syntactic analysis assignment upgrad github

SICP 4.17 2024-07-28 Sun

Separating syntactic analysis from execution.

Our evaluator is inefficient in that it interleaves syntactic analysis and execution of expressions.
If a program is executed many times, then its syntax is expensively and wastefully analyzed each of those times.

Each time factorial is called the evaluator must determine that the body is an if statement and act accordingly. Each time (* (factorial (- n 1)) n) , (factorial (- n 1)) , and (- n 1) are evaluated, the evaluator must determine that they are applications and act accordingly.

Authors present a way, used by Jonathan Rees in 1982 and independently invented by Marc Feeley in 1986, to perform syntactic analysis only once.
Eval is split into two parts.
``The procedure analyze takes only the expression. It performs the syntactic analysis and returns a new procedure, the execution procedure , that encapsulates the work to be done in executing the analyzed expression. The execution procedure takes an environment as its argument and completes the evaluation. This saves work because analyze will be called only once on an expression, while the execution procedure may be called many times.'' (394)

Here is the code:

Exercise 4.22

Extend the evaluator in this section to support the special form let . (See Exercise 4.6)

Exercise 4.23

Alyssa P. Hacker doesn't understand why analyze-sequence needs to be so complicated. All the other analysis procedures are straightforward transformations of the corresponding evaluation procedures (or eval clauses) in section 4.1.1. She expected analyze-sequence to look like this: ( define ( analyze-sequence exps) ( define ( execute-sequence procs env) ( cond ((null? (cdr procs)) ((car procs) env)) ( else ((car procs) env) (execute-sequence (cdr procs) env)))) ( let ((procs ( map analyze exps))) ( if (null? procs) (error "Empty sequence -- ANALYZE" )) ( lambda (env) (execute-sequence procs env)))) Eva Lu Ator explains to Alyssa that the version in the text does more of the work of evaluating a sequence at analysis time. Alyssa's sequence-execution procedure, rather than having the calls to the individual execution procedures built in, loops through the procedures in order to call them: In effect, although the individual expressions in the sequence have been analyzed, the sequence itself has not been. Compare the two versions of analyze-sequence . For example, consider the common case (typical of procedure bodies) where the sequence has just one expression. What work will the execution procedure produced by Alyssa's program do? What about the execution procedure produced by the program in the text above? How do the two versions compare for a sequence with two expressions?

Let's consider a sequence with one expression, the sequence which contains the self-evaluating expression 1 : (1) .

This is what happens when the program in the main text is applied to that sequence:

procs is assigned this value:

loop is called:

final value:

If we apply this latter value (which is a lambda ) to an environment, then it evaluates to 1 .

This, instead, is what happens with Alyssa's program:

procs are assigned the same value they are assigned by the program in the main text;

if this latter value is applied to an environment, then it evaluates to this call:

which evaluates to 1.

Let's now consider the sequence with the self-evaluting expression 1 and the self-evaluting expression 2 : (1 2) .

procs is set to this value:

we perform this application:

then we perform this application:

This is the final value:

This is what happens with Alyssa's program:

procs is set to the same value as above;

Final value:

When we apply this final value (which is a lambda ) to an environment, we evaluate this application, which evaluates to 1:

But also also this one:

which evaluates to

which evaluates to 2.

The program in the main text and Alyssa's program give the same result. However, Alyssa's program returns a lambda which does more work when it is called; it has to construct the final lambda calls. The program in the main text returns a lambda whose body already contains those final lambda calls.

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	package main

	import (
	"bufio"
	"fmt"
	"io"
	"os"
	"strconv"
	"strings"
	)

	type Kind int

	const (
	Print Kind = iota + 1
	Lparen
	Rparen
	Plus
	Minus
	Multi
	Divi
	Assign
	VarName
	IntNum
	EOF
	Others
	)

	type Token struct {
	Kind Kind
	Value string
	}

	type Calc struct {
	tokens []*Token
	variables map[string]int
	stack []int
	generator *TokenGenerator
	}

	func newCalc() *Calc {
	c := new(Calc)
	c.tokens = make([]*Token, 0)
	c.variables = make(map[string]int)
	c.stack = make([]int, 0, 10)
	c.generator = nil
	return c
	}

	func (c *Calc) setNewLine(line string) {
	c.generator = newTokenGenerator(line)
	}

	func (c *Calc) push(val int) {
	c.stack = append(c.stack, val)
	}

	func (c *Calc) pop() (int, bool) {
	length := len(c.stack)
	if length > 0 {
	val := c.stack[length-1]
	c.stack = c.stack[:length-1]
	return val, true
	}
	return 0, false
	}

	func (c *Calc) statement() {
	if tk, ok := c.generator.readNext(); ok {
	switch tk.Kind {
	case VarName:
	c.generator.next()
	varName := tk.Value
	if tk, ok := c.generator.next(); ok && tk.Kind == Assign {
	c.expression()
	if c.variables[varName], ok = c.pop(); !ok {
	fmt.Printf("variables not found:%s\n", varName)
	}
	} else {
	fmt.Println("token should be '='")
	}
	case Print:
	c.generator.next()
	c.expression()
	if val, ok := c.pop(); ok {
	fmt.Printf("Answer: %d\n", val)
	}
	default:
	c.expression()
	}
	}
	}

	func (c *Calc) expression() {
	c.term()
	for tk, ok := c.generator.readNext(); ok && (tk.Kind == Plus \|\| tk.Kind == Minus); tk, ok = c.generator.readNext() {
	c.generator.next()
	op := tk.Kind
	c.term()
	c.operate(op)
	}
	}

	func (c *Calc) term() {
	c.factor()

	for tk, ok := c.generator.readNext(); ok && (tk.Kind == Multi \|\| tk.Kind == Divi); tk, ok = c.generator.readNext() {
	c.generator.next()
	op := tk.Kind
	c.factor()
	c.operate(op)
	}
	}

	func (c *Calc) factor() {
	if tk, ok := c.generator.next(); ok {
	switch tk.Kind {
	case VarName:
	if val, ok := c.variables[tk.Value]; ok {
	c.push(val)
	} else {
	fmt.Printf("variables not found:%s\n", tk.Value)
	}
	case IntNum:
	if num, err := strconv.Atoi(tk.Value); err == nil {
	c.push(num)
	} else {
	fmt.Printf("value cannot convert to int:%s\n", tk.Value)
	}
	case Lparen:
	c.expression()
	if tk, ok := c.generator.next(); tk.Kind != Rparen && ok {
	fmt.Println("Syntax Error: missing ')' ")
	}
	}
	} else {
	fmt.Println("missing token")
	}

	}

	func (c *Calc) operate(op Kind) bool {
	var d1, d2 int
	var ok bool
	if d2, ok = c.pop(); ok {
	} else {
	fmt.Println("Invalid argument")
	return false
	}

	if d1, ok = c.pop(); ok {
	} else {
	fmt.Println("Invalid argument")
	return false
	}

	if op == Divi && d2 == 0 {
	fmt.Println("Zero Divide Error")
	return false
	}

	switch op {
	case Plus:
	c.push(d1 + d2)
	case Minus:
	c.push(d1 - d2)
	case Multi:
	c.push(d1 * d2)
	case Divi:
	c.push(d1 / d2)
	default:
	fmt.Println("Invalid operator")
	return false
	}
	return true
	}

	type TokenGenerator struct {
	source []rune
	maxIndex int
	currentIndex int
	}

	func newTokenGenerator(text string) *TokenGenerator {
	tg := new(TokenGenerator)
	tg.source = []rune(text)
	tg.maxIndex = len(text)
	tg.currentIndex = 0
	return tg
	}

	func (tg TokenGenerator) tokenGenerator(index int) (Token, bool, int) {
	//skip spaces
	for index < tg.maxIndex && tg.source[index] == ' ' {
	index++
	}
	if index >= tg.maxIndex {
	return nil, false, index
	}
	var token = &Token{Kind: Others, Value: ""}
	ch := tg.source[index]
	if isDigit(ch) {
	token.Kind = IntNum
	for isDigit(ch) {
	token.Value = token.Value + string(ch)
	index++
	if index >= tg.maxIndex {
	break
	} else {
	ch = tg.source[index]
	}
	}
	} else if num, ok := toLower(ch); ok {
	token.Value = string(num)
	token.Kind = VarName
	index++
	} else {
	switch ch {
	case '(':
	token.Kind = Lparen
	case ')':
	token.Kind = Rparen
	case '+':
	token.Kind = Plus
	case '-':
	token.Kind = Minus
	case '*':
	token.Kind = Multi
	case '/':
	token.Kind = Divi
	case '=':
	token.Kind = Assign
	case '?':
	token.Kind = Print
	}
	index++
	}
	return token, true, index
	}

	func (tg TokenGenerator) next() (Token, bool) {
	tk, ok, index := tg.tokenGenerator(tg.currentIndex)
	if ok {
	tg.currentIndex = index
	}
	return tk, ok
	}

	func (tg TokenGenerator) readNext() (Token, bool) {
	tk, ok, _ := tg.tokenGenerator(tg.currentIndex)
	return tk, ok
	}

	func (tg TokenGenerator) readFirstToken() (Token, bool) {
	tk, ok, _ := tg.tokenGenerator(0)
	return tk, ok
	}

	// Utils
	func isDigit(r rune) bool {
	if _, err := strconv.Atoi(string(r)); err == nil {
	return true
	}
	return false
	}

	func toLower(r rune) (rune, bool) {
	if 'a' <= r && r <= 'z' {
	return r, true
	} else if 'A' <= r && r <= 'Z' {
	return []rune(strings.ToLower(string(r)))[0], false
	} else {
	return '0', false
	}
	}

	func main() {
	var err error
	calc := newCalc()
	reader := bufio.NewReaderSize(os.Stdin, 4096)
	for line := ""; err == nil; line, err = reader.ReadString('\n') {
	calc.setNewLine(line)
	calc.statement()
	fmt.Print("input>")
	}
	if err != io.EOF {
	panic(err)
	} else {
	fmt.Println("\nbyebye!")
	os.Exit(0)
	}
	}

Natural Language Processing Tutorial
NLP - Introduction
NLP - Linguistic Resources
NLP - Word Level Analysis
NLP - Syntactic Analysis
NLP - Semantic Analysis
NLP - Word Sense Disambiguation
NLP - Discourse Processing
NLP - Part of Speech (PoS) Tagging
NLP - Inception
NLP - Information Retrieval
NLP - Applications of NLP
NLP - Python
Natural Language Processing Resources
NLP - Quick Guide
NLP - Useful Resources
NLP - Discussion
Selected Reading
UPSC IAS Exams Notes
Developer's Best Practices
Questions and Answers
Effective Resume Writing
HR Interview Questions
Computer Glossary

Natural Language Processing - Syntactic Analysis

Syntactic analysis or parsing or syntax analysis is the third phase of NLP. The purpose of this phase is to draw exact meaning, or you can say dictionary meaning from the text. Syntax analysis checks the text for meaningfulness comparing to the rules of formal grammar. For example, the sentence like “hot ice-cream” would be rejected by semantic analyzer.

In this sense, syntactic analysis or parsing may be defined as the process of analyzing the strings of symbols in natural language conforming to the rules of formal grammar. The origin of the word ‘parsing’ is from Latin word ‘pars’ which means ‘part’ .

Concept of Parser

It is used to implement the task of parsing. It may be defined as the software component designed for taking input data (text) and giving structural representation of the input after checking for correct syntax as per formal grammar. It also builds a data structure generally in the form of parse tree or abstract syntax tree or other hierarchical structure.

The main roles of the parse include −

To report any syntax error.

To recover from commonly occurring error so that the processing of the remainder of program can be continued.

To create parse tree.

To create symbol table.

To produce intermediate representations (IR).

Types of Parsing

Derivation divides parsing into the followings two types −

Top-down Parsing

Bottom-up parsing.

In this kind of parsing, the parser starts constructing the parse tree from the start symbol and then tries to transform the start symbol to the input. The most common form of topdown parsing uses recursive procedure to process the input. The main disadvantage of recursive descent parsing is backtracking.

In this kind of parsing, the parser starts with the input symbol and tries to construct the parser tree up to the start symbol.

Concept of Derivation

In order to get the input string, we need a sequence of production rules. Derivation is a set of production rules. During parsing, we need to decide the non-terminal, which is to be replaced along with deciding the production rule with the help of which the non-terminal will be replaced.

Types of Derivation

In this section, we will learn about the two types of derivations, which can be used to decide which non-terminal to be replaced with production rule −

Left-most Derivation

In the left-most derivation, the sentential form of an input is scanned and replaced from the left to the right. The sentential form in this case is called the left-sentential form.

Right-most Derivation

In the left-most derivation, the sentential form of an input is scanned and replaced from right to left. The sentential form in this case is called the right-sentential form.

Concept of Parse Tree

It may be defined as the graphical depiction of a derivation. The start symbol of derivation serves as the root of the parse tree. In every parse tree, the leaf nodes are terminals and interior nodes are non-terminals. A property of parse tree is that in-order traversal will produce the original input string.

Concept of Grammar

Grammar is very essential and important to describe the syntactic structure of well-formed programs. In the literary sense, they denote syntactical rules for conversation in natural languages. Linguistics have attempted to define grammars since the inception of natural languages like English, Hindi, etc.

The theory of formal languages is also applicable in the fields of Computer Science mainly in programming languages and data structure. For example, in ‘C’ language, the precise grammar rules state how functions are made from lists and statements.

A mathematical model of grammar was given by Noam Chomsky in 1956, which is effective for writing computer languages.

Mathematically, a grammar G can be formally written as a 4-tuple (N, T, S, P) where −

N or V N = set of non-terminal symbols, i.e., variables.

T or ∑ = set of terminal symbols.

S = Start symbol where S ∈ N

P denotes the Production rules for Terminals as well as Non-terminals. It has the form α → β, where α and β are strings on V N ∪ ∑ and least one symbol of α belongs to V N

Phrase Structure or Constituency Grammar

Phrase structure grammar, introduced by Noam Chomsky, is based on the constituency relation. That is why it is also called constituency grammar. It is opposite to dependency grammar.

Before giving an example of constituency grammar, we need to know the fundamental points about constituency grammar and constituency relation.

All the related frameworks view the sentence structure in terms of constituency relation.

The constituency relation is derived from the subject-predicate division of Latin as well as Greek grammar.

The basic clause structure is understood in terms of noun phrase NP and verb phrase VP .

We can write the sentence “This tree is illustrating the constituency relation” as follows −

Dependency Grammar

It is opposite to the constituency grammar and based on dependency relation. It was introduced by Lucien Tesniere. Dependency grammar (DG) is opposite to the constituency grammar because it lacks phrasal nodes.

Before giving an example of Dependency grammar, we need to know the fundamental points about Dependency grammar and Dependency relation.

In DG, the linguistic units, i.e., words are connected to each other by directed links.

The verb becomes the center of the clause structure.

Every other syntactic units are connected to the verb in terms of directed link. These syntactic units are called dependencies .

We can write the sentence “This tree is illustrating the dependency relation” as follows;

Parse tree that uses Constituency grammar is called constituency-based parse tree; and the parse trees that uses dependency grammar is called dependency-based parse tree.

Context Free Grammar

Context free grammar, also called CFG, is a notation for describing languages and a superset of Regular grammar. It can be seen in the following diagram −

Definition of CFG

CFG consists of finite set of grammar rules with the following four components −

Set of Non-terminals

It is denoted by V. The non-terminals are syntactic variables that denote the sets of strings, which further help defining the language, generated by the grammar.

Set of Terminals

It is also called tokens and defined by Σ. Strings are formed with the basic symbols of terminals.

Set of Productions

It is denoted by P. The set defines how the terminals and non-terminals can be combined. Every production(P) consists of non-terminals, an arrow, and terminals (the sequence of terminals). Non-terminals are called the left side of the production and terminals are called the right side of the production.

Start Symbol

The production begins from the start symbol. It is denoted by symbol S. Non-terminal symbol is always designated as start symbol.

Docs »

Syntax analysis

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Description

Syntax analysis is the second phase in compiler design where the lexical tokens generated by the lexical analyzer are validated against a grammar defining the language syntax.

I - Grammar

A language syntax is determined by a set of productions forming a grammar. Constructed grammars must satisfy the LL(1) (left to right, leftmost derivation, 1 lookahead) conditions.

LL(1) Conditions

A - no left recursion.

Example of a left recursion

Solution for a left recursion

B - Intersection of First sets in same production must be empty

Example of a non-empty intersection of First sets in a same production

In the above grammar, First(F) ∈ First(B) and First(F) ∈ First(D), therefore First(B) ∩ First(D) ≠ {}

One solution for this problem

C - Intersection of First and Follow sets of a non-terminal must be empty

Example of a non-empty intersection of First and Follow sets of a non-terminal

In the above grammar, First(B) ∩ Follow(B) = {a}

JSON structure

Terminals and Non-terminals

Non-terminals must be composed of upper case letters and underscore only. (Cannot be part of Reserved syntax keywords )
Terminals must begin and end with a single quote. The text in between the single quotes defines the lexical token name (case sensitive) and should not contain spaces. (Cannot be part of Reserved syntax keywords )
EPSILON represents an epsilon production.
Whitespaces between tokens are delimiters.

Key	Key description	Value
non-terminal (e.g. )	The non-terminal that can be replaced by its value.	Array of non-terminals string (e.g. )
		Array of a terminal string (e.g. )
		Array of an epsilon string (e.g. )
		Array of a mix strings (e.g. )

Lexical Keyword

II - Syntax error messages

When the user input does not align with the language grammar, the syntax analyzer will try to recover from the panic mode and will report customized error messages describing each situation.

Key	Description	Value	Value description	Required
default_message	A default message, used if no specific message is defined for a particular situation.	string	General error message. The string can contain .	Yes
error_messages	Specific message for a particular situation.	Array of	Check table	Yes

Key	Key description	Value	Value description
non_terminal	Non-terminal expected by the syntax analyzer at a specific location in the input	string	Non-terminal (e.g. )
			Any non-terminal
			End of grammar
terminal	Actual terminal read by the syntax analyzer	string	Terminal (e.g. )
			Any terminal
			End of file
message	Error message	string	Meaningful message on what was added or unexpected. (e.g. 'Missing variable name before the assignment operator at line ${lexical.line}'). The string can contain .

Placeholder	Description
	Display the value of the token object
	Display the column number in the line starting from 1
	Display the line number in the text starting from 1
	Input file name.

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Learn HTML: A Comprehensive Tutorial for Beginners | Step-by-Step Guide

Learn HTML from scratch! Our tutorial covers basics to advanced concepts. Start coding websites today with step-by-step guidance.

Tutorial Playlist

1 . HTML Tutorial

2 . HTML Basics

3 . HTML Syntax

4 . HTML Elements

5 . HTML Attributes

6 . HTML Comments

7 . HTML Semantic

8 . HTML Form Elements

9 . HTML Head

10 . HTML Title

11 . HTML Styles

12 . HTML Paragraphs

13 . HTML Symbols

14 . HTML Emojis

15 . HTML Formatting

16 . HTML Entities

17 . HTML Audio

18 . HTML Images

19 . HTML Lists

20 . HTML Links

21 . SVG in HTML

22 . HTML Forms

23 . HTML Video

24 . HTML Canvas

25 . Adjacency Lists

26 . HTML Input Types

27 . HTML Tables

28 . HTML Table Border

29 . Cell Spacing and Cell Padding

30 . HTML Semantic Elements

31 . HTML Layout

32 . html blocks and inline

33 . HTML Div

34 . Difference Between HTML and CSS

35 . Image Map in HTML

36 . HTML Drag and Drop

37 . HTML Iframes

38 . Divide and Conquer Algorithm

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40 . HTML Code

41 . HTML Colors

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46 . HTML Exercises

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48 . Understanding HTML Encoding: A Comprehensive Guide

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HTML Exercises

Advantages of doing html exercises, html practice exercises with solutions, frequently asked questions.

The only way we get better at something is by practicing it. The same goes for HTML programming . An effective way to get better at HTML programming is to practice HTML exercises. Even when I started my coding journey, I solved HTML examples for practice. It is a great way to get better at coding using HTML.

With that being said, let’s talk about some HTML exercises today in this guide.

Before delving into examples of HTML exercises you need to know about the benefits. In this section of the tutorial, I’ve discussed some of them with you.

Hands-on experience

Exercises give you practical, hands-on experience coding HTML, which helps you understand concepts better than passive learning methods.

Skill development

Regular practice helps you improve your HTML skills, including understanding tags, elements, attributes, and page structure, allowing you to become more proficient in web development.

Problem-solving

Exercises frequently present challenges or tasks to complete, encouraging you to think critically and develop problem-solving abilities that are essential for real-world web development scenarios.

Code Efficiency

HTML exercises help you write cleaner, more efficient code, which improves readability and maintainability in your HTML projects .

Through exercises, you can experiment with different ways to structure and design web content, encouraging creativity and innovation in your HTML coding.

Portfolio building

Completing HTML exercises allows you to create a portfolio of HTML projects that demonstrate your skills to potential employers or clients, thereby expanding your career opportunities in web development.

Let us start from the very basics. In this section of the tutorial, I will discuss all the basic concepts of HTML programming. Solutions are attached after every exercise, but try to solve the problem yourself first. This will really push you to learn the concept yourself.

These HTML assignments for students are sure to help you get better at HTML programming.

HTML headings

Question 1: Create an HTML document containing three headings:

Heading 1: "Welcome to HTML practice worksheets"

Heading 2, with the text "Practice Makes Perfect"

Heading 3 has the text "HTML Basics"

Solution:

Question 2 : Add the text "Keep Doing HTML Exercises for Practice Every Day" as a subheading under "Practice Makes Perfect".

Solution to the second question on HTML headings

Question 1 : Create an HTML page using the following headings

A heading labeled "My Favorite Websites"
Create a list (unordered or ordered) with three items:
The first item should include a link to "upGrad" (https://www.upgrad.com/).
The second item should include a link to "GitHub" (https://github.com).
The third item should include a link to "Wikipedia" (https://www.wikipedia.org).

Solution for the first question in HTML link

HTML tables

Question 1 : Create an HTML document with a table displaying a simple list of Cars. Include the following information about each car:

HTML images

Question 1 : Create an HTML page containing the following image-related tasks:

Use the tag to insert your desired image.
Create a gallery section with at least three images arranged in a grid layout.
Use the alt attribute to provide alternative text for each image.
Use CSS to style the images and gallery for better presentation.

HTML styles and formatting

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Published: 26 August 2024

Neural populations in the language network differ in the size of their temporal receptive windows

Tamar I. Regev ORCID: orcid.org/0000-0003-0639-0890 1 , 2 na1 ,
Colton Casto ORCID: orcid.org/0000-0001-6966-1470 1 , 2 , 3 , 4 na1 ,
Eghbal A. Hosseini 1 , 2 ,
Markus Adamek ORCID: orcid.org/0000-0001-8519-9212 5 , 6 ,
Anthony L. Ritaccio 7 ,
Jon T. Willie ORCID: orcid.org/0000-0001-9565-4338 5 , 6 ,
Peter Brunner ORCID: orcid.org/0000-0002-2588-2754 5 , 6 , 8 &
Evelina Fedorenko ORCID: orcid.org/0000-0003-3823-514X 1 , 2 , 3

Nature Human Behaviour ( 2024 ) Cite this article

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Despite long knowing what brain areas support language comprehension, our knowledge of the neural computations that these frontal and temporal regions implement remains limited. One important unresolved question concerns functional differences among the neural populations that comprise the language network. Here we leveraged the high spatiotemporal resolution of human intracranial recordings ( n = 22) to examine responses to sentences and linguistically degraded conditions. We discovered three response profiles that differ in their temporal dynamics. These profiles appear to reflect different temporal receptive windows, with average windows of about 1, 4 and 6 words, respectively. Neural populations exhibiting these profiles are interleaved across the language network, which suggests that all language regions have direct access to distinct, multiscale representations of linguistic input—a property that may be critical for the efficiency and robustness of language processing.

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The cortical representation of language timescales is shared between reading and listening

Brains and algorithms partially converge in natural language processing

Semantic encoding during language comprehension at single-cell resolution

Data availability.

Preprocessed data, all stimuli and statistical results, as well as selected additional analyses are available on OSF at https://osf.io/xfbr8/ (ref. 37 ). Raw data may be provided upon request to the corresponding authors and institutional approval of a data-sharing agreement.

Code availability

Code used to conduct analyses and generate figures from the preprocessed data is available publicly on GitHub at https://github.com/coltoncasto/ecog_clustering_PUBLIC (ref. 93 ). The VERA software suite used to perform electrode localization can also be found on GitHub at https://github.com/neurotechcenter/VERA (ref. 82 ).

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Acknowledgements

We thank the participants for agreeing to take part in our study, as well as N. Kanwisher, former and current EvLab members, especially C. Shain and A. Ivanova, and the audience at the Neurobiology of Language conference (2022, Philadelphia) for helpful discussions and comments on the analyses and manuscript. T.I.R. was supported by the Zuckerman-CHE STEM Leadership Program and by the Poitras Center for Psychiatric Disorders Research. C.C. was supported by the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. A.L.R. was supported by NIH award U01-NS108916. J.T.W. was supported by NIH awards R01-MH120194 and P41-EB018783, and the American Epilepsy Society Research and Training Fellowship for clinicians. P.B. was supported by NIH awards R01-EB026439, U24-NS109103, U01-NS108916, U01-NS128612 and P41-EB018783, the McDonnell Center for Systems Neuroscience, and Fondazione Neurone. E.F. was supported by NIH awards R01-DC016607, R01-DC016950 and U01-NS121471, and research funds from the McGovern Institute for Brain Research, Brain and Cognitive Sciences Department, and the Simons Center for the Social Brain. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

These authors contributed equally: Tamar I. Regev, Colton Casto.

Authors and Affiliations

Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA

Tamar I. Regev, Colton Casto, Eghbal A. Hosseini & Evelina Fedorenko

McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Program in Speech and Hearing Bioscience and Technology (SHBT), Harvard University, Boston, MA, USA

Colton Casto & Evelina Fedorenko

Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA, USA

Colton Casto

National Center for Adaptive Neurotechnologies, Albany, NY, USA

Markus Adamek, Jon T. Willie & Peter Brunner

Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA

Department of Neurology, Mayo Clinic, Jacksonville, FL, USA

Anthony L. Ritaccio

Department of Neurology, Albany Medical College, Albany, NY, USA

Peter Brunner

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Contributions

T.I.R. and C.C. equally contributed to study conception and design, data analysis and interpretation of results, and manuscript writing. E.A.H. contributed to data analysis and manuscript editing; M.A. to data collection and analysis; A.L.R., J.T.W. and P.B. to data collection and manuscript editing. E.F. contributed to study conception and design, supervision, interpretation of results and manuscript writing.

Corresponding authors

Correspondence to Tamar I. Regev , Colton Casto or Evelina Fedorenko .

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Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Human Behaviour thanks Nima Mesgarani, Jonathan Venezia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended data fig. 1 dataset 1 k-medoids (k = 3) cluster assignments by participant..

Average cluster responses as in Fig. 2e grouped by participant. Shaded areas around the signal reflect a 99% confidence interval over electrodes. The number of electrodes constructing the average (n) is denoted above each signal in parenthesis. Prototypical responses for each of the three clusters were found in nearly all participants individually. However, for participants with only a few electrodes assigned to a given cluster (for example, P5 Cluster 3), the responses were more variable.

Extended Data Fig. 2 Dataset 1 k-medoids clustering with k = 10.

a) Clustering mean electrode responses (S + W + J + N) using k-medoids with k = 10 and a correlation-based distance. Shading of the data matrix reflects normalized high-gamma power (70–150 Hz). b) Electrode responses visualized on their first two principal components, colored by cluster. c) Timecourses of best representative electrodes (‘medoids’) selected by the algorithm from each of the ten clusters. d) Timecourses averaged across all electrodes in each cluster. Shaded areas around the signal reflect a 99% confidence interval over electrodes. Correlation with the k = 3 cluster averages are shown to the right of the timecourses. Many clusters exhibited high correlations with the k = 3 response profiles from Fig. 2 .

Extended Data Fig. 3 All Dataset 1 responses.

a-c) All Dataset 1 electrode responses. The timecourses (concatenated across the four conditions, ordered: sentences, word lists, Jabberwocky sentences, non-word lists) of all electrodes in Dataset 1 sorted by their correlation to the cluster medoid (medoid shown at the bottom of each cluster). Colors reflect the reliability of the measured neural signal, computed by correlating responses to odd and even trials (Fig. 1d ). The estimated temporal receptive window (TRW) using the toy model from Fig. 4 is displayed to the left, and the participant who contributed the electrode is displayed to the right. There was strong consistency in the responses from individual electrodes within a cluster (with more variability in the less reliable electrodes), and electrodes with responses that were more similar to the cluster medoid tended to be more reliable (more pink). Note that there were two reliable response profiles (relatively pink) that showed a pattern that was distinct from the three prototypical response profiles: One electrode in Cluster 2 (the 10th electrode from the top in panel B) responded only to the onset of the first word/nonword in each trial; and one electrode in Cluster 3 (the 4th electrode from the top in panel C) was highly locked to all onsets except the first word/nonword. These profiles indicate that although the prototypical clusters explain a substantial amount of the functional heterogeneity of responses in the language network, they were not the only observed responses.

Extended Data Fig. 4 Partial correlations of individual response profiles with each of the cluster medoids.

a) Pearson correlations of all response profiles with each of the cluster medoids, grouped by cluster assignment. b) Partial correlations ( Methods ) of all response profiles with each of the cluster medoids, controlling for the other two cluster medoids, grouped by cluster assignment. c) Response profiles from electrodes assigned to Cluster 1 that had a high partial correlation ( > 0.2, arbitrarily defined threshold) with the Cluster 2 medoid (and split-half reliability>0.3). Top: Average over all electrodes that met these criteria (n = 18, black). The Cluster 1 medoid is shown in red, and the Cluster 2 medoid is shown in green. Bottom: Four sample electrodes (black). d) Response profiles assigned to Cluster 2 that had a high partial correlation ( > 0.2, arbitrarily defined threshold) with the Cluster 1 medoid (and split-half reliability>0.3). Top: Average over all electrodes that meet these criteria (n = 12, black). The Cluster 1 medoid is shown in red, and the Cluster 2 medoid is shown in green. Bottom: Four sample electrodes (black; see osf.io/xfbr8/ for all mixed response profiles with split-half reliability>0.3). e) Anatomical distribution of electrodes in Dataset 1 colored by their partial correlation with a given cluster medoid (controlling for the other two medoids). Cluster-1- and Cluster-2-like responses were present throughout frontal and temporal areas (with Cluster 1 responses having a slightly higher concentration in the temporal pole and Cluster 2 responses having a slightly higher concentration in the superior temporal gyrus (STG)), whereas Cluster-3-like responses were localized to the posterior STG.

Extended Data Fig. 5 N-gram frequencies of sentences and word lists diverge with n-gram length.

N-gram frequencies were extracted from the Google n-gram online platform ( https://books.google.com/ngrams/ ), averaging across Google books corpora between the years 2010 and 2020. For each individual word, the n-gram frequency for n = 1 was the frequency of that word in the corpus; for n = 2 it was the frequency of the bigram (sequence of 2 words) ending in that word; for n = 3 it was the frequency of the trigram (sequence of 3 words) ending in that word; and so on. Sequences that were not found in the corpus were assigned a value of 0. Results are only presented until n = 4 because for n > 4 most of the string sequences, both from the Sentence and Word-list conditions, were not found in the corpora. The plot shows that the difference between the log n-gram values for the sentences and word lists in our stimulus set grows as a function of N. Error bars represent the standard error of the mean across all n-grams extracted from the stimuli used (640, 560, 480, 399 n-grams for n-gram length = 1, 2, 3, and 4, respectively).

Extended Data Fig. 6 Temporal receptive window (TRW) estimates with kernels of different shapes.

The toy TRW model from Fig. 4 was applied using five different kernel shapes: cosine ( a ), ‘wide’ Gaussian (Gaussian curves with a standard deviation of σ /2 that were truncated at +/− 1 standard deviation, as used in Fig. 4 ; b ), ‘narrow’ Gaussian (Gaussian curves with a standard deviation of σ /16 that were truncated at +/− 8 standard deviations; c ), a square (that is, boxcar) function (1 for the entire window; d ) and a linear asymmetric function (linear function with a value of 0 initially and a value of 1 at the end of the window; e ). For each kernel ( a-e ), the plots represent (left to right, all details are identical to Fig. 4 in the manuscript): 1) The kernel shapes for TRW = 1, 2, 3, 4, 6 and 8 words, superimposed on the simplified stimulus train; 2) The simulated neural signals for each of those TRWs; 3) violin plots of best fitted TRW values across electrodes (each dot represents an electrode, horizontal black lines are means across the electrodes, white dots are medians, vertical thin box represents lower and upper quartiles and ‘x’ marks indicate outliers; more than 1.5 interquartile ranges above the upper quartile or less than 1.5 interquartile ranges below the lower quartile) for all electrodes (black), or electrodes from only Clusters 1 (red) 2 (green) or 3 (blue); and 4) Estimated TRW as a function of goodness of fit. Each dot is an electrode, its size represents the reliability of its neural response, computed via correlation between the mean signals when using only odd vs. only even trials, x-axis is the electrode’s best fitted TRW, y-axis is the goodness of fit, computed via correlation between the neural signal and the closest simulated signal. For all kernels the TRWs showed a decreasing trend from Cluster 1 to 3.

Extended Data Fig. 7 Dataset 1 k-medoids clustering results with only S and N conditions.

a) Search for optimal k using the ‘elbow method’. Top: variance (sum of the distances of all electrodes to their assigned cluster centre) normalized by the variance when k = 1 as a function of k (normalized variance (NV)). Bottom: change in NV as a function of k (NV(k + 1) – NV(k)). After k = 3 the change in variance became more moderate, suggesting that 3 clusters appropriately described Dataset 1 when using only the responses to sentences and non-words (as was the case when all four conditions were used). b) Clustering mean electrode responses (only S and N, importantly) using k-medoids (k = 3) with a correlation-based distance. Shading of the data matrix reflects normalized high-gamma power (70–150 Hz). c) Average timecourse by cluster. Shaded areas around the signal reflect a 99% confidence interval over electrodes (n = 99, n = 61, and n = 17 electrodes for Cluster 1, 2, and 3, respectively). Clusters 1-3 showed a strong similarity to the clusters reported in Fig. 2 . d) Mean condition responses by cluster. Error bars reflect standard error of the mean over electrodes. e) Electrode responses visualized on their first two principal components, colored by cluster. f) Anatomical distribution of clusters across all participants (n = 6). g) Robustness of clusters to electrode omission (random subsets of electrodes were removed in increments of 5). Stars reflect significant similarity with the full dataset (with a p threshold of 0.05; evaluated with a one-sided permutation test, n = 1000 permutations; Methods ). Shaded regions reflect standard error of the mean over randomly sampled subsets of electrodes. Relative to when all conditions were used, Cluster 2 was less robust to electrode omission (although still more robust than Cluster 3), suggesting that responses to word lists and Jabberwocky sentences (both not present here) are particularly important for distinguishing Cluster 2 electrodes from Cluster 1 and 3 electrodes.

Extended Data Fig. 8 Dataset 2 electrode assignment to most correlated Dataset 1 cluster under ‘winner-take-all’ (WTA) approach.

a) Assigning electrodes from Dataset 2 to the most correlated cluster from Dataset 1. Assignment was performed using the correlation with the Dataset 1 cluster average, not the cluster medoid. Shading of the data matrix reflects normalized high-gamma power (70–150 Hz). b) Average timecourse by group. Shaded areas around the signal reflect a 99% confidence interval over electrodes (n = 142, n = 95, and n = 125 electrodes for groups 1, 2, and 3, respectively). c) Mean condition responses by group. Error bars reflect standard error of the mean over electrodes (n = 142, n = 95, and n = 125 electrodes for groups 1, 2, and 3, respectively, as in b ). d) Electrode responses visualized on their first two principal components, colored by group. e) Anatomical distribution of groups across all participants (n = 16). f-g) Comparison of cluster assignment of electrodes from Dataset 2 using clustering vs. winner-take-all (WTA) approach. f) The numbers in the matrix correspond to the number of electrodes assigned to cluster y during clustering (y-axis) versus the number electrodes assigned to group x during the WTA approach (x-axis). For instance, there were 44 electrodes that were assigned to Cluster 1 during clustering but were ‘pulled out’ to Group 2 (the analog of Cluster 2) during the WTA approach. The total number of electrodes assigned to each cluster during the clustering approach are shown to the right of each row. The total number of electrodes assigned to each group during the WTA approach are shown at the top of each column. N = 362 is the total number of electrodes in Dataset 2. g) Similar to F , but here the average timecourse across all electrodes assigned to the corresponding cluster/group during both procedures is presented. Shaded areas around the signals reflect a 99% confidence interval over electrodes.

Extended Data Fig. 9 Anatomical distribution of the clusters in Dataset 2.

a) Anatomical distribution of language-responsive electrodes in Dataset 2 across all subjects in MNI space, colored by cluster. Only Clusters 1 and 3 (those from Dataset 1 that replicate to Dataset 2) are shown. b) Anatomical distribution of language-responsive electrodes in subject-specific space for eight sample participants. c-h) Violin plots of MNI coordinate values for Clusters 1 and 3 in the left and right hemisphere ( c-e and f-h , respectively), where plotted points (n = 16 participants) represent the mean of all coordinate values for a given participant and cluster. The mean across participants is plotted with a black horizontal line, and the median is shown with a white circle. Vertical thin black boxes within violins plots represent the upper and lower quartiles. Significance is evaluated with a LME model ( Methods , Supplementary Tables 3 and 4 ). The Cluster 3 posterior bias from Dataset 1 was weakly present but not statistically reliable.

Extended Data Fig. 10 Estimation of temporal receptive window (TRW) sizes for electrodes in Dataset 2.

As in Fig. 4 but for electrodes in Dataset 2. a) Best TRW fit (using the toy model from Fig. 4 ) for all electrodes, colored by cluster (when k-medoids clustering with k = 3 was applied, Fig. 6 ) and sized by the reliability of the neural signal as estimated by correlating responses to odd and even trials (Fig. 6c ). The ‘goodness of fit’, or correlation between the simulated and observed neural signal (Sentence condition only), is shown on the y-axis. b) Estimated TRW sizes across all electrodes (grey) and per cluster (red, green, and blue). Black vertical lines correspond to the mean window size and the white dots correspond to the median. ‘x’ marks indicate outliers (more than 1.5 interquartile ranges above the upper quartile or less than 1.5 interquartile ranges below the lower quartile). Significance values were calculated using a linear mixed-effects model (comparing estimate values, two-sided ANOVA for LME, Methods , see Supplementary Table 8 for exact p-values). c-d) Same as A and B , respectively, except that clusters were assigned by highest correlation with Dataset 1 clusters (Extended Data Fig. 8 ). Under this procedure, Cluster 2 reliably separated from Cluster 3 in terms of its TRW (all ps<0.001, evaluated with a LME model, Methods , see Supplementary Table 9 for exact p-values).

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Regev, T.I., Casto, C., Hosseini, E.A. et al. Neural populations in the language network differ in the size of their temporal receptive windows. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01944-2

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