This Research Topic aimed at deepening our understanding of the levels and explanations that are of interest for cognitive sci- entists, neuroscientists, psychologists, behavioral scientists, and philosophers of science. Indeed, contemporary developments in neuroscience and psy- chology suggest that scientists are likely to deal with a multiplicity of levels, where each of the different levels entails laws of behavior appropriate to that level (Berntson et al., 2012). Also, gathering and modeling data at the different levels of analysis is not suffi- (...) cient: the integration of information across levels of analysis is a crucial issue. Given such state of affairs, a number of interesting questions arise. How can the autonomy of explanatory levels be properly understood in behavioral explanation? Is reductionism a satis- factory strategy? How can high-level and low-level models be constrained in order to be actually explanatory of both behav- ioral and neurological or molecular evidence? What is the kind of relationship between those models? (shrink)

Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.

Is any unified theory of brain function possible? Following a line of thought dat- ing back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gain- ing the “Grand Unified Theory of the Mind”—or “painting the big picture,” as Edelman (2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with experimental (...) findings and explana- tory capabilities (Edelman, 2012). The point we are making in this note is that a brain with predictive capa- bilities is certainly necessary to endow the agent situated in the environment with forethought or foresight, a crucial issue to outline the unified account advocated by Clark. But the capacity for fore- thought is deeply entangled with the capacity for emotions and when emotions are brought into the game, cogni- tive functions become part of a large-scale functional brain network. However, for such complex networks a consistent view of hierarchical organization in large-scale functional networks has yet to emerge (Bressler and Menon, 2010), whilst heterarchical organization is likely to play a strategic role (Berntson et al., 2012). This raises the necessity of a multilevel approach that embraces causal relations across levels of explanation in either direc- tion (bottom–up or top–down), endorsing mutual calibration of constructs across levels (Berntson et al., 2012). Which, in turn, calls for a revised perspective on Marr’s levels of analysis framework (Marr, 1982). In the following we highlight some drawbacks of Clark’s proposal in address- ing the above issues. (shrink)

Science urges philosophy to be more empirical and philosophy urges science to be more reflective. This markedly occurred along the “discovery of the artificial” (CORDESCHI 2002): in the early days of Cybernetics and Artificial Intelligence (AI) researchers aimed at making machines more cognizant while setting up a framework to better understand human intelligence. By and large, those genuine goals still hold today, whereas AI has become more concerned with specific aspects of intelligence, such as (machine) learning, reasoning, vision, and action. (...) As a matter of fact, the field suffers from a chasm between two formerly integrated aspects. One is the engineering endeavour involving the development of tools, e.g., autonomous systems for driving cars as well as software for semantic information retrieval. The other is the philosophical debate that tries to answer questions concerning the nature of intelligence. Bridging these two levels can indeed be crucial in developing a deeper understanding of minds. An opportunity might be offered by the cogent theme of emotions. Traditionally, computer science, psychological and philosophical research have been compelled to investigate mental processes that do not involve mood, emotions and feelings, in spite of Simon’s early caveat (SIMON 1967) that a general theory of cognition must incorporate the influences of emotion. Given recent neurobiological findings and technological advances, the time is ripe to seriously weigh this promising, albeit controversial, opportunity. (shrink)