(2013). Statistical models as cognitive models of individual differences in reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 89-102. doi: 10.1080/19462166.2012.674061.

Cognitive Models of Science resulted from a workshop on the implications of the cognitive sciences for the philosophy of science held in October 1989 under the ...

Modelling human reasoning characterizes the fundamental human cognitive capacity to describe our past experience and use it to form expectations as well as plan and direct our future actions. Natural language semantics analyzes dynamic forms of reasoning in which the real-time order determines the temporal relations between the described events, when reported with telic simple past-tense clauses. It provides models of human reasoning that could supplement ACT-R models.

This paper presents a cognitive model of the planning process. The model generalizes the theoretical architecture of the Hearsay‐ll system. Thus, it assumes that planning comprises the activities of a variety of cognitive “specialists.” Each specialist can suggest certain kinds of decisions for incorporation into the plan in progress. These include decisions about: (a) how to approach the planning problem; (b) what knowledge bears on the problem; (c) what kinds of actions to try to plan; (d) what specific (...) actions to plan; and (e) how to allocate cognitive resources during planning. Within each of these categories, different specialists suggest decisions at different levels of abstraction. The activities of the various specialists are not coordinated in any systematic way. Instead, the specialists operate opportunistically, suggesting decisions whenever promising opportunities arise. The paper presents a detailed account of the model and illustrates its assumptions with a “thinking aloud” protocol. It also describes the performance of a computer simulation of the model. The paper contrasts the proposed model with successive refinement models and attempts to resolve apparent differences between the two points of view. (shrink)

Recent cognitive models of delusions have attempted to explain why delusions are commonly circumscribed, in that they tend to center on isolated beliefs, and incorrigible, as they often persist in the face of countervailing evidence. In this effort, the suggestion has been made that ‘modularity’ – a concept originating in seminal accounts of the architecture of perceptual and cognitive processes – may help explain delusions. The aim of this paper is to take up this suggestion, critically evaluating the (...) applicability of modularity and its attendant features to the explanation of delusions. The conclusion presented here is that although a modular explanation of delusions may be relatively consistent with models that implicate dysfunctional cognitive components such as motor control and ‘theory of mind’ (ToM) systems, ultimately the application of modularity to delusions conflicts with traditional understandings of cognitive architecture and does not help provide satisfactory or sufficient explanations of delusional symptomatology. (shrink)

Inferring an individual's preferences from their observable behavior is a key step in the development of assistive decision-making technology. Although machine learning models such as neural networks could in principle be deployed toward this inference, a large amount of data is required to train such models. Here, we present an approach in which a cognitive model generates simulated data to augment limited human data. Using these data, we train a neural network to invert the model, making it possible to (...) infer preferences from behavior. We show how this approach can be used to infer the value that people assign to food items from their eye movements when choosing between those items. We demonstrate first that neural networks can infer the latent preferences used by the model to generate simulated fixations, and second that simulated data can be beneficial in pretraining a network for predicting human-reported preferences from real fixations. Compared to inferring preferences from choice alone, this approach confers a slight improvement in predicting preferences and also allows prediction to take place prior to the choice being made. Overall, our results suggest that using a combination of neural networks and model-simulated training data is a promising approach for developing technology that infers human preferences. (shrink)

We analyze the dynamics of repeated interaction of two players in the Prisoner's Dilemma under various levels of interdependency information and propose an instance-based learning cognitive model to explain how cooperation emerges over time. Six hypotheses are tested regarding how a player accounts for an opponent's outcomes: the selfish hypothesis suggests ignoring information about the opponent and utilizing only the player's own outcomes; the extreme fairness hypothesis weighs the player's own and the opponent's outcomes equally; the moderate fairness hypothesis (...) weighs the opponent's outcomes less than the player's own outcomes to various extents; the linear increasing hypothesis increasingly weighs the opponent's outcomes at a constant rate with repeated interactions; the hyperbolic discounting hypothesis increasingly and nonlinearly weighs the opponent's outcomes over time; and the dynamic expectations hypothesis dynamically adjusts the weight a player gives to the opponent's outcomes, according to the gap between the expected and the actual outcomes in each interaction. When players lack explicit feedback about their opponent's choices and outcomes, results are consistent with the selfish hypothesis; however, when this information is made explicit, the best predictions result from the dynamic expectations hypothesis. (shrink)

Throws down a challenge to religious studies, offering a multidisciplinary approach - including developmental psychology, neuropsychology, philosophy of mind, and anthropology.

People often face preferential decisions under risk. To further our understanding of the cognitive processes underlying these preferential choices, two prominent cognitive models, decision field theory (DFT; Busemeyer & Townsend, 1993) and the proportional difference model (PD; González‐Vallejo, 2002), were rigorously tested against each other. In two consecutive experiments, the participants repeatedly had to choose between monetary gambles. The first experiment provided the reference to estimate the models’ free parameters. From these estimations, new gamble pairs were generated for (...) the second experiment such that the two models made maximally divergent predictions. In the first experiment, both models explained the data equally well. However, in the second generalization experiment, the participants’ choices were much closer to the predictions of DFT. The results indicate that the stochastic process assumed by DFT, in which evidence in favor of or against each option accumulates over time, described people’s choice behavior better than the trade‐offs between proportional differences assumed by PD. (shrink)

Cognitive scientists often describe the mind as constructing and using models of aspects of the environment, but it is not obvious what makes something a model as opposed to a mere representation....

Theories concerning the structure, or format, of mental representation should (1) be formulated in mechanistic, rather than metaphorical terms; (2) do justice to several philosophical intuitions about mental representation; and (3) explain the human capacity to predict the consequences of worldly alterations (i.e., to think before we act). The hypothesis that thinking involves the application of syntax-sensitive inference rules to syntactically structured mental representations has been said to satisfy all three conditions. An alternative hypothesis is that thinking requires the construction (...) and manipulation of the cognitive equivalent of scale models. A reading of this hypothesis is provided that satisfies condition (1) and which, even though it may not fully satisfy condition (2), turns out (in light of the frame problem) to be the only known way to satisfy condition (3). (shrink)

I will present the rationale followed for the conceptualization and the following development the Dual PECCS system that relies on the cognitively grounded heterogeneous proxytypes representational hypothesis. Such hypothesis allows integrating exemplars and prototype theories of categorization and has provided useful insights in the context of cognitive modelling for what concerns the typicality effects in categorization. As argued in [Chella et al., 2017] [Lieto et al., 2018b] [Lieto et al., 2018a] a pivotal role in this respect is played (...) by the use of the conceptual spaces framework and by its integration with a symbolic knowledge representation layer. (shrink)

Models of psychological time / Richard A. Block -- Implicit and explicit representations of time / John A. Michon -- The evasive art of subjective time...

This paper intends to present a cognitive model of anuvyavasāya through causal and logical analysis of the moment examinations (kṣaṇavicāra), remaining consistent with the fundamental presuppositions of the Nyāya system. The Naiyāyikas hold that no cognition is self-revealing in nature. A subsequent mental perception, introspection or after-perception (anuvyavasāya) reveals the determinate cognition. In anuvyavasāya, along with the cognition and Self, the object of determinate cognition (vyavasāya) also is known. The vyavasāya itself, working as cognition-induced extraordinary sensory connection (jñānalakṣaṇa alaukika (...) sannikarṣa), connects its object to the mental sense-organ. We can formulate the moment examination of the after-perception ‘I know that it is a pitcher’ in the following way: (M1) sense-pitcher connection, (M2) indeterminate perception of pitcher, (M3) determinate perception of pitcher, (M4) indeterminate perception of the vyavasāya, (M5) determinate mental perception of vyavasāya, i.e. anuvyavasāya. But, according to the Naiyāyikas cognition exists only for two moments. So at M5, the vyavasāya is destroyed, which is the object or cause of anuvyavasāya. Gaṅgeśopādhyāya replies that a cause is only required to exist at the immediately previous moment of the effect. The paper analyses the moment examinations of anuvyavasāya offered by Udayanācārya and Vardhamānopādhyāya and tries to arrive at a consistent model of anuvyavasāya. The paper also discusses whether at all anuvyavasāya is an instance of jñānalakṣaṇa. Udayana’s account suggests that it is so, but Gadādhara does not admit it. On the other hand, Harirāma Tarkavāgīśa and Raghunātha Śiromaṇi hold that like vyavasāya all the internal states having contents (saviṣayaka) may work as jñānalakṣaṇa sannikarṣa. (shrink)

This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can (...) be defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools. (shrink)

An Interdisciplinary Approach to Cognitive Modelling presents a new approach to cognition that challenges long-held views. It systematically develops a broad-based framework to model cognition, which is mathematically equivalent to the emerging 'quantum-like modelling' of the human mind. The book argues that a satisfactory physical and philosophical basis of such an approach is missing, a particular issue being the application of quantization to the mind for which there is no empirical evidence as yet. In response to this (...) issue, the book adopts a COM (classical optical modelling) approach, broad-based but mathematically equivalent to quantum-like modelling while avoiding its problematic features. It presents a philosophically informed and empirically motivated mathematical model of cognition, mainly concerning decision making processes. It also deals with applications to different areas of the social sciences. It will be of interest to scholars and research students interested in the mathematical modelling of cognition and decision making, and also interdisciplinary researchers interested in broader issues of cognition. (shrink)

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used (...) to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data). (shrink)

This paper presents an attempt to bridge the gap between logical and cognitive treatments of strategic reasoning in games. There have been extensive formal debates about the merits of the principle of backward induction among game theorists and logicians. Experimental economists and psychologists have shown that human subjects, perhaps due to their bounded resources, do not always follow the backward induction strategy, leading to unexpected outcomes. Recently, based on an eye-tracking study, it has turned out that even human subjects (...) who produce the outwardly correct ‘backward induction answer’ use a different internal reasoning strategy to achieve it. The paper presents a formal language to represent different strategies on a finer-grained level than was possible before. The language and its semantics help to precisely distinguish different cognitive reasoning strategies, that can then be tested on the basis of computational cognitive models and experiments with human subjects. The syntactic framework of the formal system provides a generic way of constructing computational cognitive models of the participants of the Marble Drop game. (shrink)

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress (...) of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results. (shrink)

Several accounts of representation in cognitive systems have recently been proposed. These look for a theory that will establish how a representation comes to have a certain content, and how these representations are used by cognitive systems. Covariation accounts are unsatisfactory, as they make intelligent reasoning and cognition impossible. Cummins' interpretation-based account cannot explain the distinction between cognitive and non-cognitive systems, nor how certain cognitive representations appear to have intrinsic meaning. Cognitive systems can be (...) defined as model-constructers, or systems that use information from interpreted models as arguments in the functions they execute. An account based on this definition solves many of the problems raised by the earlier proposals. (shrink)

In this paper, we propose a new mathematical model of retrieval of intentions from long-term memory. We model retrieval as a stochastic race between a plurality of potentially relevant intentions stored in long-term memory. Psychological theories are dominated by two opposing conceptions of the role of memory in temporally extended agency – as when a person has to remember to make a phone call in the afternoon because, in the morning, she promised she would do so. According to the Working (...) Memory conception, remembering to make the phone call is explained in terms of the construction and maintenance of intentions in working-memory. According to the Long-Term Memory conception, we should explain the episode in terms of an ability to store intentions in long-term memory. The two conceptions predict different processing profiles. The aim of this paper is to present a new mathematical model of the type of memory mechanism that could realise the long-term memory representations of intentions necessary for the Long-Term Memory conception. We present and illustrate the formal model and propose a new type of experimental paradigm that could allow us to test which of the two conceptions provides the best explanation of the role of memory in temporally extended agency. (shrink)

Here is a puzzle about aesthetic education. In a variety of contexts, we commit significant time, energy, and resources to aesthetic education. We teach (and in many cases publicly subsidize) university courses and degrees that have aesthetic education as their primary aim; we also invest public resources into museums, including enrichment programs that are also designed to afford aesthetic education. It would seem that if our commitment to aesthetic education is rational, then aesthetic appreciation is something that can be done (...) better or worse. However, we also, in a variety of contexts (oddly enough, some of them being the same sorts of contexts that are designed to abet aesthetic education), act as if it is true that there is no disputing taste. We may try to persuade students to come around to particular judgments, but we do not penalize students for judging one way or another. The aim of this paper is to dissolve the apparent puzzle of aesthetic education by clarifying its aims and advancing a conception of it that deemphasizes the role of taste. I claim that, pace “the default view of aesthetic education” (as I shall call it), the primary purpose of aesthetic education is not to educate taste. It is, rather, to facilitate the development of certain perceptual-cognitive capacities so as to enhance aesthetic experience and improve aesthetic appreciation. Thus, I call the view of aesthetic education advanced here “a perceptual-cognitive model”. (shrink)

This paper provides a general defense of the idea that the cognitive sciences provide models that are useful for exploring issues that have traditionally occupied philosophers of science. Questions about the nature of theories, for example, are assimilated into studies of the nature of cognitive representations, while questions concerning the choice of theories fall under studies of human judgment and decision making. The implications of adopting "a cognitive approach" are explored, particularly the rejection of foundationist epistemologies which (...) might provide a philosophical justification of science. Instead I suggest a scientific foundation provided by evolutionary biology and the scientific goal of explaining science as a human phenomenon. (shrink)

The purpose of cognitive models is to make predictive simulations of human behaviour, but this is often done at the aggregate level. Cranford, Gonzalez, Aggarwal, Cooney, Tambe, and Lebiere show that they can automatically customize a model to a particular individual on‐the‐fly, and use it to make specific predictions about their next actions, in the context of a particular cybersecurity game.

In the last seven years, philosophers have discussed the topic of transformative experiences. In this paper, we contribute to a crucial issue that is currently under-researched: transformative experiences' influence on cognitive modelling. We argue that cognitive modelling can be operationalized as affective forecasting, and we compare transformative and non-transformative experiences with respect to the ability of affective forecasting. Our finding is that decision-makers’ performance in cognitively modelling transformative experiences does not systematically differ from decision-makers’ performance (...) in cognitively modelling non-transformative experiences. This claim stands in strict opposition to L.A. Paul’s main argument. (shrink)

The rubber hand illusion (RHI) is the experience of an artificial body part as being a real body part and the experience of touch coming from that artificial body part. An explanation of this illusion would take significant steps towards explaining the experience of embodiment in one’s own body. I present a new cognitive model to explain the RHI. I argue that the sense of embodiment arises when an on-line representation of the candidate body part is represented as matching (...) an off-line prototype representation of what one’s body is usually like. The cause of the sense of embodiment in the model body part only partially overlaps with the causes of proprioceptive drift, which commonly accompanies the RHI, and so is compatible with observed dissociations between the illusion and proprioceptive drift. The distinguishing features of this model are the off-line body representation, and the process of matching an on-line model to an off-line model, both of which are to be understood in terms of a conceptual space. -/- Key Words: Rubber Hand Illusion; RHI; Sense of Embodiment; Self Consciousness; Body Experience. (shrink)

The Iowa Gambling Task (IGT) and the Soochow Gambling Task (SGT) are two experience-based risky decision-making tasks for examining decision-making deficits in clinical populations. Several cognitive models, including the expectancy-valence learning model (EVL) and the prospect valence learning model (PVL), have been developed to disentangle the motivational, cognitive, and response processes underlying the explicit choices in these tasks. The purpose of the current study was to develop an improved model that can fit empirical data better than the EVL (...) and PVL models and, in addition, produce more consistent parameter estimates across the IGT and SGT. Twenty-six opiate users (mean age 34.23; SD 8.79) and 27 control participants (mean age 35; SD 10.44) completed both tasks. Eighteen cognitive models varying in evaluation, updating, and choice rules were fit to individual data and their performances were compared to that of a statistical baseline model to find a best fitting model. The results showed that the model combining the prospect utility function treating gains and losses separately, the decay-reinforcement updating rule, and the trial-independent choice rule performed the best in both tasks. Furthermore, the winning model produced more consistent individual parameter estimates across the two tasks than any of the other models. (shrink)

The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact to (...) raise additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm. -/- ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition. -/- The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour. -/- ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized. (shrink)

Automated moral inference is an emerging topic of critical importance in artificial intelligence. The contemporary approach typically relies on language models to infer moral relevance or moral properties of a concept. This approach demands complex parameterization and costly computation, and it tends to disconnect with existing psychological accounts of moralization. We present a simple cognitive model for moral inference, Moral Association Graph (MAG), inspired by psychological work on moralization. Our model builds on word association network for inferring moral relevance (...) and draws on rich psychological data. We demonstrate that MAG performs competitively to state-of-the-art language models when evaluated against a comprehensive set of data for automated inference of moral norms and moral judgment of concepts, and in-context moral inference. We also show that our model yields interpretable outputs and is applicable to informing short-term moral change. (shrink)

The psycholinguistic literature has identified two syntactic adaptation effects in language production: rapidly decaying short-term priming and long-lasting adaptation. To explain both effects, we present an ACT-R model of syntactic priming based on a wide-coverage, lexicalized syntactic theory that explains priming as facilitation of lexical access. In this model, two well-established ACT-R mechanisms, base-level learning and spreading activation, account for long-term adaptation and short-term priming, respectively. Our model simulates incremental language production and in a series of modeling studies, we show (...) that it accounts for (a) the inverse frequency interaction; (b) the absence of a decay in long-term priming; and (c) the cumulativity of long-term adaptation. The model also explains the lexical boost effect and the fact that it only applies to short-term priming. We also present corpus data that verify a prediction of the model, that is, that the lexical boost affects all lexical material, rather than just heads. (shrink)

A major challenge for research in artificial intelligence is to develop systems that can infer the goals, beliefs, and intentions of others (i.e., systems that have theory of mind, ToM). In this research, we propose a cognitive ToM framework that uses a well-known theory of decisions from experience to construct a computational representation of ToM. Instance-based learning theory (IBLT) is used to construct a cognitive model that generates ToM from the observation of other agents' behavior. The IBL model (...) of the observer distinguishes itself from previous models of ToM that make unreasonable assumptions about human cognition, are hand-crafted for particular settings, complex, or unable to explain a cognitive development of ToM compared to human's ToM. The IBL model learns from the observation of goal-directed agents' behavior in a gridworld navigation task, and it infers and predicts the behaviors of the agents in new gridworlds across different degrees of decision complexity in similar ways to the way human observers do. We provide evidence for the alignment of the IBL observer's predictions under various levels of decision complexity. We also advance the demonstration of the IBL predictions using a classic test of false beliefs (the Sally–Anne test), which is commonly used to test ToM in humans. We discuss our results and the potential of the IBL observer model to improve human–machine interactions. (shrink)

It is suggested that the impetus to generate models is probably the most fundamental point of connection between mysticism and psychology. In their concern with the relation between ‘unseen’ realms and the ‘seen’, mystical maps parallel cognitive models of the relation between ‘unconscious’ and ‘conscious’ processes. The map or model constitutes an explanation employing terms current within the respective canon. The case of language mysticism is examined to illustrate the premise that cognitive models may benefit from an understanding (...) of the kinds of experiences gained, and explanatory concepts advanced, within mystical traditions. Language mysticism is of particular interest on account of the central role thought to be played by language in relation to self and the individual's construction of reality. The discussion focuses on traditions of language mysticism within Judaism, in which emphasis is placed on the deconstruction of language into primary elements and the overarching significance of the divine Name. Analysis of the detailed techniques used suggests ways in which multiple associations to any given word/concept were consciously explored in an altered state. It appears that these mystics were consciously engaging with what are normally preconscious cognitive processes, whereby schematic associations to sensory images or thoughts are activated. The testimony from their writings implies that these mystics experienced distortions of the sense of self , which may suggest that, in the normal state, ‘I’ is constructed in relation to the preconscious system of associations. Moreover, an important feature of Hebrew language mysticism is its emphasis on embodiment -- specific associations were deemed to exist between the letters and each structure of the body. Implications, first, for the relationship between language and self, and, second, for the role of embodiment in relation to self are discussed. The importance of the continual emphasis on the Name of God throughout the linguistic practices may have provided a means for effectively replacing the cognitive indexing function hypothesized here to be normally played by ‘I’ with a more transpersonal cognitive index, especially in relation to memory. (shrink)