Studying Visual Imagery With Cognitive Science: Benefits and Drawbacks

Observations abound about the power of visual imagery in human intelligence, from how Nobel prize-winning physicists make their discoveries to how children understand bedtime stories. These observations raise an important question for cognitive science, which is, what are the computations taking place in someone’s mind when they use visual imagery? Answering this question is not easy and will require much continued research across the multiple disciplines of cognitive science. Here, we focus on a related and more circumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual imagery-based knowledge representations and reasoning operations, then what kinds of problem solving might be possible, and how would such problem solving work? We highlight recent progress in AI toward answering these questions in the domain of visuospatial reasoning, looking at a case study of how imagery-based artificial agents can solve visuospatial intelligence tests. In particular, we first examine several variations of imagery-based knowledge representations and problem-solving strategies that are sufficient for solving problems from the Raven’s Progressive Matrices intelligence test. We then look at how artificial agents, instead of being designed manually by AI researchers, might learn portions of their own knowledge and reasoning procedures from experience, including learning visuospatial domain knowledge, learning and generalizing problem-solving strategies, and learning the actual definition of the task in the first place.

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The imagery debate: Déjà-vu all over again?

Behavioral and Brain Sciences ◽

10.1017/s0140525x02490048 ◽

2002 ◽

Vol 25 (2) ◽

pp. 209-210 ◽

Cited By ~ 3

Author(s):

Peter P. Slezak

Keyword(s):

Cognitive Science ◽

Visual Imagery ◽

Imagery Debate ◽

Deja Vu ◽

Déjà Vu

The imagery debate re-enacts controversies persisting since Descartes. The controversy remains important less for what we can learn about visual imagery than about cognitive science itself. In the tradition of Arnauld, Reid, Bartlett, Austin and Ryle, Pylyshyn's critique exposes notorious mistakes being unwittingly rehearsed not only regarding imagery but also in several independent domains of research in modern cognitive science.

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Language as a mental travel guide

Behavioral and Brain Sciences ◽

10.1017/s0140525x19003182 ◽

2020 ◽

Vol 43 ◽

Author(s):

Charles P. Davis ◽

Gerry T. M. Altmann ◽

Eiling Yee

Keyword(s):

Cognitive Science ◽

Human Cognition ◽

Travel Guide

Abstract Gilead et al.'s approach to human cognition places abstraction and prediction at the heart of “mental travel” under a “representational diversity” perspective that embraces foundational concepts in cognitive science. But, it gives insufficient credit to the possibility that the process of abstraction produces a gradient, and underestimates the importance of a highly influential domain in predictive cognition: language, and related, the emergence of experientially based structure through time.

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Cognitive Science and Ancient Israelite Religion

10.1017/9781108767972 ◽

2020 ◽

Author(s):

Brett E. Maiden

Keyword(s):

Cognitive Science ◽

Israelite Religion

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Embodiment and Cognitive Science

10.1017/cbo9780511805844 ◽

2005 ◽

Cited By ~ 276

Author(s):

Raymond W. Gibbs, Jr

Keyword(s):

Cognitive Science

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Vivid Imagers Are Better at Detecting Salient Changes

Journal of Individual Differences ◽

10.1027/1614-0001.27.4.218 ◽

2006 ◽

Vol 27 (4) ◽

pp. 218-228 ◽

Cited By ~ 13

Author(s):

Paul Rodway ◽

Karen Gillies ◽

Astrid Schepman

Keyword(s):

Individual Differences ◽

Change Detection ◽

Visual Imagery ◽

Detection Task ◽

Level Of Detail ◽

Detection Accuracy ◽

Change Detection Task ◽

Term Change ◽

High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17 s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

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Self-Report Imagery Questionnaire Scores and Subtypes of Social-Desirable Responding

Journal of Individual Differences ◽

10.1027/1614-0001.29.4.181 ◽

2008 ◽

Vol 29 (4) ◽

pp. 181-188 ◽

Cited By ~ 8

Author(s):

John Allbutt ◽

Jonathan Ling ◽

Thomas M. Heffernan ◽

Mohammed Shafiullah

Keyword(s):

Visual Imagery ◽

Correlational Analysis ◽

Self Report ◽

Auditory Imagery ◽

Auditory Modality ◽

Visual Thinking ◽

Thinking Style ◽

Social Desirable Responding

Allbutt, Ling, and Shafiullah (2006) and Allbutt, Shafiullah, and Ling (2006) found that scores on self-report measures of visual imagery experience correlate primarily with the egoistic form of social-desirable responding. Here, three studies are reported which investigated whether this pattern of findings generalized to the ratings of imagery vividness in the auditory modality, a new version of the Vividness of Visual Imagery Questionnaire ( Marks, 1995 ), and reports of visual thinking style. The measure of social-desirable responding used was the Balanced Inventory of Desirable Responding (BIDR; Paulhus, 2002 ). Correlational analysis replicated the pattern seen in our earlier work and of the correlations with the egoistic bias, the correlation with vividness of visual imagery was largest and significant, the correlation with visual thinking style next largest and approached significance, and the correlation with vividness of auditory imagery was the smallest and not significant. The size of these correlations mirrored the extent to which the three aspects of imagery were valued by participants.

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