Models of associative learning that use an error correcting learning algorithm, such as Rescorla-Wagner (Rescorla and Wagner, 1972; and see McLaren and
Dickinson, 1990; McLaren, 1989 for arguments that this assumption is correct) propose that conditioned associations between stimuli and outcomes can either be excitatory or inhibitory. The establishment of an excitatory association is straightforward: If a given stimulus (A) is repeatedly paired with an outcome (+), an association develops that, upon presentation of the stimulus, activates a representation of the outcome. To establish an inhibitory association a feature-negative design, A+ AB-, where a stimulus, B, is presented in compound with an excitatory stimulus, A, in the absence of the outcome can be used. The stimulus, B, termed a negative feature as its presence predicts the absence of the outcome, comes to reduce responding when presented in compound with the cue it was trained with (i.e. A) or with another cue that had predicted the same outcome. This reduction is assumed to reflect the summed excitatory and inhibitory associations between said cues and the outcome (i.e. a summation test). Thus, B becomes a conditioned inhibitor, having the opposite effect on the outcome to that of its excitatory counterpart, A (Rescorla, 1969).
One interesting property of the feature-negative design is that it is harder to learn than its excitatory counterpart; a result referred to as the feature-positive effect. Put simply, both humans (Fiedler, Eckert & Poysiak, 1989; Lotz, Uengoer, Koenig, Pearce & Lachnit, 2012; Newman, Wolff & Hearst, 1980; Richardson &
Massel, 1982) and other animals (Abramson et al., 2013; Jenkins & Sainsbury, 1969; Pace, McCoy & Nallan, 1980) will learn to discriminate that the presence of a feature, i.e. A- AB+, signals an outcome more readily, than they will learn that the presence of a feature, i.e. P+ PQ-, signals the omission of the outcome. That is, the difference in responding between A- and AB+ is usually larger than the difference in responding between P+ and PQ-. Whilst most experiments contrast the absolute presence or absence of the outcome, a reduction in the magnitude of the outcome, such as a lower shock amplitude, is also sufficient to demonstrate the feature-positive effect (Cotton, Goodall & Mackintosh, 1982; Harris, Kwok &
Andrew, 2014). In a recent demonstration, Lotz et al. (2012) presented human subjects with a predictive learning task, where they were shown a letter (or pair of letters) and asked to click if they thought the outcome (a green circle) would follow or wait five seconds if they thought it would not. As predicted, subjects were faster to learn that the presence of an additional letter predicted the green circle, than they were to learn that the additional letter predicted its absence. In this chapter, I confirm that the feature-positive effect in humans can be obtained using the incidental learning paradigm described in the previous chapter (which guards against unwanted contamination from more cognitive, rule-based processes), and then investigate the mechanisms responsible for the phenomenon.
Rescorla-Wagner is one algorithmic mechanism often invoked to explain the feature-positive effect as it readily predicts A- AB+ will be solved more easily than P+ PQ-. Essentially, because the feature-positive discrimination is dependent on the establishment of an excitatory B association and the simultaneous extinction of A; whereas the feature-negative discrimination requires both the establishment of an excitatory P association and an inhibitory Q association; the latter develops more slowly because it is inherently dependent on the prior establishment of P as an excitor before Q can become an inhibitor. The demonstrations of the feature-positive effect in humans and infra-humans reviewed thus far can be explained in this way, though the results obtained by Lotz et al. demand additional assumptions or processes to deal with their data. Inhibition is thus central to accounts of the feature-positive effect from an associative learning perspective, but there is another way of viewing this result that offers a quite different interpretation.
So far I have assumed that in the design A- AB+ P+ PQ-, it is perfectly clear what is meant by + and -. The outcome is +, the absence of the outcome is -. In Lotz et al. (2012), for example, the outcome is the appearance of a green circle.
But why could not the outcome be the omission of the green circle? That is also an outcome in some sense, though the reader will no doubt object that it does
not feel quite right to accord it that status. Nevertheless, logically it is quite possible to take the absence of the green circle (a white background perhaps) as the outcome. In this scenario the design becomes A+ AB- P- PQ+ and could be interpreted as a demonstration of the feature-negative effect; the reverse of that conventionally reported. The Rescorla-Wagner algorithm would struggle to explain this result, and so one can argue that the use of the appearance of the green circle as the outcome in these experiments goes hand-in-hand with the ability of models such as Rescorla-Wagner to explain the data. They are mutually dependent on one another. But this analysis does draw into question why the appearance of the green circle should be considered the outcome in Lotz et al. (2012) experiments, and to consider other possible implications of the feature-positive effect.