Propositional Accounts of Implicit Evaluation: Taking Stock and Looking Ahead

Associative accounts suggest that implicit (indirectly measured) evaluations are sensitive primarily to co-occurrence information (e.g., pairings of gorges with positive experiences) and are represented associatively (e.g., Gorge–Nice). By contrast, recent propositional accounts have argued that implicit evaluations are also responsive to relational information (e.g., gorges causing vs. preventing ennui) and are represented propositionally (e.g., “I find gorges fascinating”). In a review of 30 empirical papers involving exposure to contradictory co-occurrence information and relational information, we found overwhelming evidence for the latter dominating the updating of implicit evaluations, supporting the propositional perspective. However, unlike explicit evaluations, implicit evaluations seem recalcitrant in the face of relational information that requires retrospective revaluation of already encoded co-occurrence information. These findings may be jointly explained by a “common currency” hypothesis under which implicit evaluations emerge from compressed summary representations, which are sensitive to relational information but are not fully propositional.

Propositional accounts of implicit evaluation: Taking stock and looking ahead

Associative accounts suggest that implicit (indirectly measured) evaluations are sensitive primarily to co-occurrence information (e.g., pairings of gorges with positive experiences) and are represented associatively (e.g., GORGE–NICE). By contrast, recent propositional accounts have argued that implicit evaluations are also responsive to relational information (e.g., gorges causing vs. preventing ennui) and are represented propositionally (e.g., “I find gorges fascinating”). In a review of 30 empirical papers involving exposure to contradictory co-occurrence information and relational information, we found overwhelming evidence for the latter dominating the updating of implicit evaluations, supporting the propositional perspective. However, unlike explicit evaluations, implicit evaluations seem recalcitrant in the face of relational information that requires retrospective revaluation of already encoded co-occurrence information. These findings may be jointly explained by a “common currency” hypothesis under which implicit evaluations emerge from compressed summary representations, which are sensitive to relational information but are not fully propositional.

Associative Learning from Replayed Experience

We develop an extension of the Rescorla-Wagner model of associative learning. In addition to learning from the current trial, the new model supposes that animals store and replay previous trials, learning from the replayed trials using the same learning rule. This simple idea provides a unified explanation for diverse phenomena that have proved challenging to earlier associative models, including spontaneous recovery, latent inhibition, retrospective revaluation, and trial spacing effects. For example, spontaneous recovery is explained by supposing that the animal replays its previous trials during the interval between extinction and test. These include earlier acquisition trials as well as recent extinction trials, and thus there is a gradual re-acquisition of the conditioned response. We present simulation results for the simplest version of this replay idea, where the trial memory is assumed empty at the beginning of an experiment, all experienced trials are stored and none removed, and sampling from the memory is performed at random. Even this minimal replay model is able to explain the challenging phenomena, illustrating the explanatory power of an associative model enhanced by learning from remembered as well as real experiences.