I study computational bases of human cognition and am interested in what “intelligent behaviour” means in terms of human cognitive mechanisms.
My research focuses on topics such as cognitive control and automaticity, selective attention, skill acquisition, and transfer of learning.
They are fundamental to our understanding of how people regulate (or fail to regulate) their behaviors.
My research also investigates social and emotional influences on cognitive control.
My recent projects examine cognitive processes behind joint tasking (performing a task with other people) and modulations of automatic/implicit processes of socially related factors (e.g., stereotypes, implicit bias).
My applied work is concerned mainly with human factors and interface design issues.
Timeline
2019-Present
University of Essex
I joined the Department of Psychology at the University of Essex as a Senior Lecturer (associate professor) in July 2019. We have a really nice campus within an hour from London.
After 2 years of the COVID-19 lockdown, our lab is finally back in full operation! I am looking forward to working with highly motivated PhD students. Feel free to get in touch at cog.yamaguchi@gmail or motonori.yamaguchi@essex.ac.uk, if you are interested in cognitive psychology and neuroscience.
2013-2019
Edge Hill University
In 2013, I took up a lecturer post (assistant professor) at Edge Hill University and moved to Northwest of England, near Liverpool (yes, famous for Beatles and football!). I became a senior lecturer (associate professor) next year. UK decided to leave the EU in 2016, and I was awarded a Readership and had the second child that year.
2010-2013
Vanderbilt University
I completed my PhD in 2010 and started working as a postdoctoral scholar at Vanderbilt University in Nashville, Tennessee (USA), a centre of country music. My research in this period focused on skill control/acquisition and computational modelling of saccade control. I bought two new guitars before I left the town...
2005-2010
Purdue University
I started my PhD program at Purdue University, Indiana (USA) in 2005. I studied something called “stimulus-response compatibility” and developed a math model for the phenomenon. I also got married and had my first child in this snowy town.
Sometimes we do things without knowing why we do them. Sometimes we are not even aware of what we have just done. In these occasions, our actions seem to happen automatically without our control. In other occasions, we are aware of what we are doing or about to do. We seem to have good control over our actions. Some of our actions appear automatic and others controlled. This is a common intuition many people may have about their own actions. Many psychological theories are built on this idea of the duality in human behaviour.
The dual nature of human cognition has been a very popular idea in many areas of psychology. One of the examples of the dual nature of cognition was expressed by Freud’s contrast between the conscious and unconscious minds. In cognitive psychology, the distinction between automatic and controlled processes was proposed in virtually every major area, such as attention (Posner & Snyder, 1975), memory (Schneider & Shiffrin, 1977), language (LaBerge & Samuels, 1974), decision making (Tversky & Kahneman, 1974), and so on. In 1980s and 1990s, it became a major driving force in theorising how human cognition works (e.g., Bargh, 1989; Cohen, Dunbar, & McClelland, 1990; Kornblum, Hasbroucq, & Osman, 1990; Greenwald, McGhee, & Schwartz, 1998; Logan, 1988). While the distinction has a strong appeal and makes a good intuitive sense, however, scientific definitions of automaticity and control remain extremely elusive. To date, no empirical method has been established to distinguish between automatic and controlled processes. None of human cognitive processes can be categorised neatly into a purely automatic or controlled process.
Although the concept of automaticity is elusive, it is still possible to observe certain behavioural phenomena as manifestations of automaticity of behaviour. That is, automaticity is a hypothetical entity of human cognition that describes or merely characterises specific type of behaviour rather than a theoretical construct that explains how the human mind works. Indeed, much of my research interest has evolved around the idea of automaticity. I’m particularly interested in when people’s behaviour appears to rely on irrelevant aspects of a task context; how they come to influence our behaviour and how we control their influences.
One of the robust behavioural phenomena that manifests the influence of irrelevant task context is the Simon effect (try it here). In this task, you press left and right keys according to the colours of squares that appear on the left or right side of screen. Note that which key you press should depend only on the colour, but the locations of squares do influence how quickly you can press the keys. For most people, responses are faster when the locations of squares and keys correspond than when they do not. This is a version of a wider phenomenon called stimulus-response compatibility; that is, task performance is better (in terms of speed and accuracy) if responses are compatible with stimuli than if they are incompatible. Compatibility is usually defined as similarity between stimuli and responses. In the Simon task, stimuli and responses can be similar in terms of their spatial properties. In fact, spatial properties do not need to be their physical locations; for example, the words “LEFT” and “RIGHT” are still responded faster by pressing the left and right keys, respectively, even when these words appear in the screen centre. Some abstract concepts still produce the Simon effect. Therefore, it is not a very specific effect that only occurs under a special condition, but it is a general phenomenon that can be observed widely in everyday activities.
If the Simon effect is a manifestation of automaticity, how difficult is it to control? The distinction between automatic and controlled processes is misleading because automaticity is contrasted to deliberate control; as the opposite of controlled process, automatic process seems to be uncontrollable. But that is not the case at all. We have known that the Simon effect (and stimulus-response compatibility effect in general) depends on several factors that can exist simultaneously in a task environment. For instance, a square may appear on the left side of screen, but it may appear on the right side of a salient reference point within the left portion of screen (see Figure 1 below). If so, is the square compatible with a left response or with a right response? A similar ambiguity arises when there are multiple attributes in responding actions (e.g., pressing a left key to turn on a light on the right). Is this action compatible with a square on the left or a square on the right? It turned out that it depends on the context, and how people are instructed on the task can invert the direction of the Simon effect (Hommel, 1993). These findings imply that the Simon effect as a manifestation of automaticity depends on where people pay attention. This contradicts the traditional notion of automaticity as uncontrolled, or non-attentional, processes.
There are multiple factors that could potentially produce automatic responses, but which of these factors dominate in a given condition depends on the extent to which these factors draw the actor's attention in performing the task (called the frame of reference). This is a motivation behind the development of my model of stimulus-response compatibility, the multidimensional vector model (Yamaguchi & Proctor, 2012). The main idea is that stimuli and responses are inherently multidimensional and are represented in a multidimensional psychological space. The attentional state of the actor is represented as a vector pointing a specific direction within the psychological space. This vector serves as a weighting function when people make a decision. Thus, even when the similarity between stimuli and responses is fixed, the Simon effect can emerge on one factor or another, depending on how the actor controls the attention weights associated with different psychological dimensions. I will describe the mechanics of the model and its relationship to other types of cognitive models in more detail in the Cogntive Modelling section.
Updated: 20/12/2020
Visual Attention
Coming up soon!
Date: January 2020
Skill Control & Acquisition
Coming up soon!
Date: January 2020
Transfer of Learning
Coming up soon!
Date: January 2020
Joint Tasking
Coming up soon!
Date: January 2020
Computational Modelling
A cognitive model is a model of human behaviour based on theoretical cognitive processes, such as memory and attention. It is supposed to “predict” human behaviour, but human behaviour is “random,” so it is very hard to “predict” it. For example, if you insult someone, what does that person do next? Maybe, they punch you or kick you? They could also start crying or may just walk away. If you can see into the future, you can probably “predict” which of these is the correct outcome, but you cannot do that, so we can use a cognitive model to simulate human behaviour and find out what happens next moment, like you are looking into the future! But that is not how a cognitive model predicts human behaviour. To understand what a cognitive model actually does, you first need to understand the idea of “randomness.” When I said “human behaviour is random” it simply means that there are many (potentially infinite) possibilities. An important thing to remember is that although there are many possibilities, not all possibilities are equally likely. These likelihoods of different actions can be quantified (expressed in numbers called “probabilities”), and that is what a cognitive model does.
You probably know someone who is more likely than others to punch into your nose if you insult them. Your knowledge of a person who would punch into your nose is already a kind of model of human behaviour of that particular person. How do you build the model? It may not be based on any psychological theory but on your own experience with the person. How accurate is your prediction? It may be pretty good (at least as you believe so), but not perfect for sure. A cognitive model is similar to this kind of model that everyone has about people around them. So, what special about a cognitive model? A cognitive model is usually based on theories of human cognition, so it is built on concepts like “memory” and “attention.” A cognitive model also uses numbers (i.e., probabilities) that are assigned to possible outcomes, which are usually actions (like “punching” and “kicking”). It is really a good way to do science, but someone has said that less than 10% of cognitive psychologists actually try to develop one.
Some cognitive models are built on abstract concepts, and others are based on ideas inspired by how the brain works. An example of abstract cognitive model is the multidimensional vector model (MDV); this is the one that I developed for cognitive processes underlying choice-reaction tasks (Yamaguchi & Proctor, 2012). It came out of my PhD project at Purdue University. An example of brain-inspired cognitive model is the interactive race model (Logan, Yamaguchi, Schall, & Palmeri, 2015); this is a work that came out of my postdoc project at Vanderbilt University. These are my old days. I introduce MDV in this section. I will introduce the interactive race model in the Visual Attention section (it will be there someday…).
MDV is a mathematical model for choice-reaction tasks that are very often used to study human cognition. A choice-reaction task involves presenting an arbitrary stimulus to participants who then selects one of several alternative responses to the given stimulus as quickly as possible (see the Automaticity and Control section). For example, participants may be asked to respond to coloured circles displayed on a computer monitor by pressing one of several response keys. Participants’ performance is measured in terms of response time and error rates. With rigorous experimental manipulations, these two measures provide rich information about the underlying cognitive processes (e.g., Luce, 1986; Murdock, 1972). MDV accounts for these two performance measures simultaneously. Although the precise mathematical derivations are omitted here, it is a mathematical model that generates detailed response outcomes. There are two main components in MDV. The first component is a multidimensional psychological space in which stimuli are represented as fixed points and the state of the participant is represented as a vector pointing to a specific direction in the space (see Figure 1a). This vector fluctuates and produces the probabilistic nature of human performance. The process of selecting a response is modelled mathematically as the orthogonal projection of the stimulus onto the decision axis. Because of the random fluctuation of the decision axis, the projected point of a given stimulus is also randomly distributed, which is expressed as a density function along the decision axis (see Figure 1b). This results in a representation as in classical signal detection theory, or also called a Thurstonian model (Thurston, 1927), which is one of the most widely used mathematical theories in experimental psychology. The second component of MDV takes the distribution along the decision axis as the sampling space for the decision-making process. Sampling occurs over time and accumulates evidence for one or the other response. This process is modelled as a sequential sampling model and is conceptualised as a race between competing accumulators (see Figure 1c).
The innovation of this approach is that it accounts for the two sources of the variabilities in human decision making (stimulus properties and the internal state of the human agent) at the same time within a single framework. For example, selective attention is an internal state of the agent that weighs one’s decision on a set of decision factors more than others. For example, to decide a house, one may focus more on the price whereas others may focus more on the location. It may also be the case that the same buyer would rely on different factors in different economic situations. These differences can be accounted for in MDV as the variabilities in the angle of the decision axis in the psychological space. Attention to one decision factor (e.g., price) increases as the angle between the axis representing the feature and the decision axis that represents the internal state of the human agent gets smaller. Thus, full attention to one feature dimension is achieved when the decision axis is perfectly aligned to the dimensional axis and the angle between the two axes becomes zero. Hence, the cosine of the angle between the decision axis and the dimensional axis is a quantitative measure of selective attention to a particular decision factor.
I hope to develop this approach further to include mechanisms of spatial attention. Spatial attention is a process that enhances the representation of sensory information (visual, auditory, or tactile) that falls within an area of the space. In principle, this can be implemented in MDV but has not been done so as yet. Many theories of visual attention assume that visual objects are first processed in separate analysers that represent different dimensions of visual features (e.g., colour, spatial orientation, luminance), and the results of the analysers are pooled, or bound, into a single representation to form objects (e.g., Treisman & Gelade, 1980). The psychological space in MDV can be considered to be the pooled representational structure in the theory. By incorporating the mechanisms of spatial attention and feature representations, we will be able to push MDV toward a more comprehensive theory of memory and attention.
Well, I have more to talk about MDV (like how MDV is related to neural network) and about cognitive modelling more generally, but I will continue it some other time…
Updated: 15/10/2022
Journal Articles
48. Yamaguchi, M., & Swainson, R. (2024). The task-switch cost is still absent after selectively stopping a response in cued task switching. Journal of Experimental Psychology: Learning, Memory, and Cognition,50, 1579-1591. https://doi.org/10.1037/xlm0001383
47. Cusimano, K., Freeman, P., Moran, J., & Yamaguchi, M. (2024). Differences in approach and avoidance motivation sensitivities predicting participation and performance in strength sport. Journal of Strength and Conditioning Research, 38, 180-184. doi: 10.1519/JSC.0000000000004710.
46. Swainson, R., Prosser, L. J., & Yamaguchi, M. (2024). Preparing a task is sufficient to generate a subsequent task-switch cost affecting task performance. Journal of Experimental Psychology: Learning, Memory, and Cognition, 50, 39-51. doi: 10.1037/xlm0001277
45. Prosser, L. J., Yamaguchi, M., & Swainson, R. (2023). Investigating task preparation and task performance as triggers of the backward inhibition effect. Psychological Research, 87, 1816-1835. doi: 10.1007/s00426-022-01780-x
44. Chen, J., Šabić, E., Mishler, S., Jeffcoat, C., & Yamaguchi, M. (2022). Effectiveness of lateral auditory collision warnings: Should warnings be toward danger or toward safety? Human Factors, 64, 418-435. doi: 10.1177/0018720820941618
43. Debnath, B., O'Brien, M., Yamaguchi, M., & Behera, A. (2021). A review of computer vision-based approaches for physical rehabilitation and assessment. Multimedia Systems, 28, 209-239. doi: 10.1007/s00530-021-00815-4
42. Yamaguchi, M., Shah, H. H., & Hommel, B. (2021). When two actors perform different tasks: Still no evidence for shared task-sets in joint task switching. Quarterly Journal of Experimental Psychology, 21, 1914-1923. doi: 10.1177/17470218211031545
41. Yamaguchi, M., & Beattie, G. (2020). The role of explicit categorization in the Implicit Association Test. Journal of Experimental Psychology: General, 149, 809-827. doi: 10.1037/xge0000685
40. Yamaguchi, M., & Proctor, R. W. (2019). Modes of spatial coding in the Simon task. Journal of Cognitive Psychology, 31, 343-352. doi: 10.1080/20445911.2019.1595631
39. Yamaguchi, M., & Chen, J. (2019). Affective influences without approach-avoidance actions: On the congruence between valence and stimulus-response mappings. Psychonomic Bulletin & Review, 26, 545-551. doi: 10.3758/s13423-018-1547-1
38. Yamaguchi, M., Wall, H. J., & Hommel, B. (2019). The roles of action selection and actor selection in joint task settings. Cognition, 182, 184-192. doi: 10.1016/j.cognition.2018.10.010
37. Yamaguchi, M., & Nishimura, A. (2019). Modulating proactive cognitive control by reward: Differential anticipatory effects of performance contingent and non-contingent rewards. Psychological Research, 83, 258-274. doi: 10.1007/s00426-018-1027-2
36. Fang, H., Wang, V., & Yamaguchi, M. (2018). Dissecting deep learning networks–Visualizing mutual information. Entropy, 20, 813-823. doi: 10.3390/e20110823 (In Special Issue: “Information theory application in visualization,” edited by M. Sbert, M. Chen, & H.-W. Shen)
35. Yamaguchi, M., Clarke, E. L., & Egan, D. L. (2018). Is your color my color? Dividing the labor of the Stroop task between co-actors. Frontiers in Psychology, 9, Article 1407. doi: 10.3389/fpsyg.2018.01407 (In Research Topic: “What’s Shared in Sharing Tasks and Actions? Processes and Representations Underlying Joint Performance,” edited by M. Yamaguchi, T. Welsh, K. Dittrich, & K. C. Klauer.)
34. Yamaguchi, M., Chen, J., Mishler, S., & Proctor, R. W. (2018). Flowers and spiders in spatial stimulus-response compatibility: Does affective valence influence selection of task-sets or selection of responses? Cognition and Emotion, 32, 1003-1017. doi: 10.1080/02699931.2017.1381073 (The first two authors contributed equally to the study.)
33. Yamaguchi, M., Valji, A., & Wolohan, F. D. A. (2018). Top-down contributions to attention shifting and disengagement: A template model of visual attention. Journal of Experimental Psychology: General, 147, 859-887. doi: 10.1037/xge0000393
32. Yamaguchi, M., Wall, H. J., & Hommel, B. (2018). Sharing tasks or sharing actions? Evidence from the joint Simon task. Psychological Research, 82, 385-394. doi: 10.1007/s00426-016-0821-y
31. Yamaguchi, M., Wall, H. J., & Hommel, B. (2017). Action-effect sharing induces task-set sharing in joint task switching. Cognition, 165, 113-120. doi: 10.1016/j.cognition.2017.05.022
30. Yamaguchi, M., Wall, H. J., & Hommel, B. (2017). No evidence for shared task-sets in joint task switching. Psychological Research, 81, 1166-1177. doi: 10.1007/s00426-016-0813-y
29. Yamaguchi, M., *Randle, J. M., Wilson, T. J., & Logan, G. D. (2017). Pushing typists back on the learning curve: Memory chunking improves retrieval of prior typing episodes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43, 1432-1447. doi: 10.1037/xlm0000385
28. Larkin, D., Kirtchk, G., Yamaguchi, M., & Martin, C. (2017). A proposal for the inclusion of ‘Obesity Dysmorphia’ in the DSM. Australian and New Zealand Journal of Psychiatry, 51, 1085-1086. doi: 10.1177/0004867417722641.
27. Yamaguchi, M., & Harwood, S. L. (2017). Threat captures attention but does not affect learning of contextual regularities. Cognition and Emotion, 31, 564-571. doi: 10.1080/02699931.2015.1115752
26. Yamaguchi, M., & Logan, G. D. (2016). Pushing typists back on the learning curve: Memory chunking in the hierarchical control of skilled typewriting. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42, 1919-1936. doi: 10.1037/xlm0000288
25. Yamaguchi, M., Chen, J., & Proctor, R. W. (2015). Transfer of learning in choice reactions: The roles of stimulus type, response mode, and set-level compatibility. Memory & Cognition, 43, 825-836. doi:10.3758/s13421-015-0518-2
24. Logan, G. D., Yamaguchi, M., Schall, J. D., & Palmeri, T. J. (2015). Inhibitory control in mind and brain 2.0: Blocked-input models of saccadic countermanding. Psychological Review, 122, 115-147. doi: 10.1037/a0038893
23. Janczyk, M., Yamaguchi, M., Proctor, R. W., & Pfister, R. (2015). Response-effect compatibility with complex actions: the case of wheel rotations. Attention, Perception, & Psychophysics, 77, 930-940. doi: 10.3758/s13414-014-0828-7
22. Snyder, K. M., Logan, G. D., & Yamaguchi, M. (2015). Watch what you type: The role of visual feedback from the screen and the hands in skilled typewriting. Attention, Perception & Psychophysics, 77, 282-292. doi: 10.3758/s13414-014-0756-6.
21. Yamaguchi, M., & Logan, G. D. (2014). Pushing typists back on the learning curve: Contributions of multiple linguistic units in the acquisition of typing skill. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40, 1713-1732. doi: 10.1037/xlm0000026
20. Yamaguchi, M., & Logan, G. D. (2014). Pushing typists back on the learning curve: Revealing chunking in skilled typewriting. Journal of Experimental Psychology: Human Perception and Performance, 40, 592-612. doi: 10.1037/a0033809
19. Schweickert, R., Han, H. J., Yamaguchi, M., & Fortin, C. (2014). Estimating averages from distributions of tone durations. Attention, Perception, & Psychophysics, 76, 605-620. doi: 10.3758/s13414-013-0591-1
18. Baroni, G., Yamaguchi, M., Chen, J., & Proctor, R. W. (2013). Mechanisms underlying transfer of task-defined rules across feature dimensions. Experimental Psychology, 60, 410-424. doi: 10.1027/1618-3169/a000214
17. Yamaguchi, M., Logan, G. D., & Li, V. (2013). Multiple bottlenecks in hierarchical control of action sequences: What does “response selection” select in skilled typewriting? Journal of Experimental Psychology: Human Perception and Performance, 39, 1059-1084. doi: 10.1037/a0030431
16. Yamaguchi, M., Crump, M. J. C., & Logan, G. D. (2013). Speed-accuracy tradeoff in skilled typewriting: Decomposing the contributions of hierarchical control loops. Journal of Experimental Psychology: Human Perception and Performance, 39, 678-699. doi: 10.1037/a0030512
15. Proctor, R. W., Yamaguchi, M., Dutt, V., & Gonzalez, C. (2013). Dissociation of S-R compatibility and Simon effects with mixed tasks and mappings. Journal of Experimental Psychology: Human Perception and Performance, 39, 593-609. doi: 10.1037/a0029923
14. Proctor, R. W., Dunston, P. S., So, J. C. Y., Lopez-Santamaria, B. N., Yamaguchi, M., & Wang, X. (2013). Specificity of transfer in basic and applied perceptual-motor tasks. American Journal of Psychology (special issue), 126, 401-415. doi: 10.5406/amerjpsyc.126.4.0401
13. Yamaguchi, M., & Proctor, R. W. (2012). Multidimensional vector model of stimulus-response compatibility. Psychological Review, 119, 272-303. doi: 10.1037/a0026620
12. Yamaguchi, M., Logan, G. D., & Bissett, P. G. (2012). Stopping while going! Response inhibition does not suffer dual-task interference. Journal of Experimental Psychology: Human Perception and Performance, 38, 123-134. doi: 10.1037/a0023918
11. Yamaguchi, M., & Proctor, R. W. (2011). Automaticity without extensive training: The role of memory retrieval in automatic implementation of task-defined rules. Psychonomic Bulletin & Review, 18, 347-354. doi: 10.3758/s13423-011-0050-8
10. Yamaguchi, M., & Proctor, R. W. (2011). The Simon task with multi-component responses: Two loci of response-effect compatibility. Psychological Research, 75, 214-226. doi: 10.1007/s00426-010-0299-y
9. Yamaguchi, M., & Proctor, R. W. (2010). Compatibility of motion information in two aircraft attitude displays for tracking task. American Journal of Psychology, 123, 81-92. doi: 10.5406/amerjpsyc.123.1.0081
8. Miles, J. D., Yamaguchi, M., & Proctor, R. W. (2009). Dilution of compatibility effects in Simon-type tasks depends on categorical similarity between distractors and diluters. Attention, Perception, & Psychophysics, 71, 1598-1606. doi: 10.3758/APP.71.7.1598.
7. Proctor, R. W., Yamaguchi, M., Zhang, Y., & Vu, K. P.-L. (2009). Influence of visual stimulus mode on transfer of acquired spatial associations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 434-445. doi: 10.1037/a0014529.
6. Yamaguchi, M., & Proctor, R. W. (2009). Transfer of learning in choice-reactions: Contributions of specific and general components of manual responses. Acta Psychologica, 130, 1-10. doi: 10.1016/j.actpsy.2008.09.008.
5. Kim, H., Cho, Y. S., Yamaguchi, M., & Proctor, R. W. (2008). Influence of color word availability on the Stroop effect. Perception & Psychophysics, 70, 1540-1551. doi: 10.3758/PP.70.8.1540.
4. Cho, Y.-S., Proctor, R. W., & Yamaguchi, M. (2008). Influences of response position and hand posture on the orthogonal Simon effect. Quarterly Journal of Experimental Psychology, 61, 1020-1035. doi: 10.1080/17470210701467979.
3. Proctor, R. W., Koch, I., Vu, K. P.-L., & Yamaguchi, M. (2008). Influence of display type and cue format on task-cuing effects: Dissociating switch cost and right-left prevalence effects. Memory & Cognition, 36, 998-1012. doi: 10.3758/MC.36.5.998
2. Proctor, R. W., Yamaguchi, M., & Vu, K. P.-L. (2007). Transfer of noncorresponding spatial associations to the auditory Simon task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 245-253. doi: 10.1037/0278-7393.33.1.245
1. Yamaguchi, M., & Proctor, R. W. (2006). Stimulus-response compatibility with pure and mixed mappings in a flight task environment. Journal of Experimental Psychology: Applied, 12, 207-222. doi: 10.1037/1076-898X.12.4.207
Preprints
Yamaguchi, M. (2024). Item-level implicit affective measures reveal the uncanny valley of robot faces. PsyArXiv, https://osf.io/preprints/osf/rkp7a
Fukuda, K., Kozlova, O., Gillies, G., Liesefeld, H. R., & Yamaguchi, M. (2024). More liberal and less sensitive: Individual differences in visual working memory capacity predicts the metacognitive assessment of representational accuracy. PsyArXiv. https://osf.io/preprints/psyarxiv/jxh9v
Yamaguchi, M., & Swainson, R. (2024). Does preparation generate the cost of task switching? A recipe for a switch cost after cue-only trials. PsyArXiv. https://doi.org/10.31234/osf.io/4zeq6
Yamaguchi, M., Liesefeld, H. R., & Fukuda, K. (2024). Attentional boost in visual working memory? Concurrent target detection enhances the precision but reduces the retention probability. PsyArXiv. https://doi.org/10.31234/osf.io/6zy9f
Yamaguchi, M., & Swainson, R. (2023). What determines a task-switch cost after selectively inhibiting a response? PsyArXiv. https://doi.org/10.31234/osf.io/h8qk7
Constant, M., Mandal, A., Asanowicz, D., Yamaguchi, M., Gillmeister, H., Kerzel, D., Luque, D., Pesciarelli, F., Fehr, T., Mushtaq, F., Pavlov, Y. G., & Liesefeld, H. R. (2023). A multilab investigation into the N2pc as an indicator of attentional selectivity: Direct replication of Eimer (1996). PsyArXiv. https://doi.org/10.31234/osf.io/3472y
Yamaguchi, M., Moore, J. D., Hendry, S., & Wolohan, F. (2020). Exploring an Emotional Basis of Cognitive Control in the Flanker Task. PsyArXiv. https://doi.org/10.31234/osf.io/nkjy6
Book Chapters
Yamaguchi, M., & Schweickert, R. (2018). Reaction time measures in memory research. In H. Otani, & B. L. Schwartz (Eds), Handbook of Research methods in human memory. New York: Routledge.
Yamaguchi, M., & Proctor, R. W. (2015). Perception and attention: A multidimensional approach to human performance modeling. In R. R. Hoffman, J. Szalma, M. Scerbo, P. Hancock, & R. Parasuraman (Eds.), Cambridge handbook of applied perception research (pp. 107-125). Cambridge, UK: Cambridge University Press.
Proctor, R. W., Yamaguchi, M., & Miles, J. D. (2012). Training and transfer of basic components of skill. In A. F. Healy & L. E. Bourne, Jr. (Eds.), Training cognition: Optimizing efficiency, durability, and generalizability (pp. 89-111). London, UK: Psychology Press.
Proctor, R. W., & Yamaguchi, M. (2010). Factors affecting speed and accuracy of response selection in operational environments. In D. H. Andrews, R. P. Herz, & M. B. Wolf (Eds.), Human factors issues in combat identification (pp. 31-46). Aldershot, UK: Ashgate.
Yamaguchi, M., & Proctor, R. W. (2009). Modeling response times and accuracy for digital human models. In V. Duffy (Ed.), Handbook of digital human modeling: Research for applied ergonomics and human factors engineering (pp. 15-1 – 15-11). Boca Raton, FL: CRC Press.
Editorial
Yamaguchi, M., Welsh, T. N., Klauer, K. C., & Dittrich, K. (2019). Editorial: What’s shared in sharing tasks and actions? Processes and representations underlying joint performance. In M. Yamaguchi, T. Welsh, K. Dittrich, & K. C. Klauer, (eds). What’s Shared in Sharing Tasks and Actions? Processes and Representations Underlying Joint Performance. Frontiers in Psychology, 10, Article 659. doi: 10.3389/fpsyg.2019.00659
Confenrence Proceedings
Debnath, B., O’Brien, M., Yamaguchi, M., & Behera, A. (2018). Adapting MobileNets for mobile based upper body pose estimation. In the Proceedings of the 15th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS), 27-30 November, Aukland, New Zealand: IEEE.
Dunston, P. S., Proctor, R. W., Su, X., Yamaguchi, M., Wang, X., & Chen, R. (2010). Principles for utilization of construction equipment operator training simulators. In J. Ruwanpura, Y. Mohamed, and S. H. Lee (Eds.), Proceedings of the 2010 Construction Research Congress Volume 2 (pp. 1039-1046). May 8-10, 2010. Alberta, Canada: American Society of Civil Engineers.
Dutt, V, Yamaguchi, M., Gonzalez, C., & Proctor, R. W. (2009). An instance-based learning model of stimulus-response compatibility effects in mixed location-relevant and location-irrelevant tasks. In A. Howes, D. Peebles, and R. Cooper (Eds.), 9th International Conference on Cognitive Modeling – ICCM2009. July 24-26, 2009. Manchester, UK: University of Manchester.
Yamaguchi, M., & Proctor, R. W. (2009). A compatibility analysis of attitude display formats. In J. Flach (Ed.), Proceedings of the 15th International Symposium of Aviation Psychology (pp. 302-307). April 27-30, 2009. Dayton, OH: Write State University.
Proctor, R. W., & Yamaguchi, M. (2008). Factors affecting speed and accuracy of response selection in operational environments. In D. Andrews & N. Cooke (Eds.), Proceedings of Human Factors Issues in Combat Identification and Imagery Analysis Using Unmanned Aerial Vehicles (CD format). May 13-15, 2008. Mesa, AZ: Cognitive Engineering Research Institute.
Yamaguchi, M., & Proctor, R. W. (2007). Effect of display format on attitude maintenance performance. In R. Jensen (Ed.), Proceedings of the 14th International Symposium of Aviation Psychology (pp. 776-781). April 23-26, 2007. Dayton, OH: Write State University.
Tech reports/periodicals
Yamaguchi, M. (2019). What 100 years of typing research can tell us. Cognitive Bulletin, 4, 25-33.
Proctor, R. W., Yamaguchi, M., & Miles, J. D. (2010). Acquisition and transfer of basic skill components. (Technical Report). University of Colorado, Boulder, Center for Research on Training.
