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Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual (...) No categories |
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Emotion Review, Volume 14, Issue 2, Page 99-110, April 2022. Researchers often disagree as to whether emotions are largely consistent across people and over time, or whether they are variable. They also disagree as to whether emotions are initiated by appraisals, or whether they may be initiated in diverse ways. We draw upon Parallel-Distributed-Processing to offer an algorithmic account in which features of an emotion instance are bi-directionally connected to each other via conjunction units. We propose that such indirect connections (...) |
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This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...) |
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A long-standing question in cognitive science is how high-level knowledge is integrated with sensory input. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech sound, but do such effects reflect direct top-down influences on perception or merely postperceptual biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation for coarticulation (LCfC). Previous LCfC studies have shown that a lexically restored context phoneme (e.g., /s/ in Christma#) can alter the perceived place of (...) |
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Visual word recognition is facilitated by the presence of orthographic neighbors that mismatch the target word by a single letter substitution. However, researchers typically do not consider where neighbors mismatch the target. In light of evidence that some letter positions are more informative than others, we investigate whether the influence of orthographic neighbors differs across letter positions. To do so, we quantify the number of enemies at each letter position (how many neighbors mismatch the target word at that position). Analyses (...) |
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As Macnamara (1978) once asked, how can we talk about what we see? We report on a study manipulating realistic dynamic scenes and sentences aiming to understand the interaction between linguistic and visual representations in real-world situations. Specifically, we monitored participants’ eye movements as they watched video clips of everyday scenes while listening to sentences describing these scenes. We manipulated two main variables. The first was the semantic class of the verb in the sentence and the second was the action/motion (...) |
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Computational modeling and eye-tracking were used to investigate how phonological and semantic information interact to influence the time course of spoken word recognition. We extended our recent models to account for new evidence that competition among phonological neighbors influences activation of semantically related concepts during spoken word recognition . The model made a novel prediction: Semantic input modulates the effect of phonological neighbors on target word processing, producing an approximately inverted-U-shaped pattern with a high phonological density advantage at an intermediate (...) |
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