Generalising what is learned about one stimulus to other but perceptually related stimuli is a basic behavioural phenomenon. We evaluated whether a rule learning mechanism may serve to explain such generalisation. To this end, we assessed whether inference rules communicated through verbal instructions affect generalisation. Expectancy ratings, but not valence ratings, proved sensitive to this manipulation. In addition to revealing a role for inference rules in generalisation, our study has clinical implications as well. More specifically, we argue that targeting inference (...) rules might prove to be an effective strategy to affect the excessive generalisation that is often observed in psychopathology. (shrink)

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that (...) learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. (shrink)

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical diffusion model and apply it to RT data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally for (...) individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach–Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data-sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest. (shrink)

ABSTRACTIndividual differences in fear generalisation have been proposed to play a role in the aetiology and/or maintenance of anxiety disorders, but few data are available to directly support that claim. The research that is available has focused mostly on generalisation of peripheral and central physiological fear responses. Far less is known about the generalisation of avoidance, the behavioural component of fear. In two experiments, we evaluated how neuroticism, a known vulnerability factor for anxiety, modulates an array of fear responses, including (...) avoidance tendencies, towards generalisation stimuli. Participants underwent differential fear conditioning, in which one conditioned stimulus was repeatedly paired with an aversive outcome, whereas another was not. Fear generalisation was observed across measures in Experiment 1 and Experiment 2, with overall highest responding to the CS+, lowest to the CS− and intermediate responding to the GSs. Neuroticism had very little impact on fear generalisation, in line with the idea that fear generalisation is largely an adaptive process. (shrink)

ABSTRACTAffective states influence how individuals process information and behave. Some theories predict emotional congruency effects. Emotional congruency should theoretically obstruct the learning of reward associations and their ability to guide behaviour under negative mood. Two studies tested the effects of the induction of a negative affective state on appetitive Pavlovian learning, in which neutral stimuli were associated with chocolate or alcohol rewards. In both experiments, participants showed enhanced approach tendencies towards predictors of reward after a negative relative to a positive (...) performance feedback manipulation. This increase was related to a reduction in positive affect in Experiment 1 only. No effects of the manipulation on conditioned reward expectancies, craving, or consumption were observed. Overall, our findings support the idea of counter-regu... (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.