Vividness and Behavioral Specificity in Visual Imagery: Not what you’d expect
Bibliographic record
Abstract
Vividness and Behavioral Specificity in Visual Imagery: Not what you’d expect Amedeo D’Angiulli (amedeo@connect.carleton.ca) Department of Psychology, 1125 Colonel By Drive Ottawa, ON, K1S 5B6, Canada and “dorsal” imagery, and the involvement of the complex underlying working and long-term memory dynamics. A major threat to the vivid-is-fast relationship is that it may really reflect various types of participants‟ expectations during lab experiments, not at all generation and use of mental images. Following up to previous research (D‟Angiulli & Reeves, 2005), I show that the current evidence on the vivid-is-fast relationship, and its selective variations in some conditions, is incompatible with the main accounts based on expectations and tied to the alleged epiphenomenalism of imagery experience. In addition, presenting evidence from multiple measures, I show that vividness fits well within the causal theory approach to validity (Borsboom, Mellenbergh & van Heerden, 2004). To explain the data reviewed here (as well as other recent literature evidence), I develop a minimalist approach, dubbed vividness-core principle. This consists in a parsimonious set of propositions that: 1) builds on the vivid- is-fast relationship and Levesque‟s (1986) formalization of vividness in AI; 2) accounts for most everyday imagery, explaining how imagery could be useful for everyday incidental memory and undetermined object-based reasoning. When remembering specific everyday objects or events linked to past experiences, for example personal events (e.g., the face of a relative or a pet), people generally report “seeing with the mind's eye”. A pervasive aspect of people‟s report is the vividness of their mental images. Images may come from the imagination (e.g., a pink dog) or from retrieved episodic and specific representations which refer to everyday objects (e.g., your breakfast this morning). Setting aside “imagination imagery”, whose vividness is entirely subjective, we can define the vividness of “realistic” imagery as: (i) The extent to which mental images reflect the composite quality (including specificity, detail, and richness) of visual representations that would have been generated if the object had actually been perceived. Proposition (i) requires no presumptions about the underlying format of mental images (e.g., propositional) other than that they are a type of analogue. All that one needs to assume is some elementary properties of databases (Brachman & Levesque, 2004). That is, a memory database containing information about a given domain (of objects and relationships between these objects in the world) will contain individual images that consistently designate individual objects in the world and relationships between individual objects that designate the respective relationships in the world. Second, according to (i), vividness can be interpreted as a crude proxy for what is available in the memory database, a report about a represented object X or relationship involving X will be more or less vivid depending on the extent to which information about X is perceived to be complete (see Levesque, 1986), in turn this should be reflected in behaviour, for example, the time needed to respond to a query about X. Some recent research (D‟Angiulli, in press; 2002; D‟Angiulli & Reeves, 2007; 2002; Reeves & D‟Angiulli, 2003) has shown conditions in which the relationship between vividness ratings and image latency response reflects some properties of the visual systems: the system that is dedicated to process object-properties (ventral pathway) and the system that is dedicated to process locative properties of mental images (dorsal pathway). In particular, the results of these studies showed that for small images expected to recruit mainly the ventral pathway (i.e., requiring size-scaling of less than 10 o ) the higher the rated vividness, the faster their generation. This vivid-is-fast relation, it was also found, changed for large images expected to recruit mainly the dorsal pathway (i.e., requiring size-scaling of 10 o or more). While the size-dependent effects gradually disappeared over the course of repeated image generation, the vivid-is-fast relation remained, although it corresponded to a much weaker effect. Based on these findings, it was concluded that differential patterns of vividness-image latency relationship can reflect “ventral” References Brachman, R.J. & Levesque, H., J. (2004). Knowledge, representation and reasoning. NY: Morgan & Kaufmann. Borsboom, D. Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111, D‟Angiulli, A. (in press). Is the spotlight an obsolete metaphor of „seeing with the mind‟s eye”? A constructive naturalistic approach to the inspection of visual mental images. Imagination, Cognition & Personality. D'Angiulli, A., & Reeves, A. (2007). The relationship between self-reported vividness and latency during mental size scaling of everyday items: Phenomenological evidence of different types of imagery. American Journal of Psychology, 120(4), 521-551. D‟Angiulli A., & Reeves A. (2005). Picture theory, tacit knowledge or vividness-core? Three hypotheses on the mind‟s eye and its elusive size. Proceedings of the 27 th Cognitive Science Society Annual Meeting, 536-541 D'Angiulli, A. (2002). Mental image generation and the contrast sensitivity function. Cognition, 85, B11-B19. D‟Angiulli, A., & Reeves, A. (2002). Generating mental images: Latency and vividness are inversely related. Memory & Cognition, 30, 1179-1188. Levesque, H. (1986). Making believers out of computers. Artificial Intelligence, 30, 81-108. Reeves, A., & D‟Angiulli, A. (2003). What does the visual buffer tell the mind‟s eye? Abstracts of The Psychonomic Society, 8, 82.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".