Predictive brains: forethought and the levels of explanation

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

AI turns fifty: Revisiting its origins

The expression "artificial intelligence" (AI) was introduced by John McCarthy, and the official birth of AI is unanimously considered to be the 1956 Dartmouth Conference. Thus, AI turned fifty in 2006. How did AI begin? Several differently motivated analyses have been proposed as to its origins. In this paper a brief look at those that might be considered steps towards Dartmouth is attempted, with the aim of showing how a number of research topics and controversies that marked the short history of AI were touched on, or fairly well stated, during the year immediately preceding Dartmouth. The framework within which those steps were taken was the development of digital computers. Earlier computer applications in areas such as complex decision making and management, at that time dealt with by operations research techniques, were important in this story. The time was ripe for AI's intriguingly tumultuous development, marked as it has been by hopes and defeats, successes and difficulties

Coping with levels of explanation in the behavioral sciences

This Research Topic aimed at deepening our understanding of the levels and explanations that are of interest for cognitive scientists, neuroscientists, psychologists, behavioral scientists, and philosophers of science. Indeed, contemporary developments in neuroscience and psychology 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 sufficient: 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 satisfactory strategy? How can high-level and low-level models be constrained in order to be actually explanatory of both behavioral and neurological or molecular evidence? What is the kind of relationship between those models

Computationalism under attack

A partire dalla prima metà degli anni ottanta nella comunità della scienza cognitiva il computazionalismo è stato messo “sotto attacco” da parte di diversi critici di quelle impostazioni della scienza cognitiva che a partire da allora si sono dette “classiche” o “simboliche”. Da allora la comunità degli scienziati cognitivi si è divisa sui diversi “paradigmi” che di volta in volta sono stati opposti al computazionalismo (classico). Nel nostro intervento precisiamo in primo luogo che il computazionalismo non deve essere identificato con ciò che potremmo chiamare il “paradigma del computer”, ossia con la tesi secondo la quale la mente funzionerebbe “come un computer”. Le contrapposizioni sopra menzionate traggono parte della loro forza dall’identificare il computazionalismo con il paradigma del computer. Ossia, la loro plausibilità si basa sull’assumere come obiettivo polemico qualche visione ristretta del computazionalismo. Ciò può essere esemplificato discutendo alcune affermazioni di Tim van Gelder tese ad opporre l’impostazione computazionale a quella dinamicista. Riteniamo che possa essere chiarificatrice al proposito la distinzione di Marr tra spiegazioni al livello della teoria computazionale (livello 1) e al livello delle rappresentazioni e degli algoritmi (livello 2). Sulla base di questa distinzione, alcuni asseriti paradigmi alternativi al computazionalismo possono essere considerati consistenti con una teoria computazionale nel senso del livello 1 di Marr, ma risolversi in scelte differenti per quel che concerne gli algoritmi e le rappresentazioni (livello 2). Ad esempio, l’impostazione dinamicista tende a ritenere che il livello 2 sia prescindibile nelle spiegazioni cognitive

Bayesian models and simulations in cognitive science

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

Una lezione per la scienza cognitiva

Il ruolo di H.A. Simon nella Scienza Cognitiva, in un numero della rivista a lui dedicato in occasione della sua scomparsa

Alan M. Turing

La figura e l'opera di A.M. Turing con una breve scelta antologica di suoi testi