Two experiments were conducted to examine whether the resistance to extinction obtained in evaluative conditioning (EC) studies implies that EC is a qualitatively distinct form of classical conditioning (Baeyens, Eelen, & Crombez, 1995 Baeyens, F, Eelen, P, and Crombez, G, (1995a). Pavlovian associations are forever: On classical conditioning and extinction, Journal of Psychophysiology 9 ((1995a)), pp. 127–141.[Web of Science ®], [Google Scholar]a) or whether it is the result of an nonassociative artefact (Field & Davey, 1997 Field, AP, and Davey, GCL, (...) (1997). Conceptual conditioning: Evidence for an artifactual account of evaluative learning, Learning and Motivation 25 ((1997)), pp. 446–464.[Crossref], [Web of Science ®], [Google Scholar], 1998 Field, AP, and Davey, GCL, (1998). Evaluative conditioning: Arti‐fact or ‐fiction?‐ A reply to Baeyens, De Houwer, Vansteenwegen, and Eelen (1998), Learning and Motivation 29 ((1998)), pp. 475–491.[Crossref], [Web of Science ®], [Google Scholar], 1999 Field, AP, and Davey, GCL, (1999). Reevaluating evaluative conditioning: A nonassociative explanation of conditioning effects in the visual evaluative conditioning paradigm, Journal of Experimental Psychology: Animal Behavior Processes 25 ((1999)), pp. 211–224.[Crossref], [PubMed], [Web of Science ®], [Google Scholar]). Both experiments included between‐subjects control groups in addition to standard within‐subjects control conditions. In Experiment 1, only verbal ratings were measured in order to evaluate the effect of postacquisition CS‐only exposures on EC whereas in Experiment 2, verbal ratings and postextinction priming effects were measured. The results showed that the EC effects are demonstrable in a between‐subjects design and that the extinction procedure did not have any influence on the acquired evaluative value of CSs regardless of whether the verbal ratings or the priming effects were used as dependent variables. The present results provide evidence that EC is resistant to extinction and suggest an interpretation of EC as a qualitatively distinct form of associative learning. (shrink)

Evaluative learning refers to the change in the affective evaluation of a previously neutral stimulus that occurs after the stimulus has been associated with a second, positive or negative, affective stimulus . Four experiments are reported in which the AS was presented very briefly. Significant evaluative learning was observed in participants who did not notice the presentation of the affective stimuli or could not discriminate between the briefly presented positive and negative ASi when asked to do so . In two (...) other experiments , no significant learning effect was obtained. A meta-analysis performed on the present and previously reported results gave evidence for a small, though statistically reliable evaluative learning effect when ASi are presented “subliminally.” This finding supports the hypothesis that evaluative associations can be learned implicitly. (shrink)

Evaluative conditioning (EC) is one of the terms that is used to refer to associatively induced changes in liking. Many controversies have arisen in the literature on EC. Do associatively induced changes in liking actually exist? Does EC depend on awareness of the fact that stimuli are associated? Is EC resistant to extinction? Does attention help or hinder EC? As an introduction to this special issue, we will discuss the extent to which the papers that are published in this issue (...) help to resolve some of the controversies that surround EC. We also speculate about possible boundary conditions of EC and attempt to reconcile conflicting results on the functional properties of EC. (shrink)

Several forms of dietary learning have been identified in humans. These include flavor–flavor learning, flavor–postingestive learning , and learned satiety. Generally, learning is thought to occur in the absence of contingency or demand awareness. However, a review of the literature suggests that this conclusion may be premature because measures of awareness lack the rigor that is found in studies of other kinds of human learning. If associations do configure outside awareness then this should be regarded as a rare instance of (...) automatic learning. Conversely, if awareness is important, then successful learning may be governed by an individual’s beliefs and predilection to attend to stimulus relationships. For researchers of dietary learning this could be critical because it might explain why learning paradigms have a reputation for being unreliable. Since most food preferences are learned, asking questions about awareness can also tell us something fundamental about everyday dietary control. (shrink)

In this commentary, we assess the propositional approach to associative learning not only in terms of veridicality and falsifiability, but also in heuristic value. We remark that it has furthered our knowledge and understanding of human, as well as animal, associative learning. At the same time, we maintain that models developed from the association formation tradition continue to bear great heuristic value as well.

Mitchell et al.'s claim, that their propositional theory is a single-process theory, is illusory because they relegate some learning to a secondary memory process. This renders the single-process theory untestable. The propositional account is not a process theory of learning, but rather, a heuristic that has led to interesting research.

Evaluative conditioning (EC) is one of the terms that is used to refer to associatively induced changes in liking. Many controversies have arisen in the literature on EC. Do associatively induced changes in liking actually exist? Does EC depend on awareness of the fact that stimuli are associated? Is EC resistant to extinction? Does attention help or hinder EC? As an introduction to this special issue, we will discuss the extent to which the papers that are published in this issue (...) help to resolve some of the controversies that surround EC. We also speculate about possible boundary conditions of EC and attempt to reconcile conflicting results on the functional properties of EC. (shrink)

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning (...) derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, between learning that takes place with versus without concurrent awareness, and between learning that involves the encoding of instances versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, (...) conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

In three experiments, each of a set colour-unrelated distracting words was presented most often in a particular target print colour . In Experiment 1, half of the participants were told the word-colour contingencies in advance and half were not . The instructed group showed a larger learning effect. This instruction effect was fully explained by increases in subjective awareness with instruction. In Experiment 2, contingency instructions were again given, but no contingencies were actually present. Although many participants claimed to be (...) aware of these contingencies, they did not produce an instructed contingency effect. In Experiment 3, half of the participants were given contingency instructions that did not correspond to the correct contingencies. Participants with these false instructions learned the actual contingencies worse than controls. Collectively, our results suggest that conscious contingency knowledge might play a moderating role in the strength of implicit learning. (shrink)

I present an overview of emotion theories, organised around the question of emotion causation. I argue that theories of emotion causation should ideally address the problems of elicitation, intensity, and differentiation. Each of these problems can be divided into a subquestion that asks about the relation between stimuli and emotions (i.e., the functional level of process description, cf. Marr, 1982) and a subquestion that asks about the mechanism and representations that intervene (i.e., the algorithmic level of process description). The overview (...) reveals that theories of emotion causation sometimes differ with regard to the kind of process that they hold responsible for emotion causation. More precisely, they hold different assumptions regarding the conditions under which the process is supposed to operate (optimal versus suboptimal), the format of the representations involved (propositional versus perceptual), and the object or input of the central process (stimulus versus responses/experience). Further, the overview reveals that theories of emotion causation sometimes differ with regard to the level of process description that they focus on. Finally, the overview brings to light several similarities among the theories discussed. (shrink)

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved (...) in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research. (shrink)

During evaluative conditioning, a neutral conditional stimulus becomes pleasant or unpleasant after pairings with a positive/negative unconditional stimulus. Measures of US expectancy are...

Dual process accounts of affective learning state that the learning of likes and dislikes reflects a learning mechanism that is distinct from the one reflected in expectancy learning, the learning of signal relationships, and has different empirical characteristics. Affective learning, for example, is said not to be affected by: (a) extinction training; (b) occasion setting; (c) cue competition; and (d) awareness of the CS-US contingencies. These predictions were tested in a series of experiments that employed simple Pavlovian conditioning procedures. Neutral (...) visual pictures of geometric shapes, or tactile conditional stimuli (CS) were paired with aversive electrotactile unconditional stimuli (US). Dependent measures were physiological (skin conductance, blink startle modulation) or verbal (US expectancy, on-line and off-line ratings of CS pleasantness). Different combinations of these dependent measures were employed across different experiments in an attempt to assess affective and expectancy learning simultaneously. Changes in CS pleasantness as indexed by ratings or blink startle modulation were readily observed. However, contrary to the predictions from dual-process accounts, results indicated that acquired CS unpleasantness is subject to extinction, occasion setting, cue competition, and not found in absence of CS-US contingency awareness. (shrink)