Search Results

Many philosophers have said that the reference to an object depends on knowing what sort of thing it is. But there seems to be many cases in which we refer to objects without knowing the sort; conscious attention to an object does not demand knowledge of its sort. There has to be an explanation of how it is that the subject is visually singling out one object rather than any other in the environment at any one time; but this is provided by the use of one rather than another mechanism for cross‐referencing processing streams. This use of various strategies for solving the binding problem is a more low‐level phenomenon than sortal classification.

In this chapter,Frédérique de Vignemont argues that an experience can qualify as multimodal on the basis of either additive binding or integrative binding. To what extent do then the classic binding problems apply to multimodal experiences? And more particularly, how does the perceptual system select the relevant information to integrate together? This Chapter discusses various candidates for the multimodal binding parameter, that is, for the characteristic of the object that the various sensory modalities treat as distinctive of that object, and use in combining or integrating together information as information about the same object. Does the perceptual system bind together the sensory states that carry information about the same location? Or about the same object? And in the latter case, how is the sameness of object determined? Through conceptual identification? Or through non-conceptual individuation?

This chapter argues that brain systems must meet several 'hard' requirements to qualify as conscious, while other requirements are 'soft' in that they are important for sustaining normal, daily-life awareness but not strictly necessary for having the most basic form of conscious experience. The hard requirements include, first, the ability to interpret (or reconstruct) sensory inputs as having particular qualities or content, within a rich repertoire of modalities or (sub)modalities, such as visual motion, shape, depth and color. Second, this process of attributing sensory “feel” or meaning to inputs occurs in a dynamic or stable state, depending on the constancy of variables governing the sensory flux. Projection of interpreted sensory inputs into an external, perspectival space (vision) or body map (somatosensation) is seen as a relatively basic process, but patient studies indicate that core consciousness does not strictly depend on this ability, as applies as well to normal requirements on the grouping of similar features and binding of different submodalities into objects. Also the “unity” of consciousness and self-awareness are not classified as an essential feature but rather as a constantly maintained “illusion” of the healthy brain empowered by proper multimodal and motor alignment.

This chapter considers possible defenses of the claim that the conscious aspects of experience supervene on the experiencing subject's brain. Considerations discussed include the very occurrence of hallucinations, thought experiments, brain stimulation experiments, and facts about the way the brain processes visual information. It is then shown how the theory of hallucination presented in Chapter 4 can explain conditions such as akinetopsia and achromatopsia and how the ‘binding problem’ can be solved. The chapter concludes with an explanation of how this theory can accommodate the intuition that a subject whose brain was artificially stimulated in exactly the same way as it would have been stimulated in a veridical case would have an utterly convincing hallucination.

This is the first of the final three chapters of the book, which are concerned with evaluating the presented approach in the context of current cognitive modeling. This chapter re-presents the Core Cognitive Criteria (CCC) from chapter one, discussing and justifying each in more detail. The criteria are broken into three main sections, representational structure, performance concerns, and scientific merit. They cover familiar constraints, such as systematicity, compositionality and productivity, and include less common criteria, such as robustness, scalability, and compactness. Tutorial: How to build a brain – a practical guide

The project in this chapter is to build a reasonably detailed response to the famous binding problem entirely from the perspective of the prediction error minimization scheme. The binding problem concerns the ability of the brain to bind together sensory attributes in spite of processing them in different regions throughout the brain. It turns out that prediction error minimization dissolves this problem by virtue of what was called in Chapter 2 the profound reversal of the way we normally conceptualize the role of sensory input. Instead of binding disparate sensory attributes, the brain assumes bound attributes on the basis of sparse input and prior learning, and queries the sensory input on the basis of this assumption—the disparate parts of sensory input are feedback to the brain’s hypothesis. This means binding is treated as just more causal inference, albeit causal inference concerning properties at little causal depth. This is discussed through use of example of multimodal sensory integration.

This chapter examines the neurocognition of language comprehension by using a broad-coverage model in combination with neuropsychological data on sentence processing. It first looks at the main language-relevant waveforms on recording of event-related potentials (ERPs) before discussing the contrast on syntactic processing between syntax-first and more interactive models of sentence processing. It proposes a cognitive model and links it to particular regions of the brain, suggesting that the left posterior superior temporal gyrus may be responsible for storage and retrieval of lexical information whereas the left posterior inferior frontal cortex is responsible for integrating the retrieved information. The chapter also considers the binding problem for language.

The final chapter is about the notoriously difficult problem of distal content (the sixth of the content determinacy challenges listed in chapter 7). In relation to causal theories of reference, the problem is to explain why a representation counts as representing its content, rather than some other item that carries information about it to the sensory-perceptual system that produces the representation. After discussing how the problem concerns nonconceptual representations, as opposed to conceptualized thought, the author offers a solution suitable for nonconceptual representations. She explains why several well-known proposals do not seem to be even designed to assign appropriate distal contents to nonconceptual representations (whatever their success might or might not be for concepts). Neander alsoalso discusses several closely related issues, such as how a hallucinated red square might be experienced as “in the world,” versus “just in the head,” and how distal contents and perceptual constancies are related. Before concluding, the author discusses the issue of how informational teleosemantics handles the content of complex contents produced by the so-called “binding” of perceived features to perceived objects.

A remarkable feature of memory is its ability to group related experiences and differentiate them from the wealth of other, often similar, experiences that are also stored. In effect, when we see the face of a person we know we may recall his or her profession, we may recollect events that occurred when we last met the person, and certain feelings and bodily states which relate to how we feel about the person may be induced. The issue of how information that belongs to the same experience is bound in memory has attracted substantial interest. This chapter reviews studies which support the view that the way the brain is activated during the initial perception-encoding of an event is in part reproduced when episodic information is subsequently retrieved. In other words, the activity pattern at encoding is reactivated in test conditions. These observations of reactivation, in particular incidental reactivation, have potential implications for our understanding of the binding problem.

This chapter presents an overview of the research on visual attention, which has been studied extensively in cognitive psychology and neuroscience. The studies discussed are limited to the major empirical findings on visual attention that have implications for the scientific understanding of consciousness. The chapter includes studies on feature-based attention, spatial attention, object-based attention, effortless attention, the mechanisms supporting the different forms of attention (e.g., neural structures and pathways), and the evolution of these mechanisms. This review is important for the book’s primary argument that consciousness and attention must be dissociated at some level, as there are functionally different forms of attention that seem to operate independently and to have evolved at different times from each other—an argument that is difficult to make for consciousness.

With the motivation to develop computational and algorithmic levels of understanding how the mind comes into being, this chapter considers computer science, artificial intelligence, and cognitive systems perspectives. Questions are addressed, such as what ‘intelligence’ may actually be and how, and when an artificial system may be considered to be intelligent and to have a mind on its own. May it even be alive? Out of these considerations, the chapter derives three fundamental problems for cognitive systems: the symbol grounding problem, the frame problem, and the binding problem. We show that symbol-processing artificial systems cannot solve these problems satisfactorily. Neural networks and embodied systems offer alternatives. Moreover, biological observations and studies with embodied robotic systems imply that behavioral capabilities can foster and facilitate the development of suitably abstracted, symbolic structures. We finally consider Alan Turing’s question “Can machines think?” and emphasize that such machines must at least solve the three considered fundamental cognitive systems problems. The rest of the book addresses how the human brain, equipped with a suitably-structured body and body–brain interface, manages to solve these problems, and thus manages to develop a mind.

This chapter presents a model of working memory and change detection in visual scenes that addresses the binding problem: When memorizing a group of objects, each with a location and multiple surface features, how are the properties of each individual object bound together and kept separate from the properties of other objects? In the proposed architecture a stack of feature maps forms the neural substrate for scene working memory. Binding makes use of a spatial dimension shared by all feature maps. The operations of attentional selection and space-feature integration act jointly to memorize multiple objects in a bound fashion and detect changes in visual scenes. The need for attentional selection in some task implies sequential processing of individual items, whereas in other tasks, items can be processed in parallel. This aspect of the model’s behavior provides qualitative predictions about human performance.

In this chapter, the author reflects on visual irredentism within the context of the brain's cortices, thalamic nuclei, and brainstem structures. He discusses the so-called binding problem in cortical neuroscience, Christoph von der Malsburg's talk about modeling collections of neurons that were bound together through an oscillation, and conventional brain theory. He also mentions his research project that focused on how neurons in the parietal cortex flashed on and off as a monkey looked around, along with the 1989 experiment with cats by Charles Gray and Wolf Singer about oscillations triggered by masses of neurons firing in synchrony. Finally, he considers two levels to vision science, the analysis of neurons and areas and the stratification of the responses of these areas into one culture of a visually inspired brain.

In this chapter we propose precise operational criteria of systematicity that reveal a connection between the notion of systematicity and causal roles for category membership. We argue that neural network approaches that build on the assumption that grammatical knowledge is encoded implicitly, such as Elman's SRN, fall short of demonstrating systematic behavior precisely because such implicit knowledge plays no causal role in the network dynamics. On the other hand neural networks that employ explicit, encapsulated representations (i.e., representations that encapsulate contextual details) do enable categories to play causal roles. We draw upon insights from neurobiology to show how the hierarchical, columnar organization of the cortex in fact provides a basis for encapsulated representations that are invariant. We then sketch a novel approach to neural network modeling that illustrates how encapsulated representations can be operated on and dynamically bound into complex representations, producing rule-like, systematic behavior capable of dealing with hierarchical syntax.