What type of reasoning is the process of reaching logical conclusions by employing logical reasoning?

Logical (Deductive) Reasoning

Logical or deductive reasoning involves using a given set of facts or data to deduce other facts by reasoning logically. It involves drawing specific conclusions based on premises. Transitive inference, or linear syllogistic reasoning, is one of the simplest forms of logical reasoning, and involves combining two premises R(a,b) and R(b,c) to draw the conclusion that R(a,c). Transitive inference is often used to examine young children's abilities to use previous knowledge and basic logic to determine a missing piece of information. Transitive inference is evident if a child can infer that if John is taller than Mary, and Mary is taller than Sue, then John is taller than Sue, even if information about the relative height of Sue and John is not available.

Preschoolers engage in transitive inference, as evident in Bryant and Trabasso's study (1971) that showed that children as young as 4 years of age could solve transitive tasks with minimal memory demands. Similarly, Halford (1993) showed that reducing the information processing load by utilizing tasks in everyday contexts and with familiar objects, and by reducing the relational complexity of the tasks (e.g., requiring 1 instead of 2 premise relations), facilitated young children's transitive inference. These findings confirmed that information processing load is a factor that influences young children's deductive reasoning. Studies have also demonstrated the role of mapping between relations in preschoolers' transitive inference. Four- and 5-year-olds exhibited the capacity to make transitive reasoning when inferences were made by mapping relations between analogous tasks (blocks and sticks). The role of analogy in logical reasoning was further examined in a study by Goswami (1995). Preschoolers were found to be capable of solving a transitive inference task by analogizing from the story of Goldilocks and the Three Bears. The findings that exposure to analogical tasks improved young children's ability to make transitive inferences further demonstrated that the core deductive reasoning competence is in place in early life. These researchers further confirmed that young children engage in analogical mapping in order to solve various problems.

A recent study (Gazes et al., 2015), used visual scenarios with puppet characters and a looking-time method to create events that were either consistent with or violated the expected transitive-inference relationship. Ten- and 13-month-olds saw a video with two dominance interactions between three puppets (bear > elephant; hippo > bear) in a social hierarchy: first the elephant was seen holding a toy, the bear reached over and forcibly took the toy from the elephant, and then the hippopotamus took the toy from the bear. The infants were then presented with different scenarios that either violated the expected transitive-inference relationship (e.g., the elephant took the toy from the hippo), or that were not inconsistent with the transitive inference. Infants looked longer at displays that violated the transitive inference than at other scenarios. The researchers interpreted the pattern of looking longer to suggest that 1-year-olds were engaging in transitive inference when they viewed scenarios of unexpected behavior by the puppets, compared to other displays. Infants as young as 10 months of age, therefore, showed the rudimentary ability to make transitive inferences about which character should dominate another character, even when their interaction was not directly observed.

Another form of logical reasoning involves conditional reasoning, which involves drawing a conclusion based on a conditional, or “if … then,” proposition. This has been tested in studies of young children's logical inference. Harris and Núñez (1996), for example, examined young children's ability to engage in conditional reasoning by presenting a simplified, child-appropriate version of the four-card selection task. The child was given a conditional sentence such as “If Julie goes out (p), she should put her coat on” (q),” and was shown four pictures: “a child in a house not wearing a coat (not-p, not-q),” “a child in a house wearing a coat (not-p, q),” “a child outside a house not wearing a coat (p, not-q)” and “a child outside a house wearing a coat (p, q)”. The child was then asked to select the picture that violated the conditional sentence (p, not-q) from these cards. Three- and 4-year-olds were capable of identifying the protagonist as being naughty in the picture that showed the condition not being met and displayed an understanding of actions that would breach a permission or set of rules.

Children's logical thinking continues to develop through elementary school. Pillow et al. (2000) examined preschool and elementary school children's ability to identify deductive inference or guessing as a source of a belief. Children were presented with an event that involved a puppet and two different-colored objects. The objects were then hidden in two separate boxes. The child and the puppet could not see the objects. The puppet then made a statement about the color of one of the hidden objects after looking directly at the object (the direct looking condition), looking at the other object (the inference condition), or looking at neither object (the guessing condition). Four- and 5-year-olds failed to distinguish inference from guessing, while 8- and 9-year-olds referred to the premises of deductive inference in their explanations of the puppet's knowledge. These findings showed the continued progress of children's explicit deductive reasoning ability from infancy and toddlerhood to preschool and elementary school ages, in response to increasingly complex reasoning tasks.

2 Where are the Rules?

These findings from cognitive research in three areas of logical reasoning show that people are far more rational than is often assumed. Our inability to solve some impoverished symbolic problems does not mean that we are not rational. It does mean that if there is a logic within us—and we must be following some set of rules or procedures to allow us to be rational under normal circumstances—it is probably not the standard logic we are taught in schools. The logic of the head need not be the same as the philosopher's logic because human inference occurs in rich and varied contexts, with cognitive and social constraints operating all around us and directing our attention to critical assertions and providing us with just the right inference procedures to evaluate them. These pragmatic influences may at first sight seem impurities in the crystal clarity of inference, but we must be mindful that even logic rules can propagate errors. Rationality and the possession of general reasoning rules do not assure us that the premises are not false, or that situations are not misinterpreted, or that cognitive load will not contribute to erroneous processing. While natural logic may be the basis for rational judgments (Rips 1994, Braine and O'Brien 1998), this does not mean we cannot make errors and do irrational things. An error in the input can be propagated promiscuously throughout our knowledge. Without some pragmatic knowledge to direct our processing or the identification of degrees of truthfulness of propositions, our knowledge base would be completely suspect. To provide a kind of cognitive firewall (Cosmides and Tooby 2000), our inference system relies on the myriad of special representations of information that codes those things that are conjectures, possible truths, fantasies, and what other minds might know and how they may seek to influence us. These not only comprise the fabric of human inference, they are also domains of investigation of modern cognitive psychology. To understand human logical thought, we must also understand our system of knowledge and how it is able to represent the world as it is and as we imagine it to be.

(iv) Problem solving and executive functions

A complex domain of cognitive functioning involves problem solving and logical reasoning, including inductive as well as deductive components. Problem solving encompasses all behaviors executed when facing old problems that we have learned how to solve, as well as novel problems requiring reorganization of already established modes of thinking and acting. Problem solving includes concrete as well as abstract thinking, and therefore constitutes a broad category of intelligent behavior.

Various tests of reasoning and problem solving are often included as subtests in comprehensive test batteries. For example, in Schaie's longitudinal Seattle study (Schaie, 1995), estimates of age changes in reasoning show a general decline with age. A more marked decline was found for those in their late sixties. This pattern resembles that of other abilities, suggesting that this aspect of problem solving is equally prone to aging as other cognitive abilities. In the Swedish H70 study, inductive reasoning remained remarkably stable across times of measurement in individuals with longer survival. That is, when retrospectively excluding those individuals who died after a certain occasion in the series of measurements, those who survived to the next occasion in the series showed an almost stable performance in reasoning, an ability often considered sensitive to age. This finding suggests that decline is more closely associated with subsequent mortality, rather than with age per se (see Berg, 1996, and Figure 5).

A well-known reasoning test is that of Raven's Progressive Matrices (Raven, 1982), including the original and the coloured matrices versions. Subjects are instructed to find a principle among certain patterns and to choose a pattern that corresponds to this principle in identifying a missing pattern. Significant age differences suggest lower performance with age in the original version of the test (see Lezak, 1983, 1995; Salthouse, 1983). This finding was also confirmed in a study by Denney and Heidrich (1990), who interestingly employed a simple training strategy before administering the test. Following training, equal improvements were found for young, middle-aged, and elderly individuals.

Another aspect of intelligent behavior is that of executive functions which refers to abilities that successfully enable a person to engage in “ independent, purposive, self-serving behavior” (Lezak, 1983, p. 38). Questions about whether people will execute a certain behavior and if so, how they are doing it, capture the dimension of executive functioning. Lezak (1983) articulated four components of executive activity-related behaviors: goal formulation, planning, carrying out goal-directed plans, and effective performance. Given these components the question arises regarding how to evaluate the aspects of “ whether” and “ how.” The interest in executive functions emerged in neuropsychology and is largely associated with interest in cerebral dysfunction, especially with dementia and frontal lobe lesions (e.g., Moss, Albert, & Kemper, 1992; Shallice & Burgess, 1991)

Symptoms like distractability, passivity and apathy, apraxia, and perseverations, affecting self-care behaviors and leading to impaired activities of daily living have often been the focus. In patients suffering from frontal lobe lesions, impairments become manifest in a more obvious manner. Executive functioning is, however, usually maintained in primary aging although affected by a slower speed of behavior. The complexity of the executive dimension requires close scrutinity of sensory and motor handicaps, and of other noncognitive factors that potentially may influence the individual's adaptation to demands in everyday life as well as performance on cognitive tests (LaRue, 1992).

The generation of alternate behaviors necessary to overcome and compensate for deficits in certain domains can be seen as an indicator of the flexibility aspect of executive functioning. Cognitive flexibility, or the ability to shift strategy in order to solve and overcome certain problems, is an essential component in many tests, as well as in real-life situations. Rigidity, the converse of flexibility, is a more visible marker for the lack of flexibility in the ecological context. Tests that require switching from one strategy to another or tasks in which over-learned responses are to be inhibited in favour of new responses generally show superior performance in younger individuals. The Wisconsin Card Sorting Test (WCST, Grant & Berg, 1948) is another test requiring cognitive flexibility. Subjects are here instructed to organize cards according to different principles, for example, color and form. Only slight age differences were found in a study of healthy, well-educated elderly individuals' performance on a modified WCST version (Haaland, Vranes, Goodwin, & Garry, 1987). The study provides support for the conclusion that executive functions in primary aging remain relatively unaffected.

Although verbal fluency and certain tests of construction and motor performance can serve as markers of executive functioning (Royall, Mahurin, & Gray, 1992; see also Lezak, 1995), noncognitive influences must be taken into account in evaluating performance. A powerful approach for examining executive functioning is the observation of naturalistic behaviors interpreted in the context of previous ability, sensory and motor functioning. An elderly person unwilling or unable to perform as before and exhibiting lower cognitive flexibility has to be assessed in a broad context, including information on coping, perceived personal control, and well-being, before any definite conclusions can be drawn about deficient executive functioning.

VI.D. The Appealing Technique

These three logotherapeutic techniques are more likely to be effective, when the therapist appeals to the client's defiant power of the human spirit. The therapist makes use of the power of suggestion and directly appeals to the client to change for the better, regardless of the client's current circumstances, and physical-emotional condition. The therapist expresses trust in the client's dignity, freedom, responsibility, meaning orientation, and potential for positive change.

Frankl claims that “Logotherapy is neither teaching nor preaching. It is far removed from logical reasoning as it is from moral exhortation.” However, appealing often involves exhortation on the value of taking a heroic stand against suffering. For example, a nurse suffered from an inoperable tumor and experienced despair because of her incapacity to work. Frankl tried to appeal to her sense of pride and moral obligation to her patients:

I tried to explain to her that to work eight or ten hours per day is no great thing—many people can do that But to be as eager to work as, and so incapable of work, and yet not be despair—that would be as achievement few could attain. And then I asked her: “Are you not being unfair to all those sick people to whom you have dedicated your life; are you not being unfair to act now as if the life of an invalid were without meaning? If you behave as if the meaning of our life consisted in being able to work so many hours a day, you take away from all sick people the right to live and the justification for their existence.

35.7 Logical Weakness and Classification of Errors

The question that automatically arises from such results is: why are we so weak at logical reasoning? Is it because we are too sensitive to the atmosphere, or too inclined to make false conversions? Or is it because of the weakness of our working memory? If we use models for reasoning, it is clear that it should be demanding on our working memory. But given that we are all more or less poor in working memory, how can some of us be very weak at logical reasoning and others very strong? In order to answer these questions, we can compare the results of our two experiments and study the correlation between working memory and logical capacities. The Pearson degrees of correlation were the following: 0.20 between working memory and performance in logic of classes, 0.19 between working memory and categorical syllogisms and −0.04 for working memory and hypothetical reasoning. So, in all three categories, there was no significant correlation between the power of working memory and the logical performances. Indeed, many individuals appeared to be good reasoners and poor memorizers, or, vice versa: bad reasoners and good memorizers.

On the other hand, it is very relevant to analyze the comments given by the subjects about the procedures they used to find the answers. A strong correlation between the use of schematic procedures and logical skill is easily noticeable. In other words, the subjects who said that they used charts, diagrams, circles, informal kinds of truth tables, and so on, were much more successful in logic. On the contrary, the ones who gave fuzzier or more literary comments were much weaker in logical matters. A first conclusion that can be reached is that the good reasoners are definitely the ones who can translate the linguistically formulated premises in figurative representations rather than the good memorizers.

Trying to understand better the sources of logical errors, I suggest a classification of their syntactic structure. I make the hypothesis that we have a tendency to use specifically three invalid inferential principles in our logical errors: the principles of induction, of analogy, and of symmetry. The principle of induction (or extensional generalization) can be formulated as: “what is worth for some, is worth for all.” For example, from “all the criminals that I have known were liars” I conclude that “all criminals are liars.” The principle of analogy (or intensional generalization) corresponds to: “The ones that are similar are identical.” From “elm trees and beech trees have the same kind of leaves” we could easily conclude that “elm trees and beech trees belong to the same species.” The principle of symmetry (or abduction) can be stated as: “what is logically valid in one direction is valid in the other.” Using this third principle one could perform the following fallacies: “if you challenge him, he gets mad; and noboby challenges him, so he does not get mad” or “if you challenge him, he gets mad; and he gets mad, so someone challenges him.” These two last fallacies are exactly the fallacies on conditionals that were explained above, respectively, the fallacy of the negation of the antecedent and the fallacy of the affirmation of the consequent. This way, the third principle treats an implication, that is an asymmetric relation between an antecedent and a consequent, as an equivalence, as a symmetric implication that would hold not only between the antecedent and the consequent, but also reciprocally between the consequent and the antecedent.

Convergent thinking and logical deduction

Convergent thinking occurs when the solution to a problem can be deduced by applying established rules and logical reasoning. This type of reasoning involves solving a problem within the context of known information and narrowing down the solution based on logical inference. Harrison and Horne (2000b) reviewed an extensive literature on the effects of sleep deprivation on decision making and concluded that these processes are not significantly affected by sleep loss. Thus, complex cognitive processes, as quantified by broad intellectual functioning or IQ tests, reading comprehension, logical deduction and critical reasoning processes do not appear to be significantly degraded by even as much as two nights of sleep deprivation (Harrison and Horne, 2000b). This finding was supported by a recent meta-analysis that reported essentially no significant effect of sleep deprivation on reasoning and crystallized intelligence, including vocabulary, grammatical reasoning and non-verbal problem-solving (Lim and Dinges, 2010). Harrison and Horne (2000b) suggest that convergent thinking tasks tend to be less dependent upon prefrontal resources than other types of executive tasks, and may therefore, show little effect of sleep deprivation.

8 Logical Weakness and Classification of Errors

The question that automatically arises when we see such results is why are we so weak at logical reasoning? Is it because we are too sensitive to the atmosphere, or too inclined to make false conversions? Or is it because of the weakness of our working memory? If we use models for reasoning, this will obviously make demands on our working memory. But, given that most of us have poor working memories, why are some of us very weak at logical reasoning and others very strong? In order to answer these questions, we can compare the results of our two experiments and study the correlation between working memory and logical capacities. The Pearson degrees of correlation were as follows: 0.20 between working memory and performance in logic of classes, 0.19 between working memory and categorical syllogisms, and – 0.04 for working memory and hypothetical reasoning. Thus, there was no significant correlation between the power of working memory and logical performance in any of the three categories. Indeed, many individuals appeared to be good reasoners and poor memorizers or vice versa.

On the other hand, it is very relevant to analyze the comments the subjects made about the procedures they used to find the answers. A strong correlation between the use of schematic procedures and logical skill is very noticeable. In other words, the subjects who said that they used charts, diagrams, circles, informal kinds of truth tables, and so on were much better at logic. On the other hand, the participants who made fuzzier or more literary comments were much weaker in logical matters. An initial conclusion that can be reached is that the good reasoners are definitely the ones who can translate linguistically formulated premises into figurative representations, rather than the good memorizers.

In an effort to better understand the sources of logical errors, I suggest a classification of their syntactic structure. I hypothesize that we have a tendency to use three specific invalid inferential principles that lead to logical errors, the principles of induction, analogy, and symmetry. The principle of induction (or extensional generalization) can be formulated as “what is valid for some, is valid for all.” For example, from “all the criminals that I have known were liars,” I conclude that “all criminals are liars.” The principle of analogy (or intensional generalization) corresponds to “Objects that are similar are identical.” From “elm trees and beech trees have the same shaped leaves,” we could easily conclude that “elm trees and beech trees belong to the same species.” The principle of symmetry (or abduction) can be stated as “what is logically valid in one direction is valid in the other.” Using this third principle, one could derive the following fallacies: “if you challenge him, he gets angry; and nobody has challenged him, so he will not get angry” or “if you challenge him, he gets angry; and he is angry, so someone has challenged him.” These latter two fallacies are exactly the fallacies of implication that were explained above: the fallacy of the negation of the antecedent and the fallacy of the affirmation of the consequent, respectively. In this way, the third principle handles an implication, that is, an asymmetric relation between an antecedent and a consequent, as an equivalence – a symmetric implication that holds not only between the antecedent and the consequent, but also reciprocally between the consequent and the antecedent.

Measuring Motor Learning in Sports

The first 20 interviews Jill and I conducted included in-person meetings with numerous athletic programs from the University of Michigan and other programs of the Big 10 group. We also interviewed the owners and trainers of some Gyms specialized on the training of top athletes that at some point suffered an injury and required special re-training of specific muscle groups.

During those interviews, we discovered that our biometrics, while desirable to some, were not attractive to others. The notion of being able to predict the consequences of a training session on the athletes’ performance (including the possibility of injury) or even unveiling information on sleep patterns, was not welcomed by several of the coaches and individual athletes we interviewed in person and over the phone. Phone interviews were also included in our data collection because sometimes it was too late in the day to set up an in-person interview and we had to for example call the West Coast and interview folks there. The goal we had of achieving a minimum of 100 interviews to discover the market fit for our technology within a few weeks was very difficult to attain, but somehow doable if we expanded the data acquisition to other states and countries. To discover the market fit for our biometrics, we were working against the clock.

Many of the coaches and athletes that we interviewed did not feel comfortable with technology dictating scores of performances to advance decisions on their place within a team; not even as mere complementary information on what the coach was already detecting by the naked eye. Besides potential legal implication (e.g., lawsuits because of infringement on privacy matters by surveilling the athletes 24-7), the prospect of for example losing the top player in a team the day of the competition, “just because some smart gadget predicted imminent injury” was not going to cut it. “This technology could never replace us”—said the coaches and trainers we interviewed. Clearly, the role of the technology would be merely to monitor performance and indicate the rate of change of adaptive learning (e.g., the degree of plasticity of the nervous systems of the athlete when training within new contexts); but it was consistently taken as a form of intrusion by all those people we interviewed across the US college athletics system.

I decided nonetheless to pursuit the use of the biometrics in sports research, as a springboard to generate ideas for clinical research. Given the complexity of the sports routines, the data generated from the athletes in motion, along with their narratives on how they learned the routines and what they felt throughout that adaptive process, proved to be a very rich testbed for ideas that I could later use in the development of clinical motor biomarkers.

Uri Yarmush

Genius is not confined to academic performance or intellectual capacity to solve written problems or to formalize logical reasoning. The bodily performance is something to take very seriously as it entails the solution of many difficult computational problems. One of them, which we saw in Chapter 4, is the optimal recruitment-release and coordinated control of the many DoF of the human body to produce timely responses to the sensory flow of the environment, as well as to control the internally generated sensory flow. As I was planning on extending the studies of the motions of the reaching family to the full body, a new undergraduate student knocked at the door of our SMIL. He was Uri Yarmush, a martial arts expert who set up the Jab-Cross-Hook-Uppercut (J-C-H-U) (and other combination) boxing routines to study in the lab. Fig. 5.18 shows the digitization of the (J-C-H-U) using the Motion Monitor software (from SportsInn, Chicago).

Figure 5.18. Three-dimensional digital rendering frames from the expert’s performance of one trial of J-C-H-U beginner’s white belt technique using the real-time sensor outputs. (A–J) Arrows mark the locations of 15 electromagnetic sensors recording at 240 Hz. The first isolated technique is called a Jab. The Jab starts with the front hand extending toward the imaginary opponent’s nose (J1, forward Jab segment), keeping the hand in a tight fist, making sure that the elbow does not hyperextend; the hand should be retracted while it is still slightly bent. At the same time, that the Jab is being retracted (J2, Jab backwards), the Cross is being extended forward (C1, Cross forward). Again, the imaginary target is used and the Cross is directed toward the nose. Simultaneously, the body is twisting, beginning with the back foot, then the torso and ending with the back of the hand extending forward. Because the body is already twisted, this motion naturally sets up the staged portion of the Hook (H1) aimed at the opponent. As the Cross (right hand) reverts back to its original position (C2, Cross backwards), the left forearm is made into a C-shape with the hand in a fist and the palm facing down, and the body untwists itself, using the momentum of the body rather than the force of the hand to achieve the intended goal (to reach the opponent’s face) in the H1 Hook forward segment. As the body untwists itself in a supplemental motion (H2) Hook backwards segment, the knees bend slightly in preparation for the intended Uppercut (U1, Uppercut forward segment which goes upwards to the chin of the opponent). After the knees are bent and the left hand is returning to its original positioning to protect the face, the right-hand fist shoots up in a motion that resembles throwing a bowling ball in U1; but the hand is kept tighter aligned to the body and the palm is facing the body. The supplemental portion (U2, Uppercut backwards) brings the hand back, and the body adopts the defense position again. It is important to note that all routines where done in the presence of an expert instructor in order to minimize risk of injury [27,28].

Uri was an undergraduate student in Psychology who rotated through the SMIL and taught us a great deal about sports from the vantage point of a highly skilled athlete. His family owned a gym and taught martial arts. Among their pupils were children with neurodevelopmental disabilities, such as those in the spectrum of autism. Uri had been practicing since he was 4 years of age. By the time he came to work with us, he held a black belt second degree level in karate and was a competitive athlete in kick boxing. In his free time, he taught children and because my lab was interested in autism, he dropped by and offered his help.

We were extremely impressed by his exquisite sense of time and coordination. If I asked Uri to cut the time of a routine by half and measured it with millisecond time precision, his performance indeed complied with the request with exactness. His motions readily adapted to any of the manipulations I used (darkness, mirror, avatars, sound and others) and within a matter of seconds, his movement trajectories would be as efficient as they were in full trained mode. We had some fun with Uri’s teachings in the lab. He coached novices to martial arts and Rutgers athletes who were part of some other sports. One of the questions we were both curious to answer was whether being an expert on other sports would help or interfere with the learning of the boxing routines. From the few people we tested in the sports category, we felt they varied widely. They were all really good, but those who played in a group (e.g., lacrosse, volleyball and basketball) asked very different questions during the instructions than those who played in dyads (e.g., tennis) or alone (e.g., swimmers and runners). It seemed to me that the most important thing all athletes were trying to learn was how to situate the opponent relative to their own body and reference everything egocentrically. Interestingly, we tested a couple of performing artists too. They took longer to learn these routines and were extremely well-aware of their bodies. They were also very aware of their external surroundings. It was as though they were “acting” for an audience and the allocentric vantage point of that audience did matter to them. Further, the form of the routines was rapidly acquired but the effectiveness of the punch took some time to master.

We discovered a few interesting things during the experimental sessions regarding how people may learn these routines in general. Uri was a very good instructor and explained the logic behind each configuration of the body postures, so it was possible to extract the precise goals of each segment. He would explain as well why one should move in a certain way to attain effectiveness in the punches, etc. And when we got our first adolescent with autism, he certainly excelled. He had a very good sense on how to engage the participant and keep his interest in the structuring of the routines. Years after my experience with Uri and the participants in the spectrum, I traveled to Buenos Aires and met a couple of physical education instructors who had created a program specially tailored to teach children in the spectrum of autism how to play in groups [18]. They did so using sports as well. It has been the best program I have ever seen and it reminded me of how Uri taught these fellows with autism, here in the lab.

I would say that by far, the biggest discovery in this project was the empirical finding that the fluctuations in the hand’s speed amplitude of individuals with poor proprioceptive feedback was well characterized by the exponential distribution. Among the participants showing this signature, was our first individual with autism [19], a result that we then reproduced in many others across ages, sex, levels of severity and spoken language abilities [12,20,21]. This feature, present in neurotypical infants would transition into skewed and towards symmetric distributions as children passed the 4 years of age; but it would remain exponential in autism [19,20].

I decided to further explore the issue of speed variations and find out how it would help me track learning in a personalized manner and across sections of a given cohort of individuals.

3 COVIS

As mentioned earlier, COVIS postulates two systems that compete throughout learning – an explicit system that uses logical reasoning and an implicit system that uses a form of procedural learning. The explicit system generates and tests hypotheses about category membership, a process that requires working memory and executive attention, and it controls performance in rule-based tasks, primarily because the optimal rule in these tasks is easy to reason about logically. The procedural learning that forms the basis of the implicit system has previously been associated with motor learning [e.g., Willingham, Nissen and Bullemer (1989), Willingham (1998)]. COVIS assumes that the implicit system learns in an incremental fashion and, unlike the explicit system, is not constrained to learning any particular class of categorization rules. In information-integration tasks, the optimal rule is difficult to describe verbally, so it is unlikely to be discovered by the hypothesis generation process of the explicit system. As a result, COVIS assumes that the procedural-learning system dominates (asymptotic) performance in information-integration tasks.

The next two major sections describe the COVIS explicit and implicit systems, respectively. Computational versions of the two systems are described in the appendix.

Deficits in verbal fluency and naming, memory and comprehension of verbal information, as well impairment in verbal and logical reasoning are frequently associated with linguistic performance in Parkinson's disease, a neurodegenerative disorder primarily involving subcortical structures and the depletion of dopaminergic neurons in the substantia nigra. Impairment is more pronounced in more complex aspects of language processing as in sentence comprehension, understanding of relational terms and relative clauses, and in aspects of logical reasoning. It is believed that constraints in accessing the cognitive resources needed to implement the processing of syntactic and semantic information, as well as the disruption of reciprocating neural connections between the basal ganglia with the prefrontal cortex, may explain these observed deficits.