Abstract thinking facilitates aggregation of information.

Many situations in life (such as considering which stock to invest in, or which people to befriend) require averaging across series of values. Here, we examined predictions derived from construal level theory, and tested whether abstract compared with concrete thinking facilitates the process of aggregating values into a unified summary representation. In four experiments, participants were induced to think more abstractly (vs. concretely) and performed different variations of an averaging task with numerical values (Experiments 1-2 and 4), and emotional faces (Experiment 3). We found that the induction of abstract, compared with concrete thinking, improved aggregation accuracy (Experiments 1-3), but did not improve memory for specific items (Experiment 4). In particular, in concrete thinking, averaging was characterized by increased regression toward the mean and lower signal-to-noise ratio, compared with abstract thinking. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

[1]  Supplemental Material for Abstract Thinking Facilitates Aggregation of Information , 2021, Journal of Experimental Psychology: General.

[2]  M. Usher,et al.  Extracting Summary Statistics of Rapid Numerical Sequences , 2020, Frontiers in Psychology.

[3]  S. C. Chong,et al.  Ensemble perception and focused attention: Two different modes of visual processing to cope with limited capacity , 2020, Psychonomic Bulletin & Review.

[4]  S. Robinson,et al.  Putting time in perspective: How and why construal level buffers the relationship between wait time and aggressive tendencies , 2020 .

[5]  N. Liberman,et al.  Concrete mindset impairs filtering in visual working memory , 2019, Psychonomic Bulletin & Review.

[6]  Michael Gilead,et al.  Above and beyond the concrete: The diverse representational substrates of the predictive brain , 2019, Behavioral and Brain Sciences.

[7]  Marius Usher,et al.  Constructing preference from sequential samples: The impact of evaluation format on risk attitudes. , 2019, Decision.

[8]  Shaul Hochstein,et al.  Perceiving set mean and range: Automaticity and precision. , 2018, Journal of vision.

[9]  Y. Trope,et al.  Better off and far away: Reactions to others’ outcomes depends on their distance , 2018, Organizational Behavior and Human Decision Processes.

[10]  Jaime L. Napier,et al.  Construing the Essence: The Effects of Construal Level on Genetic Attributions for Individual and Social Group Differences , 2018, Personality & social psychology bulletin.

[11]  M. Usher,et al.  Fast and effective: Intuitive processes in complex decisions , 2018, Psychonomic Bulletin & Review.

[12]  Marius Usher,et al.  A Perceptual-Like Population-Coding Mechanism of Approximate Numerical Averaging , 2018, Neural Computation.

[13]  D. Whitney,et al.  Ensemble Perception , 2018, Annual review of psychology.

[14]  Adam Wierman,et al.  Thinking Fast and Slow , 2017, SIGMETRICS Perform. Evaluation Rev..

[15]  Paul E. Stillman,et al.  Resisting Temptation: Tracking How Self-Control Conflicts Are Successfully Resolved in Real Time , 2017, Psychological science.

[16]  Christopher Summerfield,et al.  Selective overweighting of larger magnitudes during noisy numerical comparison , 2017, Nature Human Behaviour.

[17]  Hean Tat Keh,et al.  Consumer reliance on intangible versus tangible attributes in service evaluation: the role of construal level , 2017 .

[18]  J. G. Holmes,et al.  Who can't take a compliment? The role of construal level and self-esteem in accepting positive feedback from close others , 2017 .

[19]  Allison Yamanashi Leib,et al.  Fast ensemble representations for abstract visual impressions , 2016, Nature Communications.

[20]  Marius Usher,et al.  Transcranial Direct Current Stimulation over the Parietal Cortex Improves Approximate Numerical Averaging , 2016, Journal of Cognitive Neuroscience.

[21]  D. Whitney,et al.  Fleeting impressions of economic value via summary statistical representations , 2016 .

[22]  Steven Dakin,et al.  Spatial limitations in averaging social cues , 2016, Scientific Reports.

[23]  Robert P. Spunt,et al.  The neural basis of conceptualizing the same action at different levels of abstraction. , 2016, Social cognitive and affective neuroscience.

[24]  Marius Usher,et al.  Adaptive Spontaneous Transitions between Two Mechanisms of Numerical Averaging , 2015, Scientific Reports.

[25]  Bjorn Hubert-Wallander,et al.  Not all summary statistics are made equal: Evidence from extracting summaries across time. , 2015, Journal of vision.

[26]  Timothy F. Brady,et al.  Individual differences in ensemble perception reveal multiple, independent levels of ensemble representation. , 2015, Journal of experimental psychology. General.

[27]  I. Blanken,et al.  A Group’s Physical Attractiveness Is Greater Than the Average Attractiveness of Its Members , 2015, Personality & social psychology bulletin.

[28]  Amy Beth Warriner,et al.  Concreteness ratings for 40 thousand generally known English word lemmas , 2014, Behavior research methods.

[29]  Yaacov Trope,et al.  Traversing psychological distance , 2014, Trends in Cognitive Sciences.

[30]  Nira Liberman,et al.  From mind to matter: neural correlates of abstract and concrete mindsets. , 2014, Social cognitive and affective neuroscience.

[31]  A. Franklin,et al.  Getting the gist of multiple hues: metric and categorical effects on ensemble perception of hue. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[32]  Edward Vul,et al.  Hierarchical Encoding Makes Individuals in a Group Seem More Attractive , 2014, Psychological science.

[33]  Timothy D. Sweeny,et al.  Perceiving Crowd Attention: Ensemble Perception of a Crowd’s Gaze , 2014 .

[34]  Marlone D. Henderson,et al.  There Are Many Ways to See the Forest for the Trees , 2013, Perspectives on psychological science : a journal of the Association for Psychological Science.

[35]  Markus F. Neumann,et al.  Viewers extract mean and individual identity from sets of famous faces , 2013, Cognition.

[36]  Y. Trope,et al.  Journal of Experimental Psychology: General When Time Flies: How Abstract and Concrete Mental Construal Affect the Perception of Time , 2012 .

[37]  David C. Burr,et al.  Linear mapping of numbers onto space requires attention , 2012, Cognition.

[38]  R. B. Post,et al.  The Frozen Face Effect: Why Static Photographs May Not Do You Justice , 2012, Front. Psychology.

[39]  Stella F. Lourenco,et al.  Common spatial organization of number and emotional expression: A mental magnitude line , 2011, Brain and Cognition.

[40]  Nicolas Robitaille,et al.  When more is less: extraction of summary statistics benefits from larger sets. , 2011, Journal of vision.

[41]  K. Fujita,et al.  The effect of distance-dependent construals on schema-driven impression formation , 2011 .

[42]  Joshua A Solomon,et al.  Visual discrimination of orientation statistics in crowded and uncrowded arrays. , 2010, Journal of vision.

[43]  Michael N. Shadlen,et al.  Temporal context calibrates interval timing , 2010, Nature Neuroscience.

[44]  Y. Trope,et al.  Construal-level theory of psychological distance. , 2010, Psychological review.

[45]  Jason M Haberman,et al.  Averaging facial expression over time. , 2009, Journal of vision.

[46]  J. D. de Fockert,et al.  Rapid extraction of mean identity from sets of faces. , 2009, Quarterly journal of experimental psychology.

[47]  Sung Jun Joo,et al.  Statistical processing: Not so implausible after all , 2008, Perception & Psychophysics.

[48]  Jason M Haberman,et al.  Correspondences Rapid extraction of mean emotion and gender from sets of faces , 2007 .

[49]  A. Treisman,et al.  Statistical processing: computing the average size in perceptual groups , 2005, Vision Research.

[50]  Y. Trope,et al.  The influence of abstract and concrete mindsets on anticipating and guiding others' self-regulatory efforts ☆ , 2004 .

[51]  A. Treisman,et al.  Representation of statistical properties , 2003, Vision Research.

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

[53]  J. Lund,et al.  Compulsory averaging of crowded orientation signals in human vision , 2001, Nature Neuroscience.

[54]  D. Ariely.  Seeing Sets: Representation by Statistical Properties , 2001, Psychological science.

[55]  H. Plessner,et al.  PERSONALITY AND SOCIAL PSYCHOLOGY BULLETIN Betsch et al. / IMPLICIT ATTITUDE FORMATION I Like It but I Don’t Know Why: A Value-Account Approach to Implicit Attitude Formation , 2022 .

[56]  S. McKee,et al.  Simultaneous encoding of direction at a local and global scale , 1998, Perception & psychophysics.

[57]  R. Watt,et al.  The computation of orientation statistics from visual texture , 1997, Vision Research.

[58]  D. Hamilton,et al.  Perceiving persons and groups. , 1996, Psychological review.

[59]  S. Dehaene.  Varieties of numerical abilities , 1992, Cognition.

[60]  Andrew P. Duchon,et al.  The human visual system averages speed information , 1992, Vision Research.

[61]  Stanislas Dehaene,et al.  Two mental calculation systems: A case study of severe acalculia with preserved approximation , 1991, Neuropsychologia.

[62]  Scott N. J. Watamaniuk,et al.  Direction Perception in Complex Dynamic Displays: the Integration of Dir~~tion Information , 1988 .

[63]  David J. Samson,et al.  Intuitive averaging of categorized numerical stimuli , 1983 .

[64]  P. Ekman,et al.  Measuring facial movement , 1976 .

[65]  H. Akaike.  A new look at the statistical model identification , 1974 .

[66]  N. Anderson.  Integration theory and attitude change. , 1971 .