105 research outputs found
Structural and effective connectivity reveals potential network-based influences on category-sensitive visual areas
No full textVisual category perception is thought to depend on brain areas that respond specifically when certain categories are viewed. These category-sensitive areas are often assumed to be “modules” (with some degree of processing autonomy) and to act predominantly on feedforward visual input. This modular view can be complemented by a view that treats brain areas as elements within more complex networks and as influenced by network properties. This network-oriented viewpoint is emerging from studies using either diffusion tensor imaging to map structural connections or effective connectivity analyses to measure how their functional responses influence each other. This literature motivates several hypotheses that predict category-sensitive activity based on network properties. Large, long-range fiber bundles such as inferior fronto-occipital, arcuate and inferior longitudinal fasciculi are associated with behavioural recognition and could play crucial roles in conveying backward influences on visual cortex from anterior temporal and frontal areas. Such backward influences could support top-down functions such as visual search and emotion-based visual modulation. Within visual cortex itself, areas sensitive to different categories appear well-connected (e.g., face areas connect to object- and motion sensitive areas) and their responses can be predicted by backward modulation. Evidence supporting these propositions remains incomplete and underscores the need for better integration of DTI and functional imaging
Modulation of perception and brain activity by predictable trajectories of facial expressions
No full textPeople track facial expression dynamics with ease to accurately perceive distinct emotions. Although the superior temporal sulcus (STS) appears to possess mechanisms for perceiving changeable facial attributes such as expressions, the nature of the underlying neural computations is not known. Motivated by novel theoretical accounts, we hypothesized that visual and motor areas represent expressions as anticipated motion trajectories. Using magnetoencephalography, we show predictable transitions between fearful and neutral expressions (compared with scrambled and static presentations) heighten activity in visual cortex as quickly as 165 ms poststimulus onset and later (237 ms) engage fusiform gyrus, STS and premotor areas. Consistent with proposed models of biological motion representation, we suggest that visual areas predictively represent coherent facial trajectories. We show that such representations bias emotion perception of subsequent static faces, suggesting that facial movements elicit predictions that bias perception. Our findings reveal critical processes evoked in the perception of dynamic stimuli such as facial expressions, which can endow perception with temporal continuity
Facial-Attractiveness Choices Are Predicted by Divisive Normalization
No full textDo people appear more or less attractive depending on the company they keep? I employed normalization models to predict context dependence of facial attractiveness preferences. Divisive normalization – where representation of stimulus intensity is normalized (divided) by concurrent stimulus intensities – predicts that choice preferences between options increase with the range of option values. I manipulated attractiveness range trial-by-trial by varying the attractiveness of undesirable distractor faces, presented simultaneously with two attractive targets. The more unattractive the distractor, the more one of the targets was preferred, suggesting that divisive normalization (a potential canonical computation in the brain) influences social evaluations. We obtained the same result when participants chose the most “average” face, suggesting that divisive normalization is not restricted to value-based decisions (e.g., attractiveness). This new application to social evaluation of a classic theory opens possibilities for predicting social decisions in naturalistic contexts such as advertising or dating
A manipulation of sequence length: An extension of: How we model prior belief is crucial for predicting decision biases in realistic contexts"
No full textThis study looks at the economic best choice task developed by Dr van de Wouw in https://osf.io/b9qha/. In this task, participants may sequentially sample from a limited sequence of smartphone prices and must choice the best one they can, and hence stop the sequence of options, ONLY when the preferred option is presented. Here, we hypothesise that longer sequence lengths will result in more options sampled before stopping the sequence at a decision
A manipulation of sequence length: An extension of: How we model prior belief is crucial for predicting decision biases in realistic contexts"
No full textThis study looks at the economic best choice task developed by Dr van de Wouw in https://osf.io/b9qha/. In this task, participants may sequentially sample from a limited sequence of smartphone prices and must choice the best one they can, and hence stop the sequence of options, ONLY when the preferred option is presented. Here, we hypothesise that longer sequence lengths will result in more options sampled before stopping the sequence at a decision
A manipulation of sequence length: Part of "Part 2: How we model prior belief is crucial for predicting decision biases in realistic contexts"
No full textThis study looks at the economic best choice task developed by Dr van de Wouw in https://osf.io/b9qha/. In this task, participants may sequentially sample from a limited sequence of smartphone prices and must choice the best one they can, and hence stop the sequence of options, ONLY when the preferred option is presented. Here, we hypothesise that longer sequence lengths will result in more options sampled before stopping the sequence at a decision
Parietal cortex and insula relate to evidence seeking relevant to reward-related decisions.
No full textDecisions are most effective after collecting sufficient evidence to accurately predict rewarding outcomes. We investigated whether human participants optimally seek evidence and we characterized the brain areas associated with their evidence seeking. Participants viewed sequences of bead colors drawn from hidden urns and attempted to infer the majority bead color in each urn. When viewing each bead color, participants chose either to seek more evidence about the urn by drawing another bead (draw choices) or to infer the urn contents (urn choices). We then compared their evidence seeking against that predicted by a Bayesian ideal observer model. By this standard, participants sampled less evidence than optimal. Also, when faced with urns that had bead color splits closer to chance (60/40 versus 80/20) or potential monetary losses, participants increased their evidence seeking, but they showed less increase than predicted by the ideal observer model. Functional magnetic resonance imaging showed that urn choices evoked larger hemodynamic responses than draw choices in the insula, striatum, anterior cingulate, and parietal cortex. These parietal responses were greater for participants who sought more evidence on average and for participants who increased more their evidence seeking when draws came from 60/40 urns. The parietal cortex and insula were associated with potential monetary loss. Insula responses also showed modulation with estimates of the expected gains of urn choices. Our findings show that participants sought less evidence than predicted by an ideal observer model and their evidence-seeking behavior may relate to responses in the insula and parietal cortex.</jats:p
Knowing when to stop: neural mechanisms of information sampling in best-choice problems
No full textThe study investigates the neural mechanisms and behavioural aspects of optimal-stopping problems using an economic best-choice task, EEG and Bayesian modellin
Knowing when to stop: neural mechanisms of information sampling in best-choice problems
No full textThe study investigates the neural mechanisms and behavioural aspects of optimal-stopping problems using an economic best-choice task, EEG and Bayesian modellin
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