The Relation between the Functional and Cognitive Approaches in Learning Psychology

The relation between the functional and cognitive approaches is currently less than ideal.

Both traditions have their own scientific associations, conferences, journals, and textbooks, and supporters of one approach have little or no contact with supporters of the other.

Although there are historical reasons why the relation between the two approaches is so problematic (see box 0.3), there is in principle no reason why this must be the case. Indeed, from the perspective of our functional- cognitive framework, these two approaches are not competing for scientific legitimacy but are simply playing two different scientific games (De Houwer, 2011b; Hughes et al., 2016). On the one hand, the functional approach in learn-ing research wants to explain behavior by first examinlearn-ing which environmental regularities influence behavior under which environmental conditions and then abstracting out general principles that can account for classes of behavior that differ across time and situations. On the other hand, the cognitive approach wants to explain learning (the impact of regularities on behavior) in terms of mental mechanisms. The two approaches are thus situated at dif-ferent levels of explanation, each with their own explanandum (i.e., the concept that has to be explained) and explanans (i.e., the concept used to explain; see table 0.1).¹⁰

What is clear is that both levels of explanation have their respective merits. Therefore, it is difficult, if not impossible, to decide which approach is the “best” or “most important” (ques-tions about what is “best” are prescientific and are decided on the basis of one’s philosophical

Box 0.3 (continued)

Introduction 27

assumptions; Hayes & Brownstein, 1986). Instead of pitting the two approaches against each other, we believe a more productive strategy would be one in which people accept that (a) the two approaches have very different scientific objectives, and that (b) the objectives important to one approach can be used to evaluate only the products that emerge from that approach and not others, in much the same way that the rules that make sense in one sport (e.g., soccer) can-not be used to govern the activity of others (e.g., basketball; see Hughes, 2018). In other words, neither approach is strengthened by showing the weakness of the other.

Once one accepts that these two approaches are not in competition, it quickly becomes clear that the functional approach can reinforce progress within the cognitive, and vice versa. Take the functional approach: functional knowledge about learning can provide new insights into the mental mechanisms that are assumed to mediate learning. For instance, one can compare existing cognitive theories and evaluate the extent to which they are able to account for the existing body of functional knowledge. After all, a good mental process theory is a theory that can explain why a certain regularity in the environment has an impact on behavior only under certain conditions (i.e., a good mental process theory has a high heuristic value insofar as it can explain existing functional knowledge). The more functional knowledge we accumulate, the better we are able to evaluate and compare mental process theories with one another. This explains why cognitive learning psychologists also carry out experiments in which they manipulate environmental factors and check whether this has an influence on learning (i.e., because doing so allows them to test their mental process theories; see Hughes et al., 2016, for a more nuanced view). What is important to appreciate here is that cognitive learning psychologists do not have to restrict themselves to accumulating their own functional knowledge by conducting experiments. Rather, they can also use the wealth of experimental data already collected by functional learning psy-chologists. But functional learning psychologists have more to offer than just their data. As we noted earlier, functional psychologists strive to formulate abstract principles on the basis Table 0.1

The concepts that need to be explained (explanandum) and the concepts used to explain (explanans) in the functional and cognitive approaches to the psychology of learning

Explanandum

28 Introduction

of individual findings. The abstract concepts that they develop when formulating these principles can also be useful for cognitive psychologists. This is because cognitive psycholo-gists tend to describe their data either in very superficial terms or in terms of their cognitive theories, which has all sorts of disadvantages that we will not discuss here. Describing data in terms of abstract functional concepts overcomes these disadvantages and can thus be useful for cognitive psychologists (see De Houwer, 2011b; De Houwer et al., 2017; Hughes et al., 2016).

Cognitive learning research can also help functional psychologists to discover the mod-erators of learning. This is because a good mental process theory has, in addition to a high heuristic value, a high predictive value (i.e., the ability to make novel predictions about the conditions under which learning occurs). Therefore, testing predictions derived from mental process theories leads to new functional knowledge about the conditions under which learn-ing occurs. This functional knowledge can be used by functionally oriented researchers to refine their own analytic- abstractive concepts, theories, and procedures (see Barnes- Holmes

& Hussey, 2016, for a discussion about the possibilities and limitations of what functional psychologists can learn from cognitive psychologists).

The key point here is that there is a natural interaction between observation, functional knowledge about learning (knowledge about the moderators of the impact of regularities in the environment on behavior), and mental process theories about learning (hypotheses about the mental mechanisms responsible for the impact of environmental regularities on behavior), and this interaction can be useful for functional and cognitive learning psycholo-gists alike. Both conduct experiments in which they manipulate environmental factors and observe behavior. Provided there are sufficient controls, they both can arrive at functional knowledge. Because functional knowledge provides insight into the causes of behavior, one can use this knowledge to predict new observations (which behavior will occur under which conditions), which can in turn lead to new functional knowledge. Thus, an interaction between the level of observation and functional knowledge is possible.

But there can also be an interaction between the functional and cognitive level of expla-nation. Based on existing functional knowledge, cognitive psychologists can try to design theories about the mental causes of learning. These theories can in turn lead to new predic-tions about functional relapredic-tions (i.e., about moderators of learning), which are tested in new experiments, leading to observations that in turn can result in new functional knowledge.

This interaction between observation, functional knowledge, and mental process theories is depicted in figure 0.5 (for more on this idea, see box 0.4).

Introduction 29

Functional knowledge Mental processes

Observations Figure 0.5

The interaction between observation, functional knowledge, and theories about mental processes.

Box 0.4 Distinguishing Two Levels of Functional Knowledge

If researchers stick to the results of individual experiments, they will be able to say something only about the specific effects that occur in those experiments— that is, the impact of those specific aspects of the environment (e.g., the pairing of bell and food) on those specific types of behavior (e.g., salivation). The functional knowledge that is generated by individual experiments has some merit (i.e., it allows for prediction and influence within that particular setting), but its merit is restricted to the effect that is being studied. Hughes et al. (2016) therefore referred to the func-tional level of the individual experiment as the effect- centric funcfunc-tional level. Scientists, how-ever, also want to use the data of individual experiments to arrive at more abstract knowledge that can be applied to a wider range of situations, including situations never encountered before. As noted earlier, functional psychologists achieve this by formulating abstract principles that refer to the function of events. For instance, the principle of reinforcement states that behavior increases in frequency if it is followed by a reinforcer; this principle can be applied not only to rats pressing a lever for food but also to any other organism that emits behaviors that have consequences. This type of knowledge is functional in nature (it is about environment- behavior relations) but it is formulated in abstract functional terms rather than in terms that refer to the stimuli and behavior involved in a specific experimental setup. Hughes et al. (2016) referred to this level of functional knowledge as the analytic- abstractive functional level. Developments at the analytic- abstractive functional level are driven by developments at the effect- centric functional level. Likewise, devel-opments at the analytic- abstractive functional level give rise to new predictions that produce new effect- centric functional knowledge. In sum, the two functional levels interact in mutually supportive ways.

Cognitive psychologists also aim to develop abstract knowledge that can be applied in a wide range of situations. They also formulate this knowledge on the basis of individual experiments and thus also draw on effect- centric functional knowledge. However, cognitive psychologists typi-cally formulate their abstract knowledge in terms of mental constructs (e.g., association forma-tion, inhibiforma-tion, working memory) that (ideally) can be applied across a range of situations.

(continued)

30 Introduction

Figure 0.6 provides a schematic representation of this extended analysis of levels of explana-tion in psychological research. The increase in complexity of this analysis is counterbalanced by a more fine- grained view on ways in which functional and cognitive researchers can interact. First, both approaches can feed into the effect- centric functional level and thus into developments at both the cognitive level and the analytic- abstractive functional level. Second, cognitive psycholo-gists can benefit from knowledge and concepts developed at the analytic- abstractive functional level, just like functional psychologists can find inspiration in cognitive theories when formulat-ing abstract functional principles.

Functional level (analytic-abstractive)

Functional level (effect-centric)

Cognitive level (mental-abstractive)

Observational level Figure 0.6

An extended analysis of levels of explanation in psychology.

Critically, this interaction between the two approaches can succeed only if they are clearly distinguished from each other. Take the interaction between functional knowledge and mental process theories. Separating these two levels implies that elements of functional knowledge (i.e., behavioral effects) are not treated as interchangeable with elements of mental process theories.

Mixing the functional and mental levels occurs whenever one starts to define behavioral effects in terms of mental processes. Unfortunately, this tendency is all too common in the psychol-ogy of learning and elsewhere in psychological science. To illustrate, take classical conditioning.

Many researchers mistakenly believe that classical conditioning effects are synonymous with the formation of associations between mental representations in memory. Conflating the thing that needs to be explained (effect) with the thing that is used to explain (mental process) has many disadvantages (see De Houwer, 2007, 2011b; De Houwer et al., 2017).

One major disadvantage is that it quickly becomes difficult to actually study classical con-ditioning. After all, from this perspective, it is not enough to establish that a regularity in the presence of multiple stimuli leads to a change in behavior. Researchers also have to show that Box 0.4 (continued)

Introduction 31