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Complex Behaviors

Animal Models of Executive Function

By July 5, 2021July 23rd, 2021No Comments

The brain acts as a “black box” where sensory inputs are processed and transformed into action to survive and succeed. As the organism becomes more complex, there is an equal evolutionary demand to increase the repertoire and flexibility of behaviors to better take advantage of available resources.

Executive functions are a collection of cognitive processes that help us to find new ways to solve problems and hone our behaviors to adapt and optimize returns in an ever-changing environment.

Going beyond simple stimulus-response contingencies, some situations may require holding and using pieces of information to deal with an ongoing challenge (working memory),[3, 30] some require switching between multiple tasks (behavioral flexibility),[42] others would benefit from “holding your horses” to inhibit a prepotent action not fit for the current situation (impulse control),[22] and all these processes require an awareness of cues (attention[44]) to interpret the best course of action.

In essence, executive functions allow us to navigate through complex and ambiguous real-life situations where simple and habitual stimulus-response relationships are inadequate to maximize outcomes.

Biological Basis of Executive Function

The Frontal Cortex and Circuits

As with most allocated brain functions to structure, lesion studies have paved the way to suggest a central role for the frontal cortex to be the seat of higher cognitive functions in general, including executive functions.

While many lesion and imaging studies support a major contribution from various parts of the frontal cortex in working memory,[10, 55] behavioral flexibility,[13, 15] impulse control, and attention,[13, 61] it is clear that different aspects and definitions of executive function are mediated by distributed circuits.

The complication arises where many tests on executive function tap into more than one domain and may depend on the integrity of other cognitive (e.g. reward sensitivity) and perceptual (e.g. stimulus detection) processes, meaning it is difficult to experimentally isolate a single process. Certainly, existing studies suggest that not all frontal areas are involved in executive functions defined here, but many of the same structures appear to support multiple executive functions[17, 41, 48]  – the observation holds true down to single-cell representations in the frontal cortex.[51]

Relevance of Executive Functions in Disease Models

As described, executive functions are crucial for adapting our behaviors to changing environments. In many neurological and psychiatric disorders, brain pathology leads to direct, or maladaptive (indirect) behavioral control that has an enormous impact on the quality of life.

The most relevant brain disorders with executive function deficits are perhaps schizophrenia and attention deficit hyperactive disorder (ADHD). Both of these disorders have shown working memory,[47] attentional, impulse control,[57] and set-shifting[9, 59] deficits. Many behavioral tasks designed to probe executive functions in these disorders have been reverse-translated for testing rodents, mostly with good success in recapitulating different aspects of the deficit and treatments.

Neurodegenerative conditions such as Alzheimer’s[53, 54] and Parkinson’s disease[16, 43, 46] (AD and PD) also result in cognitive decline. Some of the deficits can be attributed to the loss of cholinergic[32, 36] and dopaminergic[34] tone[2] responsible for arousal and plasticity for AD and PD, respectively, but there are also treatment-related side-effects such as an increase in impulsivity in PD patients treated with deep brain stimulation.[27]

These neurodegenerative diseases are good examples of how system-level degeneration in the neuromodulatory systems and outside the frontal cortex can impact executive function.

Do Rodents Have the Right Stuff?

By combining executive function tests and preclinical models of brain disorders, we learn more about the brain through dysfunction and explore potential therapies to correct them. Despite good face, construct, and predictive validity of at least some tests of executive function in rodents relating to humans,[57] it cannot be denied that there is a clear lack of frontal cortical structural homology between rodents and humans (or even between humans and other primates).[6, 14, 60]

However, despite the controversy on the homology of rodent, primate, and human prefrontal cortex function and organisation,[31, 49, 60] and it is clear that frontal cortex damage does impact rodent executive function.[12, 25, 29, 50]

Therefore, the goal of using preclinical tests of executive function serves as a model analogous to the human functions, where parcellated understanding through rodent studies may provide insight and points of convergence to the target human processes.

Testing Executive Function in Rodents

Working Memory

Memory is a crucial component of any cognitive process, as it provides context from our experiences to guide behavior. Working memory reflects our ability to maintain and manipulate our most recent experiences to support “online” processing to solve tasks at hand.

The most used tasks in preclinical working memory research depend on the use of time delay between cue presentation and responding as a measure of the ability to hold and use task-relevant information.

Spatial working memory

One of the earliest developed working memory tests for rodents takes advantage of natural foraging behavior in rats through reward baiting at multiple spatial locations. The most well-known is the radial arm maze.

The most common implementation of this paradigm is the use of an eight-arm radial maze[45] where all the arms are equally distributed around a central area and extended at the same length. Rewards are placed in half (four) of the arms pseudo-randomly and the rodents are forced (by blocking the unbaited arms) to visit the baited arms for the rewards.

After consuming all rewards, a delay is applied (usually in the central area, but some remove animals to a holding cage) and the animal is allowed back to forage again, but this time the rewards are only available at the ends of the previously unbaited arms and that they have free access to all the arms.

The best strategy would be to avoid the cost in time and effort associated with visiting non-rewarded arms. The number of times the animal visits non-rewarded arms after the delay is taken as an error – the number of errors is used as a measure of working memory since it is assumed that the animals need to keep track of which spatial locations have been visited.

A variation of the task is baiting some of the arms in the maze and allow free foraging over a longer period of time. In this implementation, animals have to actively remember which arms they have already visited (not baited or rewards already consumed) and which arms may contain rewards (unvisited, but potentially baited arms).

The behavioral measure is the same – the number of times revisiting a previously visited arm is counted as an error and used to assess working memory.

Delayed alternation

Essentially an abbreviated version of the radial arm maze, delayed alternation takes advantage of the rodents’ natural tendency to spatially alternate between two choices. This task is usually implemented in a “T” or “Y” shaped maze, where one (“bottom”) end of the maze serves as the starting point, where the animals travel down the central stem and make a binary choice between going left or right.

Similar to the radial arm maze, the left, and right arms are alternately baited so that the animals would be rewarded by alternating their choices. Once the reward has been consumed, the animal is returned to the “bottom” end of the maze with access to the rest of the apparatus blocked off, or a holding cage for the delay period.

To reduce the effects of repeated handling and to support the use of tethered animals (e.g. for microdialysis, recording, or stimulation), some use a modified version of the T-maze, termed figure-of-eight maze, to loop the path to the reward arm back to the initiation/start area.

Lastly, it is also possible to implement this paradigm through operant conditioning, where two choices/cues are given and the animals need to sequentially alternate their choices to receive rewards. Again, the duration of the delay implemented between trials determines the working memory load.

(Non)matching-to-sample

Since rodents naturally alternate, a more challenging variant of the delayed alternation where the correct response is dependent on the stimulus-response contingency that has to be learned. In delayed matching-to-sample (DMTS),[18] animals are exposed to a sample phase where a cue is presented.

After a period of delay, two cues are presented and the correct choice is to respond to the cue matching to the sample phase. The opposite contingency, where animals learn to respond to the cue not presented in the sample phase (delayed non-matching to sample; DNMTS) is equally as valid and widely used.[29] Both of these paradigms can be implemented spatially in the TY– or figure-of-eight maze, as well as in operant chambers.[29]

There are also touchscreen variants of the task, which offer the opportunity to tap into spatial working memory without whole-body movement. It is also possible to implement DMTS in other types of testing apparatus/arenas.

For example, there are versions of radial arm water maze and Morris water maze-based paradigms that use escape from water as a motivator (as opposed to appetitive rewards). In the radial arm water maze, animals search for an arm containing an escape platform (as in the Morris water maze) and revisit the arm in the next trial for expedited escape (essentially matching-to-sample, see below) – the Morris water maze version is similar, without the physical limitations imposed by the walls of the arms in the radial maze.

Mental or Behavioral Flexibility

When faced with a constantly changing world, it is crucial to be able to switch and adapt behaviors suited to the given context. Mental flexibility has historically been examined with the Wisconsin card sort test,[40] where cards with drawings with set variations in features (e.g. shape and color) are presented to the test subject, and rules are learned on the fly (i.e. without being disclosed by the experimenter) to sort the cards according to a shape or color, which can be changed within a session.

This has been extended in an intradimensional-extradimensional task (ID-ED), where pairs of stimuli with multiple features are presented, and the test subject needs to learn the correct response based on one of the features, which changes over the course of the session.

In this context, the rule change can be intradimensional (e.g. blue to red) or extradimensional (e.g. square to circle), as well as being reversed (e.g. square to circle, then back to square). These tests are also commonly referred to as “set-shifting”, which requires the test subject to learn different stimulus-response-outcome associations under otherwise identical spatial context and task demands and being able to dynamically switch between the “rules” of the task.

Set-shifting

Complex visual stimuli are the most often used in human/primate behavioral tasks, as in the Wisconsin card sort test. Since rodents have relatively poorer vision, an adaptation of the human task involves more ethologically relevant stimuli and behavior – namely olfaction/somatosensation and digging, respectively.

In this paradigm,[5] animals learn to associate reward in a filled bowl according to the texture of the bowl, texture of the filling material, or its odor. A pair of bowls are presented in a trial, and the animals are required to make the correct discrimination of the bowls based on bowl texture, odor, or bowl filler presented to receive the food reward (i.e. only one bowl is buried with food reward).

The number of trials required to learn the feature associated with reward is usually taken as the behavioral measure of mental flexibility. This test is homologous to the primate version of the ID-ED task and allows the full examination of intra- and extra-dimensional shifting, as well as reversal learning (see below).

Variants of this task can be implemented in more conventional or modified mazes, for example, where animals learn to switch rules between “always turn right” to “always head towards the light” in a plus/radial arm maze setting.[24, 37, 50] Texture and ambient light level discriminations[58] have also been used.

Reversal learning

As covered above, tasks with stimuli varying in a combination of features offer an excellent way to test behavioral flexibility hinging on stimulus-response-outcome remapping. Reversal learning can be implemented in such tasks, but there are other tasks specifically test reversal learning independent of set-shifting.

For example, in the probability reinforcement learning task, rats are initially trained to make a response to two operanda upon cue presentation. Once operantly conditioned, the reward probability for one response will be set higher (e.g. yielding reward at 80% of the time) and the other one lower (at 20%).

The animal has to learn which response is the most profitable and once 10 consecutive responses have been made on the high reward probability operandum, the response-reward contingency reverses; that is, the reward probability for responding to the operanda is switched.

The key measured variable is the number of trials the animals take to reach the reversal point, which can be used to assess behavioral flexibility and reward processing.

Impulse Control

Whether it is slamming on the breaks at a yellow light, or not to take one more cookie from the jar, there is a constant need for changing, slowing, or completely stopping prepotent motor actions in our lives.

Central to impulse, or inhibitory, control deficits can be further defined as impulsive action, impulsive choice, and compulsive actions. Behavioral tests used in preclinical research are largely faithful reverse-translations of human tasks, which offer excellent validity and translational value.

Go/NoGo and stop-signal tasks

Go/NoGo[35] task involves the operant conditioning of chaining two responses for a reward. Initially, animals are shaped to respond to one operandum for a reward and then shaped to respond to two operanda in sequence to receive a reward. The chaining of the two responses in the sequence is crucial for the establishment of a habitual, prepotent motor sequence.

Later, a “NoGo” stimulus is introduced to be triggered at a certain latency from the first response in the chain, and the correct behavior is to suppress the second response in the chain to obtain a reward.

The number of incorrect “NoGo” trials is taken as a measure of impulsive action. As an extension of the Go/NoGO task, the stop-signal reaction task (SSRT)[19, 20, 35] involves the variation in the latency of stop/NoGo cue presentation, where suppressing a response is more difficult as the stop cue is presented at longer latencies relative to the first response.

Since the variation in latency of the stop cue presentation is designed to elicit unsuccessful stopping, the “stop” trials are usually more sparse (~20%) to maintain compliance (i.e. animals may slow their chained response as an adaptation if there are too many incorrect stop trials).

Delay discounting task

Delay discounting task (DDT[7, 64]) is one of many discounting (e.g. probability discounting, effort discounting) tasks that presents two choices with different reward value/sizes. In DDT, the animals are trained to respond to two operanda for rewards.

Once shaped, one operandum will immediately yield a single reward (low delay, low reward) but the other will yield more rewards, albeit delivered at a delay after the response has been made. The tendency for animals to choose the more immediate, smaller rewards over the delayed, larger rewards is termed impulsive choice.

More specifically, the impulsive choice is measured by varying the delay period for the larger reward to construct a function describing the relationship between reward value and time (hyperbolic discounting[38]), and the steepness of the slope of the function estimates impulsivity.

Attention

Attention is an umbrella term that encompasses several different processes. For example, there are instances where one needs to detect and process a single relevant cue (selective attention) or many concurrently presented cues (divided attention) over a long period of time (sustained attention) – sometimes only picking out a rare change in cue feature (vigilance).

Given its crucial role in stimulus detection, it is an executive function that is difficult to isolate from other ongoing cognitive processes from behavioral measures alone.

Oddball paradigm

The oddball paradigm has widely been used as a stimulus detection task to examine higher-order functions such as error monitoring. A testing session typically contains hundreds of trials (i.e. stimulus presentations), where a “common” tone is presented at regular intervals but is sparsely replaced by the target (“rare”) tone differing in frequency in a sound-attenuated box.

In the active version of the task, appetitive operant conditioning can be used to shape a motor response (e.g. lever press) to signal the detection of the “rare” stimulus.[1] Otherwise, in the passive version of the task where a response is not required from the animal, neurophysiological recordings are needed to verify the detection of the “rare” stimulus.

The disadvantage of the passive version lies with the need to implement some form of real-time brain activity readout but circumvents behavioral variability and motivational factors associated with conditioned response.

By measuring changes in brain activities or behavioral output, the oddball paradigm can access vigilance needed to identify the target cue in a background of irrelevant ones and sustained attention by manipulating the number of trials (or inter-stimulus interval to increase the duration of a session).

Prepulse inhibition

As with the oddball paradigm, prepulse inhibition (PPI) taps into the domain of involuntary attention since modulation of attentional processes modifies PPI outcome.[11, 23] The task revolves around the presentation of stimuli (20-40 ms “pulses”, usually auditory) to induce a startle response.

Using the conventional case of auditory PPI as an example, the prepulse is presented at a reduced intensity and variable lead time compared to that of the startle pulse. Effective PPI is determined by calculating the reduction of the startle response induced by the startle pulse alone.

The intensity of the startle response can be measured by movement sensors (e.g. accelerometers and gyroscopes) or physiological measures (e.g. muscle activity) to quantify the startle response.

PPI usually requires specialized apparatus to implement, with the most important features being the capability to deliver pure tones in a sound-isolated environment (stimulus pulse/prepulse delivery) and a means to record the startle reflex induced by intense stimuli. As in the oddball paradigm, sustained attention and vigilance can be inferred from the changes in PPI over time.

Five-choice serial reaction time task

The 5-CSSRT[52] is perhaps the most used preclinical behavioral task for testing attention, owing to its introduction as a reverse-translated test from the human version – the continuous performance test (CPT).[4]

The simplest implementation of CPT involves the continuous presentation of (visual) stimuli with an infrequent “target” stimulus in a backdrop of more frequent “distractor” stimuli. Responding to the “target” (hits) is rewarded, whereas withholding response to a “distractor” (correct rejections), responding to “distractors” (false alarms), or not responding to the “target” (misses/errors of omission) is not rewarded. The rodent equivalent is the 5-CSRRT[8] was translated from a human equivalent.[33]

In the 5-CSRT, rodents perform a matching-to-sample task where they are required to respond to one of the 5 stimulus lights on different trials. Usually, the operant chamber is modified so that the distance from the food port to each of these five lights would be the same. The trial starts with a ~100 ms flash of the light stimulus in one of the five locations and the animal is required to respond at the location where the light stimulus was presented.

Similar to the CPT, hits, misses and false alarms are used to describe the stimulus-response-outcome relationship (note due to the nature of the implementation, correct rejection and hits are synonymous in 5-CSSRT), and the incorrect responses (i.e. misses and false alarms) are followed by a timeout (5 s) period.

After responding, rodents would usually check the food port for a reward, which initiates the next trial with a variable delay (3-5 s). A session is consisted of >100 trials and is suited to probe sustained and (spatially) divided attention due to the spatial separation of cue locations. In variations where an auditory distractor stimulus is presented concurrently with the target visual stimulus, selective attention can be assessed with 5-CSSRT.

To better match human CPT, another possible variation is to add an additional stimulus-response-outcome relationship, where all five light cues are lit and the correct response is to withhold. This implementation can offer the examination of correct rejections, and also shed light on impulsivity.

In addition to attention, 5-CSSRT can also be used to investigate impulsivity and compulsivity, which can be quantified by the number of premature responses and unnecessary repetitive responding, respectively.

Cross-modality discrimination task

Perhaps owing to the need to train animals on two sets of stimulus-response contingencies across two different sensory modalities, cross-modality discrimination tasks[39, 63] are not commonly used despite being an excellent tool to examine divided attention. The task consists of independent operant conditioning of discriminating pairs of visual (light) and auditory (tone frequency) stimuli by responding to different operandi.

Once both sets of discrimination are learned, trials of visual or auditory discrimination are interleaved in a session to force animals to dynamically switch between using visual or auditory cues. Latency and accuracy of the responses are used as a measure of divided attention.

Variations of the task exist[63] where the trial type (i.e. responding to visual or auditory stimuli) is signaled through background noise at the start of the trial and both visual and auditory stimuli are presented as the target/distractor, depending on the trial identity. This implementation requires more training time but offers an opportunity to probe divided attention with distractors.

Notes on Implementation

Not all rodents are created equal – but close enough

Historically it has been assumed that rats are better at learning and performing cognitive tasks than mice with more complex behaviours.[21, 62] Mice are smaller in size (for housing and apparatus/space considerations) and have a larger transgenic model repertoire than rats. Rats are easier to habituate to the experimenter and the testing environment and that most of the rodent behavioral tests were originally designed for testing rats.

However, although there is evidence that rats may be more cognitively able than mice,[21, 56, 62] it appears the difference is negligible, at least in some tests.[28] Given most of the rodent behavioral tests were originally designed to test rats, care should be taken to fully adapt, validate and optimize the same tests for mice.

In a similar vein, pigmented animals are preferred for behavioral tests involving cognition. There is evidence to support the use of pigmented rodents over albino strains, but depending on the model required, the performance and general behavior variability are more similar than different between strains, so the choice should be based on the requirements of the study (e.g. availability of transgenic lines).

In the past, the majority of scientific research has been done on male animals. While there are clear developmental, social behavior, and motivational differences, male and female executive functions are more similar than different.[26]

Conclusion

Executive function is an umbrella term for many different higher cognitive processes we depend on to choose how to act in the face of a changing environment. While rodent studies have been fruitful in shedding light on physiological principles (i.e. neuron activities and neuromodulator action) of executive function and their deficits in diseased states, these are considered more of a “model” of executive function given the lack of structural homology between the human and rodent brains – especially the frontal cortex.

Regardless, studies of executive function in any species suffer from the same problem in the lack of clear definition rooted in biology, and behavioral tests that can adequately isolate one conceptually defined function from others.

Therefore, the use of a battery of tasks is perhaps the best way forward, where convergence in behavioral measures in the same domain (e.g. attention) from different tasks provides more confidence in the validity of experimental findings.

Given most of the rodent tasks of executive function are, or can be, implemented in an operant chamber and some testing arenas can be readily modified to support more than one test (e.g. the radial arm maze), intersectional study design on executive function can be carried out in an efficient and cost-effective manner.

References

  1. Ahnaou, A, Biermans, R, & Drinkenburg, W, Cholinergic Mechanisms of Target Oddball Stimuli Detection: The Late “P300-Like” Event-Related Potential in Rats. Neural Plast, 2018. 2018: 4270263.
  2. Arnsten, AF, Catecholamine mechanisms in age-related cognitive decline. Neurobiol Aging, 1993. 14: 639-41.
  3. Baddeley, A, Working memory, thought, and action. Vol. 45. 2007: OuP Oxford.
  4. Beck, LH, Bransome, ED, Jr., Mirsky, AF, Rosvold, HE, & Sarason, I, A continuous performance test of brain damage. J Consult Psychol, 1956. 20: 343-50.
  5. Birrell, JM & Brown, VJ, Medial frontal cortex mediates perceptual attentional set shifting in the rat. J Neurosci, 2000. 20: 4320-4.
  6. Brown, VJ & Bowman, EM, Rodent models of prefrontal cortical function. Trends Neurosci, 2002. 25: 340-3.
  7. Cardinal, RN, Pennicott, DR, Sugathapala, CL, Robbins, TW, & Everitt, BJ, Impulsive choice induced in rats by lesions of the nucleus accumbens core. Science, 2001. 292: 2499-501.
  8. Carli, M, Robbins, TW, Evenden, JL, & Everitt, BJ, Effects of lesions to ascending noradrenergic neurones on performance of a 5-choice serial reaction task in rats; implications for theories of dorsal noradrenergic bundle function based on selective attention and arousal. Behav Brain Res, 1983. 9: 361-80.
  9. Ceaser, AE, Goldberg, TE, Egan, MF, McMahon, RP, Weinberger, DR, & Gold, JM, Set-shifting ability and schizophrenia: a marker of clinical illness or an intermediate phenotype? Biol Psychiatry, 2008. 64: 782-8.
  10. D’Esposito, M, Aguirre, GK, Zarahn, E, Ballard, D, Shin, RK, & Lease, J, Functional MRI studies of spatial and nonspatial working memory. Brain Res Cogn Brain Res, 1998. 7: 1-13.
  11. Dawson, ME, Hazlett, EA, Filion, DL, Nuechterlein, KH, & Schell, AM, Attention and schizophrenia: Impaired modulation of the startle reflex. Journal of Abnormal Psychology, 1993. 102: 633-41.
  12. de Bruin, JP, Sanchez-Santed, F, Heinsbroek, RP, Donker, A, & Postmes, P, A behavioural analysis of rats with damage to the medial prefrontal cortex using the Morris water maze: evidence for behavioural flexibility, but not for impaired spatial navigation. Brain Res, 1994. 652: 323-33.
  13. Dias, R, Robbins, TW, & Roberts, AC, Dissociation in prefrontal cortex of affective and attentional shifts. Nature, 1996. 380: 69-72.
  14. Donahue, CJ, Glasser, MF, Preuss, TM, Rilling, JK, & Van Essen, DC, Quantitative assessment of prefrontal cortex in humans relative to nonhuman primates. Proc Natl Acad Sci U S A, 2018. 115: E5183-E92.
  15. Dove, A, Pollmann, S, Schubert, T, Wiggins, CJ, & von Cramon, DY, Prefrontal cortex activation in task switching: an event-related fMRI study. Brain Res Cogn Brain Res, 2000. 9: 103-9.
  16. Downes, JJ, Roberts, AC, Sahakian, BJ, Evenden, JL, Morris, RG, & Robbins, TW, Impaired extra-dimensional shift performance in medicated and unmedicated Parkinson’s disease: evidence for a specific attentional dysfunction. Neuropsychologia, 1989. 27: 1329-43.
  17. Duncan, J & Owen, AM, Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci, 2000. 23: 475-83.
  18. Dunnett, SB, Comparative effects of cholinergic drugs and lesions of nucleus basalis or fimbria-fornix on delayed matching in rats. Psychopharmacology (Berl), 1985. 87: 357-63.
  19. Eagle, DM, Baunez, C, Hutcheson, DM, Lehmann, O, Shah, AP, & Robbins, TW, Stop-signal reaction-time task performance: role of prefrontal cortex and subthalamic nucleus. Cereb Cortex, 2008. 18: 178-88.
  20. Eagle, DM & Robbins, TW, Inhibitory control in rats performing a stop-signal reaction-time task: effects of lesions of the medial striatum and d-amphetamine. Behav Neurosci, 2003. 117: 1302-17.
  21. Ellenbroek, B & Youn, J, Rodent models in neuroscience research: is it a rat race? Dis Model Mech, 2016. 9: 1079-87.
  22. Evenden, JL, Varieties of impulsivity. Psychopharmacology (Berl), 1999. 146: 348-61.
  23. Filion, DL, Dawson, ME, & Schell, AM, Modification of the acoustic startle-reflex eyeblink: a tool for investigating early and late attentional processes. Biol Psychol, 1993. 35: 185-200.
  24. Floresco, SB, Magyar, O, Ghods-Sharifi, S, Vexelman, C, & Tse, MT, Multiple dopamine receptor subtypes in the medial prefrontal cortex of the rat regulate set-shifting. Neuropsychopharmacology, 2006. 31: 297-309.
  25. Granon, S, Hardouin, J, Courtier, A, & Poucet, B, Evidence for the involvement of the rat prefrontal cortex in sustained attention. Q J Exp Psychol B, 1998. 51: 219-33.
  26. Grissom, NM & Reyes, TM, Let’s call the whole thing off: evaluating gender and sex differences in executive function. Neuropsychopharmacology, 2019. 44: 86-96.
  27. Halbig, TD, Tse, W, Frisina, PG, Baker, BR, Hollander, E, Shapiro, H, Tagliati, M, Koller, WC, & Olanow, CW, Subthalamic deep brain stimulation and impulse control in Parkinson’s disease. Eur J Neurol, 2009. 16: 493-7.
  28. Jaramillo, S & Zador, AM, Mice and rats achieve similar levels of performance in an adaptive decision-making task. Front Syst Neurosci, 2014. 8: 173.
  29. Joel, D, Weiner, I, & Feldon, J, Electrolytic lesions of the medial prefrontal cortex in rats disrupt performance on an analog of the Wisconsin Card Sorting Test, but do not disrupt latent inhibition: implications for animal models of schizophrenia. Behav Brain Res, 1997. 85: 187-201.
  30. Jonides, J & Smith, EE, The architecture of working memory. 1997.
  31. Kesner, RP, Subregional analysis of mnemonic functions of the prefrontal cortex in the rat. Psychobiology, 2000. 28: 219-28.
  32. Lawrence, AD & Sahakian, BJ, Alzheimer disease, attention, and the cholinergic system. Alzheimer Dis Assoc Disord, 1995. 9 Suppl 2: 43-9.
  33. Leonard, J, Five choice serial reaction apparatus. Med. Res. Coun. Appl. Psychol. Res. Unit, 1959. 326.
  34. Leroi, I, Barraclough, M, McKie, S, Hinvest, N, Evans, J, Elliott, R, & McDonald, K, Dopaminergic influences on executive function and impulsive behaviour in impulse control disorders in Parkinson’s disease. Journal of neuropsychology, 2013. 7: 306-25.
  35. Leventhal, DK, Gage, GJ, Schmidt, R, Pettibone, JR, Case, AC, & Berke, JD, Basal Ganglia Beta oscillations accompany cue utilization. Neuron, 2012. 73: 523-36.
  36. Lim, YY, Maruff, P, Schindler, R, Ott, BR, Salloway, S, Yoo, DC, Noto, RB, Santos, CY, & Snyder, PJ, Disruption of cholinergic neurotransmission exacerbates Aβ-related cognitive impairment in preclinical Alzheimer’s disease. Neurobiology of aging, 2015. 36: 2709-15.
  37. Malagon-Vina, H, Ciocchi, S, Passecker, J, Dorffner, G, & Klausberger, T, Fluid network dynamics in the prefrontal cortex during multiple strategy switching. Nat Commun, 2018. 9: 309.
  38. Mazur, JE, Choice, delay, probability, and conditioned reinforcement. Animal Learning & Behavior, 1997. 25: 131-47.
  39. McGaughy, J, Turchi, J, & Sarter, M, Crossmodal divided attention in rats: effects of chlordiazepoxide and scopolamine. Psychopharmacology (Berl), 1994. 115: 213-20.
  40. Milner, B, Effects of Different Brain Lesions on Card Sorting: The Role of the Frontal Lobes. Archives of Neurology, 1963. 9: 90-100.
  41. Mishkin, M & Manning, FJ, Non-spatial memory after selective prefrontal lesions in monkeys. Brain Res, 1978. 143: 313-23.
  42. Monsell, S, Task switching. Trends Cogn Sci, 2003. 7: 134-40.
  43. Muller, U, Wachter, T, Barthel, H, Reuter, M, & von Cramon, DY, Striatal [123I]beta-CIT SPECT and prefrontal cognitive functions in Parkinson’s disease. J Neural Transm (Vienna), 2000. 107: 303-19.
  44. Norman, D & Shallice, T, Attention to action: Willed and automatic control of behavior. 1986.
  45. Olton, DS & Papas, BC, Spatial memory and hippocampal function. Neuropsychologia, 1979. 17: 669-82.
  46. Owen, AM, James, M, Leigh, PN, Summers, BA, Marsden, CD, Quinn, NP, Lange, KW, & Robbins, TW, Fronto-striatal cognitive deficits at different stages of Parkinson’s disease. Brain, 1992. 115 ( Pt 6): 1727-51.
  47. Park, S & Holzman, PS, Schizophrenics show spatial working memory deficits. Arch Gen Psychiatry, 1992. 49: 975-82.
  48. Passingham, R, Delayed matching after selective prefrontal lesions in monkeys (Macaca mulatta). Brain Res, 1975. 92: 89-102.
  49. Preuss, TM, Do rats have prefrontal cortex? The rose-woolsey-akert program reconsidered. J Cogn Neurosci, 1995. 7: 1-24.
  50. Ragozzino, ME, Detrick, S, & Kesner, RP, Involvement of the prelimbic-infralimbic areas of the rodent prefrontal cortex in behavioral flexibility for place and response learning. J Neurosci, 1999. 19: 4585-94.
  51. Rao, SC, Rainer, G, & Miller, EK, Integration of what and where in the primate prefrontal cortex. Science, 1997. 276: 821-4.
  52. Robbins, TW, The 5-choice serial reaction time task: behavioural pharmacology and functional neurochemistry. Psychopharmacology (Berl), 2002. 163: 362-80.
  53. Sahgal, A, Galloway, PH, McKeith, IG, Lloyd, S, Cook, JH, Ferrier, IN, & Edwardson, JA, Matching-to-sample deficits in patients with senile dementias of the Alzheimer and Lewy body types. Arch Neurol, 1992. 49: 1043-6.
  54. Schroeter, ML, Vogt, B, Frisch, S, Becker, G, Barthel, H, Mueller, K, Villringer, A, & Sabri, O, Executive deficits are related to the inferior frontal junction in early dementia. Brain, 2012. 135: 201-15.
  55. Smith, EE, Jonides, J, Koeppe, RA, Awh, E, Schumacher, EH, & Minoshima, S, Spatial versus Object Working Memory: PET Investigations. J Cogn Neurosci, 1995. 7: 337-56.
  56. Snyder, JS, Choe, JS, Clifford, MA, Jeurling, SI, Hurley, P, Brown, A, Kamhi, JF, & Cameron, HA, Adult-born hippocampal neurons are more numerous, faster maturing, and more involved in behavior in rats than in mice. J Neurosci, 2009. 29: 14484-95.
  57. Solanto, MV, Abikoff, H, Sonuga-Barke, E, Schachar, R, Logan, GD, Wigal, T, Hechtman, L, Hinshaw, S, & Turkel, E, The ecological validity of delay aversion and response inhibition as measures of impulsivity in AD/HD: a supplement to the NIMH multimodal treatment study of AD/HD. J Abnorm Child Psychol, 2001. 29: 215-28.
  58. Stefani, MR & Moghaddam, B, Rule learning and reward contingency are associated with dissociable patterns of dopamine activation in the rat prefrontal cortex, nucleus accumbens, and dorsal striatum. J Neurosci, 2006. 26: 8810-8.
  59. Turner, DC, Clark, L, Pomarol-Clotet, E, McKenna, P, Robbins, TW, & Sahakian, BJ, Modafinil improves cognition and attentional set shifting in patients with chronic schizophrenia. Neuropsychopharmacology, 2004. 29: 1363-73.
  60. Uylings, HB & van Eden, CG, Qualitative and quantitative comparison of the prefrontal cortex in rat and in primates, including humans. Prog Brain Res, 1990. 85: 31-62.
  61. Vandenberghe, R, Duncan, J, Dupont, P, Ward, R, Poline, JB, Bormans, G, Michiels, J, Mortelmans, L, & Orban, GA, Attention to one or two features in left or right visual field: a positron emission tomography study. J Neurosci, 1997. 17: 3739-50.
  62. Whishaw, IQ, Metz, GA, Kolb, B, & Pellis, SM, Accelerated nervous system development contributes to behavioral efficiency in the laboratory mouse: a behavioral review and theoretical proposal. Dev Psychobiol, 2001. 39: 151-70.
  63. Wimmer, RD, Schmitt, LI, Davidson, TJ, Nakajima, M, Deisseroth, K, & Halassa, MM, Thalamic control of sensory selection in divided attention. Nature, 2015. 526: 705-9.
  64. Winstanley, CA, Theobald, DE, Cardinal, RN, & Robbins, TW, Contrasting roles of basolateral amygdala and orbitofrontal cortex in impulsive choice. J Neurosci, 2004. 24: 4718-22.
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MazeEngineers makes behavioral mazes for all species with high precision and accuracy. Each maze is hand made for exacting specifications, with automation, AI integration and open software integration. We’re here to build the world’s best behavioral library, we’d love to help you with your experiments. Send us questions and we’ll answer!
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MazeEngineers makes behavioral mazes for all species with high precision and accuracy. Each maze is hand made for exacting specifications, with automation, AI integration and open software integration. We’re here to build the world’s best behavioral library, we’d love to help you with your experiments. Send us questions and we’ll answer!
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