Counter-intuitive moral judgement following traumatic brain injury

Several neurological patient populations, including Traumatic Brain Injury (TBI), appear to produce an abnormally ‘utilitarian’ pattern of judgements to moral dilemmas; they tend to make judgements that maximise the welfare of the majority, rather than deontological judgements based on the following of moral rules (e.g., do not harm others). However, this patient research has always used extreme dilemmas with highly valued moral rules (e.g., do not kill). Data from healthy participants, however, suggests that when a wider range of dilemmas are employed, involving less valued moral rules (e.g., do not lie), moral judgements demonstrate sensitivity to the psychological intuitiveness of the judgements, rather than their deontological or utilitarian content (Kahane et al., 2011). We sought the moral judgements of 30 TBI participants and 30 controls on moral dilemmas where content (utilitarian/deontological) and intuition (intuitive/counterintuitive) were measured concurrently. Overall TBI participants made utilitarian judgements in equal proportions to controls; disproportionately favouring utilitarian judgements only when they were counterintuitive, and deontological judgements only when they were counterintuitive. These results speak against the view that TBI causes a specific utilitarian bias, suggesting instead that moral intuition is broadly disrupted following TBI

An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss

Affect conveys important implicit information in human communication. Having the capability to correctly express affect during human-machine conversations is one of the major milestones in artificial intelligence. In recent years, extensive research on open-domain neural conversational models has been conducted. However, embedding affect into such models is still under explored. In this paper, we propose an end-to-end affect-rich open-domain neural conversational model that produces responses not only appropriate in syntax and semantics, but also with rich affect. Our model extends the Seq2Seq model and adopts VAD (Valence, Arousal and Dominance) affective notations to embed each word with affects. In addition, our model considers the effect of negators and intensifiers via a novel affective attention mechanism, which biases attention towards affect-rich words in input sentences. Lastly, we train our model with an affect-incorporated objective function to encourage the generation of affect-rich words in the output responses. Evaluations based on both perplexity and human evaluations show that our model outperforms the state-of-the-art baseline model of comparable size in producing natural and affect-rich responses.Comment: AAAI-1

Emotional agents at the square lattice

We introduce and investigate by numerical simulations a number of models of emotional agents at the square lattice. Our models describe the most general features of emotions such as the spontaneous emotional arousal, emotional relaxation, and transfers of emotions between different agents. Group emotions in the considered models are periodically fluctuating between two opposite valency levels and as result the mean value of such group emotions is zero. The oscillations amplitude depends strongly on probability ps of the individual spontaneous arousal. For small values of relaxation times tau we observed a stochastic resonance, i.e. the signal to noise ratio SNR is maximal for a non-zero ps parameter. The amplitude increases with the probability p of local affective interactions while the mean oscillations period increases with the relaxation time tau and is only weakly dependent on other system parameters. Presence of emotional antenna can enhance positive or negative emotions and for the optimal transition probability the antenna can change agents emotions at longer distances. The stochastic resonance was also observed for the influence of emotions on task execution efficiency.Comment: 28 pages, 19 figures, 3 table

A novel user-centered design for personalized video summarization

In the past, several automatic video summarization systems had been proposed to generate video summary. However, a generic video summary that is generated based only on audio, visual and textual saliencies will not satisfy every user. This paper proposes a novel system for generating semantically meaningful personalized video summaries, which are tailored to the individual user's preferences over video semantics. Each video shot is represented using a semantic multinomial which is a vector of posterior semantic concept probabilities. The proposed system stitches video summary based on summary time span and top-ranked shots that are semantically relevant to the user's preferences. The proposed summarization system is evaluated using both quantitative and subjective evaluation metrics. The experimental results on the performance of the proposed video summarization system are encouraging

Affect-LM: A Neural Language Model for Customizable Affective Text Generation

Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generating conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design parameter. Perception studies conducted using Amazon Mechanical Turk show that Affect-LM generates naturally looking emotional sentences without sacrificing grammatical correctness. Affect-LM also learns affect-discriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction

Policies and Programs Affecting the Employment of People with Disabilities - Policy Brief

The purpose of this brief is to summarize the wide range of federal programs and government policies that influence the employment and program participation decisions of people with disabilities and current research initiatives related to these programs and policies. The brief is organized by the following types of programs, policies, and initiatives: • federal programs that provide cash assistance, in-kind transfers (e.g., health insurance) and education, training, and rehabilitation support based on disability status and/or other characteristics (e.g., family structure); • federal tax policies that provide credits either directly to individuals with disabilities or to employers as an incentive to hire a person with a disability; • other employment-related programs and public policies that provide accommodation support and work incentives for people with disabilities; • recent policy changes that affect people with disabilities; and • some of the current research initiatives related to federal programs, tax policies, other employment programs and policies, and recent policy changes. We conclude with a summary of our program, policy, and research scan. This publication is based on The Economics of Policies and Programs Affecting the Employment of People with Disabilities, which provides a more comprehensive review of the policies and programs discussed here (as well as others) and analyzes the employment effects of these policies and programs within an economic framework

Comparison of Post-injection Site Pain Between Technetium Sulfur Colloid and Technetium Tilmanocept in Breast Cancer Patients Undergoing Sentinel Lymph Node Biopsy.

BackgroundNo prior studies have examined injection pain associated with Technetium-99m Tilmanocept (TcTM).MethodsThis was a randomized, double-blinded study comparing postinjection site pain between filtered Technetium Sulfur Colloid (fTcSC) and TcTM in breast cancer lymphoscintigraphy. Pain was evaluated with a visual analogue scale (VAS) (0-100 mm) and the short-form McGill Pain Questionnaire (SF-MPQ). The primary endpoint was mean difference in VAS scores at 1-min postinjection between fTcSC and TcTM. Secondary endpoints included a comparison of SF-MPQ scores between the groups at 5 min postinjection and construction of a linear mixed effects model to evaluate the changes in pain during the 5-min postinjection period.ResultsFifty-two patients underwent injection (27-fTcSC, 25-TcTM). At 1-min postinjection, patients who received fTcSC experienced a mean change in pain of 16.8 mm (standard deviation (SD) 19.5) compared with 0.2 mm (SD 7.3) in TcTM (p = 0.0002). At 5 min postinjection, the mean total score on the SF-MPQ was 2.8 (SD 3.0) for fTcSC versus 2.1 (SD 2.5) for TcTM (p = 0.36). In the mixed effects model, injection agent (p < 0.001), time (p < 0.001) and their interaction (p < 0.001) were associated with change in pain during the 5-min postinjection period. The model found fTcSC resulted in significantly more pain of 15.2 mm (p < 0.001), 11.3 mm (p = 0.001), and 7.5 mm (p = 0.013) at 1, 2, and 3 min postinjection, respectively.ConclusionsInjection with fTcSC causes significantly more pain during the first 3 min postinjection compared with TcTM in women undergoing lymphoscintigraphy for breast cancer

Dopaminergic and Non-Dopaminergic Value Systems in Conditioning and Outcome-Specific Revaluation

Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal Values Triggers Option Revaluations) neural model. MOTIVATOR describes cognitiveemotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (R29-DC02952, R01-DC007683); National Science Foundation (IIS-97-20333, SBE-0354378); Office of Naval Research (N00014-01-1-0624