Greater Disruption Due to Failure of Inhibitory Control on an Ambiguous Distractor

Tsushima, Yoshiaki; Sasaki, Yuka; Watanabe, Takeo

doi:10.1126/science.1133197

Cited by 160 publications

(202 citation statements)

References 29 publications

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“…Several experimental results on attention and awareness have been interpreted as consistent with this prediction (McCormick, 1997;Tsushima et al, 2006;Lin and Murray, 2015;Webb and Graziano, 2015;Webb et al, 2016a), though more experiments are needed. Thus far, the relevant experiments have focused on visual attention and visual awareness.…”

Section: The Adaptive Value Of An Attention Schema: Control Of Attentionmentioning

confidence: 60%

“…When people are unaware of a visual stimulus, they can still sometimes focus processing resources on it. They can direct attention to it (McCormick, 1997;Lamme, 2003;Woodman and Luck, 2003;Ansorge and Heumann, 2006;Tsushima et al, 2006;Kentridge et al, 2008;Hsieh et al, 2011;Norman et al, 2013). However, in that case, visual attention suffers deficits in control.…”

Section: The Adaptive Value Of An Attention Schema: Control Of Attentionmentioning

confidence: 99%

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The Attention Schema Theory: A Foundation for Engineering Artificial Consciousness

Graziano

2017

Front. Robot. AI

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The purpose of the attention schema theory is to explain how an information-processing device, the brain, arrives at the claim that it possesses a non-physical, subjective awareness and assigns a high degree of certainty to that extraordinary claim. The theory does not address how the brain might actually possess a non-physical essence. It is not a theory that deals in the non-physical. It is about the computations that cause a machine to make a claim and to assign a high degree of certainty to the claim. The theory is offered as a possible starting point for building artificial consciousness. Given current technology, it should be possible to build a machine that contains a rich internal model of what consciousness is, attributes that property of consciousness to itself and to the people it interacts with, and uses that attribution to make predictions about human behavior. Such a machine would "believe" it is conscious and act like it is conscious, in the same sense that the human machine believes and acts.

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Section: The Adaptive Value Of An Attention Schema: Control Of Attentionmentioning

confidence: 60%

Section: The Adaptive Value Of An Attention Schema: Control Of Attentionmentioning

confidence: 99%

The Attention Schema Theory: A Foundation for Engineering Artificial Consciousness

Graziano

2017

Front. Robot. AI

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“…(cf. Tsushima et al 2006;Bahrami et al 2007;Chun and Marois 2002;Keller 2011;Marchetti 2012;Rees 2013;Baars 2013;Tsuchiya and van Boxtel 2013), the attention-based scaling item in the SynQ measurement inventory is highly provisional and further experimental disentanglement of relations between synaesthetic induction and endogenous attention/ concentration within the stimulus/response event will be needed (see Item #8 in Exhibit 1).…”

Section: Domains and Scale Value Identificationmentioning

confidence: 99%

Synaesthesia quotient: operationalising an individual index of phenotypic expressivity of developmental synaesthesia

Sidoroff-Dorso

2014

THS

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Abstract. The primary purpose of our current study is to develop a novel selfadministered or/and interviewer-assisted instrument rating an individual degree of phenotypic expressivity of synaesthesia. A measurement index of such a degree is conceptualised as Synaesthesia Quotient (SynQ). This article will detail the initial stage of the scale development; i.e., conceptualisation, domains identification, item generation, and identification of rating values of the proposed scale. Ten preliminary domains are determined and related items are generated on the basis of empirical data from synaesthesia literature review, extant measures, and external neuroscientific results. Further work is underway to perform judgment-based item expansion (or reduction) informed by expert opinion and to assess the validity and reliability of the Synaesthesia Quotient inventory (SynQ-i). This paper is also intended to solicit postpublication feedback and generate specialist discussion.

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“…(e.g. Zihl et al 1983;Vaina et al 1998;Tsushima et al 2006;Huxlin et al 2009;Furlan and Schwarzbach 2009) and hence we assume that groups of neurons within this area might respond to stimuli moving in a specified direction. 2 For the purpose of the simulations we considered eight groups or pools of neurons which respond when a substantial amount of dots move coherently into their preferred direction.…”

Section: General Setupmentioning

confidence: 99%

“…Furthermore, neuroimaging studies demonstrated that changes in cortical activations might accompany perceptual learning in general (e.g. and that attention modulates neuronal activities for different levels of motion coherence (Furlan and Schwarzbach 2009;Tsushima et al 2006). Recently Huxlin et al (2009) even demonstrated that motion discrimination might be relearned after damage to the primary visual cortex.…”

Section: Introductionmentioning

confidence: 99%

Perceptual learning with perceptions

2010

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In this work we present an approach to understand neuronal mechanisms underlying perceptual learning. Experimental results achieved with stimulus patterns of coherently moving dots are considered to build a simple neuronal model. The design of the model is made transparent and underlying behavioral assumptions made explicit. The key aspect of the suggested neuronal model is the learning algorithm used: We evaluated an implementation of Hebbian learning and are thus able to provide a straight-forward model capable to explain the neuronal dynamics underlying perceptual learning. Moreover, the simulation results suggest a very simple explanation for the aspect of ''sub-threshold'' learning (Watanabe et al. in Nature 413:844-884, 2001) as well as the relearning of motion discrimination after damage to primary visual cortex as recently reported (Huxlin et al. in J Neurosci 29:3981-3991, 2009) and at least indicate that perceptual learning might only occur when accompanied by conscious percepts.

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Greater Disruption Due to Failure of Inhibitory Control on an Ambiguous Distractor

Cited by 160 publications

References 29 publications

The Attention Schema Theory: A Foundation for Engineering Artificial Consciousness

The Attention Schema Theory: A Foundation for Engineering Artificial Consciousness

Synaesthesia quotient: operationalising an individual index of phenotypic expressivity of developmental synaesthesia

Perceptual learning with perceptions

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