Biology subjects

Itthipuripat, S.

Publications and source records attributed to Itthipuripat, S..

11 recordsLinked to original sources

bioRxiv2026

The Prefrontal Cortex Organizes Visual Evidence by Representational Abstraction

Perceptual decisions often depend on both lower-level visual features and higher-level object representations, yet natural images entangle these levels of abstraction. We used deep neural network features to dissociate lower- and higher-level visual similarity during a naturalistic image-similarity judgment task. Participants relied on both forms of evidence, with greater weighting of higher-level similarity. Across prefrontal cortex, visual evidence was systematically organized by representational level: lower- and higher-level similarity were expressed in distinct prefrontal regions during comparison and remained spatially differentiated as they were transformed into decision signals. This organization was behaviorally relevant, with prefrontal activation predicting which level of evidence guided choice when the two conflicted. Together, these findings identify representational level as an organizing principle of prefrontal cortex during visual decisions.

neuroscience↗

bioRxiv2026

Stress Recovery Through Mindful Breathing: Convergent NeuralSignatures in Around-Ear and Scalp EEG

Mindfulness-based interventions effectively reduce stress and anxiety, yet the neural mechanisms underlying contemplative practices and their optimal implementation parameters remain poorly understood. A critical barrier to real-world application is the absence of validated minimally invasive neural recording technologies. Here, we simultaneously recorded full-coverage scalp and around-ear EEG during a 2x2x2 factorial design manipulating (1) cognitive state (mental arithmetic stress vs. passive viewing), (2) recovery strategy (mindful breathing meditation vs. mind-wandering), and (3) sensory context (eyes open vs. eyes closed). Mental arithmetic robustly elevated subjective stress and modulated canonical oscillatory patterns: increased midline frontal theta power (3-7 Hz), suppressed posterior alpha power (10-12 Hz), and enhanced posterior beta and gamma power (25-48 Hz). All rest conditions reduced subjective stress following stress induction, with eyes-closed mindful breathing producing maximal reduction. Critically, mindful breathing differentially modulated temporal beta and gamma power in a context-dependent manner, with effects determined by prior cognitive state and eye position. Eyes-closed meditation maximally suppressed gamma power within 20 seconds following arithmetic stress, whereas eyes-open meditation alone was sufficient for gamma suppression following passive viewing. Around-ear electrodes detected these stress and meditation signatures with comparable fidelity to scalp recordings. These findings reveal that mindful breathing engages rapid, context-dependent neural regulation mechanisms and establish that wearable EEG can reliably capture these dynamics, enabling real-world stress monitoring and mindfulness guidance. Impact StatementMindfulness-based interventions reduce stress, yet their neural mechanisms and optimal implementation remain unclear, partly due to limited real-world neural measurement tools. Using simultaneous scalp and around-ear EEG, we show that mindful breathing rapidly and context-dependently regulates stress-related brain activity via changes in high-frequency EEG oscillations (i.e., gamma band activity), with effects shaped by prior cognitive state and eye condition. Importantly, around-ear EEG captured these neural signatures with fidelity comparable to scalp recordings, enabling wearable neurotechnology for real-world stress monitoring and personalized mindfulness guidance.

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bioRxiv2021

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

Mild cognitive impairment (MCI) is an early stage of age-inappropriate cognitive decline, which could develop into dementia - an untreatable neurodegenerative disorder. An early detection of MCI is a crucial step for timely prevention and intervention. To tackle this problem, recent studies have developed deep learning models to detect MCI and various types of dementia using data obtained from the classic clock-drawing test (CDT), a popular neuropsychological screening tool that can be easily and rapidly implemented for assessing cognitive impairments in an aging population. While these models succeed at distinguishing severe forms of dementia, it is still difficult to predict the early stage of the disease using the CDT data alone. Also, the state-of-the-art deep learning techniques still face the black-box challenges, making it questionable to implement them in the clinical setting. Here, we propose a novel deep learning modeling framework that incorporates data from multiple drawing tasks including the CDT, cube-copying, and trail-making tasks obtained from a digital platform. Using self-attention and soft-label methods, our model achieves much higher classification performance at detecting MCI compared to those of a well-established convolutional neural network model. Moreover, our model can highlight features of the MCI data that considerably deviate from those of the healthy aging population, offering accurate predictions for detecting MCI along with visual explanation that aids the interpretation of the deep learning model.

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bioRxiv2019

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. the chocolate bar in the pantry when you are looking for a nice healthy apple). While increasing the value of task-relevant stimuli systematically up-regulates neural responses in early visual cortex to facilitate information processing, it is not clear if 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 fMRI in human visual cortex while subjects performed a value-based learning task, and applied a multivariate inverted encoding model 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. Moreover, this value-based modulation of distractor representations only occurred when the distractors were previously selected as targets on preceding trials. Together, these findings suggest that value-driven attentional capture begins with sensory modulations of distractor representations in early areas of visual cortex.

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bioRxiv2018

The speed-accuracy tradeoff reveals flexible access to accumulating sensory evidence during human decision making

Decisions made about identical perceptual stimuli can be radically different under changing task demands. For example, the need to make a fast decision undermines the accuracy of that decision, a well-documented effect termed the speed-accuracy tradeoff (SAT). Models of the SAT are generally based on theories of decision making in which responses are triggered only after sensory evidence accumulation terminates at a set threshold. Within this accumulate-to-bound framework, speed pressure operates by lowering the response threshold, allowing for faster responses at the expense of accumulated sensory evidence. To empirically examine the mechanisms necessary for adaptively adjusting the speed and accuracy of decisions, we used an event-related potential that indexes sensory evidence accumulation in the human brain. Instead of lowering response thresholds, we found that speed pressure adaptively shifts responses to occur close to where the rate of evidence accumulation peaks. Moreover, responses are not triggered automatically by the termination of the accumulation process, as sensory evidence continues to build after speeded decisions. Together these results suggest that response processes adaptively access accumulating sensory evidence depending on task demands and support parallel over serial models of decision making.

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bioRxiv2018

Context triggers the retrieval of long-term memories into working memory to guide attention

How do we know what we are looking for in familiar scenes and surroundings? Here we tested a novel hypothesis derived from theories of human memory that working memory (WM) buffers mnemonic contents retrieved from long-term memory (LTM) to control attention. To test this hypothesis, we measured the electrical fields recorded noninvasively from human subjects as they searched for specific sets of objects in learned contexts. We found that the subjects WM-indexing brain activity tracked the number of real-world objects people learned to search for in each context. Moreover, the level of this WM activity predicted the inter-subject variability in behavioral performance. Together, our results demonstrate that familiar contexts can trigger the transfer of information from LTM to WM to provide top-down attentional control.

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bioRxiv2018

Reconciling fMRI and EEG indices of attentional modulations in human visual cortex

Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are the two most popular non-invasive methods used to study the neural mechanisms underlying human cognition. These approaches are considered complementary: fMRI has higher spatial resolution but sluggish temporal resolution, whereas EEG has millisecond temporal resolution, but only at a broad spatial scale. Beyond the obvious fact that fMRI measures properties of blood and EEG measures changes in electric fields, many foundational studies assume that, aside from differences in spatial and temporal precision, these two methods index the same underlying neural modulations. We tested this assumption by using EEG and fMRI to measure attentional modulations of neural responses to stimuli of different visual contrasts. We found that equivalent experiments performed using fMRI and EEG on the same participants revealed remarkably different patterns of attentional modulations: event-related fMRI responses provided evidence for an additive increase in responses across all contrasts equally, whereas early stimulus-evoked event-related potentials (ERPs) showed larger modulations with increasing stimulus contrast and only a later negative-going ERP and low-frequency oscillatory EEG signals showed effects similar to fMRI. These results demonstrate that there is not a one-to-one correspondence between the physiological mechanisms that give rise to modulations of fMRI responses and the most commonly used ERP markers, and that the typical approach of employing fMRI and EEG to gain complementary information about localization and temporal dynamics is over-simplified. Instead, fMRI and EEG index different physiological modulations and their joint application affords synergistic insights into the neural mechanisms supporting human cognition.

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bioRxiv2017

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. However, the simultaneous influence of these factors on putative 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 find 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.\n\nSignificance StatementOften, it is necessary to orient towards bright, unique, or sudden events in the environment - that is, salient stimuli. However, we can focus processing resources on less salient visual information if it is relevant to the task at hand. We tested a theory which supposes that we represent different scene elements according to both their salience and their relevance in a series of priority maps by measuring fMRI activation patterns across the human brain and reconstructing spatial maps of the visual scene under different task conditions. 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.

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bioRxiv2017

When conflict cannot be avoided: executive control dominates early selective sensory modulations during cognitive conflict

When different sources of sensory information suggest competing behavioral responses, the efficiency of decision-making is impaired. Prior work suggests that at least two mechanisms may play a role in mitigating this interference: using early selective attention to extract the most relevant sensory inputs to avoid conflict or increasing the efficiency of the executive control network to resolve conflict during post-perceptual processing. To test these alternatives, we combined a stimulus-frequency tagging technique with a classic color-word Stroop paradigm, where color-bar targets and letter-string distractors were simultaneously flickered at different frequencies. Using electroencephalography (EEG), we measured the quality of early sensory processing by assessing the amplitude of steady-state visually evoked potentials (SSVEPs) elicited by the targets and distractors. We also measured the engagement of the executive control network by assessing changes in frontal theta (4-7Hz) and posterior alpha oscillations (8-14Hz). Counter to the early selective sensory modulation account, the amplitude of the SSVEP response was not modulated by manipulations of color/word congruency, while the frontal theta activity increased and the posterior alpha activity decreased in response to conflict. Moreover, target-related SSVEP amplitude was not correlated with response times (RTs) and a higher (not lower) distractor-related SSVEP amplitude predicted faster RTs. On the other hand, the amplitude of the frontal theta and alpha activity was highly correlated with RTs, irrespective of conflict levels. Over all, these results highlight the dominant role of the executive control network in conflict resolution during post-perceptual processing.\n\nSignificance StatementConflicting information interferes with decision-making. However, this interference can be mitigated either by extracting the most relevant inputs during early sensory processing or by increasing the efficiency of the executive control processes to resolve conflict. By measuring electroencephalography (EEG) in humans performing a modified color-word Stroop task, we examined early sensory responses evoked by targets and distractors while simultaneously monitoring frontal theta and posterior alpha oscillations to index the activation of the executive control network. We found evidence that the executive control network played a more prominent role in resolving conflict.

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bioRxiv2016

Training qualitatively shifts the neural mechanisms that support attentional selection

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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bioRxiv2016

Stimulus visibility controls the balance between attention-induced changes in contrast appearance and decision bias

While attention is known to improve information processing, whether attention can alter visual appearance has been a cornerstone of debate for 100+ years. Although recent studies suggest that attention can alter appearance, it has been argued that the reported appearance changes reflect response bias. Here, we provide a resolution to this debate by showing that attention has different effects on appearance and response bias depending on stimulus visibility. In a contrast judgment task where the contrast of attended and unattended stimuli varied across a full range of contrast values, human participants exhibited a substantial amount of response bias to the attended stimulus, when stimuli were hard to see. However, when stimuli were easier to see, response bias decreased and attention primarily increased perceived contrast. These results help constrain philosophical arguments about the cognitive penetrability of perception and reconcile the long-standing debate about the attention effect on appearance and response bias.

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