LEADER 05607nam 2200697Ia 450 001 9910818142403321 005 20200520144314.0 010 $a1-283-59358-0 010 $a9786613906038 010 $a981-4277-46-0 035 $a(CKB)2560000000093365 035 $a(EBL)1019622 035 $a(OCoLC)809977898 035 $a(SSID)ssj0000682503 035 $a(PQKBManifestationID)11409948 035 $a(PQKBTitleCode)TC0000682503 035 $a(PQKBWorkID)10677312 035 $a(PQKB)11058888 035 $a(MiAaPQ)EBC1019622 035 $a(WSP)00002770 035 $a(Au-PeEL)EBL1019622 035 $a(CaPaEBR)ebr10596904 035 $a(CaONFJC)MIL390603 035 $a(EXLCZ)992560000000093365 100 $a20120305d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDiscovering cognitive architecture by selectively influencing mental processes /$fby Richard Schweickert, Donald L. Fisher & Kyongje Sung 205 $a1st ed. 210 $aNew Jersey $cWorld Scientific$d2012 215 $a1 online resource (431 p.) 225 0 $aAdvanced series on mathematical psychology ;$vv. 4 300 $aDescription based upon print version of record. 311 $a981-4277-45-2 320 $aIncludes bibliographical references and index. 327 $aPreface; Contents; Chapter 1: Introduction to Techniques; Stretching Processes Rather Than Inserting Them; Chapter 2: Introduction to Process Schedules; Gantt Charts and Directed Acyclic Task Networks; Directed Acyclic Task Networks; Acyclic Task Networks in Human Factors; Systems Not Easily Represented in Acyclic Task Networks; Processing Trees; Systems Not Easily Represented As Processing Trees; Analyzing both reaction time and accuracy; Chapter 3: Selectively Influencing Processes in Task Networks; Effects of Selectively Influencing Processes in Task Networks; Slack; Selective influence 327 $aMonotonic Response Time MeansA note on SOA in dual tasks; A note on OR networks; Monotonic Interaction Contrasts; Calculations and simulations; Interaction Contrasts: Concurrent Processes; Example 1: Exponential distributions; Example 2: Truncated normal distributions; OR networks; Statistical considerations; Interaction contrasts: Sequential processes; Sequential processes case 1: Not in a Wheatstone bridge; Example 3: Exponential distributions; Example 4: Truncated normal distributions; Sequential processes case 2: An incomplete Wheatstone bridge; Example 5: Exponential distributions 327 $aExample 6: Truncated normal distributionsSequential processes case 3: A complete Wheatstone bridge; Distinguishing Concurrent and Sequential Processes; Limiting Values of Interaction Contrasts; Concurrent processes; Sequential processes; Building Blocks: Superprocesses and Stages in Task Networks; Superprocesses; Additive Factors and Stages; Appendix; Limits of Interaction Contrasts; Chapter 4: Theoretical Basis for Properties of Means and Interaction Contrasts; Notation and Definitions; Probability spaces; Ordering random variables; Conditional expectation 327 $aEffects of Experimental Factors on ProcessesFactors selectively influencing random variables; Factors ordering random vectors; Factors selectively influencing random vectors by increments; Monotonic reaction time means; Interaction contrasts; Concurrent processes; Sequential processes; OR networks; Chapter 5: Critical Path Models of Dual Tasks and Locus of Slack Analysis; Critical Path Network Models of Dual Tasks; Central limitations; Response limitations; Both central and response limitations; Selective Influence of Processes in Dual Tasks; Sensory and Central Processes 327 $aCentral Processing in Task 1 and SOA (B1,SOA)Later work on B1 and SOA; SOA and Task 2 Sensory Processing (SOA, A2); Locus of Slack Analysis; SOA and Task 2 Central Processing, ; Number of Task 2 alternatives; Degree of mental rotation; Stimulus 2 discriminability; Number of Task 2 alternatives again, with response modality; Sensory and central Task 2 processing, ; Central processing of Task 1, central processing of Task 2, ; PRP: Number of alternatives; PRP: Discriminability; PRP: Central Process Order; Stroop tasks; Number of alternatives and Stroop conflict 327 $aPost-Central and Response Processes 330 $aOne of the most successful methods for discovering the way mental processes are organized is to observe the effects in experiments of selectively influencing the processes. Selective influence is crucial in techniques such as Sternberg's additive factor method for reaction times and Jacoby's process dissociation procedure for accuracy. The successful uses of selective influence have encouraged application extensions to complex architectures, to dependent variables such as evoked potentials, and to complex interpretations. But the common themes have become lost in the details of separate uses a 410 0$aADVANCED SERIES ON MATHEMATICAL PSYCHOLOGY 606 $aPsychology$xMathematical models 606 $aPsychometrics 615 0$aPsychology$xMathematical models. 615 0$aPsychometrics. 676 $a150.1/5195 700 $aSchweickert$b Richard$01610626 701 $aFisher$b Donald L$01610627 701 $aSung$b Kyongje$01610628 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910818142403321 996 $aDiscovering cognitive architecture by selectively influencing mental processes$93938452 997 $aUNINA