I advance an account of quantitative character, a species of phenomenal character that presents as an intensity (cf. a quality) and includes experience dimensions such as loudness, pain intensity, and visual pop-out. I employ psychological and neuroscientific evidence to demonstrate that quantitative characters are best explained by attentional processing, and hence that they do not represent external qualities. Nonetheless, the proposed account of quantitative character is conceived as a compliment to the reductive intentionalist strategy toward qualitative states; I argue that (...) an account of perceptual experience that combines a tracking account of qualitative character with my functionalist proposal for quantitative character permits replies to some notoriously difficult problems for tracking representationalism without sacrificing its chief virtues. (shrink)

Processing the various features from different feature maps and modalities in coherent ways requires a dedicated integration mechanism . Many authors have related feature binding to conscious awareness but little is known about how tight this relationship really is. We presented subjects with asynchronous audiovisual stimuli and tested whether the two features were integrated. The results show that binding took place up to 350 ms feature-onset asynchronies, suggesting that integration covers a relatively wide temporal window. We also asked subjects to (...) explicitly judge whether the two features would belong to the same or to the different events. Unsurprisingly, synchrony judgments decreased with increasing asynchrony. Most importantly, feature binding was entirely unaffected by conscious experience: features were bound whether they were experienced as occurring together or as belonging to a separate events, suggesting that the conscious experience of unity is not a prerequisite for, or a direct consequence of binding. (shrink)

Several theories claim that dreaming is a random by-product of REM sleep physiology and that it does not serve any natural function. Phenomenal dream content, however, is not as disorganized as such views imply. The form and content of dreams is not random but organized and selective: during dreaming, the brain constructs a complex model of the world in which certain types of elements, when compared to waking life, are underrepresented whereas others are over represented. Furthermore, dream content is consistently (...) and powerfully modulated by certain types of waking experiences. On the basis of this evidence, I put forward the hypothesis that the biological function of dreaming is to simulate threatening events, and to rehearse threat perception and threat avoidance. To evaluate this hypothesis, we need to consider the original evolutionary context of dreaming and the possible traces it has left in the dream content of the present human population. In the ancestral environment human life was short and full of threats. Any behavioral advantage in dealing with highly dangerous events would have increased the probability of reproductive success. A dream-production mechanism that tends to select threatening waking events and simulate them over and over again in various combinations would have been valuable for the development and maintenance of threat-avoidance skills. Empirical evidence from normative dream content, children's dreams, recurrent dreams, nightmares, post traumatic dreams, and the dreams of hunter-gatherers indicates that our dream-production mechanisms are in fact specialized in the simulation of threatening events, and thus provides support to the threat simulation hypothesis of the function of dreaming. Key Words: dream content; dream function; evolution of consciousness; evolutionary psychology; fear; implicit learning; nightmares; rehearsal; REM; sleep; threat perception. (shrink)

A central issue in philosophy and neuroscience is the problem of unified visual consciousness. This problem has arisen because we now know that an object's stimulus features (e.g., its color, texture, shape, etc.) generate activity in separate areas of the visual cortex (Felleman & Van Essen, 1991). For example, recent evidence indicates that there are very few, if any, neural connections between specific visual areas, such as those that correlate with color and motion (Bartels & Zeki, 2006; Zeki, 2003). So (...) how do unified objects arise in visual consciousness? Some neuroscientists propose that neural synchrony is the mechanism that binds an object's features into a unity (e.g., see Crick, 1994; Crick & Koch, 1990; Engel, 2003; Roelfsema, 1998; Singer, 1996; von der Malsburg, 1996, 1999). I argue, on both empirical and philosophical grounds, that neural synchrony fails to explain the unity of visual consciousness. (shrink)

A key question in studying consciousness is how neural operations in the brain can identify streams of sensory input as belonging to distinct modalities, which contributes to the representation of qualitatively different experiences. The basis for identification of modalities is proposed to be constituted by self-organized comparative operations across a network of unimodal and multimodal sensory areas. However, such network interactions alone cannot answer the question how sensory feature detectors collectively account for an integrated, yet phenomenally differentiated experiential content. This (...) problem turns out to be different from, although related to, the binding problem. It is proposed that the neural correlate of an enriched, multimodal experience is constituted by the attractor state of a dynamic associative network. Within this network, unimodal and multimodal sensory maps continuously interact to influence each other’s attractor state, so that a feature change in one modality results in a fast re-coding of feature information in another modality. In this scheme, feature detection is coded by firing-rate, whereas firing phase codes relational aspects. (shrink)

Recent work in different fields of cognitive sciences seems to support the idea that in order to explain awareness of visually presented objects some kind of binding mechanism is needed for the correct conjunction of different sensory features into a whole percept. Selective attention is commonly invoked as the key for solving this conjunction problem. Accordingly, the cognitive neurosciences have begun to investigate what kind of neural processing could underlie such a process. I analyse the evidence provided for justifying the (...) existence of a binding problem and I argue against the claim that there is a feature binding problem to be solved in order to explain unity of object awareness. In particular, I question the definition of 'feature' and suggest some possible sources of misunderstanding related to this definition. Finally, I suggest an alternative approach in which perceived object unity does not rely exclusively on attentional conjunction of sensory features: instead of a general and unique mechanism mediating object awareness, I examine evidence that there are at least as many binding mechanisms as potential ways of interacting with visually presented objects. (shrink)

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods (...) to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed. (shrink)

In the first part ("Determination"), I consider different notions of determination, contrast and compare modal with non-modal accounts and then defend two a-modality theses concerning essence and supervenience. I argue, first, that essence is a a-modal notion, i.e. not usefully analysed in terms of metaphysical modality, and then, contra Kit Fine, that essential properties can be exemplified contingently. I argue, second, that supervenience is also an a-modal notion, and that it should be analysed in terms of constitution relations between properties. (...) In the second part ("A Theory of Truthmaking"), I first review the literature on ontological commitment, and argue that the notion of truthmaking is better suited to play its explanatory rôle. I then argue that we should take truthmaker theory seriously, and that we should provide actual truthmakers for all truths there are. In the last chapter of this part, I review Armstrong's truthmaker theories and argue that they are unsatisfactory. I generalise my criticism to an argument against truthmaker necessitarianism, the view that truthmakers necessarily make true the truths they are truthmakers of. In the third part ("Properties and their Kind(s)"), I discuss qualitative determination. I present and endorse the truthmaker argument for universals, and defend universalism against friends of tropes, states of affairs and facts. I then give a novel characterisation of the important class of intrinsic properties, building on Lewis' work. With a workable notion of intrinsicness at hand, I argue that relations are ontologically – but not "ideologically" – dispensable: qualitative determination in general is a matter of intrinsic structure. In the future, I plan to add two parts and to publish my thesis as a book: In the fourth part ("Exemplification"), I argue for the existence of an exemplification relation tying universals and particulars together. I derive theoretical benefits from this relation by providing adverbialist theories of modality and tense and identify exemplification with a type of parthood. In the fifth part ("Qua qua qua"), I investigate the curious and interesting ontological category of so-called "qua-objects" (Picasso-as-a-painter, for example) and argue that they exist, are not identical with transworld indidivuals or modal parts and that they provide (contingent) truthmakers for all true predications. (shrink)

The objectives of this article are twofold. First, by denying the dualism inherent in attempts to load metaphysical significance on the inner/outer distinction, it defends the view that scientific investigation can approach consciousness in itself, and is not somehow restricted in scope to the outward manifestations of a private and hidden realm. Second, it provisionally endorses the central tenets of global workspace theory, and recommends them as a possible basis for the sort of scientific understanding of consciousness thus legitimised. However, (...) the article goes on to argue that global workspace theory alone does not constitute a fully worked-out objective account of the conscious subject. This requires additional attention to be paid to the issue of embodiment, and to the possibility of indexicality that arises when an instantiation of the global workspace architecture inhabits a spatially localised body. (shrink)

This article sketches an idealized strategy for the identification of neural correlates of consciousness. The proposed strategy is based on a state space approach originating from the analysis of dynamical systems. The article then focuses on one constituent of consciousness, phenomenal awareness. Several rudimentary requirements for the identification of neural correlates of phenomenal awareness are suggested. These requirements are related to empirical data on selective attention, on completely intrinsic selection and on globally unconscious states. As an example, neuroscientific findings on (...) synchronized γ activity are categorized according to these requirements. (shrink)

We discuss the role of synchrony of activationin higher-level cognitive processes. Inparticular, we analyze the question of whethersynchrony of activation provides a mechanismfor compositional representation in neuralsystems. We will argue that synchrony ofactivation does not provide a mechanism forcompositional representation in neural systems.At face value, one can identify a level ofcompositional representation in the models thatintroduce synchrony of activation for thispurpose. But behavior in these models isalways produced by means conjunctiverepresentations in the form of coincidencedetectors. Therefore, models that rely onsynchrony (...) of activation lack the systematicityand productivity of true compositional systems.As a result, they cannot distinguish betweentype and token representations, which resultsin misrepresentations of spatial relations andpropositions. Furthermore, higher-levelcognitive processes will likely integrateinformation from widely distributed areas inthe brain, which puts severe restrictions onthe underlying neural dynamics if synchrony ofactivation is to play a role in theseprocesses. We will briefly discuss theserestrictions in the case of feature binding invisual cognition. (shrink)

I examine critically the application of information‐theoretic ideas to biological communication during embryonic development and in the functioning central nervous system (CNS). I show that intercellular communication relies mostly on simple signals whose role is to effect a selection among predetermined cellular states. Hence, a crucial role is played by cellular memory, which stabilizes such states. Memory in cells is partly located in the nuclear DNA; no less important however is (phenotypic) memory lying in the cell's organelles and compartments. Because (...) of combinatorial effects in gene expression patterns, cell memory is an enormously powerful mechanism, which also underlies plasticity, and thus constitutes the factor unifying genetic determination, plasticity and learning. Communication in the CNS is analyzed in some detail: here, cellular memory is embodied in anatomy (i.e., cell shape) and neurochemistry. These are the major, relatively “static” factors affecting the routing of neural impulses in the adult CNS. In addition, however, faster channeling is also required: action potentials must be directed to their targets along a few of exponentially many paths, and this dynamic routing is crucial for proper operation. I suggest that collective oscillatory modes may play a role in solving this addressing problem, in the same way that the clock signal gates the operation of man‐made computers. BioEssays 25:699–708, 2003. © 2003 Wiley Periodicals, Inc. (shrink)

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike-time (...) dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla–Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions. (shrink)

Ballard and Zhang offer a fascinating review of how computational models of human vision have evolved since David Marr proposed his Tri‐Level Hypothesis, with a focus on the refinement of algorithm descriptions over time.