Semiotic Machines - an Introduction
The behavioristic Paradigm
AUTHOR: Dr. Gerd Döben-Henisch
FIRST DATE: Aug-6, 1996
DATE of LAST CHANGE: Sept-29, 1996
The before mentioned Minimal Theoretical Framework can be shaped for many different special theories, for instance also for what is usually called the behavioristic paradigm.
This is interesting for our discussion because Morris has chosen 'behavioristics', how he calls it, as that point of view, which he wants to adopt in the case of semiotic. But he stresses that this decision is not necessary (cf. Morris 1971: 21). This implies that there exists probably other kinds of methodological strategies (see below).
Le us have a short look to a behavioristic point of view which is characterized by the following assumptions:
SR1) The objects of a behavioristic theory are distinguishable systems [O] for which one can establish habits which are classified as 'responses [R]' with respect to certain classifiable events of the environment of these systems which are understood as 'stimuli [S]' inducing these responses. The possible DATA_sr of such a behavioristic theory -(also called stimulus-response theory, SR-theory)- are therefore pairs of stimuli-sets and response-sets with SUBSET(DATA_sr, pow(STIMULI) x pow(RESPONSES)).
SR2) Based on subsets of those data one can introduce hypothetical functions f_0, ..., f_n describing relationships between stimuli and responses with: f_0: pow(S) ---> pow(R), ..., f_n: pow(S) ---> pow(R). All these partial functions put together will form the whole system function f_sr: f_0 x ... x f_n ---> pow(RESPONSES).
SR3) With these assumptions one gets a formal structure T_sr with at least the following elements: T_sr(x) iff x = <<S,R>, <f_0, ..., f_n, f_sr>, A>. In this structure T_sr all the functions would represent theoretical terms for which does not exist a direct counterpart in the data; the data are providing at best only a partial 'interpretation'.
Today in times of a strongly expanding brain-research one can explicitly include the physiological data in such a behavioristic paradigm.
The main difference between the classical SR-theories and those enhanced with physiological data consists in the explicit assumption of physiological states in the system. Let us call those enhanced SR-theories SNR-theories (stimulus-(neuro)physiological-response theories). They can be characterized through the following assumptions:
SNR1) The possible DATA_snr of an SNR-theory are pairs of stimuli-sets, physiological states-sets, and response-sets with SUBSET(DATA_snr, pow(STIMULI) x pow(INTERNAL) x pow(RESPONSES)).
SNR2) Based on subsets of those data one can again introduce hypothetical functions, e.g. the functions f_aff, f_n0, ..., f_nm, f_eff describing relationships between stimuli, internal (physiological) states, and responses. 'f_aff' represents functions of afferent processes, i.e. f_aff: pow(STIMULI) x pow(INTERNAL) x pow(RESPONSES) ---> pow(INTERNAL); 'f_eff' represents functions of efferent processes, e.g. f_eff: pow(STIMULI) x pow(INTERNAL) x pow(RESPONSES) ---> pow(RESPONSES); 'f_n' represents internal (neurological) processes) like f_n: pow(INTERNAL) ---> pow(INTERNAL). All these partial functions put together will constitute again the overall system function f_snr: f_aff x f_n x f_eff ---> pow(RESPONSES).
SNR3) We can then establish the formal structure T_snr(x) iff x = <<S, R, Io, ..., In >, <f_aff, f_n, f_eff, f_snr>, A >. This structure again contains several theoretical terms which can only partially be interpreted by the data.
After this introduction of the behavioristic theory-concepts T_sr and T_snr I will reconstruct the pragmatics of Morris within this framework and I will show which problems will arise with this approach.
Comments are welcomed to doeb@inm.de
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