Patterns of Response Times and Response Choices to Science Questions: The Influence of Relative Processing Time

We report on five experiments investigating response choices and response times to simple science questions that evoke student "misconceptions," and we construct a simple model to explain the patterns of response choices. Physics students were asked to compare a physical quantity represented by the slope, such as speed, on simple physics graphs. We found that response times of incorrect answers, resulting from comparing heights, were faster than response times of correct answers comparing slopes. This result alone might be explained by the fact that height was typically processed faster than slope for this kind of task, which we confirmed in a separate experiment. However, we hypothesize that the difference in response time is an indicator of the cause (rather than the result) of the response choice. To support this, we found that imposing a 3-s delay in responding increased the number of students comparing slopes (answering correctly) on the task. Additionally a significant proportion of students recognized the correct written rule (compare slope), but on the graph task they incorrectly compared heights. Finally, training either with repetitive examples or providing a general rule both improved scores, but only repetitive examples had a large effect on response times, thus providing evidence of dual paths or processes to a solution. Considering models of heuristics, information accumulation models, and models relevant to the Stroop effect, we construct a simple relative processing time model that could be viewed as a kind of fluency heuristic. The results suggest that misconception-like patterns of answers to some science questions commonly found on tests may be explained in part by automatic processes that involve the relative processing time of considered dimensions and a priority to answer quickly.

[1]  G Gigerenzer,et al.  Reasoning the fast and frugal way: models of bounded rationality. , 1996, Psychological review.

[2]  John R. Anderson,et al.  Rules of the Mind , 1993 .

[3]  L. McDermott,et al.  Resource Letter: PER-1: Physics Education Research , 1999 .

[4]  Priti Shah,et al.  Bar and Line Graph Comprehension: An Interaction of Top-Down and Bottom-Up Processes , 2011, Top. Cogn. Sci..

[5]  Yuko Munakata,et al.  When Actions Speak Louder Than Words , 2006, Psychological science.

[6]  Adam L. Alter.  The Benefits of Cognitive Disfluency , 2013 .

[7]  Gerd Gigerenzer,et al.  Why Heuristics Work , 2008, Perspectives on psychological science : a journal of the Association for Psychological Science.

[8]  K. Stanovich,et al.  Heuristics and Biases: Individual Differences in Reasoning: Implications for the Rationality Debate? , 2002 .

[9]  M. Hegarty,et al.  Impetus beliefs as default heuristics: Dissociation between explicit and implicit knowledge about motion , 2001, Psychonomic bulletin & review.

[10]  Stefan M. Herzog,et al.  Fluency heuristic: a model of how the mind exploits a by-product of information retrieval. , 2008, Journal of experimental psychology. Learning, memory, and cognition.

[11]  Joseph D. Novak,et al.  Meaningful learning: The essential factor for conceptual change in limited or inappropriate propositional hierarchies leading to empowerment of learners , 2002 .

[12]  Colin M. Macleod,et al.  American Psychological Association, Inc. A Horse Race of a Different Color: Stroop Interference Patterns With Transformed Words v , 2022 .

[13]  D J Gillan,et al.  A Componential Model of Human Interaction with Graphs: 1. Linear Regression Modeling , 1994, Human factors.

[14]  T. Gilovich,et al.  Heuristics and Biases: Introduction – Heuristics and Biases: Then and Now , 2002 .

[15]  R. Ratcliff.  Methods for dealing with reaction time outliers. , 1993, Psychological bulletin.

[16]  Mary Hegarty,et al.  Top-down and bottom-up influences on learning from animations , 2007, Int. J. Hum. Comput. Stud..

[17]  Ibrahim A. Halloun,et al.  Common sense concepts about motion , 1985 .

[18]  Lillian C. McDermott,et al.  Student difficulties in connecting graphs and physics: Examples from kinematics , 1987 .

[19]  R. Ollman.  Fast guesses in choice reaction time , 1966 .

[20]  Scott D. Brown,et al.  The simplest complete model of choice response time: Linear ballistic accumulation , 2008, Cognitive Psychology.

[21]  Roger Ratcliff,et al.  The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks , 2008, Neural Computation.

[22]  R. Hertwig,et al.  How forgetting aids heuristic inference. , 2005, Psychological review.

[23]  Robert J. Dufresne,et al.  Coordination of Knowledge in Judging Animated Motion , 2006 .

[24]  H. Wellman,et al.  Cognitive development: foundational theories of core domains. , 1992, Annual review of psychology.

[25]  Ruth Stavy,et al.  How Students Mis/Understand Science and Mathematics: Intuitive Rules (Ways of Knowing in Science Series) , 2000 .

[26]  Robert J. Beichner,et al.  Testing student interpretation of kinematics graphs , 1994 .

[27]  R. Driver,et al.  Theories-in-Action: Some Theoretical and Empirical Issues in the Study of Students' Conceptual Frameworks in Science , 1983 .

[28]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[29]  Reinders Duit,et al.  Bibliography. Students' Alternative Frameworks and Science Education. 2nd Edition. , 1988 .

[30]  D. Balota,et al.  Moving Beyond the Mean in Studies of Mental Chronometry , 2011 .

[31]  Jonathan St. B. T. Evans,et al.  Deciding before you think: Relevance and reasoning in the selection task , 1996 .

[32]  J. Roschelle,et al.  Misconceptions Reconceived: A Constructivist Analysis of Knowledge in Transition , 1994 .

[33]  Peter C.-H. Cheng,et al.  Modeling the Effect of Task and Graphical Representation on Response Latency in a Graph Reading Task , 2003, Hum. Factors.

[34]  Ji Y. Son,et al.  Perceptual Learning Modules in Mathematics: Enhancing Students' Pattern Recognition, Structure Extraction, and Fluency , 2010, Top. Cogn. Sci..

[35]  James T. Townsend,et al.  The Stochastic Modeling of Elementary Psychological Processes , 1983 .

[36]  S. Sloman.  The empirical case for two systems of reasoning. , 1996 .

[37]  D. Kahneman,et al.  Representativeness revisited: Attribute substitution in intuitive judgment. , 2002 .

[38]  Daniel Kahneman,et al.  Availability: A heuristic for judging frequency and probability , 1973 .

[39]  D. Kahneman,et al.  Heuristics and Biases: The Psychology of Intuitive Judgment , 2002 .

[40]  Mary Hegarty,et al.  Spatial Visualization in Physics Problem Solving , 2007, Cogn. Sci..

[41]  J. Clement.  Students’ preconceptions in introductory mechanics , 1982 .

[42]  J. Freyd.  Dynamic mental representations. , 1987, Psychological review.

[43]  Colin M. Macleod,et al.  Interdimensional interference in the Stroop effect: uncovering the cognitive and neural anatomy of attention , 2000, Trends in Cognitive Sciences.

[44]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[45]  Colin M. Macleod.  Half a century of research on the Stroop effect: an integrative review. , 1991, Psychological bulletin.

[46]  Frank C. Keil,et al.  The Feasibility of Folk Science , 2010, Cogn. Sci..

[47]  Gerd Gigerenzer,et al.  Homo Heuristicus: Why Biased Minds Make Better Inferences , 2009, Top. Cogn. Sci..

[48]  Crystal D. Oberle,et al.  The Galileo bias: a naive conceptual belief that influences people's perceptions and performance in a ball-dropping task. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[49]  M. McCloskey.  Naive Theories of Motion. , 1982 .

[50]  Ian Dennis,et al.  Explicit and implicit processes in multicue judgment , 2003, Memory & cognition.

[51]  D. Proffitt,et al.  Judgments of natural and anomalous trajectories in the presence and absence of motion. , 1985, Journal of experimental psychology. Learning, memory, and cognition.

[52]  James L. McClelland,et al.  On the control of automatic processes: a parallel distributed processing account of the Stroop effect. , 1990, Psychological review.

[53]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[54]  D. Mewhort,et al.  Analysis of Response Time Distributions: An Example Using the Stroop Task , 1991 .

[55]  F N Dyer,et al.  The Stroop phenomenon and its use in the stlldy of perceptual, cognitive, and response processes , 1973, Memory & cognition.

[56]  S. Vosniadou.  Capturing and modeling the process of conceptual change. , 1994 .

[57]  A. Heckler.  The Ubiquitous Patterns of Incorrect Answers to Science Questions : The Role of Automatic , Bottom-up Processes , 2011 .

[58]  J. Mokros,et al.  The impact of microcomputer‐based labs on children's ability to interpret graphs , 1987 .

[59]  R. Ratcliff,et al.  Retrieval Processes in Recognition Memory , 1976 .

[60]  Mary Hegarty,et al.  The Cognitive Science of Visual-Spatial Displays: Implications for Design , 2011, Top. Cogn. Sci..

[61]  Jonathan Evans,et al.  Human Reasoning: The Psychology Of Deduction , 1993 .

[62]  Wim De Neys,et al.  Dual Processing in Reasoning , 2006, Psychological science.

[63]  R. Duncan Luce,et al.  Response Times: Their Role in Inferring Elementary Mental Organization , 1986 .

[64]  D. Kahneman.  A perspective on judgment and choice: mapping bounded rationality. , 2003, The American psychologist.

[65]  Robert L. Goldstone,et al.  The Education of Perception , 2010, Top. Cogn. Sci..

[66]  H. Hecht,et al.  Influence of animation on dynamical judgments. , 1992, Journal of experimental psychology. Human perception and performance.

[67]  S. Carey.  The Origin of Concepts , 2000 .

[68]  R. Nosofsky,et al.  A response-time approach to comparing generalized rational and take-the-best models of decision making. , 2007, Journal of experimental psychology. Learning, memory, and cognition.

[69]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[70]  D. Rohrer.  The natural appearance of unnatural incline speed , 2003, Memory & cognition.

[71]  Jonathan Evans.  Dual-processing accounts of reasoning, judgment, and social cognition. , 2008, Annual review of psychology.

[72]  J R Anderson,et al.  Practice and retention: a unifying analysis. , 1999, Journal of experimental psychology. Learning, memory, and cognition.

[73]  R. Ratcliff.  Group reaction time distributions and an analysis of distribution statistics. , 1979, Psychological bulletin.

[74]  A. Heckler.  The Role of Automatic, Bottom-Up Processes: In the Ubiquitous Patterns of Incorrect Answers to Science Questions , 2011 .

[75]  Colin M. Macleod,et al.  Training and Stroop-like interference: evidence for a continuum of automaticity. , 1988, Journal of experimental psychology. Learning, memory, and cognition.

[76]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .