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

Modeling task effects in human reading with neural network-based attention

Research on human reading has long documented that reading behavior shows task-specific effects, but it has been challenging to build general models predicting what reading behavior humans will show in a given task. We introduce NEAT, a computational model of the allocation of attention in human reading, based on the hypothesis that human reading optimizes a tradeoff between economy of attention and success at a task. Our model is implemented using contemporary neural network modeling techniques, and makes explicit and testable predictions about how the allocation of attention varies across different tasks. We test this in an eyetracking study comparing two versions of a reading comprehension task, finding that our model successfully accounts for reading behavior across the tasks. Our work thus provides evidence that task effects can be modeled as optimal adaptation to task demands.

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.

Dividing Attention Between Tasks: Testing Whether Explicit Payoff Functions Elicit Optimal Dual-Task Performance

We test people's ability to optimize performance across two concurrent tasks. Participants performed a number entry task while controlling a randomly moving cursor with a joystick. Participants received explicit feedback on their performance on these tasks in the form of a single combined score. This payoff function was varied between conditions to change the value of one task relative to the other. We found that participants adapted their strategy for interleaving the two tasks, by varying how long they spent on one task before switching to the other, in order to achieve the near maximum payoff available in each condition. In a second experiment, we show that this behavior is learned quickly (within 2-3 min over several discrete trials) and remained stable for as long as the payoff function did not change. The results of this work show that people are adaptive and flexible in how they prioritize and allocate attention in a dual-task setting. However, it also demonstrates some of the limits regarding people's ability to optimize payoff functions.

Now or … later: Perceptual data are not immediately forgotten during language processing

Christiansen & Chater (C&C) propose that language comprehenders must immediately compress perceptual data by "chunking" them into higher-level categories. Effective language understanding, however, requires maintaining perceptual information long enough to integrate it with downstream cues. Indeed, recent results suggest comprehenders do this. Although cognitive systems are undoubtedly limited, frameworks that do not take into account the tasks that these systems evolved to solve risk missing important insights.

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.

It takes time to prime: semantic priming in the ocular lexical decision task

Two eye-tracking experiments were conducted in which the manual response mode typically used in lexical decision tasks (LDTs) was replaced with an eye-movement response through a sequence of 3 words. This ocular LDT combines the explicit control of task goals found in LDTs with the highly practiced ocular response used in reading text. In Experiment 1, forward saccades indicated an affirmative lexical decision (LD) on each word in the triplet. In Experiment 2, LD responses were delayed until all 3 letter strings had been read. The goal of the study was to evaluate the contribution of task goals and response mode to semantic priming. Semantic priming is very robust in tasks that involve recognition of words in isolation, such as LDT, but limited during text reading, as measured using eye movements. Gaze durations in both experiments showed robust semantic priming even though ocular response times were much shorter than manual LDs for the same words in the English Lexicon Project. Ex-Gaussian distribution fits revealed that the priming effect was concentrated in estimates of tau (τ), meaning that priming was most pronounced in the slow tail of the distribution. This pattern shows differential use of the prime information, which may be more heavily recruited in cases in which the LD is difficult, as indicated by longer response times. Compared with the manual LD responses, ocular LDs provide a more sensitive measure of this task-related influence on word recognition as measured by the LDT.

Task effects reveal cognitive flexibility responding to frequency and predictability: evidence from eye movements in reading and proofreading

It is well-known that word frequency and predictability affect processing time. These effects change magnitude across tasks, but studies testing this use tasks with different response types (e.g., lexical decision, naming, and fixation time during reading; Schilling, Rayner, & Chumbley, 1998), preventing direct comparison. Recently, Kaakinen and Hyönä (2010) overcame this problem, comparing fixation times in reading for comprehension and proofreading, showing that the frequency effect was larger in proofreading than in reading. This result could be explained by readers exhibiting substantial cognitive flexibility, and qualitatively changing how they process words in the proofreading task in a way that magnifies effects of word frequency. Alternatively, readers may not change word processing so dramatically, and instead may perform more careful identification generally, increasing the magnitude of many word processing effects (e.g., both frequency and predictability). We tested these possibilities with two experiments: subjects read for comprehension and then proofread for spelling errors (letter transpositions) that produce nonwords (e.g., trcak for track as in Kaakinen & Hyönä) or that produce real but unintended words (e.g., trial for trail) to compare how the task changes these effects. Replicating Kaakinen and Hyönä, frequency effects increased during proofreading. However, predictability effects only increased when integration with the sentence context was necessary to detect errors (i.e., when spelling errors produced words that were inappropriate in the sentence; trial for trail). The results suggest that readers adopt sophisticated word processing strategies to accommodate task demands.

Information theory of adaptation in neurons, behavior, and mood

The ability to make accurate predictions of future stimuli and consequences of one's actions are crucial for the survival and appropriate decision-making. These predictions are constantly being made at different levels of the nervous system. This is evidenced by adaptation to stimulus parameters in sensory coding, and in learning of an up-to-date model of the environment at the behavioral level. This review will discuss recent findings that actions of neurons and animals are selected based on detailed stimulus history in such a way as to maximize information for achieving the task at hand. Information maximization dictates not only how sensory coding should adapt to various statistical aspects of stimuli, but also that reward function should adapt to match the predictive information from past to future.