Do stimulus and response processing happen in stages? Or does information get continuously transmitted from stimulus-processing to response-processing over time?
This is an important question that models of performance heatedly disagree about. According to the continuous transfer view, any activation that accumulates for a stimulus code is immediately used as input to any associated response codes, which in turn has an immediate influence on response code activation. According to the discrete transfer view, on the other hand, activation of a stimulus code must accumulate to some critical level, indicating that stimulus identification has been completed with some degree of certainty, before a signal is sent to the response selection process and activation of the response codes can accumulate.
Historically, popularity has swung back and forth between these alternatives. Over thirty years ago, Sternberg (1969) proposed a method of interpreting reaction time data called the additive factors method (AFM). This framework assumes that information is transferred discretely between processes, and shows that given this assumption, if the effect of manipulating some factor A changes (i.e. becomes larger or smaller) as one manipulates another factor B, then these two manipulations must influence the same underlying process. The logic of this method was very compelling, and was used with much success for interpreting empirical results (see Sanders, 1980, 1990; Sternberg, 1971); so, people were happy, for a while, to assume discrete information transfer.
A decade later, however, McClelland (1979) developed the Cascade model, in which information is transferred continuously from stimulus to response processes, and showed that this model could account for many of the same kinds of empirical data that AFM could account for, but also allowed for different inferences to be drawn about what processes are influenced by experimental manipulations. Around the same time, a large number of other criticisms of AFM also arose, as well as new models favoring the assumption of continuous information transfer (e.g. Eriksen & Schulz, 1979; Taylor, 1976; Wickelgren, 1977). The tide had turned toward continuous models.
Approximately a decade after that, Miller (1988) brought extensive criticism against this shift, saying sharply: “We consider the en masse abandonment of discrete models in favor of continuous ones to be wholely unjustified given the evidence currently available, and thus scientifically premature.” He analyzed much of the empirical data that had been adduced in support for continuous models, and found that they could all be accounted for by models that did not assume continuous information transfer. Most often, the empirical evidence spoke against models that consisted of only a single unitary stimulus identification process, but could easily be accounted for by models that assumed the formation of multiple parallel stimulus codes. Miller shows that his Asynchronous Discrete Coding (ADC) model, in which multiple stimulus identification processes each give separate and independent discrete outputs to response processing, is able to account for much of the critical data (see also Miller 1982a, 1982b, 1983).
Increasingly sophisticated methods have been proposed to empirically test whether information transfer is discrete or continuous (Roberts & Sternberg, 1993), and increasingly complex models have been proposed that manipulate assumptions about different ways in which information transmission can be discrete, continuous, or varying from one to the other on a continuum (e.g. Liu, 1996; Miller, 1993). No overall agreement, however, has been reached in this debate.
Finally, it should be noted that the terms “discrete” and “continuous” can be, and have been, used in other ways when talking about models of mental processes. Miller (1988) distinguishes between three different ways in which a model can be “discrete” or “continuous”. First, it may have discrete or continuous representation: mental representations may either vary freely across a continuum, or may be restricted to a limited number of mental codes. Second, it may have discrete or continuous transformation: mental representations may vary continuously in the degree to which they are formed or activated, or may have only a limited number of states they can be in (e.g. formed or not, prepared or not). Third, it may have discrete or continuous transfer of information, as has been discussed so far here. Most of the empirical tests and theoretical debates have been focused on the question of information transmission, although there has been some work in trying to empirically establish whether the transformation of information within response selection is discrete or continuous (e.g. Meyer, Irwin, Osman, Kounios, 1988; Meyer, Yantis, Osman, & Smith, 1985).
Connectionist models of performance and consistency effects all have discrete representation (a finite number of units, representing discrete mental codes) and continuous transformation (continuous accumulation of activation within each unit); However, although most of these models assume continuous information transfer (e.g. Barber & O’Leary, 1993; Cohen, Dunbar & McClelland, 1990; Cohen & Huston, 1994; Cohen, Servan-Schreiber, & McClelland, 1992; O’Leary & Barber, 1997; Phaf, van der Heijden, & Hudson, 1990; Servan-Schreiber, 1990; Zhang & Kornblum, 1998), at least one of these models explicitly assumes discrete transfer (Kornblum, et al., 1999), and another implies it (Zorzi and Umilta, 1995; this case will be discussed below).
This debate is important when evaluating computational implementations of generic dimensional overlap and response selection models. The way a model implements the transfer of information could seriously impact the predictions that it makes, also influencing any comparison that is made between it and other models.