Indicators and Criteria of Consciousness in Animals and Intelligent Machines: An Inside-Out Approach

Abstract

In today’s society, it becomes increasingly important to assess which non-human and non-verbal beings possess consciousness. This review article aims to delineate criteria for consciousness especially in animals, while also taking into account intelligent artifacts. First, we circumscribe what we mean with “consciousness” and describe key features of subjective experience: qualitative richness, situatedness, intentionality and interpretation, integration and the combination of dynamic and stabilizing properties. We argue that consciousness has a biological function, which is to present the subject with a multimodal, situational survey of the surrounding world and body, subserving complex decision-making and goal-directed behavior. This survey reflects the brain’s capacity for internal modeling of external events underlying changes in sensory state. Next, we follow an inside-out approach: how can the features of conscious experience, correlating to mechanisms inside the brain, be logically coupled to externally observable (“outside”) properties? Instead of proposing criteria that would each define a “hard” threshold for consciousness, we outline six indicators: (i) goal-directed behavior and model-based learning; (ii) anatomic and physiological substrates for generating integrative multimodal representations; (iii) psychometrics and meta-cognition; (iv) episodic memory; (v) susceptibility to illusions and multistable perception; and (vi) specific visuospatial behaviors. Rather than emphasizing a particular indicator as being decisive, we propose that the consistency amongst these indicators can serve to assess consciousness in particular species. The integration of scores on the various indicators yields an overall, graded criterion for consciousness, somewhat comparable to the Glasgow Coma Scale for unresponsive patients. When considering theoretically derived measures of consciousness, it is argued that their validity should not be assessed on the basis of a single quantifiable measure, but requires cross-examination across multiple pieces of evidence, including the indicators proposed here. Current intelligent machines, including deep learning neural networks (DLNNs) and agile robots, are not indicated to be conscious yet. Instead of assessing machine consciousness by a brief Turing-type of test, evidence for it may gradually accumulate when we study machines ethologically and across time, considering multiple behaviors that require flexibility, improvisation, spontaneous problem-solving and the situational conspectus typically associated with conscious experience.

Renewed Perspectives on the Deep Roots and Broad Distribution of Animal Consciousness

“…More attention should now be turned as well to the study of other animals in their natural environments, combining ethological techniques developed over the decades with increasingly sophisticated neuroscience methodologies (Morris, 2005 ; Gallagher and Zahavi, 2008 ; Boly et al, 2013 ; Reiter et al, 2017 ; Pennartz et al, 2019 ), to reveal indicators of consciousness in other species. Several other strategic and experimental approaches for assessing comparative cognition (including consciousness) are proposed in Irwin and Irwin ( 2020 ).…”

“…A common approach to discerning the function of consciousness is to focus on the key features of subjective experience: its stabilizing properties and qualitative richness, dynamic integration, situatedness, and intentionality (Pennartz et al, 2019 ). James ( 1884 ), for instance, held that consciousness serves to direct attention and dampen chaotic cerebral activity.…”

“…Edelman saw consciousness as the integration of perceptual and motor events together with memory to construct a multimodal scene in the present (Edelman, 2003 ). For Pennartz et al ( 2019 ), the biological function of consciousness is to present the subject with a multimodal, situational survey of the surrounding world and body, subserving complex decision-making, and goal-directed behavior.…”

Anthropomorphism in AI

“…The problem with anthropomorphic language when discussing DNNs is that it risks masking important limitations intrinsic to DNN which make it fundamentally different from human intelligence (Marcus 2018). In addition to the issue of the lack of consciousness of DNN, which arguably is not just a matter of further development, but possibly of lacking the relevant architecture (Pennartz et al 2019), there are significant differences between DNN and human intelligence. It is on the basis of such differences that it has been argued that DNNs can be described as brittle, inefficient, and myopic compared to human brain (Watson 2019b).…”

From Consciousness to Brain-Sign: a Neurobiological Reconstruction

“…How it functions is 'explained' by substituting other words for it like 'awareness' or 'experience' which invoke similar nonphysical concepts (e.g. Pennartz et al 2019;Frith and Rees 2017). As a generality, neuroscientists identify active areas and conditions of the brain, then attribute to them mental categories as seeing, feeling or thinking, without specifying what they add to, or how they arise from, physical states.…”