The Emergence of Stimulus-Response Associations from Neural Activation Fields: Dynamic Field Theory

The Emergence of Stimulus-Response Associations from Neural Activation Fields: Dynamic Field Theory Claudia Wilimzig (Claudia.Wilimzig@neuroinformatik.rub.de) Institut f¨ ur Neuroinformatik; Ruhr-University of Bochum ND 04, 44780 Bochum Germany Gregor Sch¨ oner (Gregor.Schoener@neuroinformatik.rub.de) Institut f¨ ur Neuroinformatik; Ruhr-University of Bochum ND 04, 44780 Bochum Germany meaning information ”red” may provide input to neu- rons encoding the oral response ”red” (Cohen, Dunbar & McClelland, 1990; Roelofs, 2003; see Figure 1). Abstract Dynamic Field Theory is based on spatially continuous representations of parameter space and provides a math- ematical framework to study the metric structure of rep- resentations. We review evidence for such metric struc- ture and provide a Dynamic Field Theory of stimulus- response association to account for effects caused by the metric structure of representations such as the distance effect and the interaction of the Hick and Hyman effects with stimulus metrics. We show how the integration of different sources of information contributes to stimulus- response association by providing a new model of the manual Stroop effect. “red” “green” Introduction People continuously interact with their environment gen- erating simple behaviors such as pointing or reaching, but also using more complex skills such as using a com- puter. Everyday life thus requires a highly flexible neural system that may adjust and readjust its behav- ior depending on situational constraints, may continu- ously learn new responses toward sensory inputs and may adapt already existing stimulus-response mappings. Cognitive scientists and psychologists have studied how neural systems learn to react to input from sensory sur- faces and how they compute the corresponding output through stimulus-response association paradigms. If a participant is asked to respond to a red stimulus by pressing a right button and to a green stimulus by press- ing a left button, this requires a stimulus-response map- ping that the subject probably has never encountered before. Nevertheless, within a very few practice trials participants are able to perform such a task. While such tasks might appear trivial to the partic- ipant, it is not obvious how the nervous system might achieve them. One way researchers have tried to study stimulus-response associations consists of bringing into conflict multiple sources of response specification. In the classic Stroop task (review by MacLeod, 1991), for instance, the meaning of color words is brought into con- flict with the color the words are printed in. Participants have to speak out loud the latter and are slowed in their response if that color is incongruent with the meaning of the color word. Neural accounts for such conflict in connectionist mod- els are typically based on units that code for entire stim- ulus or response categories. For instance, neurons rep- resenting the print-color information ”red” or the word red green red print color green word task input Figure 1: Connectionist model of the Stroop paradigm, redrawn after Cohen, Dunbar & McClelland (1990) Neurophysiological evidence on the other hand sug- gests that features are represented in continuous feature maps, in which populations of neurons with broad tun- ing curves are activated. Across neocortex and other parts of the central nervous system, the location of neu- rons in the network determines what information neu- rons encode (space code principle). In many cortical ar- eas, topographic mapping makes that neighboring neu- rons encode similar kinds of information. Independently of topography (anatomical coordinates), neuronal repre- sentations of parameters can be constructed by sorting neurons according to what they code (functional coor- dinates) (Georgopoulos et al., 1982). Neuronal inter- action is sensitive to this metric structure of represen- tations, meaning that neurons representing similar in- formation excite each other while neurons representing dissimilar information inhibit each other. Wilson and Cowan (1973) as well as Amari (1977) have shown that the processing of information in such cortical and subcor- tical networks is mathematically well described by con- tinuous dimensions with an associated metric defined by interaction.