Dopaminergic and non-dopaminergic value systems in conditioning and outcome-specific revaluation

Dranias, Mark R.; Grossberg, Stephen; Bullock, Daniel

doi:10.1016/j.brainres.2008.07.013

Cited by 70 publications

(63 citation statements)

References 184 publications

(221 reference statements)

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“…A cognitive-emotional model, CogEM, of reinforcement learning and motivated attention (Grossberg & Schmajuk, 1987) was used alongside ART neural networks in a theory of consumer motivation (Leven & Levine, 1996). CogEM was further developed and became the precursor of the more general MOTIVATOR model (Dranias, Grossberg, & Bullock, 2008), which explained how cognitive-emotional resonances occur between brain areas that code subjective value and form the basis of behavioural choices. Levine (2006) proposed a neural model for the interaction of selfishness and empathy in economic actions.…”

Section: Introductionmentioning

confidence: 99%

Person-by-person prediction of intuitive economic choice

Mengov

2014

Neural Networks

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Section: Introductionmentioning

confidence: 99%

Person-by-person prediction of intuitive economic choice

Mengov

2014

Neural Networks

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“…In particular, activity in the orbitofrontal cortex is involved in producing of expectations that facilitate object recognition (Bechara et al, 1996; Frith and Dolan, 1997; Bischoff-Grethe et al, 2000; Carlsson et al, 2000; Petrides et al, 2002). ARTSCAN Search, and its precursors in the CogEM, MOTIVATOR, and START models, simulate how the activation of IT is capable of learning a cognitive-emotional ITa-AMYG-ORB resonance that supports motivated attention to top-down enhance an object category representation and thus facilitate its recognition (Grossberg, 1975; Grossberg and Levine, 1987; Grossberg and Merrill, 1992; Grossberg and Seidman, 2006; Dranias et al, 2008). ARTSCAN Search further clarifies how a cognitively-mediated search that engages PFC, and a motivationally-mediated search that engages AMYG, can utilize these circuits.…”

Section: Discussion and Related Modelsmentioning

confidence: 99%

“…This theoretical synthesis unifies and extends several previous neural models, notably the ARTSCAN model of view-invariant object category learning (Grossberg, 2007, 2009; Fazl et al, 2009; Foley et al, 2012), its extension to the positionally-invariant ARTSCAN, or pARTSCAN, model of view-, position-, and size-invariant object category learning (Cao et al, 2011), and the CogEM (Cognitive-Emotional-Motor) model of cognitive-emotional learning and motivated attention (Grossberg, 1971, 1972a,b, 1975, 1982, 1984; Grossberg and Levine, 1987; Grossberg and Schmajuk, 1987; Grossberg and Seidman, 2006; Dranias et al, 2008; Grossberg et al, 2008). pARTSCAN’s ability to recognize objects in multiple positions is needed as part of the Where’s Waldo search process.…”

Section: Introductionmentioning

confidence: 99%

Whereâ€™s Waldo? How perceptual, cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene

Chang

Grossberg

Cao

2014

Front. Integr. Neurosci.

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The Where’s Waldo problem concerns how individuals can rapidly learn to search a scene to detect, attend, recognize, and look at a valued target object in it. This article develops the ARTSCAN Search neural model to clarify how brain mechanisms across the What and Where cortical streams are coordinated to solve the Where’s Waldo problem. The What stream learns positionally-invariant object representations, whereas the Where stream controls positionally-selective spatial and action representations. The model overcomes deficiencies of these computationally complementary properties through What and Where stream interactions. Where stream processes of spatial attention and predictive eye movement control modulate What stream processes whereby multiple view- and positionally-specific object categories are learned and associatively linked to view- and positionally-invariant object categories through bottom-up and attentive top-down interactions. Gain fields control the coordinate transformations that enable spatial attention and predictive eye movements to carry out this role. What stream cognitive-emotional learning processes enable the focusing of motivated attention upon the invariant object categories of desired objects. What stream cognitive names or motivational drives can prime a view- and positionally-invariant object category of a desired target object. A volitional signal can convert these primes into top-down activations that can, in turn, prime What stream view- and positionally-specific categories. When it also receives bottom-up activation from a target, such a positionally-specific category can cause an attentional shift in the Where stream to the positional representation of the target, and an eye movement can then be elicited to foveate it. These processes describe interactions among brain regions that include visual cortex, parietal cortex, inferotemporal cortex, prefrontal cortex (PFC), amygdala, basal ganglia (BG), and superior colliculus (SC).

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“…This dichotomy between the evaluation of the frequency of reward delivery by the DA system, and the valuation of behavioral strategies in OFC is of particular biological relevance in order to understand the role of emotions in decision-making. Dranias et al proposed an anatomically realistic model of motivation called MOTIVATOR that successfully addresses classical conditioning, visual discrimination, devaluation, extinction and reversal learning [12]. Its architecture contains most of the structures depicted on Fig.…”

Section: Emotional Modelsmentioning

confidence: 99%

A Neuroscientific View on the Role of Emotions in Behaving Cognitive Agents

Vitay

Hamker

2011

Künstl Intell

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While classical theories systematically opposed emotion and cognition, suggesting that emotions perturbed the normal functioning of the rational thought, recent progress in neuroscience highlights on the contrary that emotional processes are at the core of cognitive processes, directing attention to emotionally-relevant stimuli, favoring the memorization of external events, valuating the association between an action and its consequences, biasing decision making by allowing to compare the motivational value of different goals and, more generally, guiding behavior towards fulfilling the needs of the organism. This article first proposes an overview of the brain areas involved in the emotional modulation of behavior and suggests a functional architecture allowing to perform efficient decision making. It then reviews a series of biologically-inspired computational models of emotion dealing with behavioral tasks like classical conditioning and decision making, which highlight the computational mechanisms involved in emotional behavior. It underlines the importance of embodied cognition in artificial intelligence, as emotional processing is at the core of the cognitive computations deciding which behavior is more appropriate for the agent.

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Dopaminergic and non-dopaminergic value systems in conditioning and outcome-specific revaluation

Cited by 70 publications

References 184 publications

Person-by-person prediction of intuitive economic choice

Person-by-person prediction of intuitive economic choice

Whereâ€™s Waldo? How perceptual, cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene

A Neuroscientific View on the Role of Emotions in Behaving Cognitive Agents

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