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Lecture 6

PSY274 Lecture 6 (Oct 15).docx

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University of Toronto Mississauga
Craig Chambers

PSY274 LECTURE 6 - UNIT 2 - Language in relation to other psychological domains o Interpersonal interaction (conversation) o Cognition (thinking) o Social decisions (case study: advertising) - Today: conversational behaviour o Starting point: it is not enough to know the inventory of symbols in your language, accompanying paralinguistic and gestural cues, etc; o Successful communication involves knowing HOW to use language in a socially sophisticated way o How to use language fluently in respect to another person using language - Topics to be covered o Using artificial intelligence (AI) to gain insights into conversational behaviour o “Cooperation” in conversation  Turn-taking  Sentence structuring  Common ground and reference o Details in the act o f speaking: disfluencies and their effects  Production  Perception - Using artificial intelligence to gain insights into the psychology of conversational interaction - Psychology of communication: goals of discipline o Essentially an enterprise of “reverse engineering” (start with fully functioning system (mind/brain), try to deconstruct how it works scientifically o Artificial intelligence  Goal runs in opposite direction: to create/build a fully functioning system  Successes and failures can highlight the important properties of the fully functioning system - Historical development o 1960s conversational simulation (e.g. ELIZA) using text-based system  “Turing Test” competition o Features  Stimulus/response (doesn’t speak unless spoken to  “canned” sentences, selected based on occurrence of particular words OR partial repetition of previous sentence  Simple pronoun reversals “I” input  “you” in response o If you use more complicated sentences, it will easily get mixed up o More contemporary versions (1990s, 2000s):  “Chatterbots”  Loebner prize  More sophisticated: e.g. “Alice”  Uses ever-expanding knowledge base to tailor sentences to the user by “guessing” age, background, interests, occupation, etc; through context clues - The “lessons” from AI initiatives o Even simple things (from the perspective of human communication) can require a lot of built in knowledge and complex decision systems o An initial example: the problem of “context” in conversation (see Napolii text for numerous illustrations) - Examples using context dependence o Elliptical utterances  Where are you going?  School o Superficial nonsequiturs  Can you come to my party on Sunday?  I have an exam Monday  (doesn’t specifically say no, but person will be studying instead of partying) o Ambiguities  There’s a cup of coffee on the table. I just made it.  Fred ate the pie on the windowsill  Fred ate his dinner in the restaurant o Enrichment in interpretation  Have you been to Paris? (i.e., ever, in your life)  Have you eaten breakfast? (i.e. today)  The new boeing 767 is very aerodynamic (outside)  The new boeing 767 is very comfortable (inside) - A second highlighting the complex “ingredients” of conversation: making a chat-room avatar produce deictic gestures at relevant points based on typed input o Discourse context  Textual  What has been said  Ex. First mention of wine  Situational  What is seen  Ex. Bottle on table  Global  Common knowledge  Ex. Wine is in bottles - A third example: embodied agents o Video based representations of individuals who ‘speak’, ‘listen’, acquire knowledge, recognize intentions, produce appropriate gestures, etc; o Used as an interface with systems having a specific application (educational, commercial, etc;) o The virtual conversational partner must be lifelike/natural to be useful (i.e., to be worth the effort, compared to a keyboard+mouse interface) - Rea’s Virtual Realty o Pictures and examples of the online program which shows a virtual realtor - Embodied agents o With more human-like interfaces, it is even more important to approximate natural conversation o One illustration: when the natural rhythm of conversation clashes with the time lags required for a computer to generate responses o Rea wasn’t fast enough and they start to make instances of why she’s slow o Ex. “is this house in a good neighborhood?”  1 second to respond: “Yes”  Sounds sincere and is the ideal response  3 seconds to respond: “Yes”  Doesn’t sound sincere anymore, slow response because of the computer program, but people start to read into it anyways  Behavioural things such as “looking away” and it seems like they’re thinking before responding - Next topic: cooperation in conversation o Turn-taking o Typical conversation: each participant speaks and listens  How do we know when it’s our turn? o “reactive” accounts of turn-taking  Current speaker provides signals that their turn is ending  Listener ‘reacts’ by responding to signals and taking over o Reactive approaches: turn-yielding signals (according to Duncan, 1972)  Drop in pitch  Lengthened syllable at turn-final utterance  Drop in loudness/getting quieter  Termination of hand gestures  Certain stereotyped expressions (e.g. “you know”, “or something”)  Completion of well-formed sentence - Testing the reactive account: can these signals predict when a listener attempts to take a turn? o 0 cues produced  listener attempts to take a turn 10% of the time o 3 cues  listener attempts to take a turn 33% of the time o All 6 cues  listener attempts to take a turn 50% of the time o Answer: sort of. (still some explaining to do) - Alternatives to “reactive” accounts: - “projective” accounts of turn-taking o Speaker and listener recognize conversation has some goal(s) o Turn
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