Property intercorrelations are viewed as central to the representation and processing of real-world object concepts. In contrast, prior research into real-world object concepts has incorporated the assumption that properties are independent and additive. In two studies, the role of correlated properties was explored. Property norms had been collected for 190 natural kinds and artifacts. In Experiment 1, property intercorrelations influenced performance in a property verification task. In Experiment 2, concept similarity, as measured by overlap of independent properties, predicted short interval priming latency for artifacts. In contrast, concept similarity, as measured by overlap of correlated property pairs, predicted short interval priming for natural kinds. The influence of property intercorrelations was stronger for natural kinds because they tended to contain a higher proportion of correlated properties. It was concluded that people encode knowledge about independent and correlated properties of real-world objects. Presently, a Hopfield netwoiic is being implemented to explore implications of allowing a system to encode property intercorrelations. Finally, results suggest that semantic relatedness can be defined in terms of property overlap between concepts.

Intentional visual imagery is a component of numerous aspects of cognition. Related to visual imagery, mental simulation plays a role in embodied theories of language comprehension that propose activation of modality-specific regions of the brain takes place as part of people understanding language. The extent to which the processes underlying conscious, voluntary visual imagery versus less conscious, more automatic mental simulation overlap is unclear. We investigated this issue by having aphantasics (people who are unable to experience conscious voluntary visual imagery) and control participants perform a property verification task in which they were asked whether a property is a physical part of an object (e.g., lion-tail). We manipulated the false trials in that the two words either were associated (semantically related) but did not form an object-part combination (monkey-banana), or were not associated (apple-cloud). Solomon and Barsalou (2004) demonstrated that word association influenced responses when the words in the false trials were not associated, whereas when they were related, perceptual measures most strongly influenced the results, indicating mental simulation. Here control participants and aphantasics demonstrated similar evidence of the use of both mental simulation and word association when verifying whether the words formed an object-part combination. These results provide evidence that visual imagery and mental simulation are at least somewhat separable cognitive processes.

We provide a computational account of the integration of various constraints proposed to be involved in the resolution of the direct object/sentence complement ambiguity. In the first part, competition-integration simulations show that a constraint-based model accounts for the results of Garnsey, Pearlmutter, Myers, and Lotocky (1997) at least as well as the garden-path model. In the second part, we compare the efficacy of norming techniques for capturing plausibility effects. Simulations show that norms designed to tap people's conceptual knowledge of events better capture plausibility effects than do norms that are biased toward tapping linguistic knowledge. We conclude that local information concerning event plausibility is an important constraint for understanding ambiguity resolution.

We present a connectionist model of event knowledge that istrained on examples of sequences of activities that are notexplicitly labeled as events. The model learns co-occurrencepatterns among the components of activities as they occur inthe moment (entities, actions, and contexts), and also learns topredict sequential patterns of activities. In so doing, the modeldisplays behaviors that in humans have been characterized asexemplifying inferencing of unmentioned event components,the prediction of upcoming components (which may or maynot ever happen or be mentioned), reconstructive memory,and the ability to flexibly accommodate novel variations frompreviously encountered experiences. All of these behaviorsemerge from what the model learns.

Representations formed during language comprehension do not always accurately reflect the linguistic input, but aresometimes just good enough (Ferreira et al., 2003). Here, we examined the electrophysiological correlates of such heuristicprocessing. Participants were presented with passive sentences where the plausibility of the fillers of the agent and patientthematic roles was manipulated. As expected, they made more errors in the interpretation of implausible sentences (e.g.,The doctor was treated by the patient). Intriguingly, N400 amplitudes patterned with (mis)interpretation, with increasedamplitudes to the second noun in correctly processed implausible sentences, and equally small amplitudes in plausiblesentences and in incorrectly interpreted implausible sentences. These results are in line with the view that N400 amplitudesreflect the change in an initial heuristic representation of sentence meaning (Rabovsky et al., 2018), but seem difficult toexplain by accounts suggesting that the N400 reflects lexical retrieval (Brouwer et al., 2017).

In an attractor model of semantic memory, semantic similarity is determined by degree of featural overlap. In contrast, in spreading activation theory, two concepts are similar if they share features or if they are linked to the same superordinate category node. We present an attractor network model of computing word meaning and use it to simulate the data of McRae and Boisvert (in press), who found that short SOA semantic similarity priming directly depends on degree of featural overlap. The two accounts of semantic similarity are then contrasted in a human experiment. In support of attractor networks, priming effects were determined by featural overlap, and no evidence was found for priming through a purported superordinate node. It is concluded that lexical concepts are not represented as static nodes in a hierarchical system.