Yea, looks like the approach of my study is really an individual differences approach. That's the slant to my story? Universal theories of reasoning aspire to explain how we humans as a whole reason. This is an individual similarities approach, intending to collapse differences and focus on understanding our shared mental architectures.
In contrast, by throwing in WM capacity, we are interested in how a proposed general rule might vary across individuals.
Perhaps this might help in my writing of the introduction. Or perhaps not. :p
Tuesday, February 17, 2009
Thursday, February 12, 2009
Senior Seminar: Lessons Learned (Pt 1)
- Start early. START EARLY. START. EARLY.
- Draft, draft, draft. Don't stop or get bogged down with details. Blah out everything that you can, process it, and then repeat the process. We've got to keep the ideas flowing.
- Carve out significant blocks of time (at least 1 hour, preferably 2-3 hrs) to sit down and JUST WORK ON THE STUFF.
- Divide and conquer. Break up the large chapters of writing into small writing assignments.
- ORGANIZE. Related to point 4.
- Related to point 2: PROTOTYPE. Use pilot studies. Try and fail. Don't fall into the trap of trying to run everything in your head.
- Read up literature on experimental tasks if they are available. It'll help you get a sense of what the instrument is designed to measure, and what conditions are optimal for getting "clean" data.
Monday, February 9, 2009
The Revenge of the Strategy Theorists
Previously, I had written off the strategy theories of reasoning (e.g., the reasoning research group at Plymouth) as irrelevant to my research problem. Today, I re-read Bacon, Handley, and McDonald's (2007) paper on reasoning and dyslexia, and traced a citation back to Bacon, Handley, and Newstead's (2004) book chapter that described protocol and behavioral data on verbal and spatial strategies in transitive reasoning.
It looks like I need to reframe my theoretical thinking. This empirical battle may not be between mental models and imagery theories of reasoning, but rather between the domain-general vs domain-specific views on reasoning competence. Mental models theory falls squarely in the domain-general camp, and the logic of the VI-hypothesis rests partially on the assumption that we (i.e., normal, healthy adults) are "not supposed" to reason with visual images. The strategy theorists, however, argue against universal theories of reasoning, preferring to allow for individual variation in the algorithm classes that are employed in reasoning, each as valid as the other but differing in "fit" for various problem contexts (Bacon et al, 2004).
This has enormous implications for my data interpretation and discussion. It would be scientifically untenable to merely ignore the arguments of the strategy camp. In fact, my data have the potential to actually support their position: if I do find the VI effect, one possible reinterpretation of it would be as evidence for difference between "verbal" and "spatial" reasoners, as identified by Bacon et al (2007). In any case, the ardent call of the strategy camp to consider their data cannot be ignored in my case.
I must read a little more from the strategy literature and perhaps reframe the structure of my review of literature. There seems to be less and less impetus for devoting special sections to "syntactic" and "semantic" classes of reasoning theories; rather, it appears that a fruitful strategy for lively intellectual discussion would be to pit universal theories (spearheaded by the mental models theory) against the domain-specific views. Perhaps a novel synthesis might be reached using this dialectical approach. :)
The Bacon et al (2004) paper is in Roberts & Newton (2005) Methods of Thought: Individual Differences in Reasoning Strategies.
It looks like I need to reframe my theoretical thinking. This empirical battle may not be between mental models and imagery theories of reasoning, but rather between the domain-general vs domain-specific views on reasoning competence. Mental models theory falls squarely in the domain-general camp, and the logic of the VI-hypothesis rests partially on the assumption that we (i.e., normal, healthy adults) are "not supposed" to reason with visual images. The strategy theorists, however, argue against universal theories of reasoning, preferring to allow for individual variation in the algorithm classes that are employed in reasoning, each as valid as the other but differing in "fit" for various problem contexts (Bacon et al, 2004).
This has enormous implications for my data interpretation and discussion. It would be scientifically untenable to merely ignore the arguments of the strategy camp. In fact, my data have the potential to actually support their position: if I do find the VI effect, one possible reinterpretation of it would be as evidence for difference between "verbal" and "spatial" reasoners, as identified by Bacon et al (2007). In any case, the ardent call of the strategy camp to consider their data cannot be ignored in my case.
I must read a little more from the strategy literature and perhaps reframe the structure of my review of literature. There seems to be less and less impetus for devoting special sections to "syntactic" and "semantic" classes of reasoning theories; rather, it appears that a fruitful strategy for lively intellectual discussion would be to pit universal theories (spearheaded by the mental models theory) against the domain-specific views. Perhaps a novel synthesis might be reached using this dialectical approach. :)
The Bacon et al (2004) paper is in Roberts & Newton (2005) Methods of Thought: Individual Differences in Reasoning Strategies.
Thursday, January 29, 2009
Automatic vs. controlled processes - Relevant to Visual-Impedance Hypothesis?
As sparked by my browsing of Cohen, Dunbar, & McClelland's (1990) "On the Control of Automatic Processes: A Parallel Distributed Processing Account of the Stroop Effect", I'm wondering if the cognitive task analysis at the level of automatic vs controlled processing is relevant to understanding the computational mechanisms putatively involved in the visual impedance effect.
It seems that Knauff and his colleagues argue that visual images can impede reasoning because the generation of visual images in response to highly visualizable premise terms is automatic, and therefore precedes controlled processing of the premises into reasoning-specific representations (e.g., mental models or spatial imagery). As far as I can understand, then, it may be that the visual images are automatically generated by the cognitive system in response to the premises, and these images are inappropriate (or perhaps cannot be used at all) for deductive reasoning computations. Inefficient systems (e.g., we're hoping measured by WM capacity) may erroneously attempt to operate on these representations before "realizing" that the spatial representations are needed in order to make the inference.
Alternatively, it may be that the visual images are not automatically generated but are instead a controlled response to verbal input; by this reasoning, it is possible that we might see disparities between high and low efficiency systems (in our study, high spans vs low spans). However, the best data source would probably be temporally marked neuroimages of participants at each stage of the reasoning process. We might hypothesize that high spans would show little or no activity in the occipital lobes or "what" visual pathway (or at least less activation than low spans) during all stages of the reasoning process, but more interestingly, at the comprehension stage of the reasoning process. In other words, if the visualization is a controlled process, then it's possible that an efficient cognitive system might learn that the visual images are unsuitable for deductive reasoning and thereafter refrain from generating them in response to the verbal input from the premises.
In terms of the dependent measures we are using, we might expect little or no priming effects in the high spans for the categorical decision task, owing to the fact that they might either suppress visual images generated in response to verbal input (if visualization is automatic), OR refrain from generating visual images at all (if visualization is controlled), and therefore no priming effect would be present in their responses to the visual representations of the target words.
It seems that Knauff and his colleagues argue that visual images can impede reasoning because the generation of visual images in response to highly visualizable premise terms is automatic, and therefore precedes controlled processing of the premises into reasoning-specific representations (e.g., mental models or spatial imagery). As far as I can understand, then, it may be that the visual images are automatically generated by the cognitive system in response to the premises, and these images are inappropriate (or perhaps cannot be used at all) for deductive reasoning computations. Inefficient systems (e.g., we're hoping measured by WM capacity) may erroneously attempt to operate on these representations before "realizing" that the spatial representations are needed in order to make the inference.
Alternatively, it may be that the visual images are not automatically generated but are instead a controlled response to verbal input; by this reasoning, it is possible that we might see disparities between high and low efficiency systems (in our study, high spans vs low spans). However, the best data source would probably be temporally marked neuroimages of participants at each stage of the reasoning process. We might hypothesize that high spans would show little or no activity in the occipital lobes or "what" visual pathway (or at least less activation than low spans) during all stages of the reasoning process, but more interestingly, at the comprehension stage of the reasoning process. In other words, if the visualization is a controlled process, then it's possible that an efficient cognitive system might learn that the visual images are unsuitable for deductive reasoning and thereafter refrain from generating them in response to the verbal input from the premises.
In terms of the dependent measures we are using, we might expect little or no priming effects in the high spans for the categorical decision task, owing to the fact that they might either suppress visual images generated in response to verbal input (if visualization is automatic), OR refrain from generating visual images at all (if visualization is controlled), and therefore no priming effect would be present in their responses to the visual representations of the target words.
Monday, January 5, 2009
How can working memory relate to the role of visual imagery in deductive reasoning?
Current views on working memory:
"flexibly deployable, limited cognitive resources, namely activation, that support both the execution of various symbolic computations and the maintenance of intermediate products generated by these computations" (Shah & Miyake, 1996, p. 4).
"the brain system for holding and manipulating a small amount of information temporarily" (Cowan & Morey, 2006, p. 139)
"a system responsible for the active maintenance, manipulation, and retrieval of task relevant information" (Unsworth & Engle, 2008, p. 616)
"is needed when control is needed to override automatic response tendencies" (Unsworth & Engle, 2007, p. 105)
"fulfills two basic functions, maintenance and retrieval: (a) Maintenance is needed to keep new and novel information in a heightened state of activity, particularly in the presence of internal and external distraction. (b) Because the system is limited by how much information can be maintained at any given time, sometimes retrieval of that information in the presence of irrelevant information is required. To retrieve task relevant information, a discrimination process is needed to differentiate between relevant and irrelevant information on the basis of a combination of cues, particularly context cues" (Unsworth & Engle, 2007, p. 105)
A system for temporarily storing and maintaining information in the performance of complex cognitive processing (Baddeley, 2001)
Thus, after Unsworth & Engle (2007), I take the view that individual differences in working memory have a lot to do with the degree to which the system is able to effectively focus on what's relevant, and keep what's irrelevant (at least at the present moment) out of focus while working on the task at hand.
This has implications for understanding the relationship between visual imagery and deductive reasoning. Regardless of the theoretical controversy surrounding computational explanations of deductive reasoning, we can probably agree that the nature of the task requires that the system focus on the logical relations between the entities described in the premises in order to make a deductive inference. For this reason, if Knauff & Johnson-Laird (2002) argue that the deductive reasoning process can be impeded by the phenomenal experience of a visual image, than it follows that working memory should be implicated, and that individual differences in working memory might constrain the direction or degree of this relationship. For instance, individuals with "high working memory capacity" or "high-spans" are, by our understanding of working memory, more effective at maintaining task-relevant processing; therefore, the impeding effect of a visual image should be smaller for high-spans. Conversely, the impeding effect might be larger for "low-spans" due to their diminished capacity to maintain task-relevant processing.
"flexibly deployable, limited cognitive resources, namely activation, that support both the execution of various symbolic computations and the maintenance of intermediate products generated by these computations" (Shah & Miyake, 1996, p. 4).
"the brain system for holding and manipulating a small amount of information temporarily" (Cowan & Morey, 2006, p. 139)
"a system responsible for the active maintenance, manipulation, and retrieval of task relevant information" (Unsworth & Engle, 2008, p. 616)
"is needed when control is needed to override automatic response tendencies" (Unsworth & Engle, 2007, p. 105)
"fulfills two basic functions, maintenance and retrieval: (a) Maintenance is needed to keep new and novel information in a heightened state of activity, particularly in the presence of internal and external distraction. (b) Because the system is limited by how much information can be maintained at any given time, sometimes retrieval of that information in the presence of irrelevant information is required. To retrieve task relevant information, a discrimination process is needed to differentiate between relevant and irrelevant information on the basis of a combination of cues, particularly context cues" (Unsworth & Engle, 2007, p. 105)
A system for temporarily storing and maintaining information in the performance of complex cognitive processing (Baddeley, 2001)
Thus, after Unsworth & Engle (2007), I take the view that individual differences in working memory have a lot to do with the degree to which the system is able to effectively focus on what's relevant, and keep what's irrelevant (at least at the present moment) out of focus while working on the task at hand.
This has implications for understanding the relationship between visual imagery and deductive reasoning. Regardless of the theoretical controversy surrounding computational explanations of deductive reasoning, we can probably agree that the nature of the task requires that the system focus on the logical relations between the entities described in the premises in order to make a deductive inference. For this reason, if Knauff & Johnson-Laird (2002) argue that the deductive reasoning process can be impeded by the phenomenal experience of a visual image, than it follows that working memory should be implicated, and that individual differences in working memory might constrain the direction or degree of this relationship. For instance, individuals with "high working memory capacity" or "high-spans" are, by our understanding of working memory, more effective at maintaining task-relevant processing; therefore, the impeding effect of a visual image should be smaller for high-spans. Conversely, the impeding effect might be larger for "low-spans" due to their diminished capacity to maintain task-relevant processing.
Introduction to Senior Seminar: Rethinking Thinking with Pictures
Despite common introspective reports of visualizing during some kinds of deductive reasoning, and empirical evidence that visualization aids other kinds of thinking (e.g., mental simulation), cognitive scientists still disagree about whether or not visual imagery is functionally related to deductive reasoning or simply a phenomenon that commonly accompanies specialized reasoning processes.
The purpose of the current research project is to investigate the role of visual imagery in deductive reasoning, and whether individual differences in working memory capacity provide constraints on this relationship.
The results of this study will contribute to understanding how humans reason deductively, and how visual imagery and working memory are involved in this process.
The purpose of the current research project is to investigate the role of visual imagery in deductive reasoning, and whether individual differences in working memory capacity provide constraints on this relationship.
The results of this study will contribute to understanding how humans reason deductively, and how visual imagery and working memory are involved in this process.
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