, consumer care) is concerned with developing artificial empathy, teaching AI’s to decode human feelings and behavior, and evince appropriate emotional responses.We propose that in the absence of affective empathy (which allows us to share in the states of others), existing approaches to artificial empathy may fail to reliably produce the pro-social, caring component of empathy, potentially resulting in increasingly cognitively complex sociopaths. We adopt the colloquial usage of the term “sociopath” to signify an intelligence possessing cognitive empathy (i.e., the ability to decode, infer, and model the mental and affective states of others), but crucially lacking pro-social, empathic concern arising from shared affect and embodiment. It is widely acknowledged that aversion to causing harm is foundational to the formation of empathy and moral behavior. However, harm aversion is itself predicated on the experience of harm, within the context of the preservation of physical integrity. Following from this, we argue that a “top-down” rule-based approach to achieving caring AI may be inherently unable to anticipate and adapt to the inevitable novel moral/logistical dilemmas faced by an expanding AI. Crucially, it may be more effective to coax caring to emerge from the bottom up, baked into an embodied, vulnerable artificial intelligence with an incentive to preserve its physical integrity. This may be achieved via iterative optimization within a series of tailored environments with incentives and contingencies inspired by the development of empathic concern in humans. Here we attempt an outline of what these training steps might look like. We speculate that work of this kind may allow for AI that surpasses empathic fatigue and the idiosyncrasies, biases, and computational limits that restrict human empathy. While for us, “a single death is a tragedy, a million deaths are a statistic”, the scaleable complexity of AI may allow it to deal proportionately with complex, large-scale ethical dilemmas. Hopefully, by addressing this problem seriously in the early stages of AI’s integration with society, we may one day be accompanied by AI that plans and behaves with a deeply ingrained weight placed on the welfare of others, coupled with the cognitive complexity necessary to understand and solve extraordinary problems.

Blended human-technology service realities in healthcare

PurposeThe healthcare sector is experiencing a major paradigm shift toward a people-centered approach. The key issue with transitioning to a people-centered approach is a lack of understanding of the ever-increasing role of technology in blended human-technology healthcare interactions and the impacts on healthcare actors' well-being. The purpose of the paper is to identify the key mechanisms and influencing factors through which blended service realities affect engaged actors' well-being in a healthcare context.Design/methodology/approachThis conceptual paper takes a human-centric perspective and a value co-creation lens and uses theory synthesis and adaptation to investigate blended human-technology service realities in healthcare services.FindingsThe authors conceptualize three blended human-technology service realities – human-dominant, balanced and technology-dominant – and identify two key mechanisms – shared control and emotional-social and cognitive complexity – and three influencing factors – meaningful human-technology experiences, agency and DART (dialogue, access, risk, transparency) – that affect the well-being outcome of engaged actors in these blended human-technology service realities.Practical implicationsManagerially, the framework provides a useful tool for the design and management of blended human-technology realities. The paper explains how healthcare services should pay attention to management and interventions of different services realities and their impact on engaged actors. Blended human-technology reality examples – telehealth, virtual reality (VR) and service robots in healthcare – are used to support and contextualize the study’s conceptual work. A future research agenda is provided.Originality/valueThis study contributes to service literature by developing a new conceptual framework that underpins the mechanisms and factors that influence the relationships between blended human-technology service realities and engaged actors' well-being.

A Convergent Approach to the Viability of the Dynamical Systems: The Cognitive Value of Complexity

The integration of information and industrial technologies, digitalization and differentiation of sciences are accompanied by an increase in various types of complexity. This limits the capabilities of computer modelling, data mining, and predictive analytics. The increasing cognitive complexity of information flows and their diversity creates problems of safety, reliability and stability of the functioning of a complex dynamic system in extreme conditions. Here we show the possibility of cognitive visualization of signals of different nature through their geometrization in the form of a topological 3D model of functioning. Its projections are spatio-temporal signatures, the configurations of which reflect the dynamic, energetic and structural features of the model. An increase in the number of components of the signature configuration and its area under external influence indicates an increase in structural and functional complexity. Therefore, the signal structure can be analyzed in real time using complementary probabilistic and deterministic methods. A set of tools for the synthesis and analysis of 3D models has innovative potential for monitoring the functioning of elements of complex dynamic systems, risk management and predictive analytics.

Magnitude, number and school mathematics

Definitions of concepts of magnitude and number used in academic mathematics are not suitable for school mathematics for reasons of their cognitive complexity. We discuss possible ways to treat magnitudes and numbers in school mathematics based on mathematical reasoning.

The Transition to Noncommunicable Disease: How to Reduce Its Unsustainable Global Burden by Increasing Cognitive Access to Health Self-Management

The global epidemic of noncommunicable diseases (NCDs), such as cardiovascular disease and diabetes, is creating unsustainable burdens on health systems worldwide. NCDs are treatable but not curable. They are less amenable to top-down prevention and control than are the infectious diseases now in retreat. NCDs are mostly preventable, but only individuals themselves have the power to prevent and manage the diseases to which the enticements of modernity and rising prosperity have made them so susceptible (e.g., tobacco, fat-salt-carbohydrate laden food products). Rates of nonadherence to healthcare regimens for controlling NCDs are high, despite the predictable long-term ravages of not self-managing an NCD effectively. I use international data on adult functional literacy to show why the cognitive demands of today’s NCD self-management (NCD-SM) regimens invite nonadherence, especially among individuals of below-average or declining cognitive capacity. I then describe ways to improve the cognitive accessibility of NCD-SM regimens, where required, so that more patients are better able and motivated to self-manage and less likely to err in life-threatening ways. For the healthcare professions, I list tools they can develop and deploy to increase patients’ cognitive access to NCD-SM. Epidemiologists could identify more WHO “best buy” interventions to slow or reverse the world’s “slow-motion disaster” of NCDs were they to add two neglected variables when modeling the rising burdens of disease. The neglected two are both cognitive: the distribution of cognitive capacity levels of people in a population and the cognitive complexity of their health environments.

Using O*NET Linkages to Advance Research: An Example Evaluating Cognitive Function and Work Transitions

The type of work older adults engage in has potential to play a key role in shaping health and wellbeing. In this presentation, using data drawn from an O*NET crosswalk linked with the Health and Retirement Study, I show how different types of transitions out of the workforce shapes cognitive function differently for individuals retiring from different types of occupations. Based on a factor analysis of 36 job-related abilities, activities, and contexts, this paper shows that retirement has a more significant consequence for cognitive function for those who retire from jobs with low levels of cognitive complexity, but no significant consequences for those who retire from jobs with high levels of cognitive complexity. I discuss these results in the context of the ways in which O*NET classifications of jobs can provide critical insights into the potential influence of changing retirement trajectories on wellbeing in later life.

High Cognitive Load Situations With Different Conversation Topics Affect Walking Speed and Cognitive Complexity

Walking and talking on the phone are common high-cognitive-load-situations (HCLS; e.g. dual-tasks), requiring extra attentional allocation and increasing perceived stress. We explored whether two load types, 1) single-task (ST) walking or talking on a phone and 2) HCLS walking while talking on a phone, influenced walking and/or cognitive performance among young (n=7; age=23.00±2.08yrs), middle-aged (n=14; age=44.79±7.42yrs), and older (n=15; age=74.47±3.91yrs) adults while controlling for perceived stress. Participants completed 3-minute trials of single-task walking (ST-W), single-task phone conversations with common (e.g., weather; ST-C) and uncommon topics (e.g., life experience; ST-U), and walking while talking on a phone (HCLS-C and HCLS-U). Walking speed was analyzed with 3(ST-W;HCLS-C;HCLS-U) x 3(Age) ANCOVA. HCLS resulted in slower walking speed (p<.001). Older adults exhibited slower speed across conditions compared to young (p=.015). Cognitive complexity (i.e., conversational tone and words greater than six letters (SIXLTR)) on the Linguistic Inquiry and Word Count (LIWC) were analyzed with 2(Cvs.U) x 2(STvs.HCLS) x 3(Age) ANCOVAs. Older age was associated with less cognitive complexity; positive tone (p=.014) and SIXLTR (p=.016), respectively in conversations. Uncommon topics reduced positive tone (p=.022) and SIXLTR (p=.003). Effects of HCLS on tone (p=.040) and SIXLTR (p=.005) varied with age. HCLS with different conversation topics resulted in reduced walking and cognitive complexity while controlling for perceived stress. The analysis of cognitive complexity using common/uncommon conversation topics is a novel method to assess the impact of HCLS. This research will disrupt the transformation of aging leading to a better understanding of attentional allocation and its effects on function.

Self-awareness in Dementia: a Taxonomy of Processes, Overview of Findings, and Integrative Framework

Purpose of Review Self-awareness, the capacity of becoming the object of one’s own awareness, has been a frontier of knowledge, but only recently scientific approaches to the theme have advanced. Self-awareness has important clinical implications, and a finer understanding of this concept may improve the clinical management of people with dementia. The current article aims to explore self-awareness, from a neurobiological perspective, in dementia. Recent Findings A taxonomy of self-awareness processes is presented, discussing how these can be structured across different levels of cognitive complexity. Findings on self-awareness in dementia are reviewed, indicating the relative preservation of capacities such as body ownership and agency, despite impairments in higher-level cognitive processes, such as autobiographical memory and emotional regulation. Summary An integrative framework, based on predictive coding and compensatory abilities linked to the resilience of self-awareness in dementia, is discussed, highlighting possible avenues for future research into the topic.