A Normalization Framework for Emotional Attention

Zhang, Xilin; Japee, Shruti; Safiullah, Zaid; Mlynaryk, Nicole; Ungerleider, Leslie G.

doi:10.1371/journal.pbio.1002578

Cited by 44 publications

(62 citation statements)

References 72 publications

(101 reference statements)

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“…While some discrepancies between the putative mechanisms are likely due to differences in stimulus display properties, task designs and methods of measuring neural activity [15,45–48,69–71], we show here that another important factor is the duration of training. Similar to previous studies, we found that early in training, attentional gain of the visual P1 component accurately predicted attention-induced behavioral benefits [9,19,31].…”

Section: Discussionmentioning

confidence: 72%

Two different mechanisms support selective attention at different phases of training

et al. 2017

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Selective attention supports the prioritized processing of relevant sensory information to facilitate goal-directed behavior. Studies in human subjects demonstrate that attentional gain of cortical responses can sufficiently account for attention-related improvements in behavior. On the other hand, studies using highly trained nonhuman primates suggest that reductions in neural noise can better explain attentional facilitation of behavior. Given the importance of selective information processing in nearly all domains of cognition, we sought to reconcile these competing accounts by testing the hypothesis that extensive behavioral training alters the neural mechanisms that support selective attention. We tested this hypothesis using electroencephalography (EEG) to measure stimulus-evoked visual responses from human subjects while they performed a selective spatial attention task over the course of~1 month. Early in training, spatial attention led to an increase in the gain of stimulusevoked visual responses. Gain was apparent within~100 ms of stimulus onset, and a quantitative model based on signal detection theory (SDT) successfully linked the magnitude of this gain modulation to attention-related improvements in behavior. However, after extensive training, this early attentional gain was eliminated even though there were still substantial attention-related improvements in behavior. Accordingly, the SDT-based model required noise reduction to account for the link between the stimulus-evoked visual responses and attentional modulations of behavior. These findings suggest that training can lead to fundamental changes in the way attention alters the early cortical responses that support selective information processing. Moreover, these data facilitate the translation of results across different species and across experimental procedures that employ different behavioral training regimes. Author summarySelective attention can enhance processing of sensory information via sensory gain and neural noise reduction. However, the extent to which these 2 mechanisms contribute to improvement in perceptual performance during attention is still debated. We hypothesized PLOS Biology | https://doi.org/10.1371/journal.pbio

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Section: Discussionmentioning

confidence: 72%

Two different mechanisms support selective attention at different phases of training

et al. 2017

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“…Attention is the main mechanism that controls this selection process. Numerous studies have demonstrated that attentional selection can be based on either a visual feature (Liu et al, 2007; Maunsell and Treue, 2006; Saenz et al, 2002; Serences and Boynton, 2007; Treue and Martinez-Trujillo, 1999; Zhang and Luck, 2009) or a spatial location (Brefczynski and DeYoe, 1999; Corbetta and Shulman, 2002; Kanwisher and Wojciulik, 2000; Kastner and Ungerleider, 2000; Martínez et al, 1999; Posner et al, 1980; Tootell et al, 1998; Zhang et al, 2012, 2016). Increasing evidence has indicated that attentional selection can also be deployed to entire objects: specific objects can be selected by directing attention to their features or to their locations (Scholl, 2001; Chen, 2012).…”

Section: Introductionmentioning

confidence: 99%

Attentional selection of multiple objects in the human visual system

et al. 2017

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Classic theories of object-based attention assume a single object of selection but real-world tasks, such as driving a car, often require attending to multiple objects simultaneously. However, whether object-based attention can operate on more than one object at a time remains unexplored. Here, we used functional magnetic resonance imaging (fMRI) to address this question as human participants performed object-based attention tasks that required simultaneous attention to two objects differing in either their features or locations. Simultaneous attention to two objects differing in features (face and house) did not show significantly different responses in the fusiform face area (FFA) or parahippocampal place area (PPA), respectively, compared to attending a single object (face or house), but did enhance the response in the inferior frontal gyrus (IFG). Simultaneous attention to two circular arcs differing in locations did not show significantly different responses in the primary visual cortex (V1) compared to attending a single circular arc, but did enhance the response in the intraparietal sulcus (IPS). These results suggest that object-based attention can simultaneously select at least two objects differing in their features or locations, processes mediated by the frontal and parietal cortex, respectively.

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“…Recent studies using quantitative modeling suggest that there are several candidate mechanisms that can link attentional modulations in visual cortex with attentional modulations of behavior (14,25,26,33,50). While some discrepancies between the putative mechanisms are likely due to differences in stimulus display properties and task designs (15,41,(65)(66)(67) and methods of measuring neural activity (14,42,43,65), we show here that another major factor is the duration of training. Going beyond previous studies of training that have shown changes in attentional modulations, we quantitatively modeled the relationship between neural activity and behavior to systematically examine the relative contributions of different attention mechanisms (68)(69)(70)(71)(72).…”

Section: Discussionmentioning

confidence: 57%

Training qualitatively shifts the neural mechanisms that support attentional selection

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Byers

et al. 2016

Preprint

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words)Attention supports the selection of relevant sensory information from competing irrelevant sensory information. This selective processing is thought to be supported via the attentional gain amplification of sensory responses evoked by attended compared to unattended stimuli. However, recent studies in highly trained subjects suggest that attentional gain plays a relatively modest role and that other types of neural modulations -such as a reduction in neural noise -better explain attention-related changes in behavior. We hypothesized that the amount of training may alter neural mechanisms that support attentional selection in visual cortex. To test this hypothesis, we investigated the influence of training on attentional modulations of stimulus-evoked visual responses by recording electroencephalography (EEG) from humans performing a selective visuospatial attention task over the course of one month. Early in training, visuospatial attention induced a robust attentional gain amplification of sensory-evoked responses in contralateral visual cortex that emerged within ~100ms after stimulus onset, and a quantitative model based on signal detection theory (SDT) successfully linked this attentional gain amplification to attention-related improvements in behavior. However, after training, this attentional gain amplification of visual responses was almost completely eliminated and modeling suggested that noise reduction was required to link the amplitude of visual responses with attentional modulations of behavior. These findings suggest that the neural mechanisms supporting selective attention can change as a function of training and expertise, and help to bridge different results from studies carried out in different model systems that require substantially different amount of training.

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A Normalization Framework for Emotional Attention

Cited by 44 publications

References 72 publications

Two different mechanisms support selective attention at different phases of training

Two different mechanisms support selective attention at different phases of training

Attentional selection of multiple objects in the human visual system

Training qualitatively shifts the neural mechanisms that support attentional selection

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