Countercurrents in Matter and Mind

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 177-177
Author(s):  
S Hochstein ◽  
M Ahissar
Keyword(s):  
Perceptual Learning ◽  
Sensory Information ◽  
External World ◽  
Learning Effects ◽  
Efficient Manner ◽  
Cortical Hierarchy ◽  
Internal Information ◽  
Task Conditions ◽  
Perceptual Systems ◽  
The Brain

An especially efficient manner of transmission of matter or energy, employed by numerous biological systems, is the countercurrent mechanism. Transfer is effected between two closely aligned streaming currents where the currents flow in opposite directions. Final transfer can be 100% rather than the 50% ceiling of concurrent streams. We now report that perceptual systems may employ a similar mechanism. Information derived from the external world by the senses is transferred to the perceptual system in a hierarchy of processing areas. Simultaneously, this information is intermixed with previously stored internal information. The degree of mixture of previously existing information, with new, unprocessed information is titrated along the hierarchy. The brain may tap various points along the countercurrents to obtain the mixtures required for different tasks. Perceptual learning affects first the inner levels of this cortical hierarchy and only later descends to their input levels to achieve better performance with more difficult task conditions. Learning effects discussed at ECVP over the last two decades are reviewed in the light of this cortical scheme. Many seemingly contradictory findings are reconciled when put in the framework of countercurrent streams which respectively process sensory information and guide perceptual learning.

Perceptual Systems, an Inexhaustible Reservoir of Information and the Importance of Art

2016 ◽  
Vol 4 (4) ◽  
pp. 265-279
Author(s):  
Sargy Mann
Keyword(s):  
Perceptual Learning ◽  
The Other ◽  
The Third ◽  
Figurative Art ◽  
Perceptual Systems ◽  
The Brain

There is my developing experience as a painter going blind which is unusual and interesting and as you know I am interested in that. But I am equally interested, possibly more interested in a conception of what figurative art can be as a way of mining new experience and in some sense or other recording it so it’s communicable. Now essentially all my drafts [of this paper] are trying to put those two together and it seems at first like a paradox, but it’s a paradox that I think I can perfectly resolve… and it’s what I want to do… the third element which is very hard to separate from the other two, is the perceptual learning applied to the perceptual systems, made possible through consciousness… That does require an analysis to do with things to do with the anatomy of the eye and the brain, which most people haven’t got a clue about but which is absolutely crucial.

scholarly journals Seeing What You Feel: Affect Drives Visual Perception of Structurally Neutral Faces

2018 ◽  
Vol 29 (4) ◽  
pp. 496-503 ◽  
Author(s):  
Erika H. Siegel ◽  
Jolie B. Wormwood ◽  
Karen S. Quigley ◽  
Lisa Feldman Barrett
Keyword(s):  
Visual Perception ◽  
Sensory Information ◽  
External World ◽  
The Body ◽  
Conscious Awareness ◽  
Interocular Suppression ◽  
Continuous Flash Suppression ◽  
Suppression Technique ◽  
Positive Stimuli ◽  
The Brain

Affective realism, the phenomenon whereby affect is integrated into an individual’s experience of the world, is a normal consequence of how the brain processes sensory information from the external world in the context of sensations from the body. In the present investigation, we provided compelling empirical evidence that affective realism involves changes in visual perception (i.e., affect changes how participants see neutral stimuli). In two studies, we used an interocular suppression technique, continuous flash suppression, to present affective images outside of participants’ conscious awareness. We demonstrated that seen neutral faces are perceived as more smiling when paired with unseen affectively positive stimuli. Study 2 also demonstrated that seen neutral faces are perceived as more scowling when paired with unseen affectively negative stimuli. These findings have implications for real-world situations and challenge beliefs that affect is a distinct psychological phenomenon that can be separated from cognition and perception.

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

Modulation of motivation and attention in perceptual learning: effects upon learning and cortical representation?

2010 ◽  
Vol 41 (01) ◽  
Author(s):  
S Lissek ◽  
O Höffken ◽  
P Stude ◽  
V Nicolas ◽  
H Dinse ◽  
...  
Keyword(s):  
Perceptual Learning ◽  
Cortical Representation ◽  
Learning Effects

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

Perception

2018 ◽  
Author(s):  
Joel Z. Leibo ◽  
Tomaso Poggio
Keyword(s):  
Computational Neuroscience ◽  
Feature Detection ◽  
Pedestrian Detection ◽  
Detection Systems ◽  
Catching Up ◽  
Feature Detectors ◽  
Recognition Systems ◽  
Engineered Systems ◽  
Perceptual Systems ◽  
The Brain

This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.

Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia

2021 ◽  
pp. 1-14
Author(s):  
Debo Dong ◽  
Dezhong Yao ◽  
Yulin Wang ◽  
Seok-Jun Hong ◽  
Sarah Genon ◽  
...  
Keyword(s):  
Sensory Information ◽  
Resting State Fmri ◽  
Brain Regions ◽  
High Order ◽  
Hierarchical Organization ◽  
System Level ◽  
Integrative System ◽  
Sensorimotor Region ◽  
Cortical Hierarchy ◽  
High Level

Abstract Background Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Methods We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). Results We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal−parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). Conclusions The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory−motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

scholarly journals The emulation theory of representation: Motor control, imagery, and perception

2004 ◽  
Vol 27 (3) ◽  
pp. 377-396 ◽  
Author(s):  
Rick Grush
Keyword(s):  
Motor Control ◽  
Sensory Feedback ◽  
Sensory Input ◽  
Neural Circuits ◽  
Sensory Information ◽  
The Body ◽  
Feedback Delay ◽  
Run Off ◽  
Effects Of Feedback ◽  
The Brain

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

scholarly journals Perceptual decisions are biased toward relevant prior choices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Helen Feigin ◽  
Shira Baror ◽  
Moshe Bar ◽  
Adam Zaidel
Keyword(s):  
Sensory Information ◽  
Irrelevant Stimulus ◽  
Stimulus Feature ◽  
Serial Dependence ◽  
Low Level ◽  
Subsequent Test ◽  
Key Factor ◽  
High Level ◽  
Motor Actions ◽  
The Brain

AbstractPerceptual decisions are biased by recent perceptual history—a phenomenon termed 'serial dependence.' Here, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor actions. Participants discriminated whether a brief visual stimulus lay to left/right of the screen center. Following a series of biased ‘prior’ location discriminations, subsequent ‘test’ location discriminations were biased toward the prior choices, even when these were reported via different motor actions (using different keys), and when the prior and test stimuli differed in color. By contrast, prior discriminations about an irrelevant stimulus feature (color) did not substantially influence subsequent location discriminations, even though these were reported via the same motor actions. Additionally, when color (not location) was discriminated, a bias in prior stimulus locations no longer influenced subsequent location discriminations. Although low-level stimuli and motor actions did not trigger serial-dependence on their own, similarity of these features across discriminations boosted the effect. These findings suggest that relevance across perceptual decisions is a key factor for serial dependence. Accordingly, serial dependence likely reflects a high-level mechanism by which the brain predicts and interprets new incoming sensory information in accordance with relevant prior choices.

Perspectives on Cognitive Informatics and Cognitive Computing

2010 ◽  
Vol 4 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Yingxu Wang ◽  
George Baciu ◽  
Yiyu Yao ◽  
Witold Kinsner ◽  
Keith Chan ◽  
...  
Keyword(s):  
Computational Linguistics ◽  
Computational Intelligence ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain ◽  
Science Information ◽  
Intelligence Science

Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

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