Elsevier

Acta Psychologica

Volume 163, January 2016, Pages 114-123
Acta Psychologica

Response trajectories capture the continuous dynamics of the size congruity effect

https://doi.org/10.1016/j.actpsy.2015.11.010Get rights and content

Highlights

  • We used computer mousetracking to analyze the dynamics of the size-congruity effect.

  • Trajectories for incongruent trials showed continuous influence from alternative.

  • Size of deflection increased monotonically as numerical size increased.

  • The data provide support for a late interaction model of the size congruity effect.

Abstract

In a comparison task involving numbers, the size congruity effect refers to the general finding that responses are usually faster when there is a match between numerical size and physical size (e.g., 2–8) than when there is a mismatch (e.g., 2–8). In the present study, we used computer mouse tracking to test two competing models of the size congruity effect: an early interaction model, where interference occurs at an early representational stage, and a late interaction model, where interference occurs as dynamic competition between response options. In three experiments, we found that the curvature of responses for incongruent trials was greater than for congruent trials. In Experiment 2 we showed that this curvature effect was reliably modulated by the numerical distance between the two stimulus numbers, with large distance pairs exhibiting a larger curvature effect than small distance pairs. In Experiment 3 we demonstrated that the congruity effects persist into response execution. These findings indicate that incongruities between numerical and physical sizes are carried throughout the response process and result from competition between parallel and partially active response options, lending further support to a late interaction model of the size congruity effect.

Section snippets

Experiment 1

The purpose of Experiment 1 was to see how the size congruity effect mapped onto a computer mousetracking task and subsequently investigate whether such data lend better support for an early interaction model or late interaction model.

Experiment 2

With Experiment 2, we attempted to replicate the results of Experiment 1 while adding the factor of numerical distance to our design. Specifically, we wanted to investigate how numerical distance interacts with physical/numerical size congruity. Schwarz and Ischebeck (2003) found that increasing the numerical distance in a physical size comparison task increased the size congruity effect, which they explained in terms of an early interaction model. Alternatively, the dual-route architecture

Experiment 3

With Experiment 3, we attempted to rule out the possibility that specific task instructions bias our results unfairly toward the late interaction model.1 While at first glance 400 ms seems long enough to allow any early representation effects to appear (especially in light of early ERP work by Schwarz & Heinze, 1998; Santens & Verguts, 2011) demonstrated that the congruity effects in a physical size comparison

General discussion

In the present study we conducted 3 experiments in which we used computer mousetracking with a physical comparison task to measure the dynamics of size congruity effect. We tracked participants' hand movements via the computer mouse as they selected the physically larger digit from among two response options varying in both physical and numerical sizes. In all 3 experiments, we found a robust size congruity effect. As is usually found in studies of the size congruity effect, responses took

References (41)

  • V. Walsh

    A theory of magnitude: Common cortical metrics of time, space and quantity

    Trends in Cognitive Sciences

    (2003)
  • X. Zhou et al.

    Holistic or compositional representation of two-digit numbers? Evidence from the distance, magnitude, and SNARC effects in a number-matching task

    Cognition

    (2008)
  • D.H. Abney et al.

    Response dynamics in prospective memory

    Psychonomic Bulletin & Review

    (2014)
  • C. Buc Calderon et al.

    Losing the boundary: Cognition biases action well after action selection

    Journal of Experimental Psychology: General

    (2015)
  • R. Cohen Kadosh et al.

    The brain locus of interaction between number and size: A combined functional magnetic resonance imaging and event-related potential study

    Journal of Cognitive Neuroscience

    (2007)
  • T.J. Faulkenberry

    Hand movements reflect competitive processing in numerical cognition

    Canadian Journal of Experimental Psychology

    (2014)
  • T.J. Faulkenberry et al.

    Mental representations in fraction comparison: Holistic versus component-based strategies

    Experimental Psychology

    (2011)
  • T.J. Faulkenberry et al.

    Extending the reach of mousetracking in numerical cognition: A comment on Fischer and Hartmann (2014)

    Frontiers in Psychology

    (2014)
  • M.H. Fischer et al.

    Pushing forward in embodied cognition: May we mouse the mathematical mind?

    Frontiers in Psychology

    (2014)
  • G.S. Foltz et al.

    Mental comparison of size and magnitude: Size congruity effects

    Journal of Experimental Psychology: Learning, Memory, and Cognition

    (1984)
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    The authors wish to thank Trina Geye, Jonathan Herring, Brie Heidingsfelder, Kate Shaw, and Heather Wilson for help with data collection.

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