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4 - Attention, Perception, and Knowledge
- Carolyn Dicey Jennings
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- Book:
- The Attending Mind
- Published online:
- 14 February 2020
- Print publication:
- 05 March 2020, pp 75-116
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Summary
Many think of the difference between sensation and perception in terms of information – perception carries information, but sensations, the raw feels that occur prior to perception, do not. A standard, biological account of information holds that it occurs only for information consumers. How is it that sensations become informational, and to whom do they become informational? In this chapter I argue that perception comes about due to attention directed by a subject. Attention is the process by which sensations are organized according to the subject’s interests, allowing them to have meaning for the subject, or to become informational for the subject. I compare this account to those that find attention to be necessary for the binding of features into objects, the creation of an objective spatial framework, or perceptual knowledge. I reject the first two accounts, ultimately arguing for a similar conclusion to those in the third, such as Campbell and Dickie. Experiential support for my account is the universal foreground/background structure of conscious perception, a structure that I argue depends on attention. Along the way I discuss at length the work of Treisman and Merleau-Ponty.
From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony
- Lokendra Shastri, Venkat Ajjanagadde
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- Journal:
- Behavioral and Brain Sciences / Volume 16 / Issue 3 / September 1993
- Published online by Cambridge University Press:
- 04 February 2010, pp. 417-451
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Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization.
Stimulus-dependent correlated firing in directionally selective retinal ganglion cells
- FRANKLIN R. AMTHOR, JOHN S. TOOTLE, NORBERTO M. GRZYWACZ
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- Journal:
- Visual Neuroscience / Volume 22 / Issue 6 / November 2005
- Published online by Cambridge University Press:
- 03 February 2006, pp. 769-787
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Synchronous spiking has been postulated to be a meta-signal in visual cortex and other CNS loci that tags neuronal spike responses to a single entity. In retina, however, synchronized spikes have been postulated to arise via mechanisms that would largely preclude their carrying such a code. One such mechanism is gap junction coupling, in which synchronous spikes would be a by-product of lateral signal sharing. Synchronous spikes have also been postulated to arise from common-source inputs to retinal ganglion cells having overlapping receptive fields, and thus code for stimulus location in the overlap area. On–Off directionally selective ganglion cells of the rabbit retina exhibit a highly precise tiling pattern in which gap junction coupling occurs between some neighboring, same-preferred-direction cells. Depending on how correlated spikes arise, and for what purpose, one could postulate that synchronized spikes in this system (1) always arise in some subset of same-direction cells because of gap junctions, but never in non-same-preferred-directional cells; (2) never arise in same-directional cells because their receptive fields do not overlap, but arise only in different-directional cells whose receptive fields overlap, as a code for location in the overlap region; or (3) arise in a stimulus-dependent manner for both same- and different-preferred-direction cells for a function similar to that postulated for neurons in visual cortex. Simultaneous, extracellular recordings were obtained from neighboring On–Off directionally selective (DS) ganglion cells having the same and different preferred directions in an isolated rabbit retinal preparation. Stimulation by large flashing spots elicited responses from DS ganglion-cell pairs that typically showed little synchronous firing. Movement of extended bars, however, often produced synchronous spikes in cells having similar or orthogonal preferred directions. Surprisingly, correlated firing could occur for the opposite contrast polarity edges of moving stimuli when the leading edge of a sweeping bar excited the receptive field of one cell as its trailing edge stimulated another. Pharmacological manipulations showed that the spike synchronization is enhanced by excitatory cholinergic amacrine-cell inputs, and reduced by inhibitory GABAergic inputs, in a motion-specific manner. One possible interpretation is that this synchronous firing could be a signal to higher centers that the outputs of the two DS ganglion cells should be “bound” together as responding to a contour of a common object.
Toward a quantitative description of large-scale neocortical dynamic function and EEG
- Paul L. Nunez
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- Journal:
- Behavioral and Brain Sciences / Volume 23 / Issue 3 / June 2000
- Published online by Cambridge University Press:
- 10 October 2000, pp. 371-398
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A general conceptual framework for large-scale neocortical dynamics based on data from many laboratories is applied to a variety of experimental designs, spatial scales, and brain states. Partly distinct, but interacting local processes (e.g., neural networks) arise from functional segregation. Global processes arise from functional integration and can facilitate (top down) synchronous activity in remote cell groups that function simultaneously at several different spatial scales. Simultaneous local processes may help drive (bottom up) macroscopic global dynamics observed with electroencephalography (EEG) or magnetoencephalography (MEG).
A local/global dynamic theory that is consistent with EEG data and the proposed conceptual framework is outlined. This theory is neutral about properties of neural networks embedded in macroscopic fields, but its global component makes several qualitative and semiquantitative predictions about EEG measures of traveling and standing wave phenomena. A more general “metatheory” suggests what large-scale quantitative theories of neocortical dynamics may be like when more accurate treatment of local and nonlinear effects is achieved.
The theory describes the dynamics of excitatory and inhibitory synaptic action fields. EEG and MEG provide large-scale estimates of modulation of these synaptic fields around background levels. Brain states are determined by neuromodulatory control parameters. Purely local states are dominated by local feedback gains and rise and decay times of postsynaptic potentials. Dominant local frequencies vary with brain region. Other states are purely global, with moderate to high coherence over large distances. Multiple global mode frequencies arise from a combination of delays in corticocortical axons and neocortical boundary conditions. Global frequencies are identical in all cortical regions, but most states involve dynamic interactions between local networks and the global system. EEG frequencies may involve a “matching” of local resonant frequencies with one or more of the many, closely spaced global frequencies.