The Feature-Label-Order Effect In Symbolic Learning - eScholarship (2009)

The Feature-Label-Order Effect In Symbolic Learning Michael Ramscar, Daniel Yarlett, Melody Dye, & Nal Kalchbrenner Department of Psychology, Stanford University, Jordan Hall, Stanford, CA 94305. Abstract when they are over-predicted (Kamin, 1969; Rescorla & Wagner, 1972). As a result, cues compete for relevance, and the outcome of this competition is shaped both by positive evidence about co-occurrences between cues and predicted events, and negative evidence about non-occurrences of predicted events. This process produces patterns of learning that are very different from what would be expected if learning were shaped by positive evidence alone (a common portrayal of Pavlovian conditioning, Rescorla, 1988). We present a formal analysis of symbolic learning that predicts significant differences in symbolic learning depending on the sequencing of semantic features and labels. A computational simulation confirms the Feature- Label-Ordering (FLO) effect in learning that our analysis predicts. Discrimination learning is facilitated when semantic features predict labels, but not when labels predict semantic features. A behavioral study confirms the predictions of the simulation. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world might only be learnable when labels are predicted from objects. Symbolic learning Introduction The ways in which symbolic knowledge is learned and represented in the mind are poorly understood. We present an analysis of symbolic learning—in particular, word learning—in terms of error-driven learning, and consider two possible ways in which symbols might be learned: learning to predict a label from the features of objects and events in the world; or learning to predict those features from a label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels, confirmed in computational simulations and an empirical study. Discrimination learning is facilitated when semantic features predict labels, but not when labels predict semantic features. We call this the Feature-Label- Ordering (FLO) effect. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. Language learning involves acquiring information about the relations between labels and their semantic features. Here we define labels as tokens of language, such as the word ‘pan,’ and semantic features as the properties of the objects and events communicated about in language. In turn, we can distinguish two possible forms that symbolic learning about labels and features can take: (i) cues are labels and events are semantic features; (ii) cues are semantic features and events labels. In (i), which we call Label-to-Feature or LF- Learning, one learns to predict and expect certain features given a label. In (ii), which we call Feature-to- Label or FL-Learning, one learns to predict and expect labels given a feature or certain set of features. To explain the difference between what is learned in LF- learning versus FL-learning, it is important to note some differences between labels, as they are employed in language, and the aspects of the environment they typically describe. Learning The structure of labels and the world Formally, learning can be conceived of as a process by which probabilistic information is acquired about the relationships between important regularities in the environment (such as objects or events) and the cues that allow those regularities to be predicted (Rescorla & Wagner, 1972). This process is driven by discrepancies between what is expected and what is actually observed in experience (termed error-driven learning). The learned value of a given cue produces expectations, and any difference between the value of what is expected and what is observed produces further learning. The predictive value of a cues are strengthened when relevant events are under-predicted, and weakened Symbolic labels are relatively discrete, and possess little cue-structure, whereas objects and events in the world are far less discrete, and possess much denser cue-structure. (By cue-structure we mean the number of potentially discriminable cues that are simultaneously present.) Consider a situation in which say, a pan is encountered in the environment. A pan presents to a learner many discriminable features: shape, color, size, etc. However, because objects are not discrete (i.e., pans share many features with things that are not pans), some of these features will cue other labels as well. By contrast, consider the label ‘pan.’ A native English speaker can parse this word into a sequence of