A novel method for analyzing sequential eye movements reveals strategic influence on Raven's Advanced Progressive Matrices

Hayes, Taylor R.; Petrov, Alexander; Sederberg, Per B.

doi:10.1167/11.10.10

Cited by 82 publications

(182 citation statements)

References 52 publications

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“…Indeed, our present results and previous results suggest that the so-called "scalar prediction error signal" may be more of a vector-valued signal, as required by a number of RL extensions such as learning of successor representations (Dayan, 1993;Hayes et al, 2011), factored representations (Koller and Parr, 1999), and HRL (Barto and Mahadevan, 2003;Botvinick et al, 2009). However, this raises the problem of spatial credit assignment: how are the different prediction error signals distinguished in downstream areas to learn separate reward predictions?…”

Section: Discussionsupporting

confidence: 67%

Hierarchical Learning Induces Two Simultaneous, But Separable, Prediction Errors in Human Basal Ganglia

et al. 2013

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Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.

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Section: Discussionsupporting

confidence: 67%

Hierarchical Learning Induces Two Simultaneous, But Separable, Prediction Errors in Human Basal Ganglia

et al. 2013

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“…The importance of the role of the relations between individual items is almost universally accepted in the analogy-making community. We believe that item-to-item saccades reveal the collecting of this relational information, a point of view also endorsed by Salvucci and Anderson (2001), Thibaut et al (2011), Hayes, Petrov, andSederberg (2011), and others.…”

Section: Mds Scatterplots Of Children's and Adults' Scanpathssupporting

confidence: 57%

An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making

2016

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In recent years, eyetracking has begun to be used to study the dynamics of analogy making. Numerous scanpathcomparison algorithms and machine-learning techniques are available that can be applied to the raw eyetracking data. We show how scanpath-comparison algorithms, combined with multidimensional scaling and a classification algorithm, can be used to resolve an outstanding question in analogy making-namely, whether or not children's and adults' strategies in solving analogy problems are different. (They are.) We show which of these scanpath-comparison algorithms is best suited to the kinds of analogy problems that have formed the basis of much analogy-making research over the years. Furthermore, we use machine-learning classification algorithms to examine the item-to-item saccade vectors making up these scanpaths. We show which of these algorithms best predicts, from very early on in a trial, on the basis of the frequency of various item-to-item saccades, whether a child or an adult is doing the problem. This type of analysis can also be used to predict, on the basis of the item-to-item saccade dynamics in the first third of a trial, whether or not a problem will be solved correctly.Keywords Eyetracking algorithms . Jarodzka algorithm . LDA . SVM . Analogy strategies Traditionally, analogy making has been studied statically. Participants typically see a pair of related images (the Bbase pair^), along with a third image and a number of candidate target images. One of these target images-the Bcorrect analogical match^-is supposed to be related to the third image in the same way that the base items were related to one another. The participant's task is to identify the correct analogical match. Correct and incorrect answers (and, sometimes, reaction times) are recorded and analyzed. However, these studies could not capture-and in fairness, were not designed to capture-the dynamic aspects of solving an analogy problem. As such, they shed essentially no light on the question of what strategies were adopted during the course of solving analogy problems.In this article, we introduce a novel means of studying the dynamic aspects of analogy making in both children and adults. The proposed methodology involves combining eyetracking, multidimensional scaling (MDS), and neuralnetwork classification algorithms, as well as using machinelearning algorithms to analyze the component vectors making up participants' scanpaths. In what follows, we will briefly describe each of these techniques and show how they can be combined successfully in the context of analogy making.Although the purpose of this article is, first and foremost, a methodological one, it is important to note that the development of these techniques has allowed us (French & Thibaut, 2014;Thibaut & French, 2016;Thibaut, French, Missault, Gérard, & Glady, 2011) to answer, for what we believe to be the first time, a long-standing question in the field of analogymaking-namely, do children and adults use the same (or very similar) search-space strategies when...

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“…This study was conducted in a larger context of several related experiments that combined think-aloud verbal protocols and eye tracking to investigate the role of strategic cognitive control during visual relational reasoning on Raven's APM (Hayes, 2015;Hayes et al, 2011. These experiments involved multiple sessions and various manipulations from Session 2 onward, but the first session was always the same: To establish a common baseline, eye-tracking and think-aloud protocols were collected while the participants worked on 14 Raven problems as detailed below.…”

Section: Methodsmentioning

confidence: 99%

“…This study is based exclusively on data from this common baseline session. Although other aspects of this large and multifaceted data set have been published elsewhere (Hayes et al, 2011, the pupillometric and verbal protocol aspects are reported here for the first time.…”

Section: Methodsmentioning

confidence: 99%

“…The APM is a geometric analogy test with excellent psychometric properties (Brouwers, Van de Viver, & Van Hemert, 2009) that has been a popular and trusted instrument in psychology for 70 years (e.g., Hayes, Petrov, & Sederberg, 2011, 2015Gray, Chabris, & Braver, 2003;Carpenter, Just, & Shell, 1990). Raven's APM is an excellent environment to induce strong shifts between exploration and exploitation because it repeatedly places participants in an unfamiliar relational environment in which they must engage in relational foraging to attempt to construct and/ or identify the correct answer.…”

Section: Introductionmentioning

confidence: 99%

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Pupil Diameter Tracks the Exploration–Exploitation Trade-off during Analogical Reasoning and Explains Individual Differences in Fluid Intelligence

Hayes¹,

Petrov²

2016

Journal of Cognitive Neuroscience

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Abstract■ The ability to adaptively shift between exploration and exploitation control states is critical for optimizing behavioral performance. Converging evidence from primate electrophysiology and computational neural modeling has suggested that this ability may be mediated by the broad norepinephrine projections emanating from the locus coeruleus (LC) [Aston-Jones, G., & Cohen, J. D. An integrative theory of locus coeruleus-norepinephrine function: Adaptive gain and optimal performance. Annual Review of Neuroscience, 28, 403-450, 2005]. There is also evidence that pupil diameter covaries systematically with LC activity. Although imperfect and indirect, this link makes pupillometry a useful tool for studying the locus coeruleus norepinephrine system in humans and in high-level tasks. Here, we present a novel paradigm that examines how the pupillary response during exploration and exploitation covaries with individual differences in fluid intelligence during analogical reasoning on Raven's Advanced Progressive Matrices. Pupillometry was used as a noninvasive proxy for LC activity, and concurrent think-aloud verbal protocols were used to identify exploratory and exploitative solution periods. This novel combination of pupillometry and verbal protocols from 40 participants revealed a decrease in pupil diameter during exploitation and an increase during exploration. The temporal dynamics of the pupillary response was characterized by a steep increase during the transition to exploratory periods, sustained dilation for many seconds afterward, and followed by gradual return to baseline. Moreover, the individual differences in the relative magnitude of pupillary dilation accounted for 16% of the variance in Advanced Progressive Matrices scores. Assuming that pupil diameter is a valid index of LC activity, these results establish promising preliminary connections between the literature on locus coeruleus norepinephrine-mediated cognitive control and the literature on analogical reasoning and fluid intelligence. ■

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A novel method for analyzing sequential eye movements reveals strategic influence on Raven's Advanced Progressive Matrices

Cited by 82 publications

References 52 publications

Hierarchical Learning Induces Two Simultaneous, But Separable, Prediction Errors in Human Basal Ganglia

Hierarchical Learning Induces Two Simultaneous, But Separable, Prediction Errors in Human Basal Ganglia

An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making

Pupil Diameter Tracks the Exploration–Exploitation Trade-off during Analogical Reasoning and Explains Individual Differences in Fluid Intelligence

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