Figure 1
Illustration of a cued task switching procedure. The colour of a cue indicates whether the participant should judge the parity or magnitude of the digit. The task can either repeat or alternate from one trial to the next.
Table 1
Trial types from Schmidt and Liefooghe (2016) with example trials.
| Condition | Repetition Type | Trial | |||||
|---|---|---|---|---|---|---|---|
| Task | Cue | Stimulus | Decision | Key | Preceding | Current | |
| cue-RR | ✓ | ✓ | ✓ | ✓ | ✓ | blue-3-odd-left | blue-3-odd-left |
| cue-AR | ✓ | ✓ | ✕ | ✓ | ✓ | blue-3-odd-left | blue-1-odd-left |
| cue-AA | ✓ | ✓ | ✕ | ✕ | ✕ | blue-3-odd-left | blue-2-even-right |
| rep-RR | ✓ | ✕ | ✓ | ✓ | ✓ | blue-3-odd-left | red-3-odd-left |
| rep-AR | ✓ | ✕ | ✕ | ✓ | ✓ | blue-3-odd-left | red-1-odd-left |
| rep-AA | ✓ | ✕ | ✕ | ✕ | ✕ | blue-3-odd-left | red-2-even-right |
| alt-RR | ✕ | ✕ | ✓ | ✕ | ✓ | blue-3-odd-left | green-3-<5-left |
| alt-RA | ✕ | ✕ | ✓ | ✕ | ✕ | blue-7-odd-left | green-7->5-right |
| alt-AR | ✕ | ✕ | ✕ | ✕ | ✓ | blue-3-odd-left | green-2-<5-left |
| alt-AA | ✕ | ✕ | ✕ | ✕ | ✕ | blue-3-odd-left | green-6->5-right |
Figure 2
The Parallel Episodic Processing (PEP) model as it applies to cued task switching. Instructed cue-goal and decision-response mappings are illustrated with dashed lines. Hardwired connections are illustrated with solid lines. Note that all node types are connected to episodes during encoding of experienced events, but this is not illustrated for simplicity. Connections ending in arrows are facilitative and connections ending in circles indicate within-layer competition (Goal nodes only).
Figure 3
Cued task switching effects, including original response times (with standard errors) from Schmidt and Liefooghe (2016) and Simulation 1 cycle times from the PEP. The grey bars indicate the true switch cost.
Figure 4
Cued task switching effects, including original error rates (with standard errors) from Schmidt and Liefooghe (2016) and Simulation 1 errors with the PEP. The grey bars indicate the true switch cost.
Figure 5
Simple switch cost contrast (bars) and unconfounded switch cost (line) in participant response times and PEP-simulated results.
Figure 6
Cue repetition benefit data, with participant response times and simulated results from the PEP.
Figure 7
Response repetition benefit data as a function of task repetition, with participant response times and simulated results from the PEP.
Figure 8
Task-rule congruency effect, including original response time and error data and simulated data from the PEP.
Figure 9
The Gilbert and Shallice (2002) model as it applies to cued task switching. All connections are hardwired. Notes: arrow = facilitative connection, circle = within-layer competition, grey arrow = all-to-all connections from stimuli to task demand nodes.
Figure 10
Simulations of cued task switching effects from the Gilbert and Shallice, CARIS, Altmann, and Schneider and Logan models. The grey bars indicate the true switch cost.
Figure 11
Simplified visual representation of the CARIS model as it applies to cued task switching. The inputs are filtered in favour of the current task (parity, white shading) and against the other task (dark shading). Actions are partially filtered on the basis of the prior task (in this example, magnitude) and also on the basis of whether the response on the prior trial was linked to the same or different action. The model contains a number of other parameters (not represented here) for fitting specific effects. Notes: brightness = filtering advantage for Actions and Inputs and activity level for Stimuli and Responses, arrow thickness = coactivation strength, dashed arrows = currently-irrelevant connections.
Figure 12
The Altmann model as it applies to cued task switching. Repeated features paired with another repeated feature count as –1 point, and repeated features paired with a non-repeated feature count as +1 point. There are no points for pairs of non-repeated features (e.g., the cue and task in this example). The example is of alt-RR trials, but each trial type is computed the same way.
Figure 13
The Schneider and Logan (2005, 2009, 2014) model as it applies to cued task switching. The cue is initially decoded and is influenced both by priming of repeated cues in short-term memory (STM) along with semantic connections between cues in long-term memory (LTM). The cue and stimulus then multiplicatively determine retrieval of a response.
Table 2
Original and newly-fitted parameters in sensitivity test with a priori range limits.
| Parameter | Original | New Fit | A Priori Range |
|---|---|---|---|
| change (toward stimulus signal) | .034 | .597 | 0–1 (proportion) |
| strength (target search) | 3.4 | .203 | 0–∞ |
| strength (cue search) | 1.14 | .311 | 0–∞ |
| noise (input) | .012 | .252 | 0–∞ |
| strength (decision search) | 3.2 | .405 | 0–∞ |
| noise (decision) | .25 | .323 | 0–∞ |
| preparation (base) | .4 | .282 | 0–∞ |
| strength (response search) | 3.5 | .389 | 0–∞ |
| noise (response) | .0026 | .357 | 0–∞ |
| decay (goal) | .007 | .229 | 0–1 (proportion) |
| threshold (goal) | .1 | .244 | 0–1 (activation range) |
| strength (goal) | 1.37 | .446 | 0–∞ |
| noise (goal) | .66 | .413 | 0–∞ |
| weight (goal-decision) | .88 | .289 | 0–∞ |
| decay (input/decision/response) | .086 | .397 | 0–1 (proportion) |
| decay (episode) | .0022 | .290 | 0–1 (proportion) |
| loss (episode connection) | .045 | .434 | 0–1 (proportion) |
| negative weights (episode) | –.11 | –.36 | –(0–∞) |
| restore (instruction episode) | .285 | .218 | 0–1 (plausible range) |
| search (goal, after goal selected) | .1 | .492 | 0–1 (proportion) |
| search (stimulus, after decision) | .95 | .252 | 0–1 (proportion) |
| search (response coding) | 4.0 | .365 | 0–∞ |
Figure 14
Parameter sensitivity test, with reverse-direction original response times (with standard errors) and Simulation 3 cycle times from the PEP. Notably, the PEP model is unable to fit reverse-direction effects.