What is comparable in comparative cognition?

Abstract

To understand how complex, or 'advanced' various forms of cognition are, and to compare them between species for evolutionary studies, we need to understand the diversity of neural-computational mechanisms that may be involved, and to identify the genetic changes that are necessary to mediate changes in cognitive functions. The same overt cognitive capacity might be mediated by entirely different neural circuitries in different species, with a many-to-one mapping between behavioural routines, computations and their neural implementations. Comparative behavioural research needs to be complemented with a bottom-up approach in which neurobiological and molecular-genetic analyses allow pinpointing of underlying neural and genetic bases that constrain cognitive variation. Often, only very minor differences in circuitry might be needed to generate major shifts in cognitive functions and the possibility that cognitive traits arise by convergence or parallel evolution needs to be taken seriously. Hereditary variation in cognitive traits between individuals of a species might be extensive, and selection experiments on cognitive traits might be a useful avenue to explore how rapidly changes in cognitive abilities occur in the face of pertinent selection pressures.

Beyond AI: Multi-Intelligence (MI) Combining Natural and Artificial Intelligences in Hybrid Beings and Systems

“…When the nature of intelligence is considered in fundamental terms such as problem solving capabilities involving self-awareness and robust adaptation, sophisticated intelligence is found in many forms of life [4][5][6][7]. For example, post-anthropocentric research indicates that dogs have intelligence attributes, which humans do not develop at any age.…”

“…This is because framing provides lasting rationale for thoughts, decisions and actions-even when risks and failings are evident [8][9][10][11][12][13][14][15]. Widening the current framing can be accomplished with reference to scientific research and theories concerned with the nature of intelligence across lifeforms [4][5][6][7]; causation amidst unplanned and planned complexity [44,45]; and emergence from edge effects between formal and informal organizations [79][80][81]. Figure 2 provides a conceptual framework encompassing alternative pathways for the development of new types of intelligence.…”

“…MI hybrid systems exist as a result of, for example, organizations deploying non-human natural intelligence as a better alternative than AI [3]. Here, intelligence is considered in fundamental terms, which scientific research reveals to be found in many forms of life, such as problem solving capabilities involving self-awareness and robust adaptation [4][5][6][7]. It is important to note that MI does not refer to two or more different manifestations of intelligence from one type of contributor, such as different manifestations of intelligence from a human being.…”

Can Bees See at a Glance?

“…Undoubtedly, such research has led to remarkable successes in placing human cognition into an evolutionary framework, and also dismissed the myth that large brains automatically convey superior cognition (Emery and Clayton, 2004;Giurfa, 2013). However, an obsession with animal 'cleverness' and a focus on human-like capacities of animals might obscure significant differences in cognition between species, and indeed unique cognitive adaptations and constraints (Bolhuis and Wynne, 2009;Chittka et al, 2012;de Waal and Ferrari, 2010).…”

Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

“…While we have a good understanding of associative learning at both the behavioural and neurobiological levels [12], we do not understand the biological mechanisms underlying cognitive capabilities such as planning. Thus, when we assign a label such as 'planning' to a behaviour, we are not specifying a mechanism (see earlier studies [9,13] for an extended discussion). In addition, because we often do not fully understand the problem domain (i.e.…”