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A study of the effect of auditory prime type on emotional facial expression recognition Another interesting effect observed was that pseudoutterances elicited greater negative ERP amplitudes than vocalizations. It is possible that vocalizations more effectively conveyed emotion, or similarly to the behavioural effect, provided emotional information that the participant found more relevant to the target faces. This would have allowed an association to be made more quickly between the prime and emotions expressed by the facial expressions when preceded by vocalizations. Absolute values for ERP amplitudes were not analyzed - however, it is likely that pseudoutterances elicited normal ERP amplitudes and that vocalizations simply elicited a relatively more positive response. Negative ERP Amplitudes in the 150 msec – 250 msec Window As with the 440 msec – 540 msec temporal window, the 150 msec-250 msec window showed greater negativity in trials with pseudoutterances as primes than for vocalizations. Moreover, the effect of congruency was also similar: there was greater negativity observed for congruent versus incongruent trials. This temporal window differed from the 440 msec – 540 msec window in that significant interactions were observed in the left and right frontal regions between type and congruency, and between emotion and type. The left frontal region is commonly associated with the processing of positive emotions while the right frontal region is commonly associated with the processing of negative emotions (12). This was not considered in this study; but based on the fact that the emotions of the stimuli were anger, sadness, and happiness, the observed ERPs on both sides of the brain are not surprising results. As for the particular interactions, the interaction between prime type and prime-target congruency showed greater negativity in incongruent trials than in congruent trials for vocalizations, but the opposite effect for pseudoutterances. As previously mentioned, the emotional meaning of an auditory stimulus is processed very early on. Differences in pattern between vocalizations and pseudoutterances – where vocalizations show the conventional greater negativity in incongruent trials – may be the result of the lengthier pseudoutterances favouring controlled processing. On the other hand, vocalizations tended to be less than 1000 msec in length, which may have favoured automatic processing and thus showed the expected pattern in ERP amplitudes. There was also interaction between the emotion and type of prime; greater negativity was seen for a sad prime in pseudoutterances than in vocalizations. This reiterates the idea that there may have been a weaker association between the pseudoutterance and facial expression simply due to the nature of the two prime types. Vocalizations may allow for a stronger association between the emotion of the prime and the facial expression seen. well as why full-duration pseudoutterances elicited greater negativities for congruent trials. Once these factors are understood, it would be interesting to conduct similar experiments on a greater range of emotions. This could be taken further by classifying them into positive and negative emotions in order to study further levels of interaction - for example whether positive vocalizations elicit greater or lower negativities than pseudoutterances. Conclusions These findings indicate that, from a behavioural standpoint, facial expressions preceded by vocalization primes are more accurately recognized than when preceded by pseudoutterance primes. This is likely due to the relations between facial expressions and vocalizations. As expected, vocalizations were shown to elicit a greater negativity for incongruent trials than for congruent trials. Interestingly, pseudoutterances caused a reversed effect. The conventional priming effect of pseudoutterances may have been absent in the averaged ERP data as a result of their long duration, as compared to previous studies. Future studies should consider capping the length of the pseudoutterances and vocalizations to see if the conventional N400 response is restored. References [1] G. Pourtois et al. NeuroReport. 11, 1329-1333 (2000). [2] B. De Gelder, J. Vroomen, Cognition & Emotion. 14, 289-311 (2000). [3] C. Brown, P. Hagoort, Journal of Cognitive Neuroscience. 5, 34-44 (1993). [4] M.D. Pell, Brain & Cognition. 48, 499-504 (2002). [5] M.D. Pell et al. Journal of Phonetics. 37, 417-435 (2009). [6] P. Belin, S. Fillion-Bilodeau, Behavior Research Methods. 40, 531- 539 (2008). [7] D. Grandjean et al. Nature Neuroscience. 8, 145-146 (2005). [8] S. Paulmann, S.A. Kotz, Brain & Language. 105, 59-69 (2008b). [9] S. Paulmann, M.D. Pell, Cognitive, Affective, & Behavioral Neuroscience. 10, 230-242 (2010). [10] Q. Zhang, A. Lawson, C. Guo, Y. Jiang, Brain Research Bulletin. 71, 316-323 (2006). [11] S. Paulmann, S.A. Kotz, NeuroReport. 19, 209-213 (2008a). [12] R. Graham, R. Cabeza, Cognitive Neuroscience and Neuropsychology. 12, 1-4 (2000). Further Investigations Future studies should seek to understand the ability of vocalizations to convey stronger emotional meaning than pseudoutterances as 54 McGill Science Undergraduate Research Journal - msurj.mcgill.ca
RESEARCH ARTICLE Proof-carrying authorization in distributed systems with Beluga: a case study Leah Weiner 1 * 1 School of Computer Science, McGill University, Montreal, QC *Email Correspondence: leah.d.weiner@gmail.com Abstract The rising popularity of distributed systems creates a need for a secure method of message passing. One approach to access control is accomplished through proof-carrying and proof authorization. The requesting party must provide a proof of authorization of access while the serving party must verify the validity of the proof. PCML 5 is a programming language which enables a programmer to encode proofs and perform proof-checking procedures more easily. The purpose of this project is to encode PCML 5 with Beluga, a new functional programming language which supports dependent types and which has built in some of the more common procedures. Such an implementation will provide formal guarantees regarding the language of PCML 5 itself. Moreover, this case study will aid in the understanding of the development and implementation of software specically for programming with proofs. It will provide insight into the tools needed to allow any programmer to easily specify and verify complex behavioral properties of programs. Introduction In order to understand the research laid out in this report, one must have a solid understanding of functional programming and of security in distributed systems. This report begins with a brief description of lambda calculus, a formal calculus of functions and the basis for functional programming. Following the discussion of lambda calculus and functional programming is a section devoted to introducing Beluga, the functional programming language in which the code for this project was written. Next is an introduction to distributed systems and security within these systems. PCML 5 , a prototype programming language which this project implements, is introduced together with the motivations behind implementing PCML 5 in Beluga. Finally, there is a detailed description of the actual steps taken towards the implementation of PCML 5 in Beluga. Lambda Calculus The lambda calculus is a formal system intended to represent and to calculate with functions. These functions can take other functions as input and/or return functions as output. Such functions are called higher-order functions. Lambda terms are the foundation of this system and are defined as follows: Variables: A variable is a lambda term Lambda Abstraction: If M is a lambda term and x is a variable, then λx.M is a lambda term Application: If M and N are lambda terms, then M N is a lambda term The lambda term λx.M represents the function f(x) = M, where M may or may not be dependent on the variable x. The identity function for example is represented as λx.x. Observe that variable names are unimportant. λx.x and λy.y are equivalent functions. This equivalence is known in lambda calculus as alpha-equivalence. Binding Consider the lambda term λx.M. If x occurs in the expression M, then x is said to be bound to M. A variable that is not bound by a lambda abstraction is said to be free. The free variables of a lambda term can be deduced by the following inductive rules: Let x be a variable and let M and N be two lambda terms. Then Volume 8 - Issue 1 - March 2013 55
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ON THE COVER As we marvel at the Fi
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