Sustained attention operates via dissociable neural mechanisms across different eccentric locations.

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

Sustained attention operates via dissociable neural mechanisms across different eccentric locations.

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

Attentive pairwise interaction network for AI-assisted clock drawing test assessment of early visuospatial deficits.

Dementia is a debilitating neurological condition which impairs the cognitive function and the ability to take care of oneself. The Clock Drawing Test (CDT) is widely used to detect dementia, but differentiating normal from borderline cases requires years of clinical experience. Misclassifying mild abnormal as normal will delay the chance to investigate for potential reversible causes or slow down the progression. To help address this issue, we propose an automatic CDT scoring system that adopts Attentive Pairwise Interaction Network (API-Net), a fine-grained deep learning model that is designed to distinguish visually similar images. Inspired by how humans often learn to recognize different objects by looking at two images side-by-side, API-Net is optimized using image pairs in a contrastive manner, as opposed to standard supervised learning, which optimizes a model using individual images. In this study, we extend API-Net to infer Shulman CDT scores from a dataset of 3108 subjects. We compare the performance of API-Net to that of convolutional neural networks: VGG16, ResNet-152, and DenseNet-121. The best API-Net achieves an F1-score of 0.79, which is a 3% absolute improvement over ResNet-152's F1-score of 0.76. The code for API-Net and the dataset used have been made available at https://github.com/cccnlab/CDT-API-Network .

Dissociable Neural Mechanisms Underlie the Effects of Attention on Visual Appearance and Response Bias.

A prominent theoretical framework spanning philosophy, psychology, and neuroscience holds that selective attention penetrates early stages of perceptual processing to alter the subjective visual experience of behaviorally relevant stimuli. For example, searching for a red apple at the grocery store might make the relevant color appear brighter and more saturated compared with seeing the exact same red apple while searching for a yellow banana. In contrast, recent proposals argue that data supporting attention-related changes in appearance reflect decision- and motor-level response biases without concurrent changes in perceptual experience. Here, we tested these accounts by evaluating attentional modulations of EEG responses recorded from male and female human subjects while they compared the perceived contrast of attended and unattended visual stimuli rendered at different levels of physical contrast. We found that attention enhanced the amplitude of the P1 component, an early evoked potential measured over visual cortex. A linking model based on signal detection theory suggests that response gain modulations of the P1 component track attention-induced changes in perceived contrast as measured with behavior. In contrast, attentional cues induced changes in the baseline amplitude of posterior alpha band oscillations (∼9-12 Hz), an effect that best accounts for cue-induced response biases, particularly when no stimuli are presented or when competing stimuli are similar and decisional uncertainty is high. The observation of dissociable neural markers that are linked to changes in subjective appearance and response bias supports a more unified theoretical account and demonstrates an approach to isolate subjective aspects of selective information processing.SIGNIFICANCE STATEMENT Does attention alter visual appearance, or does it simply induce response bias? In the present study, we examined these competing accounts using EEG and linking models based on signal detection theory. We found that response gain modulations of the visually evoked P1 component best accounted for attention-induced changes in visual appearance. In contrast, cue-induced baseline shifts in alpha band activity better explained response biases. Together, these results suggest that attention concurrently impacts visual appearance and response bias, and that these processes can be experimentally isolated.

Perceptual Difficulty Regulates Attentional Gain Modulations in Human Visual Cortex.

Perceptual difficulty is sometimes used to manipulate selective attention. However, these two factors are logically distinct. Selective attention is defined by priority given to specific stimuli based on their behavioral relevance, whereas perceptual difficulty is often determined by perceptual demands required to discriminate relevant stimuli. That said, both perceptual difficulty and selective attention are thought to modulate the gain of neural responses in early sensory areas. Previous studies found that selectively attending to a stimulus or increasing perceptual difficulty enhanced the gain of neurons in visual cortex. However, some other studies suggest that perceptual difficulty can have either a null or even reversed effect on gain modulations in visual cortex. According to Yerkes-Dodson's Law, it is possible that this discrepancy arises because of an interaction between perceptual difficulty and attentional gain modulations yielding a nonlinear inverted-U function. Here, we used EEG to measure modulations in the visual cortex of male and female human participants performing an attention-cueing task where we systematically manipulated perceptual difficulty across blocks of trials. The behavioral and neural data implicate a nonlinear inverted-U relationship between selective attention and perceptual difficulty: a focused-attention cue led to larger response gain in both neural and behavioral data at intermediate difficulty levels compared with when the task was more or less difficult. Moreover, difficulty-related changes in attentional gain positively correlated with those predicted by quantitative modeling of the behavioral data. These findings suggest that perceptual difficulty mediates attention-related changes in perceptual performance via selective neural modulations in human visual cortex.SIGNIFICANCE STATEMENT Both perceptual difficulty and selective attention are thought to influence perceptual performance by modulating response gain in early sensory areas. That said, less is known about how selective attention interacts with perceptual difficulty. Here, we measured neural gain modulations in the visual cortex of human participants performing an attention-cueing task where perceptual difficulty was systematically manipulated. Consistent with Yerkes-Dodson's Law, our behavioral and neural data implicate a nonlinear inverted-U relationship between selective attention and perceptual difficulty. These results suggest that perceptual difficulty mediates attention-related changes in perceptual performance via selective neural modulations in visual cortex, extending our understanding of the attentional operation under different levels of perceptual demands.

An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks.

BACKGROUND: Mild cognitive impairment (MCI) is an early stage of cognitive decline which could develop into dementia. An early detection of MCI is a crucial step for timely prevention and intervention. Recent studies have developed deep learning models to detect MCI and dementia using a bedside task like the classic clock drawing test (CDT). However, it remains a challenge to predict the early stage of the disease using the CDT data alone. Moreover, the state-of-the-art deep learning techniques still face black box challenges, making it questionable to implement them in a clinical setting. METHODS: We recruited 918 subjects from King Chulalongkorn Memorial Hospital (651 healthy subjects and 267 MCI patients). We propose a novel deep learning framework that incorporates data from the CDT, cube-copying, and trail-making tests. Soft label and self-attention were applied to improve the model performance and provide a visual explanation. The interpretability of the visualization of our model and the Grad-CAM approach were rated by experienced medical personnel and quantitatively evaluated using intersection over union (IoU) between the models' heat maps and the regions of interest. RESULTS: Rather than using a single CDT image in the baseline VGG16 model, using multiple drawing tasks as inputs into our proposed model with soft label significantly improves the classification performance between the healthy aging controls and the MCI patients. In particular, the classification accuracy increases from 0.75 (baseline model) to 0.81. The F1-score increases from 0.36 to 0.65, and the area under the receiver operating characteristic curve (AUC) increases from 0.74 to 0.84. Compared to the multi-input model that also offers interpretable visualization, i.e., Grad-CAM, our model receives higher interpretability scores given by experienced medical experts and higher IoUs. CONCLUSIONS: Our model achieves better classification performance at detecting MCI compared to the baseline model. In addition, the model provides visual explanations that are superior to those of the baseline model as quantitatively evaluated by experienced medical personnel. Thus, our work offers an interpretable machine learning model with high classification performance, both of which are crucial aspects of artificial intelligence in medical diagnosis.

Encoding strategy mediates the effect of electrical stimulation over posterior parietal cortex on visual short-term memory.

Over past decades, converging neuroimaging and electrophysiological findings have suggested a crucial role of posterior parietal cortex (PPC) in supporting the storage capacity of visual short-term memory (VSTM). Moreover, a few recent studies have shown that electrical stimulation over PPC can enhance VSTM capacity, making it a promising method for improving VSTM function. However, the reliability of these results is still in question because null findings have also been observed. Among studies that reported significant effects, some found increased VSTM capacity only in people with low capacity. Here, we hypothesized that subjects' encoding strategy might be a key source of these variable results. To directly test this hypothesis, we stimulated PPC using transcranial direct-current stimulation (tDCS) in male and female human subjects instructed to employ different encoding strategies during a VSTM recall task. We found that VSTM capacity was higher in subjects who were instructed to remember all items in the supra-capacity array of visual stimuli (i.e., the remember-all group), compared to subjects who were told to focus on a subset of these stimuli (i.e., the remember-subset group). As predicted, anodal tDCS over PPC significantly enhanced VSTM capacity only in the remember-subset group, but not in the remember-all group. Additionally, no effect of encoding strategy or its interaction with electrical stimulation was found on VSTM precision. Together, these results suggest that encoding strategy has a selective influence on VSTM capacity and this influence of encoding strategy mediates the effect of electrical stimulation over PPC on VSTM function.

Stimulus visibility and uncertainty mediate the influence of attention on response bias and visual contrast appearance.

Although attention is known to improve the efficacy of sensory processing, the impact of attention on subjective visual appearance is still a matter of debate. Although recent studies suggest that attention can alter the appearance of stimulus contrast, others argue that these changes reflect response bias induced by attention cues. Here, we provide evidence that attention has effects on both appearance and response bias. In a comparative judgment task in which subjects reported whether the attended or unattended visual stimulus had a higher perceived contrast, attention induced substantial baseline-offset response bias as well as small but significant changes in subjective contrast appearance when subjects viewed near-threshold stimuli. However, when subjects viewed suprathreshold stimuli, baseline-offset response bias decreased and attention primarily changed contrast appearance. To address the possibility that these changes in appearance might be influenced by uncertainty due to the attended and unattended stimuli having similar physical contrasts, subjects performed an equality judgment task in which they reported if the contrast of the two stimuli was the same or different. We found that, although there were still attention-induced changes in contrast appearance at lower contrasts, the robust changes in contrast appearance at higher contrasts observed in the comparative judgment task were diminished in the equality judgment task. Together, these results suggest that attention can impact both response bias and appearance, and these two types of attention effects are differentially mediated by stimulus visibility and uncertainty. Collectively, these findings help constrain arguments about the cognitive penetrability of perception.

Value-driven attentional capture enhances distractor representations in early visual cortex.

When a behaviorally relevant stimulus has been previously associated with reward, behavioral responses are faster and more accurate compared to equally relevant but less valuable stimuli. Conversely, task-irrelevant stimuli that were previously associated with a high reward can capture attention and distract processing away from relevant stimuli (e.g., seeing a chocolate bar in the pantry when you are looking for a nice, healthy apple). Although increasing the value of task-relevant stimuli systematically up-regulates neural responses in early visual cortex to facilitate information processing, it is not clear whether the value of task-irrelevant distractors influences behavior via competition in early visual cortex or via competition at later stages of decision-making and response selection. Here, we measured functional magnetic resonance imaging (fMRI) in human visual cortex while subjects performed a value-based learning task, and we applied a multivariate inverted encoding model (IEM) to assess the fidelity of distractor representations in early visual cortex. We found that the fidelity of neural representations related to task-irrelevant distractors increased when the distractors were previously associated with a high reward. This finding suggests that value-driven attentional capture begins with sensory modulations of distractor representations in early areas of visual cortex.

Functional MRI and EEG Index Complementary Attentional Modulations.

Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two noninvasive methods commonly used to study neural mechanisms supporting visual attention in humans. Studies using these tools, which have complementary spatial and temporal resolutions, implicitly assume they index similar underlying neural modulations related to external stimulus and internal attentional manipulations. Accordingly, they are often used interchangeably for constraining understanding about the impact of bottom-up and top-down factors on neural modulations. To test this core assumption, we simultaneously manipulated bottom-up sensory inputs by varying stimulus contrast and top-down cognitive modulations by changing the focus of spatial attention. Each of the male and female subjects participated in both fMRI and EEG sessions performing the same experimental paradigm. We found categorically different patterns of attentional modulation on fMRI activity in early visual cortex and early stimulus-evoked potentials measured via EEG (e.g., the P1 component and steady-state visually-evoked potentials): fMRI activation scaled additively with attention, whereas evoked EEG components scaled multiplicatively with attention. However, across longer time scales, a contralateral negative-going potential and oscillatory EEG signals in the alpha band revealed additive attentional modulation patterns like those observed with fMRI. These results challenge prior assumptions that fMRI and early stimulus-evoked potentials measured with EEG can be interchangeably used to index the same neural mechanisms of attentional modulations at different spatiotemporal scales. Instead, fMRI measures of attentional modulations are more closely linked with later EEG components and alpha-band oscillations. Considered together, hemodynamic and electrophysiological signals can jointly constrain understanding of the neural mechanisms supporting cognition.SIGNIFICANCE STATEMENT fMRI and EEG have been used as tools to measure the location and timing of attentional modulations in visual cortex and are often used interchangeably for constraining computational models under the assumption that they index similar underlying neural processes. However, by varying attentional and stimulus parameters, we found differential patterns of attentional modulations of fMRI activity in early visual cortex and commonly used stimulus-evoked potentials measured via EEG. Instead, across longer time scales, a contralateral negative-going potential and EEG oscillations in the alpha band exhibited attentional modulations similar to those observed with fMRI. Together, these results suggest that different physiological processes assayed by these complementary techniques must be jointly considered when making inferences about the neural underpinnings of cognitive operations.

When Conflict Cannot be Avoided: Relative Contributions of Early Selection and Frontal Executive Control in Mitigating Stroop Conflict.

When viewing familiar stimuli (e.g., common words), processing is highly automatized such that it can interfere with the processing of incompatible sensory information. At least two mechanisms may help mitigate this interference. Early selection accounts posit that attentional processes filter out distracting sensory information to avoid conflict. Alternatively, late selection accounts hold that all sensory inputs receive full semantic analysis and that frontal executive mechanisms are recruited to resolve conflict. To test how these mechanisms operate to overcome conflict induced by highly automatized processing, we developed a novel version of the color-word Stroop task, where targets and distractors were simultaneously flickered at different frequencies. We measured the quality of early sensory processing by assessing the amplitude of steady-state visually evoked potentials (SSVEPs) elicited by targets and distractors. We also indexed frontal executive processes by assessing changes in frontal theta oscillations induced by color-word incongruency. We found that target- and distractor-related SSVEPs were not modulated by changes in the level of conflict whereas frontal theta activity increased on high compared to low conflict trials. These results suggest that frontal executive processes play a more dominant role in mitigating cognitive interference driven by the automatic tendency to process highly familiar stimuli.

Electrical Stimulation Over Human Posterior Parietal Cortex Selectively Enhances the Capacity of Visual Short-Term Memory.

Visual short-term memory (VSTM) provides an on-line mental space for incoming sensory information to be temporally maintained to carry out complex behavioral tasks. Despite its essential functions, the capacity at which VSTM could maintain sensory information is limited (i.e., VSTM can hold only about three to four visual items at once). Moreover, the quality of sensory representation (i.e., precision) degrades as more information has to be maintained in VSTM. Correlational evidence suggests that the level and the pattern of neural activity measured in the posterior parietal cortex (PPC) track both VSTM capacity and precision. However, the causal contributions of the PPC to these different VSTM operations are unclear. Here, we tested whether stimulating the PPC with transcranial direct current stimulation (tDCS) could increase VSTM capacity or precision. We found that stimulating the PPC in male and female human participants selectively enhanced VSTM capacity when the number of memory items exceeded capacity limit, without significant effects on VSTM precision. Moreover, this enhancement of VSTM capacity is region specific as stimulating the prefrontal cortex did not change VSTM capacity or precision. Null stimulation effects in the sensory memory condition confirmed that the tDCS-induced enhancement of VSTM capacity was not simply due to changes in sensory or attentional processes. Altogether, these results provide causal evidence suggesting that the PPC has a more dominant role in supporting the storage capacity of VSTM compared with maintaining the quality of sensory representations. Furthermore, tDCS could be used as a promising noninvasive method to enhance this PPC VSTM-related function.SIGNIFICANCE STATEMENT Correlational evidence from neuroimaging and electrophysiology suggests that the posterior parietal cortex (PPC) supports the storage capacity of visual short-term memory (VSTM) and the precision of sensory representations maintained in VSTM. However, the causal contributions of the PPC to these different VSTM functions were unclear. Here, we found that electrical stimulation over the PPC selectively enhanced VSTM capacity without changing VSTM precision. Overall, our findings suggest that the PPC has a dominant and causal role in supporting the storage capacity of VSTM.

Having More Choices Changes How Human Observers Weight Stable Sensory Evidence.

Decision-making becomes slower when more choices are available. Existing models attribute this slowing to poor sensory processing, to attenuated rates of sensory evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). However, studies have not isolated the effects of having more choices on sensory and decision-related processes from changes in task difficulty and divided attention. Here, we controlled task difficulty while independently manipulating the distribution of attention and the number of choices available to male and female human observers. We used EEG to measure steady-state visually evoked potentials (SSVEPs) and a frontal late positive deflection (LPD), EEG markers of sensory and postsensory decision-related processes, respectively. We found that dividing attention decreased SSVEP and LPD amplitudes, consistent with dampened sensory responses and slower rates of evidence accumulation, respectively. In contrast, having more choices did not alter SSVEP amplitude and led to a larger LPD. These results suggest that having more options largely spares early sensory processing and slows down decision-making via a selective increase in decision thresholds.SIGNIFICANCE STATEMENT When more choices are available, decision-making becomes slower. We tested whether this phenomenon is due to poor sensory processing, to reduced rates of evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). We measured choice modulations of sensory and decision-related neural responses using EEG. We also minimized potential confounds from changes in the distribution of attention and task difficulty, which often covary with having more choices. Dividing attention reduced the activity levels of both sensory and decision-related responses. However, having more choices did not change sensory processing and led to larger decision-related responses. These results suggest that having more choices spares sensory processing and selectively increases decision thresholds.

Expectations Do Not Alter Early Sensory Processing during Perceptual Decision-Making.

Two factors play important roles in shaping perception: the allocation of selective attention to behaviorally relevant sensory features, and prior expectations about regularities in the environment. Signal detection theory proposes distinct roles of attention and expectation on decision-making such that attention modulates early sensory processing, whereas expectation influences the selection and execution of motor responses. Challenging this classic framework, recent studies suggest that expectations about sensory regularities enhance the encoding and accumulation of sensory evidence during decision-making. However, it is possible, that these findings reflect well documented attentional modulations in visual cortex. Here, we tested this framework in a group of male and female human participants by examining how expectations about stimulus features (orientation and color) and expectations about motor responses impacted electroencephalography (EEG) markers of early sensory processing and the accumulation of sensory evidence during decision-making (the early visual negative potential and the centro-parietal positive potential, respectively). We first demonstrate that these markers are sensitive to changes in the amount of sensory evidence in the display. Then we show, counter to recent findings, that neither marker is modulated by either feature or motor expectations, despite a robust effect of expectations on behavior. Instead, violating expectations about likely sensory features and motor responses impacts posterior alpha and frontal theta oscillations, signals thought to index overall processing time and cognitive conflict. These findings are inconsistent with recent theoretical accounts and suggest instead that expectations primarily influence decisions by modulating post-perceptual stages of information processing.SIGNIFICANCE STATEMENT Expectations about likely features or motor responses play an important role in shaping behavior. Classic theoretical frameworks posit that expectations modulate decision-making by biasing late stages of decision-making including the selection and execution of motor responses. In contrast, recent accounts suggest that expectations also modulate decisions by improving the quality of early sensory processing. However, these effects could instead reflect the influence of selective attention. Here we examine the effect of expectations about sensory features and motor responses on a set of electroencephalography (EEG) markers that index early sensory processing and later post-perceptual processing. Counter to recent empirical results, expectations have little effect on early sensory processing but instead modulate EEG markers of time-on-task and cognitive conflict.

Dissociable signatures of visual salience and behavioral relevance across attentional priority maps in human cortex.

Computational models posit that visual attention is guided by activity within spatial maps that index the image-computable salience and the behavioral relevance of objects in the scene. These spatial maps are theorized to be instantiated as activation patterns across a series of retinotopic visual regions in occipital, parietal, and frontal cortex. Whereas previous research has identified sensitivity to either the behavioral relevance or the image-computable salience of different scene elements, the simultaneous influence of these factors on neural "attentional priority maps" in human cortex is not well understood. We tested the hypothesis that visual salience and behavioral relevance independently impact the activation profile across retinotopically organized cortical regions by quantifying attentional priority maps measured in human brains using functional MRI while participants attended one of two differentially salient stimuli. We found that the topography of activation in priority maps, as reflected in the modulation of region-level patterns of population activity, independently indexed the physical salience and behavioral relevance of each scene element. Moreover, salience strongly impacted activation patterns in early visual areas, whereas later visual areas were dominated by relevance. This suggests that prioritizing spatial locations relies on distributed neural codes containing graded representations of salience and relevance across the visual hierarchy. NEW & NOTEWORTHY We tested a theory which supposes that neural systems represent scene elements according to both their salience and their relevance in a series of "priority maps" by measuring functional MRI activation patterns across human brains and reconstructing spatial maps of the visual scene. We found that different regions indexed either the salience or the relevance of scene items, but not their interaction, suggesting an evolving representation of salience and relevance across different visual areas.

Fluctuations in instantaneous frequency predict alpha amplitude during visual perception.

Rhythmic neural activity in the alpha band (8-13 Hz) is thought to have an important role in the selective processing of visual information. Typically, modulations in alpha amplitude and instantaneous frequency are thought to reflect independent mechanisms impacting dissociable aspects of visual information processing. However, in complex systems with interacting oscillators such as the brain, amplitude and frequency are mathematically dependent. Here, we record electroencephalography in human subjects and show that both alpha amplitude and instantaneous frequency predict behavioral performance in the same visual discrimination task. Consistent with a model of coupled oscillators, we show that fluctuations in instantaneous frequency predict alpha amplitude on a single trial basis, empirically demonstrating that these metrics are not independent. This interdependence suggests that changes in amplitude and instantaneous frequency reflect a common change in the excitatory and inhibitory neural activity that regulates alpha oscillations and visual information processing.

Two different mechanisms support selective attention at different phases of training.

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 stimulus-evoked 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.

High Working Memory Load Increases Intracortical Inhibition in Primary Motor Cortex and Diminishes the Motor Affordance Effect.

UNLABELLED: Motor affordances occur when the visual properties of an object elicit behaviorally relevant motor representations. Typically, motor affordances only produce subtle effects on response time or on motor activity indexed by neuroimaging/neuroelectrophysiology, but sometimes they can trigger action itself. This is apparent in "utilization behavior," where individuals with frontal cortex damage inappropriately grasp affording objects. This raises the possibility that, in healthy-functioning individuals, frontal cortex helps ensure that irrelevant affordance provocations remain below the threshold for actual movement. In Experiment 1, we tested this "frontal control" hypothesis by "loading" the frontal cortex with an effortful working memory (WM) task (which ostensibly consumes frontal resources) and examined whether this increased EEG measures of motor affordances to irrelevant affording objects. Under low WM load, there were typical motor affordance signatures: an event-related desynchronization in the mu frequency and an increased P300 amplitude for affording (vs nonaffording) objects over centroparietal electrodes. Contrary to our prediction, however, these affordance measures were diminished under high WM load. In Experiment 2, we tested competing mechanisms responsible for the diminished affordance in Experiment 1. We used paired-pulse transcranial magnetic stimulation over primary motor cortex to measure long-interval cortical inhibition. We found greater long-interval cortical inhibition for high versus low load both before and after the affording object, suggesting that a tonic inhibition state in primary motor cortex could prevent the affordance from provoking the motor system. Overall, our results suggest that a high WM load "sets" the motor system into a suppressed state that mitigates motor affordances. SIGNIFICANCE STATEMENT: Is an irrelevant motor affordance more likely to be triggered when you are under low or high cognitive load? We examined this using physiological measures of the motor affordance while working memory load was varied. We observed a typical motor affordance signature when working memory load was low; however, it was abolished when load was high. Further, there was increased intracortical inhibition in primary motor cortex under high working memory load. This suggests that being in a state of high cognitive load "sets" the motor system to be imperturbable to distracting motor influences. This makes a novel link between working memory load and the balance of excitatory/inhibitory activity in the motor cortex and potentially has implications for disorders of impulsivity.

Integrating Levels of Analysis in Systems and Cognitive Neurosciences: Selective Attention as a Case Study.

Neuroscience is inherently interdisciplinary, rapidly expanding beyond its roots in biological sciences to many areas of the social and physical sciences. This expansion has led to more sophisticated ways of thinking about the links between brains and behavior and has inspired the development of increasingly advanced tools to characterize the activity of large populations of neurons. However, along with these advances comes a heightened risk of fostering confusion unless efforts are made to better integrate findings across different model systems and to develop a better understanding about how different measurement techniques provide mutually constraining information. Here we use selective visuospatial attention as a case study to highlight the importance of these issues, and we suggest that exploiting multiple measures can better constrain models that relate neural activity to animal behavior.

Value-based attentional capture influences context-dependent decision-making.

Normative theories posit that value-based decision-making is context independent. However, decisions between two high-value options can be suboptimally biased by the introduction of a third low-value option. This context-dependent modulation is consistent with the divisive normalization of the value of each stimulus by the total value of all stimuli. In addition, an independent line of research demonstrates that pairing a stimulus with a high-value outcome can lead to attentional capture that can mediate the efficiency of visual information processing. Here we tested the hypothesis that value-based attentional capture interacts with value-based normalization to influence the optimality of decision-making. We used a binary-choice paradigm in which observers selected between two targets and the color of each target indicated the magnitude of their reward potential. Observers also had to simultaneously ignore a task-irrelevant distractor rendered in a color that was previously associated with a specific reward magnitude. When the color of the task-irrelevant distractor was previously associated with a high reward, observers responded more slowly and less optimally. Moreover, as the learned value of the distractor increased, electrophysiological data revealed an attenuation of the lateralized N1 and N2Pc responses evoked by the relevant choice stimuli and an attenuation of the late positive deflection (LPD). Collectively, these behavioral and electrophysiological data suggest that value-based attentional capture and value-based normalization jointly mediate the influence of context on free-choice decision-making.