Description
Built with Questionnaire Builder 2
This template of a participant information sheet can be cloned so that you do not have to create this from scratch.
The structure of the information sheet is in place, with only minor edits required so that the information sheet is specific to your study.
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Built with Questionnaire Builder 2
This template of this consent form can be cloned so that you do not have to create this from scratch.
Creative Commons Attribution (CC BY)
Built with Questionnaire Builder 2
This template of a demographics questionnaire can be cloned so that you do not have to create this from scratch.
Key questions about the age and gender of the participant are already provided. This questionnaire would need to be edited if additional questions are required.
Creative Commons Attribution (CC BY)
Built with Questionnaire Builder 2
This template of an experience question can be cloned so that you do not have to create this from scratch.
This questions will need to edited so that the questions and the options provided are specific to each groups members chosen Independent Variable (IV).
This questionnaire contains six questions so groups of six can have one question each. For those in a smaller group, remove questions/objects that are not required.
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This task can be cloned so that you do not have to create this from scratch. This new version can be used on mobile devices, tablets and computers/laptops.
What can Response Time (RT) performance tell us?
Response Time in this task is a reflection of the amount of time required to find a target amongst distractors. It is effectively indicating how efficiently someone can find the target amongst the distractors. For trials where they correctly identify if the target is or is not present (incorrect trials can be removed, of course), faster RTs indicates faster processing speed. The RT is a reflection of perceptual encoding (extracting early visual features such as colour, edges, orientation), attentional selection (guiding attention to specific locations or objects), decision making (committing to a decision about if the target is present or not), and the motor response (pressing “Present” or “Absent”).
How does this link with experience/expertise?
In terms of expertise/experience, RT for the visual search task may improve due to improved perceptual encoding, possibly from improved perceptual learning allowing the individual to become more attuned to what early visual characteristics are important (e.g., an expert footballer may encode hip orientation of opposition player, rather than just the ball) whilst also accounting for which characteristics are effectively noise or not necessary (see Kellman & Garrigan, 2009). It could also improve due to improved attentional selection, as their expertise can allow for better prioritization in where they direct their attention (where they look). Decision making can also improve with experience as a result of developing smarter decision thresholds, in that with experience they may have learned to strike a better balance between making quicker decisions that can include more errors (low threshold) and making slower decisions that may include fewer errors but at the expense of the time taken to respond (high/cautious threshold) (see Ratcliff & McKoon, 2008).
References:
Kellman, P. J., & Garrigan, P. (2009). Perceptual learning and human expertise. Physics of life reviews, 6(2), 53-84.
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922.
Creative Commons Attribution (CC BY)
Built with Task Builder 2
This task can be cloned so that you do not have to create this from scratch. This new version can be used on mobile devices, tablets and computers/laptops.
What can Response Time (RT) performance tell us?
Before addressing this question, it is important to acknowledge the two aspects of the Go No-Go task. Participants are required to respond as quickly as possible to the Go trials, but they are also required to not response to the No-Go trials. This distinction is important, as it affects how we interpret RTs, given that RTs represent the Go trails only (participants do not respond to the No-Go trials). RT in this task (well, the Go trials) is a reflection of the amount of time required to perceive the stimulus presented on the screen, map it onto the appropriate response (e.g., Go or No-Go response), and initiate the motor action (e.g., press or not press the “HIT” button). The RT is a reflection of stimulus encoding (extracting early visual features such as colour, edges, orientation of the stimulus), stimulus-response mapping/chunking where patterns can be mapped onto or chunked with the actions (e.g., press HIT when target appears, but do not press HIT when the distractor appears), response preparation where motor plans are partially activated (motor plan to press the Hit button is prepared, but not activated yet), decision making (committing to a decision about if the target is present or not), and the motor response (pressing or not pressing “HIT”).
How does this link with experience/expertise?
In terms of expertise/experience, RT for the Go Trials may improve due to improved stimulus encoding, possibly from improved perceptual learning allowing the individual to become more attuned to what early visual characteristics are important (e.g., an expert footballer may encode hip orientation of opposition player, rather than just the ball) whilst also accounting for which characteristics are effectively noise or not necessary (see Kellman & Garrigan, 2009). It could also improve due to improved stimulus-response mapping/chunking, in that with experience established stimulus-response mappings/chunking removes the need for decision making (see Lappi, 2022). For example, an expert footballer could quickly perform a necessary tackle due to an established stimulus-response mapping allowing the observed stimuli (e.g., orientation of opposition players hips indicating their next movement) implying the necessary response (perform tackle). Additionally, RT could also improve with experience/expertise as a result of improved response preparation. With experience/expertise, an individual can prepare the likely required motor plan in advance, allowing the required motor plan to be actionable if/when necessary (see Müller & Abernethy, 2012). Decision making can also improve with experience as a result of developing smarter decision thresholds, in that with experience they may have learned to strike a better balance between making quicker decisions that can include more errors (low threshold) and making slower decisions that may include fewer errors but at the expense of the time taken to respond (high/cautious threshold) (see Ratcliff & McKoon, 2008).
References:
Kellman, P. J., & Garrigan, P. (2009). Perceptual learning and human expertise. Physics of life reviews, 6(2), 53-84.
Lappi, O. (2022). Egocentric chunking in the predictive brain: a cognitive basis of expert performance in high-speed sports. Frontiers in Human Neuroscience, 16, 822887.
Müller, S., & Abernethy, B. (2012). Expert anticipatory skill in striking sports: A review and a model. Research quarterly for exercise and sport, 83(2), 175-187.
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922.
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Built with Task Builder 2
This task can be cloned so that you do not have to create this from scratch. This new version can be used on mobile devices, tablets and computers/laptops. The task currently uses faces as a part of a face recognition task. However, other images can be used in this visual memory task instead.
What can Response Time (RT) performance tell us?
Response Time in this task is a reflection of the amount of time required to for the participant to decide if the face they are presented with is one of the faces they were required to memorise, or if the face presented is a new face. It is effectively indicating how quickly someone can identify if they recognise the face to presented to them. For trials where they correctly identify if the face was or was not presented to them in the earlier part of the task (incorrect trials can be removed, of course), faster RTs indicates faster processing speed. The RT is a reflection of perceptual encoding (extracting early visual features such as colour, edges, orientation, lighting), representation matching (attempting to match the encoded representation of the facial image to previously stored representations), decision making (committing to a decision about if the target is present or not), and the motor response (pressing “Present” or “Absent”).
How does this link with experience/expertise?
In terms of expertise/experience, RT for the Visual Memory / Face Recognition Task may improve due to improved stimulus encoding, possibly from improved perceptual learning allowing the individual to become more attuned to what early visual characteristics are important (e.g., an expert footballer may encode hip orientation of opposition player, rather than just the ball) whilst also accounting for which characteristics are effectively noise or not necessary (see Kellman & Garrigan, 2009). It could also improve due to improved representation matching, in which with experience, repeated exposure to facial recognition allows for learning of which visual aspects matter for identification and which variations can be ignored. As a result, with less experience many more partial matches are activated and explored. Whereas with more experience, fewer stronger candidates are activated and considered (see Blauch et al., 2021). Decision making can also improve with experience as a result of developing smarter decision thresholds, in that with experience they may have learned to strike a better balance between making quicker decisions that can include more errors (low threshold) and making slower decisions that may include fewer errors but at the expense of the time taken to respond (high/cautious threshold) (see Ratcliff & McKoon, 2008).
References:
Blauch, N. M., Behrmann, M., & Plaut, D. C. (2021). Computational insights into human perceptual expertise for familiar and unfamiliar face recognition. Cognition, 208, 104341.
Kellman, P. J., & Garrigan, P. (2009). Perceptual learning and human expertise. Physics of life reviews, 6(2), 53-84.
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922.
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This template of a debrief can be cloned so that you do not have to create this from scratch.
The structure of the debrief is in place, with only minor edits required so that the debrief is specific to your study.
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