PhilosophicalPerspectives, 9, AI, Connectionism,and PhilosophicalPsychology, 1995 CONNECTIONISM AND THE COMMITMENTS OF FOLK PSYCHOLOGY TerenceHorgan and JohnTienson The Universityof Memphis Many philosophersbelieve thatconnectionism is incompatiblewith folk psychology,and hence that the success of connectionismwould support elimina- tivistconclusions about propositional attitudes. Some philosophers actually argue that(certain brands of) connectionismhas sucheliminativist implications. In this paperwe examinetwo sucharguments, due to Ramsey,Stich, and Garon(1990; hereafter,RSG). Their principalargument centers around a featureof the propositionalattitudes they call functionaldiscreteness. RSG's secondargument centerson thequestion whether the predicates of commonsense psychology are projectable. We concludethat their arguments are not successful.Common sense psy- chology,properly understood, is notincompatible with connectionism, properly understood-atleast not in the way thatRSG allege. But we are not simply concernedto refuteanother eliminativist argument, even though RSG's paperhas been quiteinfluential.' RSG's argumentsare a usefulvehicle for getting clearer aboutthe issues on whichtheir arguments turn, concerning both folk psychology and connectionism. 1. The Functional DiscretenessArgument. RSG's mainargument is thatcommon sense psychology has commitments thatare notsatisfied by an importantclass of connectionistmodels. Thus, if the correctmodels of human cognitionlie withinthat class, common sense psychologywill be shownto be seriouslyin error. In thisSection we lay outRSG's argumentin foursteps: the commitment of commonsense psychology to functionaldiscreteness, a class of connectionist modelsthat are held to lackfunctional discreteness, an exampleof a modelfrom thatclass, and theexplicit argument that such models lack functionaldiscrete- ness. In Section2 we look at commonsense functionaldiscreteness in more detail.We arguethat common sense is committedonly to certainparadigm cases of functionaldiscreteness, and that connectionistmodels of the class RSG identifydo exhibitdiscreteness of that kind. We also pointout that connectionist modelsin this class can exhibitother kinds of functional discreteness which com- 128 / TerenceHorgan and JohnTienson mon senserecognizes as possible,but to whichit is not committed. 1.1 A Commitmentof CommonSense Psychology. RSG base theirmain argumenton threecommitments of commonsense psychologyconcerning propositional attitudes: propositional attitudes are semanticallyinterpretable; they have a causal role; and theyare functionally discrete.RSG call thiscluster of featurespropositional modularity (504). The firsttwo are familiar.Propositional attitudes are thesorts of thingsthat can be trueor false,satisfied or unsatisfied,and the like; in thecurrent term of art,they havecontent. And propositional attitudes influence behavior, belief fixation, etc. in waysthat are appropriateto theircontent. To say thatpropositional attitudes are functionallydiscrete is to saythat they can haveeffects singly (or in content- based structures,as whena conclusionis drawnfrom two premises,with no otherpropositions playing a role).RSG holdthat distributed connectionist models do notsatisfy the common sense demand for functionally discrete states because in suchmodels all informationis encodedholistically-hence inseparably- throughoutthe network. They mentiontwo differentways in whichcommon sense propositional attitudesare functionally discrete. First, they can be acquiredor lostindividually (nearlyenough). For example,"Henry...had completely forgotten that the car keyswere hidden in therefrigerator," (504-5) althoughhe had forgottennothing else. And if you are toldthat the keys are in therefrigerator, you will acquirea small clusterof new beliefs,but most of yourbeliefs will be notbe altered. The second kind of functionaldiscreteness is more importantin the argument.Sometimes a personhas a totalset of beliefsand desiresthat provide morethan one reasonfor performing an action,A. And sometimesit happens thatthe person does A forone of thosereasons, with the other possible reason notfiguring in theetiology of the action at all. Likewise,sometimes a personhas severalsets of beliefsthat could lead herto infera particularnew belief,p, and she infersp fromone of thosesets, with the others not figuring in herthinking at all. Thus,according to commonsense psychology, it is a determinatequestion whichpotential reasons for an actionor changein beliefwere the actual or operativereasons. Accordingto commonsense psychology, then, the same state is semantically evaluableand has a content-appropriate,functionally discrete, causal role. Such stateshave what RSG call propositionalmodularity. Functional discreteness is the featureon whichthe argument turns. Since semanticevaluability and somekind of causal role are takenfor granted for the most part, we will usuallyspeak of functionaldiscreteness, reserving 'propositional modularity' for contexts in which semanticevaluability (or causal role) mightbe an issue. Connectionismand theCommitments of Folk Psychology/ 129 1.2 A Class of ConnectionistModels. RSG claim that distributedconnectionism must deny propositional modularity.They characterize a class ofconnectionist models, which, they claim, are incompatiblewith propositional modularity, in particularwith functional discretenessof semanticallyevaluable states. The models in this class are characterizedby threeproperties: i. Theirencoding of informationin theconnection weights and in the biases on unitsis highlydistributed rather than localist. ii. Individualhidden units in the networkhave no comfortable symbolicinterpretation; they are subsymbolic.... iii. The modelsare intendedas cognitive,not merely as implementa- tionsof cognitivemodels. (p. 508) Features(i) and(ii) aremeant to insurethat it is notpossible to associatespecific informationwith particular local partsof themodel. Connections and nodesare notto be semanticallyevaluable individually or in smallsets. Information in the modelis encodedholistically throughout the network or throughout large portions of the network.Furthermore, each node contributesto representingmany differentpropositions, and each connectionweight contributes to storingmany differentpropositions. Thus, information is contained in thenetwork holistically and globally,not locally. And this means, RSG argue,that all ofthe information in thenetwork is involvedin all of its processing,so thatit is not possibleto singleout certain bits of informationas operative-andothers as inoperative-in a tokenprocess, as folkpsychology requires. As RSG note,feature (iii) is notabout the network as such,but about how it is to be interpreted.The idea is thatthe model is supposedto tellus something about how the mindworks, not how it mightbe embodied.Consider, for instance,a classicalparser-a classicalcomputer program which is meantto take naturallanguage sentences as inputand yieldstructural descriptions of theinput sentencesas output.Such a programcan be considereda hypothesisabout the cognitiveprocesses, knowledge structures, and so forth,involved in recognizing the grammaticalstructure of sentences.The programcan be run on many differentcomputers, with differentmachine languages; the hypothesisabout cognitionis thesame in each case. The machinelanguage of thecomputer that the programhappens to be runningon is irrelevantto the cognitivestory the programproposes. One could attemptto use a connectionistnetwork to implementthe opera- tionof sucha classicalprogram. This would be to attemptto use thenetwork as an implementationofthe classical model-as an alternative,unorthodox kind of machinelanguage. There would still be no differencein the hypothesesput forwardabout cognition. This is thekind of construalof connectionistmodels that(iii) rulesout. 130 / TerenceHorgan and JohnTienson But a connectionistmodel-for instance,a parsingmodel such as Berg (1992)-can also be construedas offeringan alternativestory about the cognitive processesinvolved in recognizingthe grammatical structure of sentences,a story thatis in competitionwith the classicalmodel. This would be to construethe modelas a cognitivemodel, as requiredby (iii). Whenunderstood in thisway, RSG hold, distributedconnectionist models are incompatiblewith the propositionalmodularity of folk-psychologicalstates. 1.3 An Example. RSG describea simplethree-layered, feedforward connectionist network, whichthey describe as "a connectionistmodel of memory." The network,called NetworkA, has sixteeninput nodes, one outputnode, and a hiddenlayer of four nodes. Inputconsists of encodingsof sixteenpropositions, for example, Dogs have fur. Cats have fur. Dogs have gills. Fish have gills. Eight inputnodes are used to encodethe subjectof the proposition,eight to encodethe predicate. NetworkA was trainedup so thatits outputnode is on (> .9) whenthe inputproposition is true,and off (< .1) whenthe input proposition is false.Thus, the networkhas memorizedthe answersto a true/falsetest. The networkis capable of generalizing;it respondedcorrectly to encodingsof 'cats have legs' and 'Cats have scales,' whichwere not in thetraining set. 1.4 TheArgument RSG observethat [t]heinformation encoded in Network A is storedholistically and distributed throughoutthe network. Whenever information is extracted from Network A, bygiving it an inputstring and seeing whether it computesa highor a low valuefor the output unit, many connection strengths, many biases and many hiddenunits play a rolein