Behaviorism reborn? Statistical learning as simple conditioning

Behaviorism Reborn? Statistical Learning as Simple Conditioning Todd M. Gureckis (tgurecki@indiana.edu) Department of Psychology, Indiana University, 1101 E. 10th Street, Bloomington, IN 47405 USA Bradley C. Love (brad love@mail.utexas.edu) Consortium for Cognition and Computation, University of Texas at Austin, Austin, TX 78712 USA Abstract behaviorist tradition (Rescorla & Wagner, 1972). Cen- tral to our analysis is a detailed comparison between human learning and two computational models of sta- tistical sequence learning. While closely related, these models differ in the types of associative learning pro- cesses they utilize. The first model we consider, the sim- ple recurrent network (SRN, Elman, 1990), is a recurrent network architecture which learns via back-propagation to predict successive sequence elements on the basis of the last known element and a representation of the cur- rent context. This mechanism has been shown to posses a number of powerful computational properties. For ex- ample, the SRN can act as a “universal approximator” of arbitrary finite state machines (Cleeremans, Servan- Schreiber, & McClelland, 1989). We compare the SRN to a simple conditioning model based on direct stimulus-response mappings: the lin- ear associative-shift register (LASR) model of sequence learning (Gureckis & Love, 2005). Like the SRN, LASR is an associative model of sequential processing. How- ever, instead of relying on recurrent patterns of activa- tion, memory in the model takes the form of a simple shift-register memory (similar to the Buffer network of Cleeremans, 1993). Unlike the SRN or the Buffer net- work, this memory is directly associated with output re- sponses without mediation by an internal abstraction or recoding using hidden units. In this way LASR repre- sents a variant of Rescorla-Wagner which learns statis- tical relationships through time. Due to limited space we refer the reader to Gureckis & Love (2005) for the mathematical details of the model. A critical difference between these two models concerns their assumptions about the stages of processing that mediate stimulus and response. In LASR, sequence learning is driven through direct stimulus-stimulus or stimulus-response associa- tions while the SRN includes a set of internal “trans- formative” representations which mediate behavior. In this paper, we focus our analysis on two empirical studies of SL behavior in a standard serial reaction time (SRT) task. On each trial of our experiments, subjects were presented with a set of six response options, one of these options was cued, and subjects were instructed to quickly and accurately press a corresponding button. Unknown to the subject, the pattern of cued-responses followed simple sequential patterns which we manipu- lated. The primary variable of interest was the time be- tween presentation of the cued stimulus and the subject’s In recent years, statistical learning (SL) has emerged as a compelling alternative to nativist theories of language acquisition (i.e., Chomsky, 1980). However, in many ways the framework of statistical learning echoes aspects of classic behaviorism by stressing the role of associative learning processes and the environment in shaping be- havior. How far backwards has the needle swung? In this paper, we show how a subset of behaviors studied under the rubric of SL are in fact entirely consistent with a simple form of conditioned priming inspired by models from the behaviorist tradition (i.e. a variant of Rescola- Wagner which learns associative relationships through time). Keywords: Statistical Learning; Simple Recur- rent Network; Behaviorism; Language; Serial-Reaction Time Task In recent years, statistical learning (SL) has emerged as a compelling alternative to nativist theories of lan- guage acquisition (e.g., Chomsky, 1980). For example, young infants are able to learn the statistical patterns in short segments of artificial speech and to use these dependencies to isolate word-like units (Saffran, Aslin, & Newport, 1996). Results such as these appear to chal- lenge arguments concerning the “poverty of the stimu- lus” by demonstrating how the environment may provide subtle sources of structure which language learners can use to bootstrap into adult capabilities. The implications of this work revisit the tension in Chomsky’s (1959) attack on the behaviorist research pro- gram. Much like current SL research, Skinner’s (1957) research program placed a strong emphasis on the role of the environment and associative learning processes in shaping behavior. Interestingly, many experimental in- quiries into SL have relied on simple tasks which share much in common with stimulus-response or stimulus- stimulus conditioning. For example, in tasks such as the serial reaction time (SRT) task (Nissen & Bulle- mer, 1987), the syllable task (Saffran et al., 1996), and artificial grammar learning (A. Reber, 1967) subjects are presented with cued response sequences, auditory sequences, or letter strings with the goal of assessing learned associations between stimulus elements. Given these aspects of common theoretical alignment between SL literature and classic behaviorism, we reasonably might wonder the degree to which SL research reflects a return to behaviorist principles. In this paper, we will argue that an important sub- set of the learning behaviors studied under the rubric of SL are, in fact, consistent with a simple form of condi- tioned priming well captured by models inheriting from