Roles of implicit processes: instinct, intuition, and personality

Transcription

1 Mind Soc (2014) 13: DOI /s Roles of implicit processes: instinct, intuition, and personality Ron Sun Nick Wilson Received: 8 September 2013 / Accepted: 5 December 2013 / Published online: 24 December 2013 Ó Springer-Verlag Berlin Heidelberg 2013 Abstract The goal of this research is to explore implicit and explicit processes in shaping an individual s characteristic behavioral patterns, that is, personality. The questions addressed are how psychological processes may be separated into implicit and explicit types, and how such a separation figures into personality. In particular, it focuses on the role of instinct and intuition (two kinds of mostly implicit processes) in determining personality. This paper argues that personality may be fundamentally based on instincts resulting from basic human motivation, along with related processes, within a comprehensive cognitive architecture. This approach is implemented as a computational model. Various tests and simulations show that this model captures major personality traits and accounts for empirical data. The work shows how a cognitive architecture with the implicit explicit distinction may capture instinct, intuition, and personality. Keywords Implicit Explicit Instinct Intuition Personality Cognitive architecture Motivation 1 Introduction Our long-standing research goal has been to understand the interaction of implicit and explicit psychological processes. Sun (1995, 1994) developed a computational cognitive model that contained distinct implicit and explicit processes for modeling human everyday commonsense reasoning. Sun et al. (2001, 2005) applied a variant of the model to the understanding of human skill acquisition, capturing the synergy resulting from the interaction of implicit and explicit processes. R. Sun (&) N. Wilson Department of Cognitive Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA dr.ron.sun@gmail.com

2 110 R. Sun, N. Wilson On that basis, the present work explores implicit and explicit processes in shaping an individual s characteristic behavioral patterns, that is, personality. The questions are how psychological processes can be separated into implicit and explicit components, and how such a separation figures into personality, which may have significant implications. In particular, we want to explore the role of instinct and intuition in determining personality (characteristic behavioral patterns). We argue that personality may be fundamentally based on instinct (resulting from basic human motivation), along with related processes (including intuition), within a comprehensive cognitive architecture. This is implemented within the computational cognitive architecture CLARION. Note that instinct and intuition are both folk psychological notions. As such, they are not precisely defined; in fact they are quite elusive concepts. They have been used in a variety of different ways. We hope that, by using computational modeling, we may be able to clarify these concepts, in a mechanistic, process-based way, with the precision and concreteness that computational theories often offer. For instance, instinct, according to Merriam-Webster Dictionary, is a largely inheritable and unalterable tendency of an organism to make a complex and specific response to environmental stimuli without involving reason. According to Oxford English Dictionary, it is an innate, typically fixed pattern of behaviour in animals in response to certain stimuli. The question is how instinct may be carried out mechanistically (i.e., computationally), and moreover, how fixed or unalterable it is (i.e., whether it is tunable to some extent). Some initial work has been done to elucidate this notion. For instance, Sun (2009) discussed how an adequate, necessary, and appropriate representation of basic human motivation and related metacognitive, action decision-making, and other processes, within a generic, comprehensive computational model, captures some forms of instinct. Such representations and processes also capture the interaction of internally felt needs and external environmental factors in determining goals and actions by individuals, and therefore much of characteristic behavioral patterns (i.e., personality; Sun and Wilson 2011). Likewise, the notion of intuition needs clarification also. Although intuition has often been defined as the immediate apprehension of an object by the mind without the intervention of any reasoning process (Oxford English Dictionary), or immediate apprehension or cognition (Merriam-Webster Dictionary), we may instead view intuition as a form of reasoning (Sun 1994; Sun and Zhang 2006). In our view, reasoning encompasses both explicit processes (especially explicit rules and logics) on the one hand, and implicit processes (including intuition) on the other (Sun 1994). In fact, intuition and insight resulting from intuition are arguably important elements of human reasoning (Helie and Sun 2010). They supplement and guide explicit reasoning involving, for example, rules and logics. Related to this, previous psychological or AI theories of human problem solving (which relies on reasoning) have largely focused on explicit processes that gradually bring one closer to a solution, step by step, in an explicit, deliberative way. This approach to problem solving and reasoning is often ineffective when the problem is complex, ill-understood, or ambiguous. In such a case, an alternative approach relying more on intuition might be more appropriate. To address this issue, Helie

3 Roles of implicit processes 111 and Sun (2010) proposed a CLARION theory of creative problem solving that centers on the interaction of implicit and explicit processing, relying on intuition and insight (resulting from cumulating intuition), which has been demonstrated through computational simulation of empirical data. Based on the prior work on both intuition and instinct, it may be argued that personality, as studied in social personality psychology, may be computationally captured and consequently explained based on instinct and intuition, along with other processes. The present summary article will address how instinct and intuition determine personality (i.e., characteristic behavioral patterns). Sociocultural influences are also extremely important to understanding personality. Such influences may manifest in an individual through tuning instinct and intuition when the individual interacts with the world, sociocultural or physical, in addition to other possibilities. Therefore, personality may be influenced by sociocultural factors (Forgas et al. 2003). It is necessary to provide a framework capable of accounting for such influences. In particular, it is necessary to account for the influences in a mechanistic, process-based way. An outline of a general framework related to personality may be summarized as follows on the basis of the CLARION cognitive architecture: Within the cognitive architecture, there is the constant interaction among various subsystems. Within the motivational subsystem, there is a set of basic motives, termed drives, which are universal across individuals. Individual differences may be explained (in a large part) by the differences across this set of drives in terms of drive strengths in different situations by different individuals (Winter et al. 1998). These drives, with their different drive strengths, lead to setting of different goals as well as major cognitive parameters by the metacognitive subsystem. Individual differences in terms of drive strengths are consequently reflected in the resulting goals and major cognitive parameters. On the basis of the goals set and the major cognitive parameters chosen, an individual makes action decisions, within the action-centered subsystem. The action decisions may be examined and reasoned, within the nonaction-centered subsystem, before the action decisions are finalized. As a result, actions reflect fundamental individual differences as well as situational factors. It is our contention that such a framework (as implemented in the CLARION cognitive architecture, to be detailed later) may capture the relative invariance (such as the Big Five personality dimensions; Clark and Watson 1999; Digman 1990; John and Srivastava 1999) within a specific individual in terms of behavioral propensities and inclinations at different times and with regard to different situations (social or physical), as well as behavioral variability. The (relative) invariance of personality has been extensively argued for in social personality psychology (e.g., Caprara and Cervone 2000; Epstein 1982; Murray 1938), and with the use of a generic computational cognitive architecture (namely, CLARION), it can be captured computationally. In the remainder of the paper, first, a theory of personality is reviewed. Then, the CLARION cognitive architecture that implements this theory is sketched. Some simulations using CLARION are briefly described. Possibilities for tuning of various processes constituting personality is then discussed. Some concluding remarks end the paper.

4 112 R. Sun, N. Wilson Note that the present article is a brief summary report of a large research project. Therefore, details will necessarily be sketchy and not all aspects can be covered. In particular, many technical details have to be omitted (although citations of previous publications partially remedy this problem). 2 A theory of personality Below, a number of basic principles of the CLARION theory of personality are discussed. 2.1 A set of principles Principle 1: embodying of personality in a cognitive architecture Human personality should emerge from the interactions among various components of the mind. That is, mechanistically, it should emerge from the interactions among various subsystems (and various modules within) of a cognitive architecture. The cognitive architecture should allow for the emergence of different personality types, and the adaptation of personality through experiences (to some extent at least) Principle 2: the dichotomy of implicit and explicit psychological processes Psychological processes may be either implicit or explicit; the two types co-exist but are different in some important respects, with implicit processes being more fundamental (for personality and for many other phenomena) Principles 3: the division of drives and goals in the motivational (sub)system Human motivation may be explained by a combination of implicit drives and explicit goals, with drives being more fundamental Principle 4: goal setting on the basis of drives Goals may be determined (possibly stochastically) on the basis of the interaction and competition of different drives, as a result of situational inputs and internal factors. Such processes are, to some extent, subject to adaptation through experience Principle 5: the fundamental role of motivation (drives and goals) in determining personality Personality is deeply rooted in the motivational processes, and in particular in drives Principle 6: the role of action decision-making in personality Action decision-making (i.e., procedural processes) on the basis of the goal chosen and the situational inputs (possibly stochastically) is also important to personality;

5 Roles of implicit processes 113 it is an integral part of personality, although it is subject to learning and adaptation Principle 7: the role of declarative knowledge and reasoning in personality Declarative knowledge and reasoning also affect personality, through affecting actions of an individual. Their effects are less direct. They are subject to learning from experiences. 2.2 Justifying some principles While a complete justification of this set of principles is beyond the scope of this short summary article, we will look into some key justifications below. It seems obvious that personality should be the result of the existing psychological mechanisms and processes, and nothing else. A computational cognitive architecture, by definition, includes all essential psychological components, mechanisms, and processes of the human mind (Sun 2004). Within the cognitive architecture, the interactions among different subsystems (and components within) should be able to generate psychological phenomena of all kinds, which include personality-related phenomena (Sun and Wilson 2011). Thus, personality, if it is a valid psychological construct, should be accounted for by the cognitive architecture. In a similar fashion, Cervone (2004) argues that personality results from a complex system with dynamic interactions among multiple processes. Personality should be understood by reference to basic underlying processes that give rise to overt behavior. In addition, it is desirable that a model of personality captures more detailed psychological processes than previous work. It is necessary to go beyond abstract (sometimes ungrounded) notions. It is one thing to argue abstractly that personality traits consist of configurations of goals, plans, resources, beliefs or cognitive affective units (e.g., Shoda and Mischel 1998), it is quite another to map personality traits to more concrete, more detailed, and better grounded psychological representations, mechanisms, and processes. Thus it is useful to ground personality traits in a computational cognitive architecture, so that they are explained in a deeper and more unified way, along with many other psychological phenomena, based on the same primitives within a generic cognitive architecture. Also, coupled with the account of learning in a cognitive architecture, a model of personality should attempt to account for the emergence, shaping, and tuning of personality by a variety of factors. Such explanations should be more detailed and deeper (preferably computational). Turning to the implicit explicit distinction, it is generally agreed upon that at least some processes are not consciously accessible under normal circumstances. Voluminous experimental data testifying to this distinction can be found in Seger (1994), Cleeremans et al. (1998), and Sun (2002). Theoretical treatments may be found in Reber (1989), Evans and Frankish (2009), Kahneman (2011), and Evans and Stanovich (2013) (although some details in these treatments may be debatable).

6 114 R. Sun, N. Wilson In general, explicit processing may be described as rule-based in some way, while implicit processing is mostly associative (Sun 2002, 1994). Explicit processing may involve the manipulation of symbols. In contrast, implicit processing involves more instantiated knowledge that is holistically associated (Sun 2002, 1994). Empirical evidence in support of these points can be found in the work cited above (e.g., Reber 1989; Seger 1994; Sun 2002). Implicit processes are often believed to be more fundamental. The fundamental importance of implicit processes has been argued for by Reber (1989), as well as many others (e.g., Sun 2002; Sun et al. 2001, 2005). Implicit processes encompass folk psychological notions of instinct and intuition, as discussed earlier. With regard to drives and goals, as well as goal setting on the basis of drives, the reader is referred to extensive arguments presented in, for example, Sun (2009). Due to the consideration of length, they will not be repeated here. Regarding the role of motivational processes in personality (Winter et al. 1998), drives are the most fundamental. Other processes may be more transient, due to contextual factors, learning, and adaptation. Although drives are tunable also, they are, relatively speaking, more fundamental and more stable than other processes. Therefore, it appears reasonable to ground personality, first and foremost, in drives and then in goals on their basis. Existing work in social-personality psychology shows how personality can be related to human motivation. Deci (1980) made an elaborate case for this point, reviewing the literature on motivation and personality, arguing for their close relationship. Shoda and Mischel (1998) also argued that personality could be understood in terms of cognitive affective units, including goals, plans, expectancies, and so on. They showed how individual differences in personality might emerge on the basis of cognitive affective units (although no exact structural mapping was produced). Many existing computational models and simulations of personality also support this view. See Sun and Wilson (2011) for a review. It should be noted that personality may involve a variety of psychological mechanisms and processes beyond motivation, although they may be less important to personality. Therefore, personality types (Digman 1990; John and Srivastava 1999), besides being mapped onto motivational structures, representations, mechanisms, and processes, may also be mapped onto other mechanisms and processes. The determination of personality types involves various motivational, cognitive, perceptual, and other parameters (Sun 2003). With regard to the role of action specifically, actions (behaviors) are the ultimate measure of personality. Without them, there would be no objective way of classifying personality types. Action decision-making (procedural processes) is important to measuring personality. Moreover, it is also an important part of personality, because even given drives and goals, there are a range of possible actions. Here the notion of action is defined in a broad sense, including both physical and mental actions. Let us summarize these principles with an example. For example, consistent with Principle 1, the personality trait of being dominating may be captured by a drive state where dominating others is emphasized (Principles 3, 4, and 5), a specific goal relevant to domination being chosen (Principle 4), actions, routines, and plans for

7 Roles of implicit processes 115 achieving that goal being carried out (Principle 6), and reasoning related to that goal being conducted (Principle 7 later). In terms of actions (behaviors), a dominating person, when in relevant situations and reacting to relevant cues, regularly exhibits dominating behaviors. On that basis, the individual may be viewed as a dominating person. The label captures the way in which the person regularly responds to situations through actions. However, this configuration produces dominating behaviors across an array of situations, only if other drives and goals leading to competing behaviors are not as frequently and as strongly activated. Another prerequisite is the learning of the connection between the goal and the corresponding actions within a given sociocultural and physical environment; procedural knowledge and skills (for action selection) are highly learnable, as commonly accepted (Sun et al. 2001; Montague 1999), and they are subject to sociocultural influences. Implicit processes are fundamentally important in each of these steps (Principle 2): drive activation, goal setting, and action selection. These implicit processes involved in these steps (especially in action selection) together roughly constitute the folk psychological notion of instinct. Therefore, instinct is fundamentally important to personality. Finally, with regard to the role of declarative processes in personality, Cervone (2004) argued for the importance of knowledge, belief, schema, appraisal, reasoning, and so on as determinants of personality. As in the previous example, the personality trait of being dominating may be captured by a drive state where dominating others is emphasized, a goal of dominating others being chosen, actions for achieving that goal being carried out, and so on. But beyond that, reasoning related to that goal might also be carried out, for example, regarding whether one s actions would actually achieve the goal. Such reasoning, implicit or explicit, as well as the declarative knowledge (implicit or explicit) on which reasoning is based, is important to behavior choices and therefore to personality. But, reasoning and declarative knowledge can impact personality only on the basis of drives and goals, and affect procedural processes (action decision-making) only indirectly (Sun and Wilson 2011). Among declarative processes, implicit declarative processes (the most important part of intuition) have been studied in Sun and Zhang (2006) and Helie and Sun (2010), and are important to declarative processes, including reasoning. Therefore, they are relevant to personality as well. Declarative knowledge and processes (including intuition) are evidently learnable, and subject to sociocultural influences. 3 A cognitive architecture capturing the personality theory CLARION is a generic computational cognitive architecture a comprehensive model of psychological processes of a wide variety, specified computationally. It has been described in detail and justified on the basis of psychological data in Sun (2002, 2003; see also Sun et al. 2001, 2005; Helie and Sun 2010). It can be the framework within which we instantiate the theory of personality.

8 116 R. Sun, N. Wilson CLARION consists of a number of interacting subsystems: the action-centered subsystem (the ACS), the non-action-centered subsystem (the NACS), the motivational subsystem (the MS), and the metacognitive subsystem (the MCS). The role of the action-centered subsystem is to control actions (regardless of whether the actions are for external physical movements or for internal mental operations) utilizing and maintaining procedural knowledge. The role of the nonaction-centered subsystem is to maintain and utilize declarative knowledge. The role of the motivational subsystem is to provide underlying motivations for perception, action, and cognition (in terms of providing impetus and feedback). The role of the metacognitive subsystem is to monitor, direct, and modify the operations of the other subsystems dynamically. Each of these subsystems consists of two levels of representations (i.e., a dualprocess framework with a dual-representational structure) as theoretically posited in Sun (2002). Generally speaking, in each subsystem, the top level encodes explicit knowledge (using symbolic localist representation) and the bottom level encodes implicit knowledge (using distributed representation; Rumelhart et al. 1986). The two levels interact, for example, by cooperating in action decision-making, through integration of the action recommendations from the two levels of the ACS respectively, as well as by cooperating in learning through a bottom-up and a top-down learning process (in the ACS and the NACS, as will be discussed below). See Fig. 1 for a sketch. As has been pointed out before, existing theories of personality tend to confuse implicit (reflexive) and explicit (deliberative) processes; hence the perplexing complexity (Smillie et al. 2006). In contrast, CLARION generally separates and then integrates implicit and explicit processes in each of its subsystems (Sun 2002). With such a framework, CLARION can provide better explanations of empirical findings in a wide range of domains, including in personality (see, e.g., Sun et al. 2001, 2005; Helie and Sun 2010; Sun and Wilson 2011). Another particularly important characteristic of this cognitive architecture is its focus on the cognition motivation environment interaction, as opposed to dealing only with cognition in the narrow sense. Below, we will examine each of the subsystems in more detail (which will illustrate some of these points). 3.1 The action-centered subsystem The action-centered subsystem (the ACS) of CLARION captures the action decision-making of an individual when interacting with the world, and the implicit level of the ACS may capture an important part of human instinct. The process for action selection within the ACS is essentially as follows: Observing the current (observable) input state of the world, the two levels within the ACS (implicit or explicit) make their separate decisions in accordance with their respective procedural knowledge, and their outcomes are somehow integrated. Thus, a final selection of an action is made and the action is then performed. The action changes the world in some way. Comparing the changed input state with the

9 Roles of implicit processes 117 Fig. 1 The CLARION cognitive architecture previous input state somehow, the person learns. The cycle then repeats itself. Thus, the overall algorithm for action selection is as follows: 1. Observe the current input state x (including the current goal). 2. Compute in the bottom level the value of each of the possible actions (a i s) associated with state x: Q(x, a 1 ), Q(x, a 2 ),, Q(x, a n ). Stochastically choose one action according to these values. 3. Find out the possible actions at the top level (b 1,b 2,,b m ), based on the current input state x (which goes up from the bottom level) and the existing rules in place at the top level. Stochastically choose one action. 4. Choose an action, by stochastically selecting the outcome of either the top level or the bottom level. 5. Perform the action, and observe the next input state y and (possibly) the reinforcement r. 6. Update knowledge in the bottom level in accordance with an appropriate learning algorithm (e.g., Q-learning; to be detailed later), based on the feedback information.

10 118 R. Sun, N. Wilson 7. Update the top level using an appropriate learning algorithm (e.g., the RER algorithm; to be detailed later). 8. Go back to Step 1. The bottom level is implemented with neural networks involving distributed representations (Rumelhart et al. 1986), and the top level is implemented using symbolic localist representations. The input state (x) to the bottom level consists of several sets of information: sensory information (environmental or internal), the current goal, and so on. The current goal and the current sensory input are both important in deciding on an action. The input state is represented as a set of dimensional values: (d 1,v 1 )(d 2, v 2 ).(d n,v n ) (constituting a distributed representation in the bottom level). The output of the bottom level is the action choice. At the top level, chunk nodes are used for denoting concepts (a localist representation). A chunk node connects to its corresponding dimensional values (microfeatures) represented as separate nodes in the bottom level (distributed representation). In a neural network encoding implicit procedural knowledge at the bottom level of the ACS, actions are selected based on their Q values. A Q value is an evaluation of the quality of an action in a given input state: Q(x, a) indicates how desirable action a is in state x. At each step, given state x, the Q values of all the actions (i.e., Q(x, a) for all a s) are computed in parallel. Then the Q values are used to decide stochastically on an action to be performed, through a Boltzmann distribution of Q values (Luce s choice axiom; Watkins 1989). This implicit action decision-making captures an important part of human instinct (more later). For learning implicit procedural knowledge at the bottom level (as represented by the Q values), a reinforcement learning algorithm may be used. Q values are gradually tuned, on-line, through successive updating, which enables reactive sequential behavior to emerge through trial-and-error interaction with the world (more later). For learning explicit rules at the top level with a bottom-up learning process (Sun et al. 2001), the Rule-Extraction Refinement algorithm (RER) uses information from the bottom level. The basic idea is as follows: If an action chosen by the bottom level of the ACS is successful (i.e., it satisfies a certain criterion), then an explicit rule is extracted at the top level. Then, in subsequent interactions with the world, the rule is refined by considering the outcome of applying the rule: If the outcome is successful, the condition of the rule may be generalized to make it more universal; if the outcome is not successful, then the condition of the rule should be revised and made more specific (Sun et al. 2001). For stochastic selection of the outcomes of the two levels, at each step, with probability P BL, the outcome of the bottom level is used. Likewise, with probability P RER, if there is at least one RER rule indicating an action in the current input state, the outcome from that rule set (determined through competition) is used. Other components, if exist, may also be included in the stochastic selection. There exists some psychological evidence for such intermittent use of rules (Sun 2002).

11 Roles of implicit processes The non-action-centered subsystem The non-action-centered subsystem (the NACS) deals with declarative knowledge, which is not action-centered, for the purpose of making inferences about the world. It stores such knowledge in a dual representational form (the same as in the ACS): that is, in the form of explicit associative rules at the top level, and in the form of implicit associative memory at the bottom level. Its operation is under the direction of the ACS. On the one hand, at the bottom level of the NACS, associative memory networks encode implicit declarative (non-action-centered) knowledge (Rumelhart et al. 1986). These networks may capture an important part of human intuition in reasoning (Sun and Zhang 2006; Helie and Sun 2010). On the other hand, at the top level of the NACS, explicit declarative (non-action-centered) knowledge is stored. As in the ACS, chunk nodes (denoting concepts) at the top level are linked to dimensional values (microfeatures) represented at the bottom level. Additionally, in the top level, links between chunk nodes encode explicit associative rules. Explicit associative rules may be learned in a variety of ways (Sun 2003). As shown by Sun (2003, 1994), within the NACS, sequences of mixed similaritybased and rule-based reasoning capture essential patterns of human everyday (mundane, commonsense) reasoning. Such reasoning, as has been shown, involves both intuition and explicit, reflective thinking (Evans and Frankish 2009; Evans and Stanovich 2013). 3.3 The motivational subsystem The motivational subsystem (the MS) of CLARION is concerned with why an individual does what he/she does (Weiner 1992; Toates 1986). The relevance of the MS to the ACS lies primarily in the fact that it provides the context in which the goal and the reinforcement of the ACS are determined. It thereby influences the working of the ACS (and by extension, the working of the NACS). A dual motivational representation is in place in the MS. The explicit goals (such as finding food ), which are essential to the working of the ACS, may be generated based on implicit drives (e.g., being hungry ). The explicit goals derive from, and hinge upon, implicit drives. See Fig. 2 for a sketch of the motivational subsystem. For justifications, see the principles in Sect. 2 (see Sun 2009 for more details). In particular, within the MS, primary drives refer to those drives that are essential to an individual and are most likely built-in (hard-wired) to a significant extent to begin with. Some sample low-level primary drives include: food, water, reproduction, and so on (cf. Tyrell 1993; Murray 1938; McDougall 1936). Beyond such low-level primary drives (concerning mostly physiological needs), there are also high-level primary drives: for example, dominance and power, fairness, and so on. 1 The primary drives (low-level and high-level together) may be roughly explained as in Table 1. This set of primary drives has been extensively explored and justified 1 Note that a generalized notion of drive is adopted in CLARION, different from the stricter interpretations of drives. As discussed before, it is a generalized notion that transcends controversies surrounding the stricter notions of drive (Sun 2009).

12 120 R. Sun, N. Wilson Fig. 2 The structure of the motivational subsystem Table 1 Primary drives Food Water Sleep Reproduction Avoiding danger Avoiding unpleasant stimuli Affiliation and belongingness Dominance and power Recognition and achievement Autonomy Deference Similance Fairness Honor Nurturance Conservation Curiosity The drive to consume nourishment The drive to consume liquid The drive to rest and/or sleep The drive to mate The drive to avoid situations that have the potential to be or already are harmful The drive to avoid situations that are physically (or emotionally) uncomfortable or negative in nature The drive to associate with other individuals and to be part of social groups The drive to have power over other individuals or groups The drive to excel and be viewed as competent at something The drive to resist control or influence by others The drive to willingly follow and serve a person of a higher status of some kind The drive to identify with other individuals, to imitate others, and to go along with their actions The drive to ensure that one treats others fairly and is treated fairly by others The drive to follow social norm and code of behavior and to avoid blames The drive to care for, or attend to the needs of, others who are in need The drive to conserve, to preserve, to organize, or to structure (e.g., one s environment) The drive to explore, to discover, and to gain new knowledge in prior writings (e.g., Sun 2009; see also the principles discussed in Sect. 2). 2 It may be hypothesized that such drives are the basis of human instinct (Sun 2009; Sun and Wilson 2011). 2 Briefly, this set of primary drives is essentially the same as Murray s (1938), with only a few differences. Similarly, comparing this set of drives with Reiss s (2010) set, one can see that they are highly similar (but with some differences). So, the prior work by these and other researchers in justifying their frameworks may be applied, to a significant extent, to this set of drives as well (McDougall 1936; Murray 1938; Maslow 1943; Reiss 2010; Sun 2009).

13 Roles of implicit processes 121 The drive activation for each of these drives is determined by: ds d ¼ gain d stimulus d deficit d þ baseline d where ds d is the strength of drive d, gain d is the gain parameter for drive d, stimulus d is a value representing how pertinent the current situation is to drive d, deficit d indicates the perceived deficit in relation to drive d (which represents an individual s intrinsic inclination toward activating drive d), and baseline d is the baseline value of drive d The metacognitive subsystem The large variety of drives and the goals resulting from them lead to the need for metacognitive control and regulation. Metacognition refers to one s knowledge (implicit or explicit) concerning one s own cognitive processes. Metacognition includes active monitoring and consequent regulation and orchestration of these processes (Flavell 1976). In CLARION, the metacognitive subsystem (the MCS) is closely tied to the MS. The MCS monitors, controls, and regulates cognitive processes (Wright and Sloman 1997). Control and regulation may be in the forms of setting goals (which are then used by the ACS) on the basis of drives, setting reinforcement functions for learning within the ACS (on the basis of drives and goals), interrupting and changing on-going processes in the ACS and the NACS, setting essential parameters of the ACS and the NACS, and so on. Structurally, this subsystem may be divided into a number of functional modules, including: the goal module, the reinforcement module, the processing mode module. the input filtering module, the output filtering module, the parameter setting module, and so on. We may look into the goal module specifically. In order to select a new goal, it first determines goal strengths for some or all of the goals, based on information from the MS and the current sensory inputs. Then, a new goal is stochastically selected on the basis of the goal strengths. For the general notion of, and arguments in support of, goal setting on the basis of implicit motives (i.e., drives), see, for example, Tolman (1932) and Deci (1980). In the simplest case, the following calculation may be performed by this module: gs g ¼ Xn relevance d;s!g ds d d¼1 where gs g is the strength (activation) of goal g, relevance d,s?g is a measure of how relevant drive d is to goal g with regard to the current situation s (which represents 3 Note that drive strengths actually could be a function of the equation above; in the simplest case, an identity function may be assumed, as shown above.

14 122 R. Sun, N. Wilson the support that drive d provides to goal g), and ds d is the strength of drive d as determined by the MS. Once calculated, the goal strengths are turned into a Boltzmann distribution and the new goal is chosen stochastically from that distribution. Goal selection is often implicit. This implicit process is part of what comprises human instinct. 3.5 Model of personality according to CLARION CLARION, as sketched above, can account for many psychological phenomena. So far, CLARION has been successful in simulating, accounting for, and thereby explaining a wide variety of psychological data. While accounting for various psychological data, CLARION provides explanations that shed new light on psychological processes. In particular, CLARION has been capturing, explaining, and accounting for human data using the two-level, dual-representational perspective. For example, a number of well-known skill learning tasks have been simulated using CLARION that span the spectrum ranging from simple reactive skills to complex cognitive skills. These tasks include serial reaction time tasks, artificial grammar learning tasks, process control tasks, categorical inference tasks, alphabetical arithmetic tasks, and the Tower of Hanoi task (Sun 2002). Among them, some are typical implicit learning tasks (mainly involving implicit processes), while others are high-level cognitive skill acquisition tasks (with more explicit processes). Also, reasoning tasks, insight problem solving tasks, and social and organizational simulation tasks have been tackled. Moreover, human data concerning metacognitive monitoring and regulation have been simulated with the use of the MCS. Motivational tasks involving the MS have also been simulated. In addition, in terms of computationally explaining well known psychological regularities, Sun and Helie (2013) provide an overview of many psychological laws that CLARION accounts for, in relation to categorization, memory, inductive reasoning, deductive reasoning, decision-making, and so on. On the basis of the prior work on CLARION that established its initial validity, we can relate the CLARION cognitive architecture to the theory of personality (Principle 1 as discussed earlier). In CLARION, action decisions are made by the ACS, but the action decisions are based on the current goal, which is (mostly) set by the MCS based on the drives in the MS. Therefore, drives in the MS are the foundation of behavior, according to CLARION. The actions are directed by the flow of desires (drives), that is, various impulses on a moment-to-moment basis. Therefore, it is natural to ground the notion of personality first and foremost within the MS of CLARION (principle 5). This is consistent with our earlier argument that personality traits are very much motivationally based and personality reflects largely the dynamics of the underlying MS (Sun 2009; Sun and Wilson 2011). On the basis of drives, goal setting and action selection take place (principles 3, 4, and 6). Thus, within the CLARION framework, personality may involve a variety of parameters within the ACS, the MCS, and the MS. In these subsystems, implicit processes are

15 Roles of implicit processes of fundamental importance (principle 2), and together they capture the notion of instinct. The NACS is also involved, because reasoning is often needed in making action decisions (principle 7). In the NACS, implicit processes (capturing the most important part of intuition) play significant roles (Sun 1994; Sun and Zhang 2006). A general outline of the CLARION personality model is thus as articulated earlier in Introduction. Consequently, the notion of instinct may be made more precise by appealing to the cognitive architecture. Instinct involves mostly implicit processes and is mostly concerned with action. Within CLARION, instinct may be roughly equated with the following chain of activation: stimuli? drive? goal? action. This chain goes from stimuli received to the MS, the MCS, and eventually the ACS. That is, stimuli activate drives, drive activations lead to goal setting in an implicit way, and based on the goal set, actions are selected in an implicit way to achieve the goal. Instinct mostly involves implicit procedural processes. Instinct is mostly implicit, but it may become explicit, especially with regard to the part of goal? action (Sun et al. 2001). The notion of intuition can also be made more precise. Intuition, according to CLARION, is roughly the following chain: stimuli? drive? goal? implicit reasoning. This chain goes from stimuli received to the MS, the MCS, and the NACS. As such, intuition mostly involves implicit declarative processes within the NACS. Intuition is often complementary to explicit reasoning, and the two types are used often in conjunction with each other (Helie and Sun 2010). Therefore, personality may be mostly captured by instinct, along with intuition (for reasoning) and explicit processes (for reflective thinking of various kinds). 4 Simulations with the CLARION personality model Although not all related issues can be covered in this short summary article, we need to examine at least a few key tests of the model. Let us look into some details below. 4.1 Simulation tests The possibility of displaying different personalities by the model should be explored. In addition, the person situation interaction also needs to be explored. Process-based personality models often involve detailed constructs, such as individual differences in drives (implicit motives), goals, and knowledge (implicit or explicit), and how behavior changes across time and situations as a result of underlying processes. On the other hand, structural models of personality focus on stable individual differences captured by various trait constructs that tend to be stable across time and situations. The present model is a detailed process model based on the motivational and other processes, especially implicit processes embodying instinct and intuition. It captures personality dynamics, and generates behaviors that vary across situations and time. At the same time, the structure of the subsystems can capture stable individual differences in behavioral inclinations and tendencies, that is, stable personality traits, through differences in parameters. The present model can provide

16 124 R. Sun, N. Wilson both an account of broad, stable traits, as well as an account of the processes that lead to behavioral variability across situations and time and in particular the person situation interaction. In the present model, the person situation interaction is the interaction between the (relatively stable) characteristics of the motivational and other subsystems and the influence of situations (which are more transient). 4 Many past debates have highlighted the importance of person situation interaction (Caprara and Cervone 2000). Thus it should be tested within the model. With our model, we can vary either personality or situation (or both) in testing such interactions. For example, as having been shown before, one could keep a particular personality constant and examine how it responds differently to different situations (Read et al. 2010). Or one could keep a particular situation constant and see how different personalities respond differently to the same situation. For instance, in one of our simulations, six different personalities were set up (as in Read et al. 2010). These personalities were designed to form three complimentary pairs: Sociable Shy, Confident Anxious, and Responsible Lazy (note that these terms, as used by Read et al., may not be precise). Each of these pairs was defined to correspond to the far ends of one of the Big Five personality dimensions (Clark and Watson 1999; Digman 1990). One pair consists of the shy and the sociable, at the two ends of the extroversion dimension. Another pair consists of the anxious and the confident, at the two ends of the neuroticism dimension. The third pair consists of the lazy and the conscientious, at the two ends of the conscientiousness dimension. The drive deficit levels determined these different personality types to a significant extent (see Sun and Wilson 2011 for details). The model (for each of the six personality types) was run on a set of 15 scenarios (Read et al. 2010). Each scenario was tested for 100 iterations and the chosen behaviors were recorded. The process was repeated for 100 different runs (with different initial weights; representing 100 different simulated subjects ). Figures 3, 4, 5 show the results of the simulation using the three pairs of personality types. These figures are separated by personality type with the scenarios on the x-axis and the index of the most frequently chosen behavior plotted on the y-axis. As shown by Figs. 3, 4, 5, the two personality types in each pair behave differently across this set of 15 scenarios (Sun and Wilson 2011). We drilled down and explored how individuals of different personalities behave in a given situation. As an example, Fig. 6 shows the comparison between the sociable and the shy in the urgent project scenario. As shown in the figure, the sociable was more likely to talk about work and help others, but was less likely to stay at the periphery; the sociable and the shy were almost equally likely to put in extra effort (because this was an urgent project scenario); and so on. As shown by these figures, the model was adept at exhibiting characteristic behaviors of the different personality types. An individual of a particular personality type acting characteristically within a given situation was the result of the 4 Personality is, in part, the result of interaction among different drives, among other things. There may not necessarily be a direct relationship between the characteristics of a single drive (or a single group of drives) and a hypothesized corresponding trait (as consistent with the view of Smillie et al. 2006).

17 Roles of implicit processes 125 Fig. 3 The most frequent behaviors of the sociable and the shy personality across 15 scenarios. The Y axis shows the behavior indices Fig. 4 The most frequent behaviors of the confident and the anxious personality across 15 scenarios. The Y axis shows the behavior indices Fig. 5 The most frequent behaviors of the responsible and the lazy personality across 15 scenarios. The Y axis shows the behavior indices interaction between the (relatively stable) characteristics of the subsystems and the influence of situations. For instance, the activations of different drives were the results of stable internal motivational parameters, as well as stimuli received from

18 126 R. Sun, N. Wilson Fig. 6 The sociable and the shy in the urgent project scenario situations. Furthermore, which goals were activated at any given moment was partially a result of the competitive interaction among drives and which goal won that competition (stochastically). Behaviors were then (stochastically) determined based on both the goal and the current situation. These processes resulted in part from past learning from past situations within the ACS, as well as within the MS and the MCS. This simulation indicated the role of instinct, defined as relatively fixed patterns of behavior in response to stimuli, involving implicit processes concerning motivation and action selection, in human personality (or human behavior in general). Note that individual differences as captured by different parameters in the model might be, in part, attributed to innate ( hardwired ) differences (due to biological, including genetic, factors), and also, in part, to different individual experiences during ontogenesis (including different experiences of sociocultural influences; more later on learning). 4.2 Simulations of human data It is also important to validate the CLARION personality model based on quantitative human data. Some empirical data available were indeed used to validate the model. For instance, Moskowitz et al. (1994) hypothesized that social roles had a significant effect on an individual s behavior. The results of their experiments confirmed the hypothesis. Significant effects were found for social role. Participants

19 Roles of implicit processes 127 reported significantly more dominance toward supervisees or co-workers than toward bosses. In terms of submissiveness, there was also a significant effect: Participants reported more submissiveness toward bosses than toward co-workers or supervisees. Simulation using the CLARION personality model was conducted, which was similar to the previous simulations sketched earlier. The findings from the simulation were consistent with the human data of Moskowitz et al. In a preliminary way, simulations like this suggested some psychological validity of the CLARION personality model. Furthermore, they suggested some plausible explanations for the data patterns observed in empirical studies (for example, different behaviors exhibited given different roles were attributed to roles as input from situations, rather than personality changes). Because of the involvement of detailed representations and processes (including drives, goals, and action selection, which encompass instinct), the simulation provided deeper looks into the psychological underpinning of the human data, in a detailed, mechanistic, and process-based way, and in the process, suggested some potentially useful mechanistic explanations that might be tested by later empirical studies. 4.3 Instinct and intuition in simulation In the CLARION personality model and simulation, implicit processes play major roles, which include, in particular, the folk psychological notions mentioned before instinct and intuition. Instinct is captured within CLARION by the implicit processes of the ACS in particular, but also the implicit processes of the MS and the MCS. Intuition is captured by the implicit processes of the NACS (but also the implicit processes of the MS and the MCS as their basis). While intuition contributes to personality, instinct is the most important part of personality, as demonstrated by the model and its simulations such as those summarized above. The reader is referred to Sun (2009) and Sun and Wilson (2011) for further details of the model and simulations. CLARION is unique in that it captures and emphasizes implicit processes (especially instinct and intuition) in the human mind, unlike many of the other existing cognitive architectures. Such dual-process theories/models have been developed since the early 90 s (see Sun 1991, 2002, 1994). In particular, our work has been centering on developing computational models (computational cognitive architectures) that provide more detailed, mechanistic looks into the dual processes and their interactions. The effects of their interactions (including the synergy effects ) have been observed and modeled (Sun et al. 2005). 5 Personality shaping and sociocultural influences Beyond simulation of existing human data, the CLARION personality model allows us to explore a number of other important issues. Here we will focus on the tuning of personality, especially the tuning of instinct and intuition, and the role of sociocultural influences in such tuning.