Use of Mental Imagery in Psychotherapy: A Critical Review

The paper presents arguments in favor of the use of mental imagery for therapeutic purposes. Several existing imagery approaches to psychotherapy are critically examined and suggestions for future inquiry are offered. The intimate relation between imagery and the affective-somatic processes is stressed

The brain is a prediction machine that cares about good and bad - Any implications for neuropragmatics?

Experimental pragmatics asks how people construct contextualized meaning in communication. So what does it mean for this field to add neuroas a prefix to its name? After analyzing the options for any subfield of cognitive science, I argue that neuropragmatics can and occasionally should go beyond the instrumental use of EEG or fMRI and beyond mapping classic theoretical distinctions onto Brodmann areas. In particular, if experimental pragmatics ‘goes neuro’, it should take into account that the brain evolved as a control system that helps its bearer negotiate a highly complex, rapidly changing and often not so friendly environment. In this context, the ability to predict current unknowns, and to rapidly tell good from bad, are essential ingredients of processing. Using insights from non-linguistic areas of cognitive neuroscience as well as from EEG research on utterance comprehension, I argue that for a balanced development of experimental pragmatics, these two characteristics of the brain cannot be ignored

The Cradle of Humanity: A Psychological and Phenomenological Perspective

We present an account of the evolutionary development of the experiences of empathy that marked the beginning of morality and art. We argue that aesthetic and moral capacities provided an important foundation for later epistemic developments. The distinction between phenomenal consciousness and attention is discussed, and a role for phenomenology in cognitive archeology is justified-critical sources of evidence used in our analysis are based on the archeological record. We claim that what made our species unique was a form of meditative and empathic thinking that made large-scale human cooperation possible through pre-linguistic, empathic communication. A critical aspect of this proposal is that the transformation that led to the dawn of our species was not initially driven by semantic or epistemic factors, although clearly, these factors increased the gap between us and other species dramatically later on. Our proposal suggests that recent philosophy of mind and psychology might have "epistemicized" phenomenal consciousness too much by construing it in terms of semantic content rather than by describing it in terms of empathic and meditative thinking. Instead of the prevailing approach, we favor the type of subjectivity that is fundamentally "other-involving" as essential, because on our account, a necessary condition for subjectivity is the empathic understanding of other individuals' psychology, not through inference or judgment, but through immediate conscious engagement

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Hearing meanings: the revenge of context

According to the perceptual view of language comprehension, listeners typically recover high-level linguistic properties such as utterance meaning without inferential work. The perceptual view is subject to the Objection from Context: since utterance meaning is massively context-sensitive, and context-sensitivity requires cognitive inference, the perceptual view is false. In recent work, Berit Brogaard provides a challenging reply to this objection. She argues that in language comprehension context-sensitivity is typically exercised not through inferences, but rather through top-down perceptual modulations or perceptual learning. This paper provides a complete formulation of the Objection from Context and evaluates Brogaards reply to it. Drawing on conceptual considerations and empirical examples, we argue that the exercise of context-sensitivity in language comprehension does, in fact, typically involve inference

Detecting Sarcasm in Multimodal Social Platforms

Sarcasm is a peculiar form of sentiment expression, where the surface sentiment differs from the implied sentiment. The detection of sarcasm in social media platforms has been applied in the past mainly to textual utterances where lexical indicators (such as interjections and intensifiers), linguistic markers, and contextual information (such as user profiles, or past conversations) were used to detect the sarcastic tone. However, modern social media platforms allow to create multimodal messages where audiovisual content is integrated with the text, making the analysis of a mode in isolation partial. In our work, we first study the relationship between the textual and visual aspects in multimodal posts from three major social media platforms, i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to quantify the extent to which images are perceived as necessary by human annotators. Moreover, we propose two different computational frameworks to detect sarcasm that integrate the textual and visual modalities. The first approach exploits visual semantics trained on an external dataset, and concatenates the semantics features with state-of-the-art textual features. The second method adapts a visual neural network initialized with parameters trained on ImageNet to multimodal sarcastic posts. Results show the positive effect of combining modalities for the detection of sarcasm across platforms and methods.Comment: 10 pages, 3 figures, final version published in the Proceedings of ACM Multimedia 201