Global Cue Inconsistency Diminishes Learning of Cue Validity

We present a novel two-stage probabilistic learning task that examines the participants’ ability to learn and utilize valid cues across several levels of probabilistic feedback. In the first stage, participants sample from one of three cues that gives predictive information about the outcome of the second stage. Participants are rewarded for correct prediction of the outcome in stage two. Only one of the three cues gives valid predictive information and thus participants can maximise their reward by learning to sample from the valid cue. The validity of this predictive information, however, is reinforced across several levels of probabilistic feedback. A second manipulation involved changing the consistency of the predictive information in stage one and the outcome in stage two. The results show that participants, with higher probabilistic feedback, learned to utilise the valid cue. In inconsistent task conditions, however, participants were significantly less successful in utilising higher validity cues. We interpret this result as implying that learning in probabilistic categorization is based on developing a representation of the task that allows for goal-directed action

Constraining bridges between levels of analysis : a computational justification for locally Bayesian learning

Different levels of analysis provide different insights into behavior: computational-level analyses determine the problem an organism must solve and algorithmic-level analyses determine the mechanisms that drive behavior. However, many attempts to model behavior are pitched at a single level of analysis. Research into human and animal learning provides a prime example, with some researchers using computational-level models to understand the sensitivity organisms display to environmental statistics but other researchers using algorithmic-level models to understand organisms’ trial order effects, including effects of primacy and recency. Recently, attempts have been made to bridge these two levels of analysis. Locally Bayesian Learning (LBL) creates a bridge by taking a view inspired by evolutionary psychology: Our minds are composed of modules that are each individually Bayesian but communicate with restricted messages. A different inspiration comes from computer science and statistics: Our brains are implementing the algorithms developed for approximating complex probability distributions. We show that these different inspirations for how to bridge levels of analysis are not necessarily in conflict by developing a computational justification for LBL. We demonstrate that a scheme that maximizes computational fidelity while using a restricted factorized representation produces the trial order effects that motivated the development of LBL. This scheme uses the same modular motivation as LBL, passing messages about the attended cues between modules, but does not use the rapid shifts of attention considered key for the LBL approximation. This work illustrates a new way of tying together psychological and computational constraints

Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account.Mitsubishi Electronic Research LaboratoriesUnited States. Air Force Office of Sponsored ResearchMassachusetts Institute of Technology. Paul E. Newton ChairJames S. McDonnell Foundatio

Inferential Dependencies in Causal Inference: A Comparison of Belief-Distribution and Associative Approaches

Causal evidence is often ambiguous, and ambiguous evidence often gives rise to inferential dependencies, where learning whether one cue causes an effect leads the reasoner to make inferences about whether other cues cause the effect. There are 2 main approaches to explaining inferential dependencies. One approach, adopted by Bayesian and propositional models, distributes belief across multiple explanations, thereby representing ambiguity explicitly. The other approach, adopted by many associative models, posits within-compound associations-associations that form between potential causes-that, together with associations between causes and effects, support inferences about related cues. Although these fundamentally different approaches explain many of the same results in the causal literature, they can be distinguished, theoretically and experimentally. We present an analysis of the differences between these approaches and, through a series of experiments, demonstrate that models that distribute belief across multiple explanations provide a better characterization of human causal reasoning than models that adopt the associative approach

Rationality of cognition:a meta-theoretical and methodological analysis of formal cognitive theories

Predmet metateorijske i metodološke analize u ovoj tezi je naučni status koncepta racionalnosti saznanja u kompjutacionoj kognitivnoj psihologiji (KKP). Na prvom nivou analize, racionalnost se analizira kao predmet proučavanja kognitivne psihologije. U tom proučavanju je moguće doći do zaključka o tome da je neka kognitivna funkcija racionalna ili da je ograničeno racionalna: ovo empirijsko pitanje je karakteristično za savremenu debatu o racionalnosti. Na drugom nivou analize, racionalnost predstavlja teorijski i metodološki koncept koji je a priori ugra en u temelje savremene kompjutacione kognitivne psihologije. Centralni cilj analize koja se predstavlja u ovoj tezi jeste rasvetljavanje odnosa između ova dva koncepta racionalnosti. Analiza je organizovana u šest celina. U I delu teze uvodimo razliku izme u racionalnosti kao predmeta proučavanja i kao okvira za a priori teorijske i metodološke odluke u KKP. Kroz konceptualnu i dijahronu analizu razvoja teorija odlučivanja u društvenim naukama pokazujemo kako dolazi do razvoja savremene suprostavljenosti izme u normativnih i deskriptivnih teorija u kognitivnim naukama. Uvodimo Andersonovu paradigmu racionalne analize kao centralnu metodološku pretpostavku savremenih racionalnih teorija i dajemo kratak pregled analiza koje slede. U II delu teze dajemo kompletnu kritičku analizu teorijske strukture kognitivne psihologije kao prirodne nauke o kognitivnim funkcijama. Prvo diskutujemo osnove bihejvioralne metodologije merenja neopservabilnih, internih konstrukata, što je centralni teorijski problem psihologije kao nauke uopšte, i pokazujemo na koji način je ovaj problem povezan sa problemom aksiomatizacije teorija odlučivanja. Zatim detaljno diskutujemo teorijske koncepte KKP kroz tri aktualne paradigme: simbolicističku, konekcionističko-emergentističku i konstruktivističko-enaktivističku. Diskutujemo tipologiju naučnih objašnjenja u ovim teorijskim paradigmama. Pokazujemo da neki delovi naučnog programa standarde simbolicističke KKP uopšte ne podležu mogućnosti falsifikacije. Konačno, definišemo metateorijski okvir za analizu formalnih kognitivnih teorija: skup teorijskih pojmova na osnovu kojih se formiraju pozicije racionalnih teorija i teorija ograničene racionalnosti u debati o racionalnosti...The subject of the present meta-theoretical and methodological analysis is the scienti c status of the concept of rationality of cognition in Computational Cognitive Psychology (CCP). On the rst level of analysis rationality is constrained as a subject matter of cognitive psychology, and only on this level of analysis it is possible to reach a conclusion on whether a cognitive function is rational or not. This empirical question is characteristic of the contemporary rationality debate. On the second level of analysis, rationality stands as a theoretical and methodological concept which is built a priori in the foundations of the contemporary CCP. The central goal in this thesis is to clarify the relationship between these two concepts of rationality . In Part I we introduce the distinction between (i ) rationality constrained as a subject matter of CCP and (ii ) rationality constrained as a framework for theoretical and methodological decisions a priori in CCP. Through conceptual and diachronic analysis of the development of decision theories in social sciences we demonstrate the development of the contemporary opposition between normative and descriptive theories in cognitive science. We introduce Anderson's rational analysis as a central methodological assumption of rational theories and provide a short overview of the discussions that follow. In Part II we provide a complete critical analysis of the theoretical structure of CCP as a natural science of cognitive functions. We rst discuss the foundations of the behavioural approach to the measurement of internal constructs, which presents the central theoretical problem of scienti c psychology in general, and demonstrate the way that this problem is related to the problem of the axiomatization of decision theories. Then we provide a detailed discussion of CCP in three actual theoretical paradigms: symbolicistic, connectionist-emergentistic and constructivist-enactivistic.We discuss the typology of scienti c explanation involved in these theoretical paradigms. We demonstrate that some parts of the program of the standard symbolicistic CCP cannot be falsi ed by means of standard behavioural methodology. Finally, we de ne a meta-theoretical framework for the analysis of formal cognitive theories: a set of theoretical concepts upon which the positions of rational theories and theories of bounded rationality are developed in the rationality debate

Dynamical causal learning

Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.