Who Needs Emotions? The Brain Meets the Robot

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Cognitive Releasers goal achieved et cetera people percept Social behavior hierarchy toy percept understimulated drive looming stimulus Behavioral Response sensors vision, joint position, velocity Feature Extraction color, size, motion, skin tone Visual Attention Locus of Attention contextualized perceptual and internal state contribution Drives Social drive Behavior System Toy behavior hierarchy Fatigue behavior hierarchy Motor Skills Stimul. Fatigue drive drive behavior state color, size, motion, skin tone active emotion Emotional Expression Motor System Motor Expression face voice posture motors scolding speech praising speech robot emotion 285 Affective Releasers threatening stimulus desired stimulus Affective Appraisal [A, V, S] et cetera microphone Affective Speech Recognizer undesired stimulus et cetera affectively tagged releasers elicitor contributions Emotion Arbitration/Activation pitch, energy, phonemes Emotion Elicitors anger disgust fear joy sorrow surprise anger disgust fear joy sorrow surprise active emotion net [A, V, S] Figure 10.3. An architectural overview showing the tight integration of the cognitive system (light gray), the emotive system (white), and the motor system. The cognitive system is comprised of perception, attention, drives, and behavior systems. The emotive system is composed of affective releasers, appraisals, elicitors, and “gateway” processes that orchestrate emotive responses. A, arousal; V, valence; S, stance. It is common for biologically inspired architectures to be constructed from a network of interacting elements (e.g., subsumption architecture [Brooks, 1986], neural networks [McCulloch & Pitts, 1943], or agent architectures [Minsky, 1986]). Ours is implemented as an agent architecture where each computational element is conceptualized as a specialist (Minsky 1986). Hence, each drive, behavior, perceptual releaser, motor,
286 robots and emotion-related process is modeled as a different type of specialist that is specifically tailored for its role in the overall system architecture. Each specialist receives messages from those connected to its inputs, performs some sort of specific computation based on these messages, and then sends the results to those elements connected to its outputs. Its activation level, A, is computed by the following equation: n A = wi j j b j ⎛ ⎞ ⎜ ⎝⎜ ⎠⎟ + ∑ = 1 for integer values of inputs i j, weights w j, and bias b over the number of inputs n. The weights can be either positive or negative; a positive weight corresponds to an excitatory connection, and a negative weight corresponds to an inhibitory connection. Each process is responsible for computing its own activation level. The process is active when its activation level exceeds an activation threshold. When active, the process can send activation energy to other nodes to favor their activation. It may also perform some special computation, send output messages to connected processes, and/ or express itself through motor acts by sending outputs to actuators. Hence, although the specialists differ in function, they all follow this basic activation scheme. Units are connected to form networks of interacting processes that allow for more complex computation. This involves connecting the output(s) of one unit to the input(s) of another unit(s). When a unit is active, besides passing messages to the units connected to it, it can also pass some of its activation energy. This is called spreading activation and is a mechanism by which units can influence the activation or suppression of other units. This mechanism was originally conceptualized by Lorenz (1973) in his hydraulic model of behavior. Minsky (1986) uses a similar scheme in his ideas of memory formation using K-lines. Popular architectures of Brooks (1986) and Maes (1991) are similar in spirit. However, ours is heavily inspired by ethological models and, hence, is most similar to that of Blumberg, Todd, and Maes (1996). Ethology, comparative psychology, and neuroscience have shown that observable behavior is influenced by internal factors (i.e., motivations, past experience, etc.) and by external factors (i.e., perception). This demands that different types of systems be able to communicate and influence each other despite their different functions and modes of computation. This has led ethologists such as McFarland and Bosser (1993) and Lorenz (1973) to propose that there must be a common currency to the perceptual, motivational, and behavioral systems. Furthermore, as the system becomes more complex, it is possible that some components conflict with others (e.g., competing for shared resources such as motor or perceptual abilities of the creature). In this
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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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192 robots independent, a value on
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194 robots Implications of the Proc
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196 robots current affective state
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198 robots primitive fear at the ro
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200 robots Gray, J. A. (1990). Brai
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202 robots cesses underlying approa
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204 robots case of CogAff, conjectu
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206 robots some cases and in other
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208 robots DIRECT AND MEDIATED CONT
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210 robots Toward a Useful Ontology
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212 robots different varieties of m
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214 robots Primitive sensors provid
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216 robots Being in a state P of a
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218 robots terms of the ability to
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220 robots Varieties of Affective S
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222 robots Central Perception Actio
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224 robots control, where all the l
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226 robots layer (e.g., observing p
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228 robots are sometimes unclear, i
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230 robots for such a fast-acting s
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232 robots from mental processes ot
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Page 257 and 258: 240 robots some undesirable emotion
Page 259 and 260: 242 robots References Albus, J. S.,
Page 261 and 262: 244 robots Sloman, A. (2001b). Evol
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
Page 275 and 276: 258 robots Object of Attachment Saf
Page 277 and 278: 260 robots Mean distance to attachm
Page 279 and 280: 262 robots 2003), but the intent is
Page 281 and 282: 264 robots Affective State Emotion
Page 283 and 284: 266 robots Motivational/emotional m
Page 285 and 286: 268 robots Brooks, R. (1986). A rob
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Page 289 and 290: 272 robots responsible for perceivi
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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
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Page 299 and 300: 282 robots Expression of Affective
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Page 305 and 306: 288 robots For instance, the visual
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Page 311 and 312: 294 robots Table 10.2. Summary of t
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Page 315 and 316: 298 robots pitch, f o (kHz) pitch,
Page 317 and 318: 300 robots Table 10.3. Overall Clas
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Page 321 and 322: 304 robots Biasing Attention Kismet
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Page 325 and 326: 308 robots References Ackerman, B.,
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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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