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Ishita Singh

Publications and source records attributed to Ishita Singh.

2 recordsLinked to original sources

Structural Brain Predictors of Visual Attention Gradient Modulated by Trait Anxiety

Dynamic allocation of attention across the visual field, quantified as a visuospatial attention gradient, is essential for maintaining perceptual breadth. Disruptions to this flexibility may contribute to altered spatial attentional bias and may be influenced by trait anxiety. We investigated whether individual differences in structural brain morphology predict spatial attentional deployment as a function of trait anxiety. Sixty participants, recruited based on an a priori sample size calculation, completed a visuospatial attention gradient task incorporating brief partial facial emotion cues. Although discrete emotional cues did not significantly modulate attention gradients, structural neuroimaging analyses revealed that greater grey matter volume in bilateral cerebellar lobule VI and increased cortical thickness in the left precentral gyrus and paracentral lobule were associated with reduced interaction between the magnitude of the spatial attention gradient (averaged across emotions) and trait anxiety. Machine-learning models further predicted individual attention-anxiety profiles from these neuroanatomical features. These findings suggest that greater structural integrity in cerebellar and sensorimotor regions is associated with more flexible spatial attentional deployment in individuals with lower trait anxiety. Together, the results highlight the contribution of cerebellar and sensorimotor regions, beyond their traditional motor functions, to individual differences in visual spatial attention and cognitive-affective interactions, while demonstrating the predictive utility of structural brain markers.

q-bio.NC↗

Can Valence Reflect Morality in Natural Language? A Preliminary Annotation Study

Present implementations of artificial intelligence (AI) ethics do not adequately take feelings, or affect, into account. If AI should be aligned with human ethics, it seems reasonable to thoroughly investigate the possibility of AI behaviour that mirrors virtuous human ethical conduct, where feelings play a role in the actions, judgements or statements one makes. Furthermore, while prominent theories of normative ethics are often discussed in terms of their differences and shortcomings, Virtue, Consequentialist, and Kantian Deontological ethics all share a common feature of considering human feeling to some degree while the popular descriptive ethics theory, Moral Foundations Theory, positions feelings as central to many of its foundations. Therefore, in the present paper, a data set of moral valence is proposed, consisting of 500 annotations by six human participants for both action/judgement and consequence moral valence, ranging from -1 to 1 for text-presented scenarios from the Commonsense Norm Bank data set. The resulting valence features share significant relationships with multi-class (immoral/discretionary/moral) and binary immoral/moral categories while additionally providing a noteworthy test set Matthew's correlation coefficient of 0.764 using regularised logistic regression for binary classification. This provides early evidence of the usefulness of valence features for morality estimation of text, indicating that valenced consequences of responses for others can be considered toward more human morally-aligned AI. In the interest of promoting further affective-moral computing research, this study's annotations will be made available for research on request.

cs.CL↗