Who Needs Emotions? The Brain Meets the Robot

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architectural basis of affect 243 Ortony, A. (2002). On making believable emotional agents believable. In R. Trappl, P. Petta, P., & S. Payr (Eds.), Emotions in humans and artifacts (pp. 189–211). Cambridge, MA: MIT Press. Ortony, A., Clore, G., & Collins, A. (1988). The cognitive structure of the emotions. New York: Cambridge University Press. Panksepp, J. (1998). Affective neuroscience. The foundations of human and animal emotions. Oxford: Oxford University Press. Picard, R. (1997). Affective computing. Cambridge, MA: MIT Press. Sartre, J.-P. (1939). The emotions: A sketch of a theory. New York: Macmillan. Scheutz, M. (2001). The evolution of simple affective states in multi-agent environments. In D. Cañamero (Ed.), Proceedings of the American Association for Artificial Intelligence fall symposium 01 (pp. 123–128). Falmouth, MA: AAAI Press. Scheutz, M., & Schermerhorn, P. (2002). Steps towards a systematic investigation of possible evolutionary trajectories from reactive to deliberative control systems. In R. Standish (Ed.), Proceedings of the 8th Conference of Artificial Life. Cambridge, MA: MIT Press. Scheutz, M., &Sloman, A. (2001). Affect and agent control: Experiments with simple affective states. In N. Zhong, et al. (Eds.), Intelligent agent technology: Research and development (pp. 200–209). River Edge, NJ: World Scientific. Simon, H. A. (1979). Motivational and emotional controls of cognition. In Models of thought (pp. 29–38). New Haven, CT: Yale University Press. (Original work published 1967) Sloman, A. (1978). The computer revolution in philosophy. Hassocks, UK: Harvester Press (Humanities Press). Available online (http://www.cs.bham.ac.uk/ research/cogaff/crp) Sloman, A. (1982). Towards a grammar of emotions. New Universities Quarterly, 36, 230–238. (http://www.cs.bham.ac.uk/research/cogaff/0-INDEX96-99. html#47) Sloman, A. (1989). On designing a visual system (towards a gibsonian computational model of vision). Journal of Experimental and Theoretical AI, 1, 289–337. Sloman, A. (1993). The mind as a control system. In C. Hookway & D. Peterson (Eds.), Philosophy and the cognitive sciences (pp. 69–110). Cambridge: Cambridge University Press. Sloman, A. (1996). Towards a general theory of representations. In D. M. Peterson (Ed.), Forms of representation: An interdisciplinary theme for cognitive science (pp. 118–140). Exeter, UK: Intellect. Sloman, A. (2000a). Interacting trajectories in design space and niche space: A philosopher speculates about evolution. In M. Schoenauer et al. (Eds.), Parallel problem solving from nature—PPSN VI, lecture notes in computer science (No. 1917, pp. 3–16). Berlin: Springer-Verlag. Sloman, A. (2000b). Models of models of mind. In M. Lee (Ed.), Proceedings of symposium on how to design a functioning mind, AISB’00 (pp. 1–9). Birmingham: Artificial Intelligence and Simulation of Behaviour. Sloman, A. (2001a). Beyond shallow models of emotion. Cognitive Processing: International Quarterly of Cognitive Science, 2, 177–198.
244 robots Sloman, A. (2001b). Evolvable biologically plausible visual architectures. In T. Cootes & C. Taylor (Eds.), Proceedings of British machine vision conference (pp. 313–322). Manchester: British Machine Vision Association. Sloman, A. (2002). Architecture-based conceptions of mind. In In the Scope of Logic, Methodology, and Philosophy of Science (Vol. II, pp. 403–427; Synthese Library Vol. 316). Dordrecht: Kluwer. Sloman, A., & Chrisley, R. (2003). Virtual machines and consciousness. Journal of Consciousness Studies, 10, 113–172. Sloman, A., & Croucher, M. (1981). Why robots will have emotions. In Proceedings of the 7th International Joint Conference on AI (pp. 197–202), Vancouver: Morgan-Kaufman. Sloman, A., & Logan, B. (2000). Evolvable architectures for human-like minds. In G. Hatano, N. Okada, & H. Tanabe (Eds.), Affective minds (pp. 169–181). Amsterdam: Elsevier. Turner, T., & Ortony, A. (1992). Basic emotions: Can conflicting criteria converge? Psychological Review, 99, 566–571. Wright, I., Sloman, A., & Beaudoin, L. (2000). Towards a design-based analysis of emotional episodes. Philosophy Psychiatry and Psychology, 3(2): 101–126. Reprinted in R. L. Chrisley (Ed.), Artificial intelligence: Critical concepts in cognitive science (Vol. IV). London: Routledge. (Original work published 1996)
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TLFeBOOK Who Needs Emotions? The Br
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The Nature of Emotion: Fundamental
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3 Oxford University Press, Inc., pu
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vi preface want their computer prog
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viii preface is required because th
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x preface prefer to read Part III b
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xii contents PART III: ROBOTS 7 Aff
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xiv contributors Jean-Marc Fellous
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4 perspectives RUSSELL: I confess t
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6 perspectives EDISON: Tinkering! Y
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10 perspectives HOW COULD WE TELL I
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12 perspectives This, of course, ra
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14 perspectives of a stimulus (e.g.
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16 perspectives an emotion is neith
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18 perspectives cognitive processes
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20 perspectives that they visually
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22 perspectives A self-model that c
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24 perspectives Brothers, L. (1997)
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30 brains specificity and flexibili
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32 brains It is useful to begin wit
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34 brains the readout of that syste
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36 brains A. 5 NH 4Cl Bacteria in c
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38 brains Although instinctual beha
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40 brains Motivated behavior requir
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42 brains emerged through decades o
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44 brains and gonadal and adrenal s
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46 brains voluntary control of acti
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48 brains A. Total cerebral input t
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50 brains sensory neurons, interneu
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52 brains functions. Niall (1982) n
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54 brains motivation arousal necess
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56 brains serotonergic system is pr
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58 brains as tree jumping (Doudet e
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60 brains then have a system that a
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62 brains heroin and other opiates,
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64 brains benefits that also presen
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66 brains and brain neurotransmitte
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68 brains Cardinaud, B., Gilbert, J
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70 brains Hess, W. R. (1957). The f
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72 brains MacLean, P. D. (1990). Th
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74 brains Pfaus, J. G., Damsma, G.,
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76 brains trained monkeys: Agonist
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80 brains We conclude by discussing
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82 brains to prove, theoretical dis
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84 brains is a mammalian specializa
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86 brains processing circuits to se
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88 brains amygdala) and the control
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90 brains comparison of the amygdal
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92 brains Is the Amygdala Necessary
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94 brains the amygdala to determine
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96 brains The medial prefrontal cor
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98 brains Many questions remain to
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100 brains by the amygdala might be
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102 brains United States, pair up w
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104 brains networks that participat
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106 brains Note Portions of this ch
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108 brains Davidson, R. J., & Irwin
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110 brains mediate emotional respon
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112 brains Salinas, J. A. (1995). I
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114 brains and thalamo-cortico-amyg
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118 brains and flexible in the orbi
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120 brains Pleasure Rage Anger Frus
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122 brains of the eliciting stimulu
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124 brains or a right turn to obtai
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126 brains cortex and basal ganglia
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128 brains tex of recent (episodic)
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130 brains the right value in the c
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132 brains by the process of condit
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134 brains memories to be held in p
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136 brains However, it may be expec
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138 brains Figure 5.4. Some of the
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140 brains acting through the ventr
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142 brains (cutting white matter) w
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144 brains References Alexander, R.
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146 brains Rolls, E. T. (1999a). Th
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148 brains directed to us or when t
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150 brains Specific methods, partly
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152 brains Movements performed by l
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154 brains processing of invariant
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156 brains more about them, their r
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158 brains see Zajonc, 1985), the i
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160 brains The question now arises
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162 brains situations where they ha
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164 brains Estimation of social con
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166 brains Davies, M., & Stone, T.
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168 brains Lhermitte, F. (1983). Ut
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174 robots perform unanticipated ta
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176 Table 7.1. Principal Organism F
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178 robots and cognitive domains. A
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180 robots that they are better tho
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182 robots irregularities or discon
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184 robots 3. A (positive) feeling
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186 robots We consider the well-est
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188 robots disturbed by the approac
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190 robots processing. We view para
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Page 211 and 212: 194 robots Implications of the Proc
Page 213 and 214: 196 robots current affective state
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Page 217 and 218: 200 robots Gray, J. A. (1990). Brai
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Page 225 and 226: 208 robots DIRECT AND MEDIATED CONT
Page 227 and 228: 210 robots Toward a Useful Ontology
Page 229 and 230: 212 robots different varieties of m
Page 231 and 232: 214 robots Primitive sensors provid
Page 233 and 234: 216 robots Being in a state P of a
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Page 237 and 238: 220 robots Varieties of Affective S
Page 239 and 240: 222 robots Central Perception Actio
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Page 243 and 244: 226 robots layer (e.g., observing p
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Page 249 and 250: 232 robots from mental processes ot
Page 251 and 252: 234 robots The majority view in thi
Page 253 and 254: 236 robots Will it be a long-term f
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Page 263 and 264: 246 robots state variables such as
Page 265 and 266: 248 robots In order to make robots
Page 267 and 268: 250 robots motivational state also
Page 269 and 270: 252 robots Prey acquisition: This b
Page 271 and 272: 254 robots Figure 9.3. Top photos:
Page 273 and 274: 256 robots especially when young. E
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Page 277 and 278: 260 robots Mean distance to attachm
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Page 283 and 284: 266 robots Motivational/emotional m
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Page 293 and 294: 276 robots This endeavor does not i
Page 295 and 296: 278 robots For example, the person
Page 297 and 298: 280 robots mental states (i.e., int
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Page 301 and 302: 284 robots Negative valence High ar
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Page 305 and 306: 288 robots For instance, the visual
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294 robots Table 10.2. Summary of t
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296 robots it up (Breazeal, 2002b).
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298 robots pitch, f o (kHz) pitch,
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300 robots Table 10.3. Overall Clas
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302 robots Each emotion gateway pro
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304 robots Biasing Attention Kismet
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306 robots be to vocalize to the pe
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308 robots References Ackerman, B.,
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310 robots Center for the Study of
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312 robots When team members align
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314 robots effects that the agent h
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316 robots double arrow), which imp
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318 robots that of “robot as avat
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320 robots terms of specific apprai
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322 robots their own self-survival
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324 robots explicit, intended commu
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326 robots Although, the emotion
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328 robots planning. In Proceedings
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334 conclusions handout and pleased
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336 conclusions emotion. How can we
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338 conclusions Presumably, the fac
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340 conclusions Absence of N and N
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342 conclusions Thus, evolution yie
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344 conclusions different from such
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346 conclusions approach to the soc
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348 conclusions at the much higher
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350 conclusions mediated by distrib
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352 conclusions to the prefrontal c
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354 conclusions pathway of much mor
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356 conclusions The Motivated Toad
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358 conclusions explicitly formal r
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360 conclusions directly and by pro
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362 conclusions striatum pallidum d
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364 conclusions (A) Auditory stimul
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366 conclusions from the primary ta
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368 conclusions by an interest in u
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370 conclusions he sees as the stat
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372 conclusions to detect possible
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374 conclusions much of my behavior
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376 conclusions Consider a robot th
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378 conclusions 3. As already noted
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380 conclusions Fellous, J.-M., & S
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382 conclusions Localization of gra
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386 index amygdala back projection,
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388 index cognition (continued) evo
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390 index emotion research (continu
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392 index fear conditioning (contin
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394 index limbic system theory, 80-
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396 index prefrontal cortex and the
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398 index schema theory and Jackson
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