The brain as a symbol-processing machine

Rocha, Armando Freitas da

doi:10.1016/s0301-0082(97)00015-4

Cited by 28 publications

(20 citation statements)

References 232 publications

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“…In addition gene expression is subject to environmental influence and learning (e.j., Bjorklund, Dunnett, 2007;Graff-Guerrero et al, 2005;Judith etl al, 2012;Rocha et al, 2006;Rocha, 1997) such that both genetic and environmental variability make motivation to vary among people and during their life time.…”

Section: Inheritance and Learning Determine Motivationmentioning

confidence: 99%

“…Pleasant outcomes reinforce knowledge that maps necessity into the implemented actions, while unpleasant outcomes contribute to reinforce mapping of necessity to alternative actions other than the implemented ones. It have been shown (e.g, Bayer and Glimcher, 2005;Rocha, 1997;Rocha et al, 1998Rocha et al, , 2005Schultz, 2002;Schultz, 2004;2007) that neurons in various areas of the brain evaluate experienced benefit concerning the expected one, having their activity increased if experienced exceeds expected benefit or decreased in the opposite case.…”

Section: Inheritance and Learning Determine Motivationmentioning

confidence: 99%

“…This means that any living being has as primordial goals to at least obtain the resources to synthesize their constituent chemicals, to obtain energy to support the chemical transactions and to allow it to creating other similar living beings, that is to say, reproduce itself (Rocha andRocha, 2011, Rocha, 2013). Because resources are finite, goal achievement and maintenance creates necessity for goods and services.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Free Will from the Neuroscience Point of View

Rocha¹,

Rocha²

2013

SSRN Journal

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Section: Inheritance and Learning Determine Motivationmentioning

confidence: 99%

Section: Inheritance and Learning Determine Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Free Will from the Neuroscience Point of View

Rocha¹,

Rocha²

2013

SSRN Journal

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“…DIPS intelligence is, therefore, a function of the types of tools used by its agents, as well as how and for what purpose these tools are used [3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

The Brain as a Distributed Intelligent Processing System: An EEG Study

Rocha¹,

Rocha²,

Massad

2009

SSRN Journal

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Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS.

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“…Intelligent agents theory is a fascinating topic in modern science [75][76][77][78][79]. Decision making transitions depend to high degree on global factors influencing an ensemble of independent learners.…”

Section: Co On Nc CL Lu Ud Di In Ng G R Re Em Ma Ar Rk Ks Smentioning

confidence: 99%

Mean-Field Theory of Meta-learning

SpringerReference

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A Ab bs st tr ra ac ct tWe discuss here the mean-field theory for a cellular automata model of meta-learning. The metalearning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents, that acquire and process incoming information using various types, or different versions of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share opposite classification outcome can be observed in the system. Therefore, the probability of selecting proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are build from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents.

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The brain as a symbol-processing machine

Cited by 28 publications

References 232 publications

Free Will from the Neuroscience Point of View

Free Will from the Neuroscience Point of View

The Brain as a Distributed Intelligent Processing System: An EEG Study

Mean-Field Theory of Meta-learning

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