Autonomous construction of ecologically and socially relevant semantics

This article presents a synthetic modeling approach to the problem of grounded construction of concepts. In many computational models of grounded language acquisition and evolution, meanings are created in the process of discrimination between a chosen object and other objects present on the scene of communication. We argue that categories constructed for the purpose of identification rather than discrimination are more suitable for the detached language use (talking about things not present here and now). We describe a semantics based on so-called identification criteria constructed by extracting cross-situational similarities among instances of a category, and present several computational models. In the model of individual category construction, the instances are grouped to categories by common motor programs (affordances), while in the model of social learning, focused on the influence of naming on category formation, entities are considered members of the same category, if they are labeled with the same word by an external teacher. By these two mechanisms, the learner can construct interactionally grounded representation of objects, properties, relations, changes, complex situations and events. We also report and analyze simulation results of an experiment focused on the dynamics of meanings in iterated intergenerational transmission.     

Multiple systems of spatial memory and action

Recent findings from spatial cognition and cognitive neuroscience suggest that different types of mental representations could mediate the off-line retrieval of spatial relations from memory and the on-line guidance of motor actions in space. As a result, a number of models proposing multiple systems of spatial memory have been recently formulated. In the present article we review these models and we evaluate their postulates based on available experimental evidence. Furthermore, we discuss how a multiple-system model can apply to situations in which people reason about their immediate surroundings or non-immediate environments by incorporating a model of sensorimotor facilitation/interference. This model draws heavily on previous accounts of sensorimotor interference and takes into account findings from the stimulus-response compatibility literature.     

What you don’t know won’t hurt me: Costly (but quiet) exit in dictator games

We used simple economic games to examine pro-social behavior and the lengths that people will take to avoid engaging in it. Over two studies, we found that about one-third of participants were willing to “exit” a $10 dictator game and take $9 instead. The exit option left the receiver nothing, but also ensured that the receiver never knew that a dictator game was to be played. Because most social utility models are defined over monetary outcomes, they cannot explain choosing the ($9, $0) exit outcome over the dominating $10 dictator game, since the game includes outcomes of ($10, $0) and ($9, $1). We also studied exiting using a “private” dictator game. In the private game, the receiver never knew about the game or from where any money was received. Gifts in this game were added innocuously to a payment for a separate task. Almost no dictators exited from the private game, indicating that receivers’ beliefs are the key factor in the decision to exit. When, as in the private game, the receivers’ beliefs and expectations cannot be manipulated by exit, exit is seldom taken. We conclude that giving often reflects a desire not to violate others’ expectations rather than a concern for others’ welfare per se. We discuss the implications of our results for understanding ethical decisions and for testing and modeling social preferences. An adequate specification of social preferences should include “psychological” payoffs that directly incorporate beliefs about actions into the utility function.     

Differentiating between mixed-effects and latent-curve approaches to growth modeling

In psychology, mixed-effects models and latent-curve models are both widely used to explore growth over time. Despite this widespread popularity, some confusion remains regarding the overlap of these different approaches. Recent articles have shown that the two modeling frameworks are mathematically equivalent in many cases, which is often interpreted to mean that one’s choice of modeling framework is merely a matter of personal preference. However, some important differences in estimation and specification can lead to the models producing very different results when implemented in software. Thus, mathematical equivalence does not necessarily equate to practical equivalence in all cases. In this article, we discuss these two common approaches to growth modeling and highlight contexts in which the choice of the modeling framework (and, consequently, the software) can directly impact the model estimates, or in which certain analyses can be facilitated in one framework over the other. We show that, unless the data are pristine, with a large sample size, linear or polynomial growth, and no missing data, and unless the participants have the same number of measurements collected at the same set of time points, one framework is often more advantageous to adopt. We provide several empirical examples to illustrate these situations, as well as ample software code so that researchers can make informed decisions regarding which framework will be the most beneficial and most straightforward for their research interests.     

Simulation and counselling

Simulations are basically models of social and physical situations. Their educational value and the problems involved in using simulation techniques are analysed, and some possible applications of these techniques in counselling are outlined: these include applications in counsellor training, in one-to-one and three-person counselling situations, and in preparing pupils for such experiences as job interviews. But dangers of “faddishness” have to be avoided, and more rigorous research into the effectiveness of simulation techniques is still needed.     

The Effect of Life-Course Transitions on Delinquent Behavior among Youth in Social Withdrawal Situation

This study investigates whether young people in social withdrawal situations pass through life-course transitional events, and examines the relationship among their life-course transitions, quality of life, and delinquent behavior. To achieve these aims, quantitative analysis was used. A total of 588 young people in social withdrawal situations took part in this study. Results suggest that these people do pass through life-course transitions; their life-course events affect whether they will engage in delinquent behavior. Also, the effect of life-course transitions of these young people on their involvement in delinquent behavior is partly impacted by their quality of life.     

Mathematical models and the experimental analysis of behavior

The use of mathematical models in the experimental analysis of behavior has increased over the years, and they offer several advantages. Mathematical models require theorists to be precise and unambiguous, often allowing comparisons of competing theories that sound similar when stated in words. Sometimes different mathematical models may make equally accurate predictions for a large body of data. In such cases, it is important to find and investigate situations for which the competing models make different predictions because, unless two models are actually mathematically equivalent, they are based on different assumptions about the psychological processes that underlie an observed behavior. Mathematical models developed in basic behavioral research have been used to predict and control behavior in applied settings, and they have guided research in other areas of psychology. A good mathematical model can provide a common framework for understanding what might otherwise appear to be diverse and unrelated behavioral phenomena. Because psychologists vary in their quantitative skills and in their tolerance for mathematical equations, it is important for those who develop mathematical models of behavior to find ways (such as verbal analogies, pictorial representations, or concrete examples) to communicate the key premises of their models to nonspecialists.     

Negative Correlations between Control Performance and Verbalizable Knowledge: Indicators for Implicit Learning in Process Control Tasks?

Negative correlations between task performance in dynamic control tasks and verbalizable knowledge, as assessed by a post-task questionnaire, have been interpreted as dissociations that indicate two antagonistic modes of learning, one being “explicit”, the other “implicit”. This paper views the control tasks as finite-state automata and offers an alternative interpretation of these negative correlations. It is argued that “good controllers” observe fewer different state transitions and, consequently, can answer fewer post-task questions about system transitions than can “bad controllers”. Two experiments demonstrate the validity of the argument by showing the predicted negative relationship between control performance and the number of explored state transitions, and the predicted positive relationship between the number of explored state transitions and questionnaire scores. However, the experiments also elucidate important boundary conditions for the critical effects. We discuss the implications of these findings, and of other problems arising from the process control paradigm, for conclusions about implicit versus explicit learning processes.     

The association between ruminative thinking and negative interpretation bias in social anxiety

Cognitive models propose that both, negative interpretations of ambiguous social situations and ruminative thoughts about social events contribute to the maintenance of social anxiety disorder. It has further been postulated that ruminative thoughts fuel biased negative interpretations, however, evidence is rare. The present study used a multi-method approach to assess ruminative processing following a social interaction (post-event processing by self-report questionnaire and social rumination by experience sampling method) and negative interpretation bias (via two separate tasks) in a student sample (n?=?51) screened for high (HSA) and low social anxiety (LSA). Results support the hypothesis that group differences in negative interpretations of ambiguous social situations in HSAs vs. LSAs are mediated by higher levels of post-event processing assessed in the questionnaire. Exploratory analyses highlight the potential role of comorbid depressive symptoms. The current findings help to advance the understanding of the association between two cognitive processes involved in social anxiety and stress the importance of ruminative post-event processing.     

Interpersonal Expectations as the Building Blocks of Social Cognition: An Interdependence Theory Perspective

In this paper I use interdependence theory as an analytic framework for depicting the logically interconnected network of expectations that determines social interaction. The framework focuses on expectations about a partner’s goals (B) relevant to particular interdependence situations (S), and suggests that expectations about these two elements define the social situation that activates a person’s own goals (A). Together, these elements determine interaction behavior (I). This SABI framework is complementary to Mischel and Shoda’s (1995) CAPS theory of personality in its logic. It depicts a person’s interpersonal dispositions as having profiles or signatures dependent on both the expected features of situations and the expected dispositions of partners. A taxonomic theory for classifying both situations and the functionally relevant goals of interaction partners is outlined. Research on attachment theory and trust is used to illustrate the model. Finally, I suggest that people’s expectations about partners’ prosocial motivations—their perceived responsiveness toward the self—play an imperial role in social cognition, and, further, that complex SABI models can be seen as detailing a set of security operations that serve as a program for social action. SABI models detail the set of mechanisms that constitute the basic survival kit of interpersonal relations.     

A Computationally Grounded,Weighted Doxastic Logic

Modelling, reasoning and verifying complex situations involving a system of agents is crucial in all phases of the development of a number of safety-critical systems. In particular, it is of fundamental importance to have tools and techniques to reason about the doxastic and epistemic states of agents, to make sure that the agents behave as intended. In this paper we introduce a computationally grounded logic called COGWED and we present two types of semantics that support a range of practical situations. We provide model checking algorithms, complexity characterisations and a prototype implementation. We validate our proposal against a case study from the avionic domain: we assess and verify the situational awareness of pilots flying an aircraft with several automated components in off-nominal conditions.     

Incompatibility Semantics from Agreement

In this paper, I discuss the analysis of logic in the pragmatic approach recently proposed by Brandom. I consider different consequence relations, formalized by classical, intuitionistic and linear logic, and I will argue that the formal theory developed by Brandom, even if provides powerful foundational insights on the relationship between logic and discursive practices, cannot account for important reasoning patterns represented by non-monotonic or resource-sensitive inferences. Then, I will present an incompatibility semantics in the framework of linear logic which allow to refine Brandom’s concept of defeasible inference and to account for those non-monotonic and relevant inferences that are expressible in linear logic. Moreover, I will suggest an interpretation of discursive practices based on an abstract notion of agreement on what counts as a reason which is deeply connected with linear logic semantics.     

Perception: The interface between cognition and the external world

In contrast to researchers who emphasize the distinction between perception and cognition it is claimed that the perceptual system is closely intertwined with cognition and with our comprehension of the external world. This claim is supported by evidence for two characteristic features of human information processing. One of these features is the use of mental models. If mental models are assumed to be anchored in the perceptual system it will not be necessary to assume a "little man in the head" who reads off the model. Moreover, perceptually based mental models will provide a referential semantics by anchoring mentally represented concepts in the external world. The other feature is the use of perceptually based perspectives in cognition, such as actor or observer perspectives in social cognition. It is concluded that human beings consistently interact within the real world--also when they are just thinking--and that this becomes possible via perceptual processes.     

Imagination inflation: Imagining a childhood event inflates confidence that it occurred

Counterfactual imaginings are known to have far-reaching implications. In the present experiment, we ask if imagining events from one’s past can affect memory for childhood events. We draw on the social psychology literature showing that imagining a future event increases the subjective likelihood that the event will occur. The concepts of cognitive availability and the source-monitoring framework provide reasons to expect that imagination may inflate confidence that a childhood event occurred. However, people routinely produce myriad counterfactual imaginings (i.e., daydreams and fantasies) but usually do not confuse them with past experiences. To determine the effects of imagining a childhood event, we pretested subjects on how confident they were that a number of childhood events had happened, asked them to imagine some of those events, and then gathered new confidence measures. For each of the target items, imagination inflated confidence that the event had occurred in childhood. We discuss implications for situations in which imagination is used as an aid in searching for presumably lost memories.     

Reasoning from Suppositions

Two experiments investigated inferences based on suppositions. In Experiment 1, the subjects decided whether suppositions about individuals' veracity were consistent with their assertions—for example, whether the supposition “Ann is telling the truth and Beth is telling a lie”, is consistent with the premises: “Ann asserts: I am telling the truth and Beth is telling the truth. Beth asserts: Ann is telling the truth”. It showed that these inferences are more difficult than ones based on factual premises: “Ann asserts: I live in Dublin and Beth lives in Dublin”. There was no difference between problems about truthtellers and liars, who always told the truth or always lied, and normals, who sometimes told the truth and sometimes lied. In Experiment 2, the subjects made inferences about factual matters set in three contexts: a truth-inducing context in which friends confided their personality characteristics, a lie-inducing context in which business rivals advertised their products, and a neutral context in which computers printed their program characteristics. Given the supposition that the individuals were lying, it was more difficult to make inferences in a truth-inducing context than in the other two contexts. We discuss the implications of our results for everyday reasoning from suppositions, and for theories of reasoning based on models or inference rules.     

Multivalent Semantics for Vagueness and Presupposition

Both the phenomenon of presupposition and that of vagueness have motivated the use of one form or another of trivalent logic, in which a declarative sentence can not only receive the standard values true (1) and false (0), but also a third, non-standard truth-value which is usually understood as ‘undefined’ (#). The goal of this paper is to propose a multivalent framework which can deal simultaneously with presupposition and vagueness, and, more specifically, capture their projection properties as well as their different roles in language. Now, there is a prima facie simple way of doing this, which simply consists in assimilating the two phenomena, and using an appropriate type of trivalent logic. On this view, we just need a compositional system that deals with the ‘undefined’ truth-value, and does not care about whether the source of undefinedness is ‘presuppositional’ or related to vagueness. I will argue that such a simple solution cannot succeed, and point out a number of desiderata that any successful approach must meet. I will then present and discuss two seven-valued semantics, inspired, respectively, by the Strong Kleene semantics and by supervaluationism, which meet these desiderata.     

Is Human Learning Rational?

We can predict and control events in the world via associative learning. Such learning is rational if we come to believe that an associative relationship exists between a pair of events only when it truly does. The statistical metric ΔP, the difference between the probability of an outcome event in the presence of the predictor and its probability in the absence of the predictor tells us when and to what extent events are indeed related. Contrary to what is often claimed, humans' associative judgements compare very favourably with the ΔP metric, even in situations where multiple predictive cues are in competition for association with the outcome. How do humans achieve this judgemental accuracy? I argue that it is not via the application of an explicit mental version of the ΔP rule. Instead, accurate judgements are an emergent property of an associationist learning process of the sort that has become common in adaptive network models of cognition. Such an associationist mechanism is the “means” to a normative or statistical “end”.     

The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning

Determining the knowledge that guides human judgments is fundamental to understanding how people reason, make decisions, and form predictions. We use an experimental procedure called 'iterated learning,' in which the responses that people give on one trial are used to generate the data they see on the next, to pinpoint the knowledge that informs people's predictions about everyday events (e.g., predicting the total box office gross of a movie from its current take). In particular, we use this method to discriminate between two models of human judgments: a simple Bayesian model ( Griffiths & Tenenbaum, 2006 ) and a recently proposed alternative model that assumes people store only a few instances of each type of event in memory (Min K ; Mozer, Pashler, & Homaei, 2008 ). Although testing these models using standard experimental procedures is difficult due to differences in the number of free parameters and the need to make assumptions about the knowledge of individual learners, we show that the two models make very different predictions about the outcome of iterated learning. The results of an experiment using this methodology provide a rich picture of how much people know about the distributions of everyday quantities, and they are inconsistent with the predictions of the Min K model. The results suggest that accurate predictions about everyday events reflect relatively sophisticated knowledge on the part of individuals.