The Special Theory of Relativity (STR) holds sway as a theory of time due to its apparently successful predictive structure regarding time-related phenomena such as the increased life spans of mesons or retarded clocks on jets circling the globe, and due to the relativization of simultaneity intrinsic to this theoretical structure. Yet the very structure of the theory demands that such very real physical effects be construed as non-ontological. The scope and depth of this contradiction is explored and, if these (...) time-changes are indeed viewed as ontological effects within STR, an additional problem for the theory is introduced in the context of perception. The origins of this confused situation arise as a result of the fact that STR is an expression of a classical, spatial metaphysic – a framework that equally underpins current discussions of the hard problem. This metaphysic holds an inadequate concept of time and a failure to acknowledge the reality of simultaneous causal flows. These problems are developed against the background of an alternative, namely, the temporal metaphysic of Bergson – a framework that provides a profoundly different base for viewing both relativity and consciousness. (shrink)

This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...) developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called “deep networks”—systems that build on key ideas presented in the PDP volumes. (shrink)

I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification has no downsides; (...) it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000‐fold increase? (shrink)

The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A learning system must be constrained to learn efficiently, but some of these constraints are themselves learnable. To know how something will behave, a learner must know what kind of thing it is. Although this has led previous researchers to argue for domain-specific constraints that are tied to different kinds/domains, an exciting possibility is that kinds/domains themselves can be (...) learned. General cognitive constraints, when combined with rich inputs, can establish domains, rather than these domains necessarily preexisting prior to learning. Knowledge is structured and richly differentiated, but its “skeleton” must not always be preestablished. Instead, the skeleton may be adapted to fit patterns of co-occurrence, task requirements, and goals. Finally, we argue that for models of development to demonstrate genuine cognitive novelty, it will be helpful for them to move beyond highly preprocessed and symbolic encodings that limit flexibility. We consider two physical models that learn to make tone discriminations. They are mechanistic models that preserve rich spatial, perceptual, dynamic, and concrete information, allowing them to form surprising new classes of hypotheses and encodings. (shrink)

Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to (...) a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information. (shrink)

Do perceiving subjects represent kind properties in the content of their conscious visual experience when they see and recognize instances of those natural kinds? In Part 1 of my thesis I clarify this question, in Part 2 I answer it, and in Part 3 I raise a problem for previous answers. Part 1 conceives of conscious experience in an internalist way, and the unified conscious episode does not exclude having beliefs about what one sees. Following Siegel and Bayne, Part 2 (...) formulates two arguments in support of representing kind properties in the content of experience. In Part 3, I argue that attempts to distinguish visual experiences from visual beliefs might fail to account for the interplay of sensory and cognitive elements in visual object recognition. I conclude by suggesting it has not been established that visual experiences can be distinguished from visual beliefs. (shrink)

On observe une nouvelle tendance dans la philosophie des sciences cognitives, manifeste dans les écrits de Betchel et al. qui met en avant l’importance des explications mécanistes au détriment du rôle explicatif des principes. Cet article est un plaidoyer pour rétablir l’équilibre. Il met l’accent sur l’effort d’explication des effets du niveau de base, l’une des plus importantes découvertes empiriques dans l’histoire de la recherche en catégorisation. L’analyse de trois différentes périodes de cette histoire révèle que le recours aux principes (...) y a joué un rôle crucial. Cependant, afin de reconnaître pleinement le rôle explicatif des principes, nous devrions nous préparer à admettre que les explications déductives-nomologiques ne sont pas les seuls types d’explications basées sur des principes. (shrink)

Our goal is to investigate the biological correlates of the first-person experience of time or phenomenal time. ‘Time’ differs in various domains, such as (i) physical time (e.g., clock time), (ii) biological time, such as the suprachiasmatic nucleus, and (iii) the perceptual rate of time. One psychophysical-measure of the perceptual rate is the critical flicker frequency (CFF), in which a flashing light is perceived as unchanging. Focusing on the inability to detect change, as in CFF, may give us insight into (...) phenomenal time. CFF varies from 24 Hz for dim light and 60 Hz in bright light and is lower for colored lights. We propose that problem of the phenomenal time can be addressed using two contrasting but complementary approaches (inability to detect changes vs. ability to detect changes): (1) The soliton-catalytic model that entails invariant quantum coherent state for temporal frequencies (TFs) >= CFF, where flickering light is perceived as unchanging, similar to a Bose-Einstein condensate (BEC). (2) Temporal frequency tuned mechanisms model, which starts with ability to detect changes for TFs < CFF and then their sensitivities decreases to zero at CFF. For a subject who has CFF of 60 Hz, the duration of one cycle or time-period of the flickering light is approximately 16.7 ms. Phenomenal time may be quantized into ‘subjective occasions of experience’ (SE), which arise out of the interaction of the individual with situation (environment). Pioneering work examining the complex interaction of neurons suggests the possibility that macroscopic quantum states similar to a BEC may also occur in the brain (Davia, 2006; Freeman & Vitiello, 2006; Georgiev, 2004; Vimal & Davia, 2008) . (shrink)