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Distinguishing quantum and Markov models of human decision making Jerome R. Busemeyer1, Efrain Santuy1, Ariane Lambert Mogiliansky2 1 Cognitive Science, Indiana University 1101 E. 10th Street, Bloomington Indiana, 47405 jbusemey@indiana.edu ellorent@indiana.edu 2 PSE Paris-Jourdan Sciences Economiques ariane.LM@gmail.com Abstract models are Markov models, and they are also closely A general property for empirically distinguishing Markov related to the optimal Bayesian sequential sampling model and quantum models of dynamic decision making is (Bogatz, et. al., 2006). derived. A critical test is based on measuring the decision process at two distinct time points, and recording the Recently, we (Busemeyer, Townsend, & Wang, 2006) disturbance effect of the first measurement on the second. developed a quantum dynamic model for the signal The test is presented within the context of a signal detection decision task. Thus an important question is what paradigm, in which a human or robotic operator must decide fundamental properties distinguish these two very different whether or not a target is present on the basis of a sequence classes of models? of noisy observations. Previously, this task has been modeled as a random walk (Markov) evidence accumulation process, but more recently we developed a quantum First we describe the formal characteristics of a signal dynamic model for this task. Parameter free predictions are detection task. Second, we describe Markov and quantum derived from each model for this test. Experimental models of signal detection. Third we derive theoretical methods for conducting the proposed tests are described, properties from each model that provide a parameter free and previous empirical research is reviewed. test of the two models. Finally, we summarize experimental results that provide initial evidence for Interference is a signature of quantum processes. One way interference effects in human decision making. to produce interference effects is to perturb a target measurement by an earlier probe. In this article we develop The Signal Detection Task Markov and quantum models of human decision making Signal detection can be a very complex task. But our goal and prove that only the latter is capable of producing this is to describe a simple setting for testing models of this type of interference effect. The decision models are task. Imagine, for example, a security agent who is developed within the context of a decision paradigm called checking baggage for weapons. With this idea in mind, we the signal detection paradigm. assume that a decision maker is faced with a series of choice trials. On each trial, a noisy stimulus is presented Signal detection is a fundamentally important decision for some fixed period of time, tf, and at the end of the fixed problem in cognitive and engineering sciences. In this time period, the decision maker has to immediately decide dynamic decision situation, a human or robotic operator whether or not a target was present. The evidence monitors rapidly incoming information to decide whether generated by the stimulus is assumed to be stationary or not a potential target is present (e.g., missiles in the during the duration of the stimulus. The amount of time military, or cancer in medicine, or malfunctions in given to observe the stimulus can be experimentally industry). The incoming information provides uncertain and conflicting (noisy) evidence, but at some point in time, manipulated. a decision must be made, and the sooner the better. Incorrectly deciding that the target is present (i.e. a false The operator can respond by choosing one of 2n+1 ordered alarm) could result in deadly consequences, but so could levels of confidence (n is assumed to be an even number). the failure to detect a real target (i.e. a miss). For example, n = 2 produces five levels that might be labeled: 2 = no target with high confidence, 1 = no In statistics, Bayesian sequential sampling models provide target with low confidence, 0 = uncertain, +1 = yes target an optimal model for this task (DeGroot, 1970). During the with low confidence, +2 = yes target with high past 35 years, cognitive scientists (see, Ratcliff & Smith, confidence). The choice probabilities of the 2n+1 2004, for recent review) have developed random walk categories are estimated from each person pooled across model and diffusion models to describe human responses made on several thousand choice trials. performance on this task. The random walk/diffusion Models of Signal Detection from state |j. The intensities must satisfy kij 0 for i j and i kij = 0 to guarantee that T(t) remains a transition A random walk (Markov) model matrix. To construct a random walk (Markov) model for this task, we postulate a set of (2m+1) states of confidence about the The random walk model is a special case of a Markov process which assumes that intensities are positive only presence or absence of the target: { |m, |m+1 , …, |1, |0, |+1, …, |+m1, |+m }. between adjacent states: kij = 0 for |ij| > 1. For |j| < m, we The state |j can be interpreted as a (2m+1) column vector make the following assumptions: If the target is present then we assume that kj+1,j > kj1,j; if the target is absent then with zeros everywhere, except that it has 1.0 located at the row corresponding to index j. Positive indices, j > 0, we assume that kj1,j > kj+1,j. At the boundaries, we set represent a state of evidence favoring target present; km1,+m > 0 and km+1,m > 0 (reflecting boundaries). negative indices, j < 0, represent a state of evidence for target absent, and zero represents a neutral state of Note that the discrete state random walk model is closely evidence. The number of states could be as small as the related to a continuous state diffusion model. If we allow the number of states to become arbitrarily large, and at the number of response categories (m = n), but the participant may be able to employ a more refined scale of confidence, same time let the increment between states become and so the number could also be much larger (m = 10n). arbitrarily small, then the distribution produced by the random walk model converges to the distribution produced At the start of a single trial, the process starts in some by the diffusion model (see Bhattacharya & Waymire, particular state, for example the neutral state |0. Then as 1990, pp. 386-388). time progresses within the trial, the process either steps up one index, or steps down one index, or stays at the same The response measured at time tf is determined by the state, depending on the information or evidence that is following choice rule. The entire set of states is partitioned into 2n+1subsets, with the first subset define by Rn = { |j sampled at that moment. The process continues moving up or down the evidence scale until the fixed time period tf | m j cn), and subsequent subsets defined by Rk = { |j ends and a decision is requested. At that point, a choice | ck-1 j ck) with cn = +m. If the state of confidence at time tf equals |jRk, then the response category k is and a rating are made based on the state existing at the time of decision. selected. The probability of this event is simply Pr[R(tf) = k] = j Rk Pj(tf). The initial state of the decision maker on each choice trial is not known, and this initial state may even change from For later comparisons with the quantum model, it will be trial to trial. The probability of starting in state |j is helpful to redefine the computation of the response probabilities in matrix notation. Define Mk as a projection denoted Pj(0), and the (2m+1) column vector P(0) = j Pj(0)|j matrix with elements Mjj = 1 if |jRk, and zero otherwise. represents the initial probability distribution over states so Note that k=1,n Mk = I, where I is the identity matrix. Then MkP(tf) is the projection of the probability vector P(tf) onto that j Pj(0) = 1. the subspace defined by the states that are assigned to During an observation period of time t of a single trial, the category k. Define 1 as a (2m+1) row vector with all entries equal to one. Then the desired probability equals the sum probability distribution over states, P(t), evolves in a direction guided by the incoming evidence from the of the elements in the projection: stimulus. This evolution is determined by a transition Pr[R(tf) = k] = 1MkP(tf) . matrix T(t) as follows: P(t) = T(t) P(0). A quantum dynamic decision model The entry in row i and column j of T(t), Tij(t), determines To construct a quantum model for this task, we again the probability of transiting to state |i from state |j after a postulate a set of (2m+1) states of confidence about the period of time t. The transition matrix must satisfy 0 presence or absence of the target: Tij(t) 1 and i Tij(t) = 1 to guarantee that P(t) remains a { |m, |m+1 , …, |1, |0, |+1, …, |+m1, |+m }. probability distribution. In this case, we assume that these states form an orthonormal basis for a (2m+1) dimensional Hilbert space. The transition matrix satisfies the group property More specifically, i|j = 0 for every i j and i|i = 1 for all T(tf) = T(tf ti)T(ti), i, where i|j denotes the inner product between two and from this it follows that it satisfies the Kolmogorov vectors. As before, states with positive indices represent forward equation: evidence favoring target present, states with negative dT(t)/d(t) = KT(t), indices represent evidence for target absent, and zero which has the solution represents a neutral state of evidence. T(t) = exp(tK). The matrix K is called the intensity matrix with element Kij The initial state of the quantum system is represented by a determining the rate of change in probability to state |i superposition of the confidence states |(0) = j |jj|(0) = j j(0)|j. H = P2/2m + V(x). The coefficient j(0) = j|(0) is the probability amplitude The off diagonal elements of the Hamiltonian correspond of being in state |j at start of the trial. The complex to the P2 operator, and the diagonal elements correspond to (2m+1) column vector (0) represents the initial the potential function V(x) (see Feynman et al. (1966, Ch. probability amplitude distribution over the states. The 16). coordinate in the j-th row of (0) is j(0) = j|(0). The initial state vector has unit length so that (0)|(0) = The quantum response probabilities measured at time tf are (0)†(0) = 1.0. The initial probability amplitude determined as follows. Once again the entire set of states is distribution over states represents the decision maker’s partitioned into 2n+1subsets, with the first subset define by initial beliefs at the beginning of the trial. Rn = { |j | m j cn), and subsequent subsets defined by Rk = { |j | ck-1 < j ck) with cn = +m. The probability During an observation period of time t of a single trial, the of choosing category k at time tf is simply probability amplitude distribution over states, (t), evolves Pr[R(tf) = k] = j Rk |j(tf)|2. in a direction guided by the incoming evidence from the stimulus. This evolution is determined by a unitary matrix As before, it will be helpful to describe these computations U(t) as follows: in matrix formalism. Define Mk as a projection matrix with (t) = U(t) (0). elements Mjj = 1 if |jRk, and zero otherwise. Note again The entry in row i and column j of U(t), Uij(t), determines that k=1,n Mk = I, where I is the identity matrix. Then the probability amplitude of transiting to state |i from state Mk(tf) is the projection of the probability amplitude |j after a period of time t. The unitary matrix must satisfy vector (tf) onto the subspace defined by the states that are U(t)†U(t) = I (where I is the identity matrix) to guarantee assigned to category k. Then the probability is given by that (t) remains unit length. the squared length of the projection: Pr[R(tf) = k] = |Mk(tf)|2 The unitary matrix satisfies the group property = (tf)†Mk†Mk(tf) = (tf)†Mk(tf). U(tf) = U(tf ti)U(ti), and from this it follows that it satisfies the Schrödinger The disturbance effect of measurement. equation: dU(t)/d(t) = iHU(t), We modify the signal detection task by asking the decision which has the solution maker to report a confidence rating at two time points, ti U(t) = exp(itH). initially and later tf. The initial confidence measurement The complex number i = 1 is needed to guarantee that will cause a ‘state collapse’ with both the Markov and the U(t) is unitary. The matrix H is called the Hamiltonian quantum models. But how does this change the final matrix with element Hij determining the rate of change in distribution of probabilities? This simple manipulation has probability amplitude to state |i from state |j. The profoundly different effects on the two models. The Hamiltonian matrix must be Hermitian (H = H†) to quantum model exhibits an interference effect that does not guarantee that U(t) remains unitary. occur with the Markov model. To construct a random walk analogue, we assume that The general idea is to compare the results from two Hamiltonian elements are non zero only between adjacent experimental conditions at time tf. Under the single states: Hi,j = 0 for |ij| > 1. For rows |j| < m, we make the measurement condition (C1), we can estimate the following assumptions: Hj1,j = Hj+1,j = and Hjj = j. If probability distribution over the category responses at time the target is present, then we assume that j+1 > j ; if the tf , that is Pr[ R(tf) = k | C1]. Under the double measurement target is absent then we assume that j+1 < j. At the condition (C2), we can obtain another estimate of the boundaries we set Hm1,+m = = Hm+1,m and Hm,m = m probability distribution over the category responses at time Hmm = m. This choice of Hamiltonian corresponds to a tf using the law of total probability crystal model discussed in Feynman et al. (1966, Ch. 16). Pr[ R(tf) = k | C2] = k’ Pr[ R(tf) = k R(ti) = k’ ], Note that the discrete state quantum walk model is closely = k’ Pr[ R(tf) = k | R(ti) = k’ ] Pr[ R(ti) = k’ ]. related to a continuous state quantum model. Once again, if we allow the number of states to become arbitrarily large, Effects of measurement on the Markov model. and at the same time let the increment between states According to the Markov model, the distribution over the become arbitrarily small, then the distribution produced by confidence states immediately before measurement at time the discrete quantum model converges to the distribution ti will be produced by the continuous quantum model (see Feynman P(ti) = T(ti) P(0). et al., 1966, Ch. 16). Also note that the Hamiltonian The probability of choosing category k’ is defined above is closely related to the Hamiltonian that is Pr[ R(ti) = k’] = 1Mk’P(ti) . commonly used to model the one dimensional movement of a particle in physics: If category k’ is selected at this point, then the probability The projection of this final distribution on the basis states distribution over states collapses to a new distribution after of response category k at time tf equals measurement = MkU(tf ti)(ti|k’) P(ti|k’) = Mk’P(ti) / 1Mk’P(ti). = MkU(tf ti)[Mk’(ti)/|Mk’(ti)|]. The joint probability of selecting category k’ and then The joint probability of selecting category k’ and then reaching one of the (2m+1) final states at time tf equals selecting category k at time tf equals [T(tf ti)P(ti|k’)]Pr[R(ti) = k’] = T(tf ti) Mk’ P(ti) . Pr[ R(tf) = k R(ti) = k’ ] The joint probability of selecting category k’ and then = Pr[ R(ti) = k ‘]|MkU(tf ti)[Mk’(ti)/|Mk’(ti)|]|2 selecting category k at time tf equals = |Mk’(ti)|2(|MkU(tf ti)Mk’(ti)|2)/|Mk’(ti)|2 Pr[ R(tf) = k R(ti) = k’ ] = 1MkT(tf ti)Mk’P(ti). = |MkU(tf ti)Mk’(ti)|2 The marginal probability of selecting category k at time tf = |MkU(tf ti)Mk’U(ti)(0)|2 . equals The marginal probability of selecting category k at time tf Pr[ R(tf) = k | C2] equals = k’ Pr[ R(tf) = k R(ti) = k’ ] Pr[ R(tf) = k | C2] = k’ 1MkT(tf ti)Mk’ P(ti) = k’ Pr[ R(tf) = k R(ti) = k’ ] , = 1MkT(tf ti)k’ Mk’P(ti) = k’ |MkU(tf ti)Mk’U(ti)(0) |2 , = 1MkT(tf ti)( k’ Mk’)P(ti) | k’ MkU(tf ti)Mk’U(ti)(0) |2 , = 1MkT(tf ti)IP(ti) = | MkU(tf ti) k’ Mk’U(ti)(0) |2 , = 1MkT(tf ti)P(ti) = | MkU(tf ti) (k’ Mk’)U(ti)(0) |2 , = 1MkT(tf ti)T(ti)P(0) = | MkU(tf ti) I U(ti)(0) |2 , = 1 Mk T(tf )P(0) = | MkU(tf ti)U(ti)(0) |2 , = Pr[ R(tf) = k | C1]. = | MkU(tf )(0) |2 , Thus the first measurement has no effect on the marginal = Pr[ R(tf) = k | C1] . distribution of the second measurement. The law of total The two results are not equal, and the first measurement probability is satisfied. However, the first measurement does effect the marginal distribution of the second does influence the distribution of the second measurement, measurement. The law of total probability is not satisfied. conditioned on the observed value of the first As noted above, it is important for us to compare models measurement. So it is important for us to compare models using the marginal distribution from the two conditions. using the marginal distribution. The difference between the two marginal distributions, d(tf ti) = Pr[ R(tf) = k | C2] Pr[ R(tf) = k | C1], It is important to note that the above results hold quite is called the interference effect, and it depends strongly on generally. It holds for any number of confidence states (as the time lag (tf ti). long as this number is equal or greater than the number of response categories). It holds for any transition matrix T(t) It is important to note that the above results hold quite and so it is not restricted to any particular form of intensity generally. Once again, it holds for any number of matrix. Finally, it holds for any initial probability confidence states (as long as this number is equal or greater distribution across the confidence states. This property than the number of response categories). It holds for any tests a basic assumption of linearity that is implicit in the unitary matrix U(t) and so it is not restricted to any Markov model: particular form of Hamiltonian matrix. Finally, it holds for 1 Mk T(t)[pP1(0) + qP2(0)] , any initial probability amplitude distribution across the = p[1 Mk T(t) P1(0)] + q[1 Mk T(t) P2(0)]. confidence states. This property tests a basic assumption of nonlinearity that is implicit in the quantum model: Effects of measurement on the quantum model. |Mk U(t)[pP1(0) + qP2(0)]|2 , According to the quantum model, the probability amplitude p|Mk U(t) P1(0)|2 + q|Mk T(t) P2(0)|2. distribution over the confidence states immediately before measurement at time ti equals Example of interference effects. (ti) = U(ti) (0). To illustrate the predicted interference effect of the The probability of choosing category k’is Pr[ R(ti) = k’] = |Mk’(ti)|2 . quantum model, the predictions for the single and double conditions were computed using the following parameters: If category k’ is selected at this point, then the amplitude (2m+1) = 51, 2K+1 = 3, c1 = 9, c+1 = +8, 1(0) = 0(0) distribution over states collapses to a new distribution after measurement = +1(0) = 1/3, = .5, j = j/51, tf = ti+50. The differences between the double versus the single conditions are shown (ti|k’) = Mk’ (ti)/ |Mk’(ti)| . in the table below for various ti. As can be seen in this The probability amplitude distribution over the (2m+1) final states at time tf given k’ observed at time ti equals table, the interference effects can be quite large. = U(tf ti)(ti|k’), = U(tf ti)[Mk’(ti)/|Mk’(ti)|] . Interference Effect Shafir and Tversky (1992) tested a property called the sure Initial time k = 1 k=0 k = +1 thing principle, which could be re-interpreted as a test of ti = 0 0 0 0 interference effects (Busemeyer, Matthew, Wang, 2006). ti = 100 .0385 .0251 -.0636 Decision makers were asked to play a prisoner dilemma ti = 200 .0177 .0865 -.1043 game under three conditions: Knowing the opponent has ti = 300 .0592 .1509 .2101 defected, knowing the opponent has cooperated, and not Note: These predictions were computed from the quantum knowing the opponent’s action. This paradigm can be used equations using Matlab’s matrix exponential function. to determine the interference effect produced by knowledge of the opponent on the choice of the decision Proposed method for investigating the interference maker. Decision makers defected 97% of the time when effect. An experiment is underway to experimentally the other was known to defect; 84% of the time when the investigate the disturbance effect of measurement. A signal other was known to cooperate; and 63% of the time when detection task was designed that requires an average the other’s action was unknown. Busemeyer et al. (2006) decision time of at least 1 second or more. Within this showed that these results also violate the predictions of a amount of time, a probe measurement can be made, say Markov model, and they explained these findings using a within 500 msec, and a final measurement can obtained quantum dynamic decision model. after 1000 msec. The participants are asked to view a complex visual scene and decide whether or not a target Conte et al. (2006) conducted an experiment to test object (e.g. a bomb) is present or not, similar to the task interference effects in perception. The task required faced by security agents at airports or government individual’s to judge whether or not two lines were buildings. The time interval between probe and final identical. The lines were in fact identical, but they were measurement will be manipulated, as well as the presented within a context that is known to produce a discriminability of the stimulus and prior probability of the perceptual illusion of a difference. Two conditions were target. According to our quantum model, the time interval examined: In one condition, line judgment task B was would affect the delay parameter (tfti), the discriminability presented alone; in another condition, line judgment task A would affect the potential of the Hamiltonian, j, and the preceded line judgment task B. The results showed prior probability would affect the initial state, (0). significant differences in the response proportions to task B. These results were interpreted within a general quantum Related empirical tests of interference effects measurement framework developed by Khrennikov (2007). in human decision making. Atmanspacher, Filk, & Romer (2004) examined a quantum zeno effect in a bi-stable perception task. The task involves Although we are not aware of any experimental tests of the presentation of a Necker cube, which is the projection interference effects that were conducted using the single of a cube onto a plane. The projection can be perceptually and double measurement conditions described above, interpreted as being viewed from a top or bottom several related lines of evidence have been reported by past viewpoint, and the viewer experiences a spontaneous researchers. switch from one view to another. The time period between switches in experienced viewpoints is the primary Townsend, Silva, & Spencer – Smith (2000) conducted a measurement of interest. According to quantum theory, closely related test of interference. Decision makers were this time period can be extended by repeated measurements presented faces belonging to one of two categories (e.g., of the perception. Atmanspacher, et. al. (2004) report some good guys, bad guys) and they were asked to decide to experimental evidence related to this effect, and they choose between two actions (e.g. attack or withdraw). Two explain these results using a quantum dynamic model. conditions were examined: In the category decision task they were asked to first categorize the face, and then decide how to act; in the decision only task, they were only asked Comparing Markov and quantum models to decide how to act and no categorization was requested. There is a surprising amount of similarity between the This paradigm was used to investigate the interference Markov and quantum models. The basic equations appear effect of the category task on the decision task. Townsend almost the same if one simply replaces probabilities with et al. (2000) reported that 72 out of 276 participants complex amplitudes. The initial states of both models can produced statistically significant deviations from the be represented as a linear combination of basis states predictions of a Markov model. Busemeyer & Wang (mixed versus superposition). The transition operators that (2006) explained these deviations using a quantum evolve the states both obey simple group properties which dynamic decision model. lead to deterministic linear differential equations (Kolmogorov versus Schrödinger). The solutions to the differential equations are matrix exponentials for both models. Measurement produces a collapse of the states for All of the above applications follow a quantum decision both models. program of research that uses only the mathematical principles of quantum theory to explain human decision Based on these similarities, one might suspect that the two making behavior (Aerts, et al., 2003; Atmanspacher, et al., models are indistinguishable. This is not the case because 2002; Khrennikov, 2007). No attempt or assumptions are there are several key differences between the models. First, made at this point about the possible neural basis for these the Markov model operates on real valued probabilities computations. This program differs radically from a more (bounded between zero and one), whereas the quantum reductionist program that attempts to explain neural model operates on complex probability amplitudes. computations using quantum physical models (Penrose, Second, the intensity matrix for the Markov model obeys 1989; Pribram, 1993; Woolf & Hammeroff, 2001) different constraints than the Hamiltonian for the quantum model. Third, the Schrödinger equation introduces a Acknowledgments complex multiplier to maintain a unitary operation. Finally, This research was supported by NIMH R01 MH068346 to the Markov model uses a linear projection to determine the the first author. final probabilities, but the quantum model uses a nonlinear operation (the squared projection) to determine the final probabilities. The latter is crucial for the interference effect References examined in this paper. The interference effect produced Aerts, D. 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