The utility of modeling word identification from visual input within models of eye movements in reading

Ongoing Cognitive Processing Influences Precise Eye-Movement Targets in Reading

Reading is a highly complex learned skill in which humans move their eyes three to four times every second in response to visual and cognitive processing. The consensus view is that the details of these rapid eye-movement decisions-which part of a word to target with a saccade-are determined solely by low-level oculomotor heuristics. But maximally efficient saccade targeting would be sensitive to ongoing word identification, sending the eyes farther into a word the farther its identification has already progressed. Here, using a covert text-shifting paradigm, we showed just such a statistical relationship between saccade targeting in reading and trial-to-trial variability in cognitive processing. This result suggests that, rather than relying purely on heuristics, the human brain has learned to optimize eye movements in reading even at the fine-grained level of character-position targeting, reflecting efficiency-based sensitivity to ongoing cognitive processing.

What do we mean by prediction in language comprehension?

We consider several key aspects of prediction in language comprehension: its computational nature, the representational level(s) at which we predict, whether we use higher level representations to predictively pre-activate lower level representations, and whether we 'commit' in any way to our predictions, beyond pre-activation. We argue that the bulk of behavioral and neural evidence suggests that we predict probabilistically and at multiple levels and grains of representation. We also argue that we can, in principle, use higher level inferences to predictively pre-activate information at multiple lower representational levels. We also suggest that the degree and level of predictive pre-activation might be a function of the expected utility of prediction, which, in turn, may depend on comprehenders' goals and their estimates of the relative reliability of their prior knowledge and the bottom-up input. Finally, we argue that all these properties of language understanding can be naturally explained and productively explored within a multi-representational hierarchical actively generative architecture whose goal is to infer the message intended by the producer, and in which predictions play a crucial role in explaining the bottom-up input.

The adaptive nature of eye movements in linguistic tasks: how payoff and architecture shape speed-accuracy trade-offs

We explore the idea that eye-movement strategies in reading are precisely adapted to the joint constraints of task structure, task payoff, and processing architecture. We present a model of saccadic control that separates a parametric control policy space from a parametric machine architecture, the latter based on a small set of assumptions derived from research on eye movements in reading (Engbert, Nuthmann, Richter, & Kliegl, 2005; Reichle, Warren, & McConnell, 2009). The eye-control model is embedded in a decision architecture (a machine and policy space) that is capable of performing a simple linguistic task integrating information across saccades. Model predictions are derived by jointly optimizing the control of eye movements and task decisions under payoffs that quantitatively express different desired speed-accuracy trade-offs. The model yields distinct eye-movement predictions for the same task under different payoffs, including single-fixation durations, frequency effects, accuracy effects, and list position effects, and their modulation by task payoff. The predictions are compared to-and found to accord with-eye-movement data obtained from human participants performing the same task under the same payoffs, but they are found not to accord as well when the assumptions concerning payoff optimization and processing architecture are varied. These results extend work on rational analysis of oculomotor control and adaptation of reading strategy (Bicknell & Levy, ; McConkie, Rayner, & Wilson, 1973; Norris, 2009; Wotschack, 2009) by providing evidence for adaptation at low levels of saccadic control that is shaped by quantitatively varying task demands and the dynamics of processing architecture.