Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that (...) they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations. (shrink)

I propose that primary conscious awareness arises from synchronized activity in dendrites of neurons in dorsal thalamic nuclei, mediated particularly by inhibitory interactions with thalamic reticular neurons. In support, I offer four evidential pillars: consciousness is restricted to the results of cortical computations; thalamus is the common locus of action of brain injury in vegetative state and of general anesthetics; the anatomy and physiology of the thalamus imply a central role in consciousness; neural synchronization is a neural correlate of consciousness.

This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out (...) aspects pertaining to marketing or sociology of research, and the remaining aspects seem to certify a genuine value of deep learning, calling for explanation. The alleged two main propelling factors for deep learning, namely computing hardware performance and neuroscience findings, are scrutinized, and evaluated as relevant but insufficient for a comprehensive explanation. We review various attempts that have been made to provide mathematical foundations able to justify the efficiency of deep learning, and we deem this is the most promising road to follow, even if the current achievements are too scattered and relevant for very limited classes of deep neural models. The authors’ take is that most of what can explain the very nature of why deep learning works at all and even very well across so many domains of application is still to be understood and further research, which addresses the theoretical foundation of artificial learning, is still very much needed. (shrink)

The McCollough effect, an orientation-contingent color aftereffect, has been known for over 30 years and, like other aftereffects, has been taken as a means of probing the brain's operations psychophysically. In this paper, we review psychophysical, neuropsychological, and neuroimaging studies of the McCollough effect. Much of the evidence suggests that the McCollough effect depends on neural mechanisms that are located early in the cortical visual pathways, probably in V1. We also review evidence showing that the aftereffect can be induced without (...) conscious perception of the induction patterns. Based on these two lines of evidence, it is argued that our conscious visual experience of the world arises in the cortical visual system beyond V1. (shrink)

Neurobiological and cognitive models of unconscious information processing suggest that subconscious threat detection can lead to cognitive misinterpretations and false alarms, while conscious processing is assumed to be perceptually and conceptually accurate and unambiguous. Furthermore, clinical theories suggest that pathological anxiety results from a crude preattentive warning system predominating over more sophisticated and controlled modes of processing. We investigated the hypothesis that subconscious detection of threat in a cognitive task is reflected by enhanced ''false signal'' detection rather than by selectively (...) enhanced discrimination of threat items in 30 patients with panic disorder and 30 healthy controls. We presented a tachistoscopic word-nonword discrimination task and a subsequent recognition task and analyzed the data by means of process-dissociation procedures. In line with our expectations, subjects of both groups showed more false signal detection to threat than to neutral stimuli as indicated by an enhanced response bias, whereas indices of discriminative sensitivity did not show this effect. In addition, patients with panic disorder showed a generally enhanced response bias in comparison to healthy controls. They also seemed to have processed the stimuli less elaborately and less differentially. Results are consistent with the assumption that subconscious threat detection can lead to misrepresentations of stimulus significance and that pathological anxiety is characterized by a hyperactive preattentive alarm system that is insufficiently controlled by higher cognitive processes. (shrink)

I criticize Bourget’s intuitive and empirical arguments for thinking that all possible conscious states are underived if intentional. An underived state is one of which it is not the case that it must be realized, at least in part, by intentional states distinct from itself. The intuitive argument depends upon a thought experiment about a subject who exists for only a split second while undergoing a single conscious experience. This, however, trades on an ambiguity in "split second." Meanwhile, Bourget's empirical (...) argument is question-begging. My critique also has implications for debates about the essential temporality and unity of consciousness experience, and, phenomenal atomism. (shrink)

I have two goals in this paper. First, I want to show by example that inferences about theoretical entities are relatively contingent affairs. Previously accepted conceptual metaphors in science set both the general form of new theories and our acceptance of the theories as plausible. In addition, they determine how we define the relevant parameters in investigating phenomena in the first place. These items then determine how we conceptualize things in the world. Second, and maybe more importantly, I want to (...) solve a puzzle that falls out of our current explication of attention, namely why we have it. Given the now widely accepted view that our brains are massively parallel, it is difficult to see why we should have evolved attentional mechanisms at all. Why gate when we can already process what we transduce in parallel? Here I answer that puzzle and suggest a perspective on attention that makes it a bit easier to understand, although this perspective also entails that we have to revise how we individuate experimental protocols and relevant data. (shrink)