This paper aims to fill a gap in the literature on situated affectivity by proposing that the notion of affective scaffold be extended to the case of companion dogs. Situated affectivity posits that emotions and affective experiences are profoundly shaped by and embedded within the social and environmental contexts in which they unfold. We argue that dogs, and likely pets more broadly, play a significant role in regulating and supporting the affective lives of their human companions – a role that can be captured through key dimensions identified in the affective scaffolding literature, including trust, individuation, sharing, temporal scale, and reciprocity. After defending our main thesis, we briefly consider some ethical concerns that may arise when dogs are treated as affective scaffolds, focusing in particular on the risk of affective injustice.
There is evidence that adults with spinal cord injury (SCI) have deficits in mental body representations (e.g., altered visuospatial body maps and reduced body awareness), due to the diminished or lack of sensory information reaching the brain. These mental body representation deficits are important and need to be quantified, because they can impact daily functioning and they are associated with neuropathic pain. The currently available evaluation scales measure certain aspects of mental body representations with few having been assessed in adults with spinal cord injury. Furthermore, to our knowledge, no scales have been developed specifically for adults with spinal cord injury. Therefore, to address this gap, we completed two aims. First, we developed a novel evaluation scale (the SCI-BodyMap) to measure SCI-specific deficits in mental body representations. Second, we assessed the psychometric properties of inter-rater reliability, test-retest reliability, concurrent validity, with the Revised Body Awareness Rating Questionnaire, the Multidimensional Assessment of Interoceptive Awareness-2, and the Numeric pain rating scale. We also assessed feasibility, utility, and face validity with the QQ-10. We found good to excellent inter-rater and test-retest reliability, with the exception of two items showing moderate test-retest reliability. We did not find any correlations between the SCI-BodyMap with the Revised Body Awareness Rating Questionnaire or the Multidimensional Assessment of Interoceptive Awareness-2 and found fair correlations between high levels of neuropathic pain on the Numeric pain rating scale with highest level of neuropathic pain on the SCI-BodyMap. The scale proved to have high feasibility, utility, and face validity. Once more psychometric analyses are performed in a bigger sample, the SCI-BodyMap could be recommended for use in research and in the clinic.
We introduce AI Harmonics (AIH), a novel metric designed to quantify the concentration of harms across stakeholder groups affected by AI systems. Unlike traditional approaches that rely on arbitrary numerical assignments to ordinal severity levels, AIH provides a principled framework grounded in inequality theory, extending concepts from the Gini index to purely ordinal data. The metric evaluates how harm is distributed among stakeholders, capturing whether severe impacts are concentrated within specific groups or more evenly spread. Experiments on annotated incident data show that the proposed metric exhibits a strong monotonic relationship with the Criticality Index (CI), preserving harm category rankings while capturing additional variation in concentration patterns. The method demonstrates high robustness, with Spearman rank correlations above 0.97 under severity perturbations and stable prioritization even under up to 80% random data removal. Political and physical harms consistently exhibit the highest concentration, indicating the need for urgent mitigation. Political harms erode public trust, while physical harms pose serious, even life-threatening risks, underscoring the real-world relevance of our approach. The AIH metric is particularly well-suited for policy-making and risk management, where only ordinal assessments are available, and it enables more informed prioritization of mitigation strategies.
Large language models grounded on attention-based architectures have outperformed recurrent neural networks (RNNs), like those based on long short-term memory (LSTM) gating systems, in various natural language processing tasks. However, despite optimization, these models (i) require unreasonable training data compared to what children need, (ii) exhibit an inverse relationship between their performance and their linguistic explanatory value, and, crucially, (iii) avoid reasonable (word-by-word) incremental sentence processing, raising doubts about their cognitive plausibility. In this paper, we address these issues starting from the intuition that RNNs directly model incrementality, a key factor in human language processing. Specifically, we discuss the performance of an RNN architecture, eMG-RNN, both during training and in minimal pairs forced-choice tasks in English and Italian. We observe that: (i) ecological training regimens lead to a decrease in cross-entropy loss, although performance on linguistic minimal pairs does not improve; (ii) the specific gating system adopted induces relevant structural biases; and (iii) while these networks outperform standard LSTM, gated recurrent unit (GRU) networks, and transformer models under the same ecological training regimens, their performance on linguistic tasks remains low compared to adults (in English) and 7-year-old children (in Italian).
Women and men pursue different but complementary forms of scientific innovation. Analyzing 261,452 solo-authored papers by U.S. scholars, with patterns confirmed by millions of multi-authored articles, we show that women more often bridge distant disciplines through novel reference combinations, while men more often recombine concepts within fields. Women's interdisciplinary innovations prove more disruptive and more prescient, yet science penalizes them for it. For equally innovative work, women's papers land in lower-prestige journals and tend to receive less downstream citation credit, though their disruptive impact is greater. These gaps narrow only at extreme levels of novelty, suggesting women must produce exceptionally surprising work to achieve parity. Men's within-field concept innovations, by contrast, attract recognition from disciplinary gatekeepers who control careers. The asymmetry reveals not a deficit in women's contributions but a reward structure that systematically undervalues the boundary-crossing work most likely to transform fields.
