37,966 research outputs found
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus
The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic orientation implies undesirability (e.g., "disturbing", "superfluous"). This paper introduces a simple algorithm for unsupervised learning of semantic orientation from extremely large corpora. The method involves issuing queries to a Web search engine and using pointwise mutual information to analyse the results. The algorithm is empirically evaluated using a training corpus of approximately one hundred billion words — the subset of the Web that is indexed by the chosen search engine. Tested with 3,596 words (1,614 positive and 1,982 negative), the algorithm attains an accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and verbs. The accuracy is comparable with the results achieved by Hatzivassiloglou and McKeown (1997), using a complex four-stage supervised learning algorithm that is restricted to determining the semantic orientation of adjectives
Autonomic arousal and attentional orienting to visual threat are predicted by awareness
The rapid detection and evaluation of threat is of fundamental importance for survival. Theories suggest that this evolutionary pressure has driven functional adaptations in a specialized visual pathway that evaluates threat independently of conscious awareness. This is supported by evidence that threat-relevant stimuli rendered invisible by backward masking can induce physiological fear responses and modulate spatial attention. The validity of these findings has since been questioned by research using stringent, objective measures of awareness. Here, we use a modified continuous flash suppression paradigm to ask whether threatening images induce adaptive changes in autonomic arousal, attention, or perception when presented outside of awareness. In trials where stimuli broke suppression to become visible, threatening stimuli induced a significantly larger skin conductance response than nonthreatening stimuli and attracted spatial attention over scrambled images. However, these effects were eliminated in trials where observers were unaware of the stimuli. In addition, concurrent behavioral data provided no evidence that threatening images gained prioritized access to awareness. Taken together, our data suggest that the evaluation and spatial detection of visual threat are predicted by awareness
Statistical Inferences for Polarity Identification in Natural Language
Information forms the basis for all human behavior, including the ubiquitous
decision-making that people constantly perform in their every day lives. It is
thus the mission of researchers to understand how humans process information to
reach decisions. In order to facilitate this task, this work proposes a novel
method of studying the reception of granular expressions in natural language.
The approach utilizes LASSO regularization as a statistical tool to extract
decisive words from textual content and draw statistical inferences based on
the correspondence between the occurrences of words and an exogenous response
variable. Accordingly, the method immediately suggests significant implications
for social sciences and Information Systems research: everyone can now identify
text segments and word choices that are statistically relevant to authors or
readers and, based on this knowledge, test hypotheses from behavioral research.
We demonstrate the contribution of our method by examining how authors
communicate subjective information through narrative materials. This allows us
to answer the question of which words to choose when communicating negative
information. On the other hand, we show that investors trade not only upon
facts in financial disclosures but are distracted by filler words and
non-informative language. Practitioners - for example those in the fields of
investor communications or marketing - can exploit our insights to enhance
their writings based on the true perception of word choice
Memory Structure and Cognitive Maps
A common way to understand memory structures in the cognitive sciences is as a cognitive map​.
Cognitive maps are representational systems organized by dimensions shared with physical space. The
appeal to these maps begins literally: as an account of how spatial information is represented and used
to inform spatial navigation. Invocations of cognitive maps, however, are often more ambitious;
cognitive maps are meant to scale up and provide the basis for our more sophisticated memory
capacities. The extension is not meant to be metaphorical, but the way in which these richer mental
structures are supposed to remain map-like is rarely made explicit. Here we investigate this missing
link, asking: how do cognitive maps represent non-spatial information?​ We begin with a survey of
foundational work on spatial cognitive maps and then provide a comparative review of alternative,
non-spatial representational structures. We then turn to several cutting-edge projects that are engaged
in the task of scaling up cognitive maps so as to accommodate non-spatial information: first, on the
spatial-isometric approach​ , encoding content that is non-spatial but in some sense isomorphic to
spatial content; second, on the ​ abstraction approach​ , encoding content that is an abstraction over
first-order spatial information; and third, on the ​ embedding approach​ , embedding non-spatial
information within a spatial context, a prominent example being the Method-of-Loci. Putting these
cases alongside one another reveals the variety of options available for building cognitive maps, and the
distinctive limitations of each. We conclude by reflecting on where these results take us in terms of
understanding the place of cognitive maps in memory
(Mind)-Reading Maps
In a two-system theory for mind-reading, a flexible system (FS) enables full-blown mind-reading, and an efficient system (ES) enables early mind-reading (Apperly and Butterfill 2009). Efficient processing differs from flexible processing in terms of restrictions on the kind of input it can take and the kinds of mental states it can ascribe (output). Thus, systems are not continuous, and each relies on different representations: the FS on beliefs and other propositional attitudes, and the ES on belief-like states or registrations. There is a conceptual problem in distinguishing the representations each system operates with. They contend that they can solve this problem by appealing to a characterization of registrations based on signature limits, but this does not work. I suggest a solution to this problem. The difference between registration and belief becomes clearer if each vehicle turns out to be different. I offer some reasons in support of this proposal related to the performance of spontaneous-response false belief tasks.Fil: Velazquez Coccia, Fernanda Maria Soledad. Universidad de Buenos Aires. Facultad de FilosofĂa y Letras. Instituto de FilosofĂa "Dr. Alejandro Korn"; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentin
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